At 42 Agency, we work with clients who run complex revenue stacks. Attribution rarely follows a clean, linear path. For one client, we kept running into the same issue: contacts associated with deals weren’t consistently receiving the correct “latest channel” attribution.

The result was familiar to any RevOps team: deals attributed to “Unknown.” Incomplete reporting. Manual, time-consuming validation to figure out which channel actually influenced the deal.

Instead of accepting this as a limitation, I built a targeted attribution audit workflow using Breeze AI in HubSpot. Here’s how it works—and what I learned along the way.

The Two Problems Breaking Our Attribution

Two scenarios were consistently breaking our attribution model. Understanding these is key to understanding why we needed the AI approach.

1. The contact-deal association gap

When a deal is created in Salesforce and syncs to HubSpot, there’s often a delay before the contact gets associated. During that gap, the contact might never enter the standard workflow responsible for assigning the latest channel.

2. Sources that happen after the deal

We also saw edge cases where the Latest Traffic Source Date was later than the deal creation date. From a RevOps perspective, this is a critical distinction: we don’t want the most recent interaction—we want the most recent interaction that actually contributed to the deal.

Together, these issues created a growing pool of “Unknown” channels and forced teams into reactive, manual investigations.

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Why We Built an Audit Agent

The goal wasn’t just to validate our existing attribution logic. It was to investigate and recover the latest meaningful source for prospects who followed non-standard paths.

Think of it as a detective:

· It retraces the prospect’s journey

· Reviews timestamps, channel history, and associations

· Produces a reasoned conclusion about the most likely source

And when the standard investigation still comes up short, a Forensic Research Agent steps in—performing a deep dissection of the prospect’s lifecycle to reconstruct the most accurate attribution possible.

This Is an Audit Workflow, Not a Scaled Process

From the start, I made a deliberate decision: this workflow should not run on every contact.

Each Breeze Data Agent execution consumes 10 credits. If a contact passes through multiple branches, that’s 20 credits. Design for intentional scarcity—one or two contacts per month, not hundreds.

The trigger:

A contact enters the workflow only when:

· Latest Channel = Unknown

· The contact has already passed through the standard attribution workflow

· The contact is not part of excluded operational segments

Scheduling and cost control:

The workflow includes a 30-minute delay, and I’ve set it to only run on Fridays between 3pm and 7pm. We need to be sure only a handful of contacts per month enter this workflow—it’s an audit, not a bulk process.

How the Attribution Audit Agent Works

Branching by Source Type

One of the biggest learnings: avoid a single “catch-all” prompt.

I originally tried to write one prompt that could handle all source types, but Breeze couldn’t process it reliably. When I separated the channels and wrote specific prompts for each, the accuracy improved significantly.

The workflow splits into clear branches:

· Organic inbound: Direct, Organic Search, Organic Social, Email, AI referrals

· Paid inbound: Paid Search and Paid Social

· Referrals and campaigns

· Offline sources: outbound prospecting and events

This structure keeps prompts focused and significantly reduces hallucinations.

The Core Logic: “Time-Travel” Attribution

Every AI agent follows strict chronological rules:

1. If the source date is before or equal to the deal creation date → the source is valid and accepted

2. If the source date is after the deal creation date → the agent must search the property history to find the value active at the moment the deal was created

3. If no reliable data exists → the contact is flagged for manual review

The Year Rule (Non-Negotiable)

I explicitly taught the agent that 2026 is always after 2025.

Without this instruction, the model occasionally misinterpreted dates like 1/8/26 and 10/15/25. For RevOps teams, this was a clear reminder: never assume AI understands time the way humans do.

Tips for Writing the Prompts

This took a lot of trial and error. A few things that made a real difference:

· Use property tokens, not just property names. The agent needs the exact data reference to pull the right information.

· Be very specific about date/timestamp formats. I had a lot of issues until I added explicit instructions like: “Both timestamps must be parsed as datetime values using the format…”

· Draft with multiple tools. I drafted my prompts with Gemini, then ran them through ChatGPT for a second review. Even then, it took multiple rounds of testing to get them working consistently.

The Final Fallback: The Forensic Research Agent

When all standard branches still return “Unknown,” the contact enters the most advanced step.

The Forensic Research Agent:

· Performs a deep analysis of the Channel History Log

· If the log is empty, applies a fallback rule: compares the month and year of the Contact Create Date with the Deal Create Date

· If they match, suggests the Original Traffic Source as the most likely contributor

It’s not perfect—but it’s significantly better than leaving attribution blank.

A Real Example: The Multi-Touch Maze

Consider this journey:

· September 2025: First visit via Organic Search

· October 10, 2025: Deal created manually (Salesforce sync)

· October 15, 2025: Returns via a Direct “Book demo” link

· January 2026: Clicks a LinkedIn ad (Paid Social)

Without an audit, HubSpot would attribute the deal to Paid Social in January 2026.

With the audit agent, post-deal interactions are ignored, and Organic Search is correctly identified as the true driver—or inferred using creation-date logic if needed.

Making the AI Accountable

To keep the system transparent, I populate several custom properties:

· AI Suggested Latest Channel Attribution — what the agent determined

· Attribution Reason — why it made that decision (e.g., “Source date occurs after deal creation date”)

· Attribution Detail — UTMs, offline event type, etc.

· Source Investigation Log — a full forensic timeline

This gives us visibility into what the AI is doing and helps catch any issues. For RevOps, this is essential: if you can’t audit the AI’s decision-making, you can’t trust it.

Honest Caveats

The prompts sometimes hallucinate. This is real and you need to plan for it.

That’s why I added a secondary data agent branch for contacts tagged as “requires manual review.” This branch tries again with a different prompt structure. But even with that, some contacts will still need a human to look at the history log and make a judgment call.

My recommendation: don’t activate a workflow like this right away. Test it manually first by enrolling specific contacts and reviewing the outputs. Make sure you understand where the AI gets things right and where it struggles.

What This Taught Me

1. Breeze can’t handle complex prompts. Smaller, focused prompts outperform one big “do everything” prompt. Every time.

2. Credits add up fast. Design workflows to be highly selective about which contacts enter AI-powered actions.

3. Always have a manual review fallback. AI tools are helpful, but they’re not 100% reliable—especially for edge cases.

4. Temporal logic must be explicit. Especially across calendar years. The AI doesn’t naturally understand that January 2026 comes after October 2025.

5. Testing is not optional. Use the “test action” feature aggressively before going live.

What’s Next

This system is still evolving. I’m continuing to refine prompts, monitor outputs, and learn from edge cases. The approach is highly adaptable for other teams facing similar attribution gaps.

If you’ve built something similar—or have opinions on prompt design and attribution logic—I’d love to compare notes. These tools are new, and in RevOps, we’re all still figuring out what truly scales.

Jessica is a Revenue Operations Analyst at 42 Agency, focused on GTM Ops and attribution.

Most Enterprise marketing teams eventually end up in the same uncomfortable meeting.

You are sitting across from your CFO. You have a dashboard showing that “Direct Traffic” and “Organic Search” are up 40% year-over-year. You know, intuitively, that your heavy investment in LinkedIn ads, Connected TV (CTV), and content is driving that growth.

But the CFO points to the attribution software.

The software says your LinkedIn ads drove zero “Last Click” conversions.

It says your CTV campaign has a CPA of infinity.

It says “Direct Traffic” is your best channel—so why are we paying for ads at all?

This is the core issue of modern B2B.

We know that enterprise buyers don’t click ads. They see them, they remember them, and three days later, when they are ready, they Google your name & find you.

But because most teams still use attribution models built for $50 e-commerce purchases.

And because they can’t prove it, they cut the budget.

At 42, we stopped arguing about “Brand Equity” and “Dark Social.” Those are soft terms for hard problems.

Instead, we started using math.

Here is how we proved—with 99.9% statistical confidence—that “Brand Spend” drives pipeline, even when the attribution software says it doesn’t.

The Invisible Lift: Why Clicks Are a Vanity Metric

If you are selling to the Enterprise (ACVs of $50,000+), you need to accept a hard truth:

Your buyers are not clicking your ads.

They are busy. They are consuming content on mobile while commuting. They are watching CTV in their living rooms. They are scrolling LinkedIn between meetings. They are on NYT or watching cable news or live sports.

When they see your message, they don’t click “Book a Demo” immediately. When

This creates a measurement gap.

The stimulus (the ad) happens in one place.

The response (the search) happens in another.

Attribution software is designed to track a single user moving in a straight line. It fails when the user journey is a scattered cloud of impressions, offline conversations, and delayed searches.

To see the truth, you have to stop looking at clicks and start looking at correlation.

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The Evidence: 27 Weeks of Hard Data

We recently conducted a Brand Impact Analysis for a FinTech client to answer the question: “What do we actually get for our LinkedIn spend?” ¹

We didn’t look at “Leads” or “MQLs” inside the ad platform. We looked at downstream behavior.

We aggregated daily LinkedIn spend into weekly totals across 6 months (June–December) and overlaid it against website traffic patterns from HubSpot.

The results were staring us in the face. No we didnt magically find MQLs. What we did find was - when we spend on LinkedIn, branded organic search, direct traffic all trend up (and leads / SQLs follow)

Here is the math behind the lift:

• Organic Search Correlation: +0.57. This is a moderate-to-strong positive correlation. It means that as LinkedIn spend rises, the volume of people searching for the brand name rises with it. ³

• Direct Traffic Correlation +0.67. This is a strong correlation. It means that when we spend on “awareness,” more people type the URL directly into their browser. ⁴

• Confidence Level: 99.9%. The p-values were 0.0001. In statistical terms, this means there is less than a 0.1% chance this relationship is random noise. ⁵

This proves that Paid Media is creating measurable demand signals—just not in the form of direct leads.

The “Natural Experiment” (Or, Why It Wasn’t Just Seasonality)

Skeptics love to claim that correlation isn’t causation. “Maybe traffic just went up because it was a busy season,” they say.

But the data gave us a perfect control group: Thanksgiving Week.

During that week, our LinkedIn spend remained flat ($2,376 vs. $2,379 the week prior). But Direct Traffic dropped by 8% (from 27k to 25k sessions).

This “drop” in the pattern validated the model. If the traffic was purely driven by ad spend, it should have stayed flat. If it were purely seasonal, it should have dropped regardless of spend.

The fact that traffic responds to both media stimulus (spend) AND human behavior (holidays) proves we are measuring real people, not bot traffic & its not random chance.

The System View: How Revenue Actually Flows

This brings us to the core philosophy of 42 Agency: GTM is a System, not a Funnel.

If you look at our GTM Systems Map below, you can see the reality of how modern revenue is engineered.

Most companies only measure the bottom box (”Marketing Influenced”). They ignore the engine that feeds it.

A healthy Enterprise GTM engine has three distinct layers:

Layer 1: Signal Generation (The “Exposure” Layer)

This is where you create familiarity. This includes Connected TV, LinkedIn Ads, and Content.

• Goal: Mental Availability.

• Metric: CPM, Reach, Frequency.

Layer 2: Signal Processing (The “Engagement” Layer)

This is where we measure if the market cares. We look at Channel Health and Cross-Channel Correlation (like the $r=0.81$ stat).

• Goal: Validation of Interest.

• Metric: Correlation ($r$), Lift, Account Intent Scores (0-100 points).

Layer 3: Activation (The “Capture” Layer)

This is where we route demand.

• High Intent (61-100 points): These go to Sales for immediate outreach or a custom demo.

• Low Intent (0-30 points): These stay in nurture.

The Fix: The 80/20 Rule

So, how do you operationalize this without getting fired for “wasting money” on unmeasurable ads?

You follow the 80/20 Rule.

This is our interpretation of the famous “95/5 Rule” in B2B marketing. At any given time, only 5% of your market is ready to buy. The other 95% are “out of market”—but they will be ready eventually. ¹⁰

We structure our Demand Generation retainers strictly:

• 80% of Effort & Spend = Direct Response.

  This covers Paid Search, Retargeting, and Conversion campaigns. This captures the demand that already exists. It feeds the sales team now.

• 20% of Effort & Spend = Demand Creation.

  This covers CTV, Podcasts, Video, and “Reach” campaigns. We do not expect these to drive a direct conversion. We measure them via correlation and lift. This balance ensures you are hitting your quarterly commit (via the 80%) while building the pipeline for next year (via the 20%).

The Bottom Line

If your agency is telling you to turn off LinkedIn/CTV because “the CPA is too high,” they are doing you a disservice.

They are optimizing for the harvest while refusing to plant seeds.

• Attribution measures the harvest (Layer 3).

• Correlation measures the growth (Layer 1 & 2).

You need both.

If you want to see the math behind your own growth—and stop guessing where your pipeline comes from—let’s talk.

We have more ABM tools than ever. Intent platforms, CDPs, programmatic display, AI-powered sequencing, identity resolution, and multi-touch attribution dashboards that make your CFO feel like they’re getting their money’s worth. The stack has never been more sophisticated.

And yet.

Are the companies actually winning enterprise deals right now? They’re hosting 10-person dinners and sending $7 bags of gummy worms.

I’m not being cute. Listen to any recent conversation with the ABM leads at Snowflake, Ramp, or Hightouch. What they’re prioritizing isn’t another layer of digital orchestration. It’s getting in the room. Physical, analog, human presence.

Here’s the thing, though: I don’t think most teams realize how deeply we over-indexed on digital coming out of COVID. Events died. Gifting felt awkward because nobody knew anyone’s home address. We built stacks instead of relationships. And now we’re all staring at the same fatigued buyers, wondering why our engagement metrics are in the toilet.

The Fatigue Is Real

Sendoso’s 2026 Digital Fatigue Report surveyed 425 marketers and analyzed 1.75 million gifts. The numbers are damning:

· 84% of buyers are fatigued by cold calls

· 83% fatigued by marketing emails

· 82% fatigued by sales emails

· 78% fatigued by digital ads

Eighty-four percent. Fatigued. By the channels we spend most of our budgets on.

We optimized for volume and burned through buyer attention in the process. This isn’t a hot take anymore. It’s just the state of B2B. Your buyers are tired of you before you’ve even said hello.

And look, I’ve written before about how paid media isn’t the problem in isolation. A channel can’t outperform the system it sits in. But the system most teams built over the last four years treats digital as the entirety of the motion rather than as one node in it. That’s the mistake.

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The Channels Nobody Uses

Here’s where it gets interesting. The same Sendoso report asked about zero fatigue by channel. Meaning: what percentage of buyers report no exhaustion whatsoever with each channel type?

· Direct mail and gifts: 40%

· In-person events: 34%

· Virtual events: 32%

· Marketing emails: 17%

· Cold calls: 16%

The two channels buyers aren’t tired of are both physical.

But here’s the kicker: only 34% of marketing teams report using gifting or direct mail regularly. It’s the least-used channel despite being the least fatiguing. We’re all fighting for scraps in channels that are saturated while ignoring the ones where buyers are actually receptive.

Why? Because physical is harder. Doesn’t scale as elegantly. Can’t be measured in the same neat dashboards. So we don’t do it.

This is backwards.

What the Winning Teams Actually Do

Let me tell you what’s actually working at the companies everyone benchmarks against.

Snowflake runs intimate dinners where existing customers share experiences directly with prospects. Casey Patterson, their Director of ABM, talks about this constantly. The model isn’t vendor pitch. It’s peer-to-peer trust. Get a happy customer in a room with three prospects who have the same problem. Let them talk. Your sales rep barely needs to speak.

They’re also doing data-driven personalized gifts. Not mass swag drops. Intentional, researched gifts that demonstrate you paid attention.

Ramp takes a different angle. Drew Pinta, their Director of Growth Data Science, describes “hyper-specific dinners where the salesperson originates the idea based on a specific pain point.” The key phrase there is salesperson originates. This isn’t marketing deciding to host a generic networking event. It’s sales saying “I have three prospects stuck on the same objection, let’s get them in a room together.”

Problem-focused gatherings. Not happy hours.

Hightouch does high-end executive experiences. Brian Kotlyar, their CMO, talks about moving beyond simple swag to meaningful experiences for strategic accounts. They layer it: scaled gifting through platforms like Sendoso for the broader ABM list, plus personalized high-effort gifts for the accounts that really matter.

The common thread? All three companies prioritize offline, high-touch tactics over digital ads for ABM engagement. They haven’t abandoned digital. But they understand it’s Layer 1 and 2 work. The closing happens in person.

The $7 Gummy Worms

There’s a story in the Sendoso report that stuck with me.

The best gift a buyer ever received from a vendor? It wasn’t a $200 bottle of whiskey. It was a $7 bag of sour gummy worms.

The context: she had mentioned her daughter loved them. Some rep was actually listening. Wrote it down. Remembered. Sent a bag with a handwritten note.

Seven dollars and active listening beat two hundred dollars and generic “thought you’d enjoy this.”

The data backs this up. When asked what gifts they prefer:

· 34% want branded high-quality swag

· 26% want a thoughtful, personalized low-value gift

· 23% want a generic gift card

· 12% want a generic high-value item

A quarter of buyers actively prefer the cheap-but-personal option over the expensive-but-generic one. And here’s the list of what to avoid:

1. Compliance violations (obviously)

2. Hassle (signatures, forms, anything requiring effort)

3. Low-quality swag (the branded stress ball nobody asked for)

4. Alcohol (only 54% of adults drink regularly, lowest in 87 years)

5. Too personal (creepy social snooping vibes)

6. Too expensive (feels like a bribe, creates obligation)

The sweet spot is thoughtful, low-friction, and personal. Not expensive.

I’ve recommended this before: research where decision makers went to school. Buy alumni swag and ship it. It’s $40 and a clear signal that you did your homework. You’re not being creepy. You’re being considerate.

Why Teams Don’t Do This

I know what you’re thinking. All of this sounds lovely, but it doesn’t scale. My board isn’t going to be impressed by “we sent some cupcakes.”

Let me address the objections directly.

“It doesn’t scale.”

True. That’s the point. ABM isn’t about scale. ABM is selling to your top 10-50 dream accounts. If you’re trying to run gifting and dinners at the scale of your programmatic display campaigns, you’ve misunderstood the exercise.

ABM is not a renamed demand gen motion. It’s a focused effort on accounts that matter disproportionately. If the list is too long to do this for, the list is wrong.

“It’s hard to measure.”

Measure deal velocity, not MQLs. Are your ABM deals closing faster? Are they larger? Did the buyer attend a dinner before they signed?

Deal velocity is everything in enterprise GTM: (Number of Deals x Average Deal Value x Conversion Rate) / Sales Cycle Length. If dinners and gifts are compressing your sales cycle or increasing your close rate, you can measure that. It just requires you to think beyond “how many leads did this generate.”

“Sales won’t do it.”

Then your ABM program is already broken. ABM is a sales activity. Marketing surrounds the market with messaging and air cover. But the actual motion is sales-led. If sales won’t participate in account planning, research, dinner hosting, and gift selection, you don’t have an ABM program. You have marketing pretending to do ABM while sales ignores you.

Fix the alignment problem first.

“It feels old school.”

Old school is the new differentiation.

Everyone else is sending the same cold emails through the same sequences with the same LinkedIn connection requests. The buyer who gets a handwritten note and an invitation to a dinner with three of their peers is going to remember you.

Where This Fits in Your System

Dinners and gifts don’t replace your digital motion. They activate it.

I’ve written about the three layers of enterprise GTM before:

Layer 1: Exposure. Creates familiarity. LinkedIn ads, CTV, podcasts, content. The buyer has heard of you.

Layer 2: Engagement. Reveals interest. Website behavior, intent signals, event attendance. The buyer is exploring.

Layer 3: Activation. Converts interest to conversation. Dinners, gifting, outbound, sales touches. The buyer becomes an opportunity.

Physical tactics belong in Layer 3. They don’t work if you skip Layers 1 and 2.

A targeted dinner invitation works because the buyer already knows who you are. A personalized gift lands because they’ve already been researching you. A well-timed SDR touch converts because you’ve lowered the cost of exploration for a buyer who’s curious but busy.

The anti-pattern is gift card spam to cold accounts. Sending a $50 Starbucks card to someone who’s never heard of you isn’t ABM. It’s expensive noise. You haven’t earned the right to their attention yet.

Do the work in Layers 1 and 2 first.

The Playbook

If you’re ready actually to do this, here’s how I’d think about it.

When to run dinners:

· Pain-point specific (Ramp model), not generic networking

· 8-12 people max

· Mix customers and prospects (Snowflake model)

· Sales-originated idea, marketing executes logistics

· Quarterly in your key markets, not one-off experiments

When to gift:

· Deal acceleration (active opportunity, stuck deal)

· Post-meeting thank you (not pre-meeting bribe)

· Milestone moments (closed-won anniversary, expansion conversations)

· Personalized beats expensive (the gummy worms rule)

When NOT to gift:

· Cold outreach to unaware accounts

· Generic blasts to a list

· Without CRM infrastructure to track it

How to measure:

· Deal velocity (are ABM deals closing faster?)

· Average deal size (are they bigger?)

· Influenced pipeline (did they attend or receive something before closing?)

· NOT: MQLs, gift acceptance rate in isolation

The measurement frame matters. If you’re trying to justify dinners and gifts with the same metrics you use for paid media, you’ll kill the program before it starts. These tactics don’t generate leads. They accelerate deals and build relationships. Measure accordingly.

The Takeaway

The ABM stack isn’t the strategy. The strategy is getting in rooms with the right people and being remembered.

Snowflake, Ramp, and Hightouch aren’t winning because they have better intent data. They’re winning because they host dinner. They’re winning because they listen well enough to remember someone’s daughter likes sour gummy worms.

The Sendoso data proves that buyers aren’t tired of physical. They’re tired of digital. But the vast majority of marketing teams are still pouring budget into the fatigued channels while ignoring the ones with open attention.

This only works as Layer 3 of a system. You still need exposure and engagement. You still need the digital infrastructure. But you also need to close the loop with something human.

The companies that win the next five years of enterprise deals will be the ones that remember how to shake hands.

So here’s my question: when’s the last time you actually got in a room with your top five accounts?

Sources: Sendoso 2026 Digital Fatigue Report; Exit Five podcast, “ABM: What Ramp, Snowflake, and Hightouch are doing in 2026” (February 2026).

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The Forward Deployed GTM Problem

Every B2B company I talk to has the same issue. They’ve spent years buying GTM tools. They haven’t spent any time architecting how those tools work together.

The result is sprawl. Overlapping systems. Data flowing in directions nobody mapped. A rev ops team that spends more time maintaining the stack than actually using it to drive growth.

And the uncomfortable truth: adding another tool won’t fix this. Neither will ripping everything out and starting over. The problem isn’t the tools. The problem is that nobody’s thinking about the system.

How We Got Here

The GTM tool stack evolved in waves.

First came the platforms. HubSpot, Marketo, Salesforce, Pardot. The promise was consolidation - one system to run your marketing and sales ops. Companies bought in, built their processes around these platforms, and for a while it worked well enough.

Then came the point solutions. The platforms couldn’t do everything, so a new generation of tools emerged to fill the gaps. Enrichment tools like ZoomInfo and Clearbit. Intent data from Bombora and 6sense. Conversational intelligence from Gong and Chorus. Outbound sequencing from Outreach and Salesloft. Each one solved a real problem. Each one “integrated” with your existing stack.

Now we’re in the AI GTM wave. Tools like Clay, Pocus, Deepline, Common Room. They promise to connect your data sources, score leads dynamically, surface intent signals, automate next-best-actions for reps. The capabilities are genuinely impressive.

But each wave added tools without adding coherence. The stack got bigger. The architecture didn’t get better. And the people responsible for making it all work - your rev ops and marketing ops teams - got stretched thinner.

The Sprawl Problem

Here’s what the sprawl actually looks like inside companies.

You have a CRM that’s supposed to be the source of truth, except half the fields are outdated, and sales uploads lists directly without going through any enrichment or routing. You have a MAP running lifecycle automation, but the lifecycle stages don’t match what’s in the CRM, so reporting tells different stories depending on which system you pull from.

You have an enrichment tool, maybe two. One fires on form fill, one runs on a schedule. Sometimes they overwrite each other. Nobody’s documented which one wins.

You have intent data coming in from a third-party provider, but it’s not wired into scoring, so it sits in a dashboard nobody checks. You have a conversational intelligence tool recording every sales call, but the insights don’t flow back into the CRM in any structured way.

Everything is technically connected. Nothing is actually architected.

The rev ops team knows where the bodies are buried. They’ve built workarounds on top of workarounds. They can tell you why that one workflow is turned off and why you can’t delete that custom field without breaking three other things.

But they’re spending all their time on maintenance. Keeping the stack from falling over. Fixing sync errors. Manually cleaning data. Answering questions about why the numbers don’t match.

They’re not doing strategy. They’re doing triage.

Why Vendors Can’t Fix This

Every GTM vendor will tell you their tool is essential. And they’re not lying - most of these tools are genuinely useful. The problem is incentives.

Vendors are paid to sell their piece of the stack. They’re not paid to ask whether you actually need it. They’re definitely not paid to tell you that your real problem is the three other tools you bought that overlap with theirs.

So you get content about why intent data is critical. Why enrichment is a must-have. Why conversational intelligence will transform your sales team. All of it’s true in isolation. None of it helps you think about the whole system.

“Integrates with everything” has become a checkbox feature. But integration isn’t architecture. Just because data can flow from one tool to another doesn’t mean it should. Doesn’t mean anyone’s thought through what happens downstream. Doesn’t mean the process exists to act on it.

I’ve seen stacks where the same lead gets enriched by three different tools, scored by two different models, and ends up in a queue nobody checks. Everything was integrated. Nothing was designed.

The vendors can’t solve this. It’s not their job. Their job is to make their tool work. Your job is to make the system work. And most companies don’t have anyone who owns that.

The Missing Layer

In software engineering, there’s a concept called the forward deployed engineer. These are technical people who sit at the intersection of the product and the customer. They don’t just implement - they understand the customer’s business deeply enough to make architecture decisions on their behalf. They translate between what the product can do and what the customer actually needs.

GTM needs the same thing.

Not a HubSpot admin. Not a Salesforce consultant running a standard playbook. Not a vendor’s customer success team trying to drive adoption of their specific tool.

What’s missing is someone who can look at the whole system and make decisions across it. Which tools do you actually need? What’s redundant? How should data flow between systems? What process runs on top? What should you measure and what’s just noise?

These aren’t admin questions. They’re strategic questions. But they fall through the cracks because everyone owns their piece and nobody owns the system.

The CRM admin owns Salesforce. The marketing ops person owns HubSpot. The sales ops person owns the sequencing tool. Maybe someone’s figured out Clay. But who owns the architecture that connects all of it?

Usually nobody. And that’s why the stack doesn’t work.

What Forward Deployed GTM Looks Like

Forward deployed GTM starts with understanding the business, not the tools.

Before touching any configuration, you need to understand how the company actually sells. What’s the ICP? What does the buyer journey look like? How does a lead become an opportunity? What does the handoff between marketing and sales look like in practice, not in theory?

Most implementations skip this step. They start with best practices and templates. They configure the tool based on how other companies use it, not based on how this company works. That’s why reps don’t trust the system six months later - because it doesn’t match their reality.

Forward-deployed GTM means being tool-agnostic. The question isn’t “how do we configure HubSpot?” The question is “what do you actually need, and what’s the simplest stack that gets you there?” Sometimes that means consolidating. Sometimes it means ripping out tools that looked good in the demo but don’t fit the motion. Sometimes it means building a process and documentation before touching any software at all.

It also means staying close enough to iterate. The initial architecture is never perfect. Scoring models drift. New tools get added. The sales process evolves. Someone needs to be watching whether the system still matches reality - and adjusting when it doesn’t.

This isn’t a one-time project. It’s an ongoing relationship with someone who understands your business well enough to make decisions on your behalf.

How We Think About This at 42 Agency

This is the model we’ve built our RevOps practice around.

We start with discovery, not implementation. Before we touch HubSpot or Salesforce, we run workshops to understand your ICP, your customer journey, your actual sales process. We align on what the lifecycle stages should mean, how leads should be scored, what attribution model makes sense for your business. The output is a blueprint, not a statement of work for configuration.

We’re tool-agnostic. We work primarily in HubSpot and Salesforce, but we also work with Clay, Common Room, Pocus, and whatever else is in your stack. If a tool isn’t earning its place, we’ll tell you. If you need something you don’t have, we’ll recommend it. We’re not trying to sell you more software. We’re trying to make your system work.

We build a process, not just a configuration. Documentation, data governance, routing rules, enrichment logic, reporting definitions - this is the stuff that makes tools actually usable. It’s also the stuff that gets skipped when you’re rushing to launch.

And we stay close. Our retainer model exists because this work isn’t one-and-done. Stacks drift. Requirements change. New tools get added. Someone needs to own the ongoing architecture, not just the initial build.

The companies we work with don’t think of us as their HubSpot consultants. They think of us as the team that makes their GTM infrastructure work.

The Shift

The framing most companies use for this work is reactive. Fix our broken attribution. Clean up our HubSpot. Get our CRM in order.

Those are symptoms. The underlying problem is that nobody’s architecting the system.

The shift is from “fix my tools” to “architect my GTM infrastructure.” From treating rev ops as maintenance to treating it as a strategy. From buying tools and hoping they work to designing a system that actually fits how you sell.

The tools are powerful. The vendors are building real capabilities. The AI GTM wave is going to add even more. But none of it matters if you don’t have someone thinking about the whole system.

That’s the layer that’s missing. That’s what forward-deployed GTM means.

Last quarter, a B2B SaaS company came to us with a familiar complaint: “Our paid media isn’t working.”

We pulled their CRM data. 47% of paid leads from the previous quarter were still sitting in “open” status. Never worked. Never touched. Just... sitting there.

Their paid media wasn’t broken. Their system was.

The Usual Suspects

When paid underperforms, teams reach for the usual fixes. Restructure campaigns. Test new creative. Tighten targeting. Refresh audiences. Audit the bid strategy.

All good stuff. All stuff that matters. We do it too.

But here’s what we’ve learned after years of running paid for B2B companies: the campaigns are rarely the whole problem. Sometimes they’re not the problem at all.

Paid media is one node in a go-to-market system. And a channel can’t outperform the system it sits in.

Three Problems Disguised as Paid Media Problems

1. The Measurement Problem

Most teams measure paid on last-touch attribution. The demo request gets credit. The pricing page visit gets credit. That bottom-funnel retargeting ad gets credit.

You know what doesn’t get credit? The LinkedIn campaign that introduced them to you six months ago. The thought leadership that got them on your newsletter. The brand impression that made them recognize your name when they finally hit market.

Last-touch says TOFU doesn’t work. So you cut it. You shift budget downstream where the “conversions” are.

Six months later, pipeline is down 30% and nobody can figure out why. Market conditions, right?

No. You starved the top of funnel because your measurement told you it was waste. It wasn’t waste — you just couldn’t see what it was doing.

We had a client whose cost per MQL was $1,240. After we rebuilt their attribution — connecting Pardot to Salesforce properly, tracking influence across touches — that same campaign structure dropped to $216. The campaigns didn’t change. The measurement did. And suddenly leadership could see which channels were actually driving pipeline, not just which ones got the last click.

2. The RevOps Problem

Here’s a question: what percentage of your paid leads from last quarter actually got worked?

Not “assigned.” Worked. A human looked at them, made a call, sent an email, tried to start a conversation.

We’ve seen 40%. 60%. Sometimes worse.

These aren’t bad leads. They’re unworked leads. They got routed to the wrong rep. Or routed to a rep who’s cherry-picking and ignoring anything that isn’t a hand-raiser. Or they sat for five days before anyone touched them and by then the buyer had moved on.

One client had contacts being marked as MQLs just for opening an email. Support tickets were triggering sales alerts. The sales team couldn’t tell the difference between an opportunity and a tire-kicker because the system was lying to them.

Your paid media looks inefficient because lead activation is broken. Your CAC is high not because acquisition is expensive — but because you’re paying to acquire leads you never actually work.

This isn’t a paid media problem. It’s a RevOps problem. But it shows up in your paid metrics.

3. The Feedback Loop Problem

When’s the last time sales told marketing which paid leads actually converted to revenue?

Not MQLs. Not SQLs. Closed-won deals traced back to the campaign that sourced them.

Most teams can’t answer this. Marketing optimizes toward leads. Sales works deals. Nobody closes the loop.

We worked with a company spending heavily on Meta. Lots of MQLs. Leadership was happy with the volume. But when we built reporting that tracked all the way to opportunities and closed-won — not just form fills — we found those Meta leads weren’t converting to revenue. The CPL looked great. The pipeline contribution was nearly zero.

They didn’t have a paid media problem. They had a “we’re measuring the wrong thing” problem. Once we shifted reporting to SQLs and pipeline, they reallocated budget to channels that actually drove revenue. Same spend, completely different outcome.

The Compound Effect

These three problems don’t just add up. They compound.

Bad measurement → wrong budget allocation → TOFU gets cut → fewer leads entering the funnel → RevOps atrophies because there’s less to work → less signal back to marketing about what’s working → even worse budget decisions.

It’s a death spiral disguised as “paid media efficiency problems.”

Compound GTM

We’ve started calling this way of thinking Compound GTM.

The core idea: execution alone doesn’t compound. You need a full stack where each layer reinforces the others.

Layer 1: Audience & Intelligence
Target accounts, CRM data, ICP definition. The foundation that tells you who to reach.

Layer 2: Orchestration & Activation
Ads, outbound, content, events. The tactics that put you in front of buyers.

Layer 3: Measurement & Operations
Attribution, reporting, lead routing, lifecycle management. The infrastructure that tells you what’s working and ensures leads actually get worked.

Most teams over-index on Layer 2. They optimize campaigns endlessly while Layers 1 and 3 are broken or missing entirely.

But when all three layers work together, the gains multiply. Better targeting means higher-quality leads. Better measurement means smarter budget allocation. Better operations means leads actually convert. Each layer amplifies the others.

10% improvement in campaign performance is nice. 10% improvement across all three layers? That compounds into something much bigger.

The Real Question

So when a team tells us “paid isn’t working,” we don’t start with the campaigns.

We ask: How are you measuring it? What happens to leads after they convert? How fast are they getting worked, and by who? Does sales ever tell you what turned into revenue?

Nine times out of ten, the campaigns aren’t the problem.

The question isn’t “how do we make paid more efficient?”

It’s “where is the system breaking down — and what would happen if we fixed all of it?”

Most agencies audit your campaigns. We audit your system.

There’s a quiet crisis happening in B2B marketing right now — one most marketers feel but rarely name.

The channels that once delivered predictable performance are now crowded, expensive, and repetitive.

Every B2B company is competing for the same few square inches of real estate in the LinkedIn feed. CPMs and CPCs rise quarter after quarter, while engagement flattens. The algorithms are efficient; the audience, numb.

It’s not that the channels stopped working.

It’s that we squeezed the juice out of them.

Marketers have optimized themselves into a corner. Everyone’s running the same playbook — the same creative, the same offers, the same cadence of remarketing. Every dashboard looks identical: LinkedIn, Google, retargeting, repeat.

The feed has become wallpaper.

And that’s the backdrop for what’s quietly becoming one of the most interesting shifts in B2B media: Connected TV (CTV).

While everyone else is driving up LinkedIn auction prices, CTV is sitting in plain sight — underleveraged, underpriced, and massively underused in B2B.

At 42, we started experimenting with LinkedIn’s new CTV placements earlier this year. What we found surprised even us:

• CPMs and Cost per 1,000 members reached came in roughly 50% lower than comparable LinkedIn in-feed video campaigns.

• CPAs were 30% lower.

• Completion rates were nearly perfect. You can’t scroll past a CTV ad. You can’t skip it. You see it — full-screen, in motion, with sound.

It’s a rare moment in marketing when a channel is both premium and cheaper than the alternatives.

And it’s giving B2B marketers something we’ve been losing for years: time, space, and attention.

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From Feed Fatigue to Full Screen

At 42, we’ve seen this story before.

Every high-performing channel — Google Search, LinkedIn, even Meta for B2B — eventually hits saturation. Costs rise, quality drops, and incremental dollars stop driving the incremental pipeline.

LinkedIn is still the best intent-based social platform for B2B, but let’s be honest: it’s become an echo chamber. Everyone’s saying the same thing to the same people in the same way.

CTV breaks that pattern.

It moves your message from the feed to the living room. It interrupts the scroll with scale. You can’t skip it. You can’t tab away. It demands attention in a different posture — the audience isn’t multitasking; they’re watching.

And there’s something powerful about that full-screen moment. It still carries prestige.

You don’t hear someone say, “I saw your sponsored post on LinkedIn.”

But you do hear, “I saw your ad on Hulu.”

That line still has weight in a B2B boardroom.

Respecting the Medium

Of course, the creative bar is higher.

What works on social doesn’t automatically translate to CTV.

We’ve seen pixelated, low-fi ads from B2B companies — clearly built for mobile — playing on a 55-inch screen. It looks cheap. It breaks immersion. It’s a waste of real estate.

CTV demands high production value.

Strong sound. Cinematic pacing. Real storytelling.

It’s not about performance gimmicks — it’s about presence. You’re not just trying to generate a click; you’re trying to imprint your brand in memory.

That’s the shift: CTV isn’t a feed format. It’s a storytelling medium for a performance-driven world.

CTV as a Diagnostic Tool

One of the most underrated benefits of CTV is what it tells you about your audience.

When you run CTV through LinkedIn, you’re not just buying impressions — you’re getting demographic and account-level reporting.

That turns it into something like a diagnostic gauge for your total addressable market.

For example, if your target job titles or accounts aren’t showing up in your in-feed campaign reports, but they do appear in your CTV delivery data, that’s a signal.

It means those people might not be active on LinkedIn in the ways your feed campaigns assume — they’re not scrolling, but they’re watching.

So you can shift your strategy: use CTV to build awareness with those hard-to-reach segments, and then recapture them later through paid search or remarketing.

In other words, CTV helps you see the shape of your market — not just the active slice.

That alone makes it worth testing.

The Mechanics: How CTV Actually Works

A lot of marketers still think CTV is too complex or expensive to test. That’s no longer true.

LinkedIn’s native CTV inventory and platforms like StackAdapt or The Trade Desk make it possible to buy Connected TV placements with the same precision as digital.

Creative Specs (LinkedIn CTV example):

• Format: 16:9 aspect ratio

• Resolution: 1920×1080px

• Duration: 45–60 seconds recommended

• Bitrate: 15 Mbps

• Max file size: 500 MB

• Audio: Stereo AAC preferred

That’s standard, broadcast-quality material — easy for most in-house or agency teams to produce.

Campaign setup is also straightforward.

On LinkedIn, you can use the same campaign manager interface, but target by company, industry, job title, and seniority. You can layer your ABM lists or use first-party audiences to reach specific accounts.

And for platforms like StackAdapt or The Trade Desk, you can even import LiveRamp audience segments, retarget site visitors, or build lookalikes from your CRM data.

It’s a blend of TV storytelling and programmatic precision.

Busting the “CTV Isn’t Measurable” Myth

Let’s address the biggest misconception head-on:

“CTV isn’t measurable.”

It is. It just requires thinking beyond the click.

You’re not tracking who clicked on your ad — you’re tracking who saw it, and what happened after.

On LinkedIn, you can measure view-through conversions. That means when someone sees your CTV ad and later fills out a form, requests a demo, or visits your site within 30 days, LinkedIn attributes that conversion to the CTV exposure.

You can also layer in CRM data to look at pipeline influence: how many opportunities were created or advanced after exposure to CTV campaigns.

And if you’re using a data visualization platform like Databox, you can track lift over time — branded search volume, direct traffic, or account engagement — before and after launching a CTV flight.

For higher-volume programs, you can even run statistical lift studies: A/B tests where one region or audience segment sees CTV and the other doesn’t, then compare pipeline impact.

The trick isn’t whether you can measure it — it’s whether you’re measuring the right thing.

Clicks don’t tell the whole story anymore. Attention does.

CTV in the ABM Playbook

CTV becomes even more powerful when you think about it as part of an account-based marketing strategy.

The best ABM campaigns create what we call surround sound: your message shows up everywhere your target audience spends time.

The problem is, LinkedIn alone can’t achieve that anymore.

Decision-makers log in sporadically. Algorithms throttle reach. There’s a hard cap on impressions.

CTV fills that gap.

You can reach executives who may never engage with your sponsored content but will still see your message in a premium, trusted environment — while watching news, sports, or streaming.

We’ve run CTV campaigns for enterprise clients where the reporting showed the exact accounts being reached — sometimes hundreds of key companies that hadn’t appeared in months of in-feed engagement data.

That’s what makes CTV a force multiplier in ABM:

• It reaches the unreachable.

• It builds familiarity before your sales team ever reaches out.

• It complements your digital mix with a brand layer that feels bigger, more credible, more real.

When combined with first-party CRM lists, StackAdapt or Trade Desk campaigns, and audience data from platforms like LiveRamp, you can push your ABM strategy well beyond the feed.

CTV becomes the awareness layer that drives better performance everywhere else.

We’ve seen LinkedIn CTV and search campaigns feed each other in interesting ways.

When CTV flights go live, branded search volume almost always spikes within two weeks.

It’s not a coincidence, it’s recognition.

CTV and YouTube: Different Tools, Same Story

We often get asked: “If I’m already running YouTube, do I really need CTV?”

The answer: it depends on what story you’re trying to tell.

YouTube is still one of the best intent-linked awareness channels out there — especially because it ties directly into your Google Search performance. You can target people based on keywords, search behavior, or remarketing lists.

That makes YouTube ideal for reinforcing performance channels.

Someone searches for your product, watches your YouTube ad, and then clicks a search ad a few days later — measurable, attributable, clean.

CTV, on the other hand, is more independent.

It’s not tethered to Google’s ecosystem or search intent. It’s built for reach and resonance.

If YouTube amplifies what people are already looking for, CTV reaches the people who aren’t looking yet.

That’s why they work beautifully together.

We often pair the two:

• CTV to drive broad awareness and emotional storytelling.

• YouTube to re-engage warm audiences, reinforce intent, and nudge them toward action.

In measurement terms: CTV lifts the ceiling; YouTube fills the funnel.

The Future: Brand and Demand, Reunited

For the last decade, B2B marketers have been trained to chase efficiency.

Every channel was judged by conversion rate and CPL. Every creative asset was designed to be “direct response.”

It worked — until it didn’t.

Today, the constraint isn’t data. It’s differentiation.

Everyone has the same targeting tools, the same automation, the same dashboards. What’s scarce is memory.

CTV brings that back into the mix.

It doesn’t replace your performance channels. It amplifies them. It’s what happens when brand and demand finally stop fighting and start collaborating.

Run a CTV ad, and then check your Google Analytics a few weeks later.

You’ll see the curve bend. Branded search goes up. Direct traffic rises. Email open rates improve.

That’s not attribution magic. That’s what happens when people remember you.

At 42, we think that’s where the next decade of B2B marketing is heading — a world where brand recall and performance data live in the same playbook.

Because attention is finite. Channels evolve. But storytelling still scales.

And for now, Connected TV is the most underpriced, underutilized storytelling surface in B2B.

It’s not a trend. It’s a reset.

The B2B feed is saturated.

Performance marketing has plateaued.

The next edge won’t come from cheaper clicks — it’ll come from deeper impressions.

CTV gives you that — premium attention at a performance price.

The question isn’t if it’ll become core to B2B marketing.

It’s when you’ll start testing it. If you're considering CTV, give us a shout.

Every B2B marketer has seen it.

The neat little spreadsheet where every dollar of paid media scales in a straight line:

💰 $100K in → 📈 $800K pipeline out.

So $200K in → $1.6M pipeline.

Easy.

I’ve been in those meetings. The models look airtight. Finance loves them. The board nods along.

And then the campaign runs.

Spend doubles. Pipeline doesn’t.

By month three, the curve flatlines completely.

Not because the media team failed. Not because the ads didn’t run. But because the model itself was broken.

The truth is: pipeline in B2B does not scale linearly. It bends.

The Mirage of Linear Growth

Linear scaling works in consumer markets. If you sell toothpaste, adding budget really does just buy more reach.

But in B2B, you’re not selling toothpaste. You’re selling to a finite, specialized audience who only buys occasionally, in cycles, often with ten other people in the room.

The first dollars you spend are your best dollars. They hit:

• In-market accounts.

• Buyers actively researching.

• ICPs already problem-aware.

But once you saturate that group, the math changes.

The next dollars don’t go to more in-market buyers (there aren’t any left). They go to:

• Adjacent personas.

• Accounts that fit ICP but aren’t ready.

• The same VP of IT who’s already seen your ad six times this week.

That’s the diminishing returns curve. And if you don’t account for it, your forecasts are already wrong.

How We Model the Curve

When we forecast for clients, we don’t stop at “$1 in = $X out.” That’s only the first step.

We build models in layers:

Step 1. Establish the Baseline

Start with real MoM data: Spend → MQLs → SQLs → Opportunities → Pipeline.

From that, we derive a baseline efficiency — pipeline-per-dollar at current spend. This gives us the linear model. Useful, but naive.

Step 2. Measure the Ceiling by Channel

Here’s where it gets interesting.

• Search (Google/Bing):

  The ceiling is hard-coded in query volume. If there are only 5,000 relevant monthly searches for your category, no amount of budget will make more people search. Once you max impression share, you’ve hit the ceiling.

• LinkedIn Ads:

  We map TAM by account list and persona, then overlay:

  • 3rd-party intent signals.

  • 1st-party intent (website visits, product interest).

  • Buying signals like open roles tied to the problem you solve.

  This defines your “addressable universe” and the frequency at which you can show up before fatigue sets in.

• Programmatic & Display:

  Looser targeting, but the ceiling is tolerance: how much spillover outside ICP are you willing to pay for before pipeline-per-dollar collapses?

Step 3. Layer Brand & Qualitative Signals

The most overlooked step.

Pipeline isn’t just math — it’s memory.

A company with strong brand recall will convert impressions to pipeline at a higher rate. A company with weak recall will struggle, no matter how targeted the spend.

We’ve run brand recall surveys where only ~20% of ICP respondents said they were “very familiar” with a company. Many said “heard of the name, not sure what they do.” That number is effectively a ceiling: you can’t expect efficient pipeline until familiarity shifts.

A Forecasting Exercise

Recently, we ran a forecasting exercise for two mid-market SaaS brands in adjacent industries.

When we applied the linear model, the math said:

• ~$1.7M spend (over 4 months) could generate ~$14M pipeline.

• ~$5.1M spend could generate ~$42M pipeline.

On paper, it looked achievable.

But when we ran the diminishing returns model — tiering efficiency down after the first $500K/month — the curve collapsed:

• $1.7M delivered closer to ~$10M, not $14M.

• $5.1M delivered closer to ~$24M, nowhere near $42M.

The lesson wasn’t “don’t spend.” It was:

• Know where the curve bends.

• Plan for diminishing returns.

• Treat later spend not as immediate pipeline, but as future demand creation.

That subtle shift — from pipeline math to demand strategy — changes the whole conversation with leadership.

Brand Recall in the Wild

Here’s another lens.

We ran a recall survey for decision-makers in one market. The results?

• Only 22% were “very familiar.”

• 38% were “somewhat familiar.”

• The rest had either just “heard the name” or “not familiar at all.”

That’s a ceiling.

Until you grow the “very familiar” segment, pipeline efficiency will be capped, no matter how much you spend.

Visual 2: Brand Recall Distribution

This is where brand and demand intersect. Paid media doesn’t just capture demand — it creates mental availability. And that availability is measurable.

The Second-Order Effects

Here’s the part that rarely makes it into a forecast deck: second-order effects.

We ran campaigns for an enterprise software company. During the flight, branded organic search spiked — materially. People weren’t just clicking ads; they were Googling the brand.

That’s demand creation at work. Paid didn’t just produce direct conversions. It nudged memory, which showed up in search volume.

Visual 3: Paid Media Spend vs. Branded Search Lift

(Drop in the second-order effects chart here)

When you ignore second-order effects, you undervalue paid media.

When you only count direct pipeline, you miss the compounding.

Why This Matters

Because your CFO is going to ask: “If $1 in gets $X pipeline, why don’t we just spend 10X?”

And your job is to explain the curve:

• Early dollars = capture.

• Later dollars = creation.

• Every channel has a ceiling.

This isn’t about underselling paid. It’s about reframing it.

Paid media is not a faucet you can just turn up. It’s a curve. And unless you map the curve — with ceilings, brand recall, and second-order effects — you’re driving blind.

Closing

The most dangerous assumption in B2B marketing is that growth is linear.

It’s not.

It curves.

And great marketers don’t just spend more.

They forecast the plateau. They measure the ceiling. They use brand and qualitative insights to expand it.

That’s the work.

Because the question isn’t “How much pipeline will $X get us?”

The real question is: “Where does the curve bend — and what do we do next?”

We spent $15,000 last month, of our own money, not on media, not on creative production, and not on more leads.

We spent it on understanding.

Message tests. Brand recall surveys. Buyer interviews. The kind of work most performance agencies avoid, not because it isn’t valuable, but because it doesn’t show up neatly in a dashboard. You can’t A/B test your way to relevance if the underlying message doesn’t resonate. You can’t optimize your way out of obscurity if buyers don’t even remember your name.

At most agencies, that’s considered “brand” work — a fuzzy line item, often pushed aside in favour of lead volume or lower CPLs.

But here, we see it differently.

We treat message clarity and brand recall the same way we treat cost per lead or conversion rate — as levers to be measured, refined, and scaled. Not because we’re sentimental about good writing or clever taglines, but because you cannot build a pipeline off a message that your buyers forget the moment they scroll past it.

The illusion of performance problems

Campaigns stall for all sorts of reasons. The CTR dips. The CPL creeps up. Sales starts to complain about lead quality. On the surface, it looks like a media problem. The natural instinct is to tweak bids, adjust targeting, try new ad formats, or shift budgets around.

But more often than not, the real issue is upstream.

It’s not that the campaign isn’t working.

It’s that no one understands what you do, or remembers you after the first touch.

We’ve seen this time and again: performance issues that aren’t actually performance issues. They’re messaging issues. Positioning issues. Confusion issues.

You can’t scale a funnel built on a fuzzy story. And yet, most teams do exactly that. Then they wonder why their metrics are stuck.

The test that told us everything

Earlier this year, we ran a brand survey for a B2B company in the employee experience space. The team had been running LinkedIn and Google campaigns for months. Traffic was fine. CTR was stable. But conversions were lagging, and the brand wasn’t showing up in sales conversations.

So we asked their audience directly.

Over 200 decision-makers, all in the ICP. Full-time professionals, mid-to-large companies, roles in HR and IT.

Here’s what we found:

• 0% unaided recall.

• Just 18% aided awareness.

• Nearly half had never heard of the brand.

• Of those who had, most couldn’t explain what they did or how they were different.

We layered this with qualitative feedback from a homepage message test.

“Still don’t know what this does.”

“Feels like buzzwords.”

“What is ‘organizational velocity’? Why should I care?”

The team had great visuals, a sleek product, and paid media running. But it wasn’t sticking. Not because of media performance, but because the message lacked clarity.

You can’t scale confusion

When messaging is unclear, everything else becomes expensive.

Your ads have to work harder. Your sales cycle gets longer. Buyers bounce before they get the value. And attribution gets noisy because even the good traffic doesn’t convert.

This is why we ask:

• What brands come to mind in this space?

• What problem do you think they solve?

• What makes them different?

• What do you remember, if anything?

These aren’t fluffy brand questions. They’re conversion diagnostics.

When unaided recall is low, the pipeline gets leaky. When value prop comprehension is weak, CAC spikes. When you sound like everyone else, the buyer picks the loudest brand, not the best one.

The two sins: no brand vs. no activation

We were customers of a major HR tech platform. It worked well enough.

But over time, we started seeing content from a competitor. We saw the founder speak. Read their LinkedIn posts. Watched short clips explaining their POV. The message stuck.

So when they eventually reached out? We switched.

No nurture required. No drawn-out pitch. Just a familiar brand, offering a better fit.

The brand work had already been done. The outbound just closed the loop.

This is the first sin: trying to activate demand without building memory. Every touch is cold. Every deal takes longer. CAC climbs. It’s a grind.

But the second sin is just as bad: building awareness with no intent to convert. You warm the market with thought leadership, podcasts, and content, then sit back and wait.

While your competitor makes the call.

We’ve seen both sides. Which is why we say:

Brand without activation is arts & crafts.

Activation without brand is a grind.

You need both.

The 5% myth (and why timing matters)

Everyone loves to quote it:

“Only 5% of your market is in buying mode at any given time.”

And in many categories, that’s true.

But when we ran a brand survey for a client in the IT and operations space, the results told a very different story:

• 79% were actively buying or planning to.

• 56% planned to purchase in the next 90 days.

This wasn’t a drip. It was a flood.

But here’s the catch: just because the market is in motion doesn’t mean they remember you. And if they don’t, they won’t buy from you.

Awareness isn’t enough. You need relevant awareness. Timed. Specific. Memorable.

What buyers actually said

Across message tests for three different B2B brands in the last quarter, the feedback was almost identical:

“What is this?”

“Too vague.”

“Feels like it’s written for investors.”

“Why should I care?”

“Still don’t know what this does.”

This isn’t a design problem. It’s not a copy problem.

It’s a clarity problem.

And once we fixed that, restructured the landing page, swapped jargon for plain language, brought in specific proof — everything downstream improved. Better engagement. More demos. Lower CPL.

Same product. Same budget. Different outcome.

Insight → Message → Media

We used to follow the classic order:

Launch media. Get leads. Adjust if things break.

Now we flip it:

1. Insight → Ask questions. Run surveys. Watch what buyers say.

2. Message → Shape your positioning around what they actually care about.

3. Media → Spend behind the story that’s proven to resonate.

This approach doesn’t just improve performance. It builds confidence.

Confidence that your funnel isn’t leaking because of a misunderstanding. Confidence that your ads will convert, not because they’re clever, but because they’re clear.

The cost of invisibility

One brand we worked with had all the signs of a category leader.

• World-class team

• Big-name logos

• Backed by strong investors

But after six months of active campaigns, they ran a recall survey.

0% unaided.

Even when prompted, most couldn’t describe what the company did. But their competitors? Recalled easily. Understood clearly. Chosen more often.

That’s the cost of invisibility.

Buyers don’t always pick the best product.

They pick the one they remember.

Brand recall is a performance metric

We don’t treat brand work as separate from performance.

We treat it as the foundation.

Because if your message doesn’t stick, your funnel will stall. If your story is unclear, your spend becomes inefficient. If your name doesn’t come up in the right moments, someone else wins by default.

That’s why we spend money on message tests.

That’s why we ask buyers what they remember.

That’s why we pair brand and activation like they’re two parts of the same machine.

Performance starts before the click.

It starts with a moment of recognition.

And that’s what brand recall really is.

Not a nice-to-have.

Not a soft metric.

A lever.

And one worth pulling.

Why this matters now (and why we’re different)

This isn’t theory. It’s how we operate.

In one recent project, we identified the top competitors in the space and launched campaigns to position our client directly against them. Not vaguely. Not aspirationally. But head-to-head. Because the brand recall data told us buyers didn’t know how to choose, and we saw that as an opportunity to shape the category.

In another, we realized that while prospects recognized the brand name, they didn’t know what the company did. Awareness was high. Clarity was low. So we launched a top-of-funnel campaign explicitly designed to say: here’s who we are, what we do, and who we do it for.

In a third case, the brand survey showed us something hard to see from inside: we had been over-investing in bottom-of-funnel campaigns. Demo CTAs, sales outreach, retargeting. But buyers weren’t ready. Not because of intent, but because they didn’t remember the brand. So we reallocated the budget to early-stage campaigns to fix the front of the funnel.

And in every case, we took what we heard from real buyers and fed it straight back into the work. The homepage copy was rewritten. Ads were refocused. Personas were sharpened.

Brand recall wasn’t a PowerPoint. It was an operating input.

Most demand agencies start with the media plan.

We start with the message.

Where others chase leads, we look upstream.

Where others separate brand and performance, we merge them into a single system.

Where others deliver impressions, we aim for memory.

We believe that the brand is not a cost center. It’s a performance layer.

That’s why we invest in message testing.

Why we run brand recall surveys.

Why we help clients define a narrative that sticks.

Not because we’re trying to win creative awards.

But because we want your campaigns to work better.

To close faster. To scale with less waste. To convert more efficiently.

If you’re tired of watching good ads fail because the message is fuzzy, you’re not alone.

If your brand shows up in front of the right people, but they don’t remember what you do, that’s fixable.

And if you’re trying to build a pipeline in a competitive market without being memorable, that’s a solvable problem.

This is what we do.

We’re not just a performance agency.

We’re the demand partner that starts with first principles.

That treats brand as infrastructure.

And builds every campaign from a foundation of clarity, resonance, and recall.

Want to work together? Drop us a note.

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One of the common issues with B2B Marketing measurement is funnel reporting. It's not that we don’t understand how the ‘funnel’ works, everyone is familiar with the lead → MQL → SQL → SAL → Opp → Customer progression. The issues happen when we don’t define the funnel well & answer questions like

• What is a lead

• How does a lead convert to SQL

• How is SQL defined

• What are the parameters for marking someone an MQL

• How do we report on the funnel

• What does our cohort report look like

It starts with a person

Even though B2B buying is all about selling to companies, a company itself can’t start a sales discovery call or be “in market” for a new solution. It always starts with a lead — a person inside the company who is expressing intent or engaging with sales.

A lead can be defined as a person with a name, email, and company who has interacted with our brand (by visiting the site, filling out a form, or engaging on social media).

In the traditional inbound model, a lead was easy to define: someone who filled out a form on your website — downloading an ebook, requesting a demo, subscribing to a newsletter.

Today, that definition is no longer enough. With the explosion of first-party and second-party intent data (1P/2P intent), a lead can (and should) be recognized much earlier and across a broader range of signals.

A modern lead is any person with a name, email, and company who has meaningfully interacted with your brand or shown intent. That interaction could take many forms:

• Visiting your pricing or product pages

• Engaging with your brand’s posts on LinkedIn

• Watching a webinar or event replay

• Clicking on a high-intent paid ad

• Being identified through tools like ClearBit, Breeze, Warmly, or Syft when they visit your website

In other words:

A lead today isn’t limited to hand-raisers.

It includes people showing buying interest even if they haven’t yet “asked” to speak to sales.

Modern B2B marketers need to rethink lead generation not as capturing only inbound, but as recognizing early intent wherever and however it surfaces — and having a system to capture, score, and act on it. The lifecycle stage is the place within the funnel where a particular account is located.

Traditional Inbound Was All About 1st-Party Data.

The traditional inbound model built everything around first-party (1P) data.

A prospect would come to your website, fill out a form (e.g., download an ebook, request a demo), and cookies would connect their visit to their identity. Once a form was filled out, the CRM could track and attribute all future behavior to that known person.

This was the core model that HubSpot built its software around, and the foundation of the inbound marketing category.

It’s also why gated content — like ebooks, templates, and whitepapers — became so central to B2B marketing.

The idea was simple:

• Not everyone is ready to talk to sales immediately.

• By offering high-value content, marketers could de-anonymize visitors early and nurture them until they were sales-ready.

Downloading an ebook was seen as an early buying signal — a lightweight conversion that identified interest without requiring a demo request.

Why the 1P Gated Model Broke Down

Over time, this model started losing effectiveness, primarily for two reasons:

1. Decline in Content Quality:

Marketers began chasing form fills at all costs, producing high volumes of low-value, generic content.

Rather than solving real problems or connecting to pain points, much of the gated content became clickbait, generating contacts, but not qualified interest.

The ebook download was no longer a reliable signal of intent.

2. Misaligned Handoff to Sales:

Early-stage leads (people downloading TOFU content) were often treated the same as demo requests.

Instead of nurturing them appropriately, marketing would pass these contacts straight to sales, creating poor buyer experiences, frustrating sales teams, and ultimately harming trust.

As buyers became more sophisticated and poor follow-up experiences became common, fewer and fewer people were willing to fill out forms at all.

The Modern Funnel: 1P, 2P, and 3P Intent Signals

Today, marketers no longer have to rely solely on form fills to identify potential buyers.

We now have a richer view of buying behavior through a combination of:

• First-party (1P) intent: Direct engagement on your owned channels (e.g., visiting the pricing page, starting a demo flow, attending your webinar).

• Second-party (2P) intent: Engagement on partner-owned channels where you can access the data (e.g., liking your company’s LinkedIn post, commenting on a team member’s post, following your brand on social).

• Third-party (3P) intent: External research activity captured via data providers (e.g., someone reading about your product category on G2, TrustRadius, Bombora).

Importantly:

• Some social signals (like engaging with your company page) are still technically 1P because you own your company’s LinkedIn or X (Twitter) profile.

• If you’re capturing aggregated social intent data through a partner or a data-sharing platform, that moves into 2P intent territory.

• 3P intent generally covers data you don’t own or don’t directly observe — it’s purchased, licensed, or syndicated data about what topics buyers are researching elsewhere.

In short:

The funnel no longer starts with a form fill.

It starts when a buyer shows any sign of interest — whether it’s on your site, in your network, or across the broader internet — often before they ever willingly identify themselves.

How the B2B Funnel Changed

The traditional B2B Funnel (as defined in Hubspot / Inbound Marketing)

Subscriber → Lead → MQL → SQL → Opp → Customer

Because of the changes in how buyers engage and how go-to-market (GTM) strategies operate today, a “lead” is no longer just a single contact who filled out a form.

A lead can now emerge from any of the three types of intent signals outlined above — 1st-party, 2nd-party, or 3rd-party.

Yet despite this evolution, the biggest mistake in B2B marketing measurement remains the same:

Treating the funnel as a single object moving cleanly through stages — Lead → MQL → SQL → Opp → Customer.

At the very beginning of the funnel, that model makes sense:

• It starts with a single lead — a known person showing intent.

• Early actions (site visit, form fill, ad engagement) are attached to that individual.

Previously, a Marketing Qualified Lead (MQL) was often defined by basic engagement thresholds:

• Opening a certain number of marketing emails

• Visiting a set number of pages on the website

• Downloading a piece of gated content

But today, marketers have access to a much wider range of data points — across 1st-party, 2nd-party, and 3rd-party intent sources.

We can now qualify leads not just by surface-level engagement, but by deeper buying signals:

• Visiting high-intent pages (pricing, demo request)

• Watching or attending product webinars

• Following or engaging with executives or brands on social media

• Researching solution categories on external review sites like G2 or TrustRadius

• Being flagged by third-party intent data providers as “in-market” based on aggregated research patterns

In short:

MQLs today need to be defined by true buying behaviors, not just generic marketing activity.

Modern MQL scoring should blend volume of engagement (how much?) with quality of engagement (how meaningful?) & ICP fit (are they from the right kind of account?) and type of signal (owned, earned, or external).

The SQL Blackhole

One of the most common breakdowns in B2B marketing and sales alignment happens right after a lead becomes an SQL.

This moment is known as the Sales <> Marketing Handoff.

Here’s what typically happens:

• Marketing identifies a lead who fits the ICP (Ideal Customer Profile) and shows buying intent based on engagement signals (e.g., website activity, content downloads, 2p/3p intent signals).

• Marketing then says:

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“This is a qualified person. Sales, it’s now your job to take this lead and determine whether there’s a real commercial opportunity here.”

At this point, the lead is handed over to a Business Development Rep (BDR/SDR) or directly to an Account Executive (AE) to qualify further.

Most sales teams use established qualification frameworks like:

• BANT (Budget, Authority, Need, Timing)

• MEDDIC (Metrics, Economic Buyer, Decision Criteria, Decision Process, Identify Pain, Champion)

• SPICED (Situation, Pain, Impact, Critical Event, Decision)

The goal of these frameworks is simple:

• Quantify the buyer’s pain or need

• Confirm urgency and timeline

• Validate whether the opportunity is real and worth pursuing

• Size the potential deal ($$ value)
  This is also where things break the most.

The danger is that without clear tracking at this stage, leads fall into the “SQL blackhole” — accepted by sales, but never properly qualified, advanced, or rejected.

SQL is often where things fall into a blackhole. Marketing and sales will yell at each other, and generally, the lovely funnel starts to break down.

This happens primarily because marketing doesn’t understand the sales qualification process and the mini funnel that comes with it, BD teams don’t enforce or follow SLA, or update the status on the lead and systems to enable automation to minimize human errors.

The SQL stage actually has a ‘mini funnel’ under it, which tracks where the lead currently sits within its BD / Sales Qualification Journey. Typically, it looks something like this.

New

The lead has been assigned to a rep but has not yet been contacted.

Contacted

Sales has attempted outreach (email, call, social DM, etc.).

Meeting Scheduled

A discovery call or meeting has been booked.

Meeting Held

The first sales conversation with the prospect has been completed.

Qualified

The lead has met qualification criteria (BANT, MEDDIC, SPICED) and an Opportunity is opened in CRM.

Disqualified

The lead was contacted but deemed not a good fit (e.g., wrong ICP, no urgency, no budget).

Recycle

The lead is not ready now but could be a good fit later; sent back to marketing for nurture and re-engagement.


Tracking Lead Status accurately matters because it provides visibility into sales activity, accountability for lead progression, and insights into where breakdowns happen.

Without it, the SQL stage becomes a black box, and it’s impossible to know whether pipeline problems are due to lead quality, sales follow-up, or buyer readiness.

From Lead to Opportunity: The Operational Shift

Going back to our funnel, this handoff is not just a change in team ownership — it’s a shift in how the system tracks and measures the buyer journey.

Up until qualification (SAL and SQL), the lead is a single object — an individual person showing intent.

However, once a lead is qualified, that individual lead ceases to be the primary object.

From an operational and systems perspective, we now transition into managing three key objects:

Opportunity (Deal)

Account (Company)

Contacts (Buying Committee)

Here’s what happens next:

• The Opportunity represents the commercial potential: a formal deal with forecasted revenue and timelines.

• The Account represents the company or organization we are selling into.

• The Contacts represent the people — decision-makers, champions, influencers — who are part of the buying process.

In other words, B2B sales no longer targets just one person; it targets the organization through multiple stakeholders.

The Internal Sales Handoff: BDR to AE

This is also where an important internal sales handoff occurs between the BDR (Business Development Representative) and the AE (Account Executive).

• The BDR’s job is to qualify whether there is a viable Opportunity.

  • They focus on early discovery: Does the account have a real need, budget, urgency?

• Once a qualified Opportunity is identified, the BDR hands off to an AE.

  • The AE’s job is to take over the sales cycle: running deeper discovery, demoing the product, building the business case, navigating procurement, and closing the deal.

• (In some models, full-cycle AEs handle both discovery and closing without a separate BDR layer.)

Why This Transition Matters for Funnel Measurement

The moment an Opportunity is created, the old single-lead model no longer applies.

• You are no longer tracking a person moving through the funnel.

• You are tracking an Account, an Opportunity, and a group of Contacts.

If your measurement, reporting, and attribution models don’t adjust at this stage, you lose visibility into what’s actually happening inside your pipeline.

You risk making marketing look ineffective (“bad leads”) or sales look slow (“bad execution”) — without the correct data to diagnose where the real problems are.

Key point:

Funnels today must be designed to evolve — from tracking individual leads to tracking opportunities driven by buying groups at the account level.

From an operational point of view, once an Opportunity is created, you must bridge your reporting from Leads to Opportunities, Accounts, and Contacts.

• Early in the funnel, marketing and sales are tracking Leads (which in HubSpot = individual Contacts).

• But after qualification, the Opportunity becomes the primary object — and it is connected to:

  • The Account (the company you’re selling to)

  • One or more Contacts (the buying committee)

If you continue reporting only at the Contact level, you create massive distortion.

For example:

• Suppose an Opportunity has 4 Contacts associated with it, and you report on “Contacts tied to Opportunities” without properly bridging to the Opportunity object. In that case, it might incorrectly appear as 4 Opportunities instead of 1 real Opportunity.

This creates inflated pipeline metrics, incorrect conversion rates, and misleading funnel reporting.

Key operational reality:

AEs don’t work just individual Contacts — they work Opportunities tied to Accounts that involve multiple Contacts.

Thus, once a deal is in play:

• The Opportunity is the “north star” object that sales works against.

• Contacts are important, but they are now attributes of the Opportunity and Account, not standalone funnel stages.

ProTip: How to Bridge Leads → Opportunities → Accounts in CRM Reporting

When working with CRM systems like HubSpot or Salesforce, make sure that lead source and attribution data are preserved after a lead is converted into a Contact, Account, and Opportunity.

In HubSpot, this can be handled with Cross-Object Reporting.

In Salesforce, you can map key Lead fields to the Contact and Opportunity objects during conversion, or use Converted Lead fields in reporting.

The Opportunity Funnel

The funnel doesn’t end at Opportunity creation.

It continues through a structured sales process — one that must be defined, measured, and managed just as tightly as the early-stage funnel.

Once an Opportunity is created, the buyer journey enters a new phase — and a new funnel begins: the Opportunity Funnel.

Unlike the early-funnel stages that track individuals (Leads, Contacts) or engagement signals (MQL → SQL), the Opportunity Funnel is about managing a commercial deal through the sales process, from discovery to close.

Each stage of the Opportunity Funnel represents a deeper level of buyer commitment and an increased probability of revenue realization.

Most revenue forecasts, revenue attribution from marketing, sales management, quota reporting & sales management actually happens here. The Opp represents the $$$ that the lead ‘promised’.

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Lifecycle Best Practices

1. The lifecycle stages should match your sales funnel. You can rename them to disqualified (DQL) if you must. What matters is the consistency between Hubspot and Salesforce so the reporting is clean.

2. Lead Status should match BDR activity: Open (not owned), working (in a BDR queue), connected (meeting booked), and recycle (not the right time), should be the lead status’ you use.

3. If BDRs don’t qualify the lead, don’t move it: There is no reason for leads to hit the next stage unless a BDR gets on the phone with them and figure out their status. If the lead is disqualified it doesn’t mean they’re an MQL, they’re just disqualified.

4. If BDRs qualify the lead as recycle, don’t move them back to MQL: This is an already qualified individual that went through the funnel. While you may continue nurturing them so when the time comes your company comes to mind, it doesn’t mean they become an MQL. This will mess up your reports.

Mistakes to avoid with Hubspot and Salesforce

1. Don’t move backward on lifecycle stages: The main reason you can't move backward is creating an accurate report where everyone understands what’s working for sales and marketing. Once sales and accepted qualify a lead, it should be managed via lead status. If it becomes a deal, manage it via deal stages or opportunity stages in SFDC.
   
   Why? You need to know what is happening at all times. Moving backwards on lifecycle stages creates redundancies, messes with stage understanding, it creates interdepartmental confusion, and messes your reports.

2. Leads vs. Opportunities: Think of a lead (contact in HS, lead in SFDC) as an individual in your funnel that has not gone through a qualification process. It’s a single object going through the funnel.
   
   But, once it becomes qualified, it stops being a single object but an opportunity. This opportunity may have multiple individual contacts that require touch-points for a deal to close (this is particularly relevant in B2B), but the opportunity is about the account you’re trying to close, not the person.

   Why? B2B works with sales committees. Reporting on opportunities as singular objects is generally inaccurate.
   

3. Don’t use leads, contacts and opportunity reports: Technically a lead and a contact should not exist at the same time. If your lead gets qualified it should become an opportunity and you should report it within that category.

   
   Why? because multiple contacts can belong to the same opportunity and opportunity

4. Understand how the funnel stage varies by object: Closed-won or closed-lost opportunities should not go back to MQL stage. Ever. If a closed-won/lost opportunity shows interest again it should become a net-new opportunity rather than become a lead or contact object.

   
   Why? Because you know they are already qualified so they don’t have to go through the entire funnel again. This will only add confusion to your funnel view and potentially trigger unnecessary sequences to help people move down a funnel they already are aware of anyway.

Funnels Still Work — But Only If We Build Them for How Buyers Actually Buy

Funnels aren’t broken.

Our old definitions of how buyers move through funnels are broken.

Today, B2B buyers start earlier, involve more people, engage across more channels, and expect more seamless handoffs between marketing and sales.

The traditional Lead → MQL → SQL → Opp → Customer model still matters — but it only works if every stage is clearly defined, properly operationalized, and measured against the reality of multi-threaded, account-based buying.

To build a modern, effective funnel, you need:

• Clear definitions of leads, MQLS, SALS, SQLS, and Opportunities.

• Rigorous tracking of buyer behaviour through both lead status mini-funnels and opportunity stages.

• Clean operational handoffs across Marketing, BDRS, and AES.

• CRM systems (HubSpot, Salesforce) that enforce lifecycle discipline and prevent reporting confusion.

Today's funnels are no longer linear—they are networks of signals, buying groups, and opportunity paths that must be captured, qualified, and progressed systematically.

If you don’t evolve your funnel operations to match modern buying behaviour, your pipeline will lie, your attribution will fail, and your growth engine will stall.

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Ready to Fix Your Funnel?

Modern B2B funnels demand more than great marketing or great sales — they demand great systems, clean processes, and tight operational alignment.

That’s exactly what we build at 42 Agency.

Here is how we can help

• Define your funnel stages properly

• Build a clean lifecycle model across marketing and sales

• Set up accurate reporting across Leads, Accounts, Contacts, and Opportunities

• Fix the gaps between marketing attribution and sales forecasting

• Scale your revenue engine without guesswork

• Set up Enrichment & Routing Rules.

Book an audit or download our RevOps Overview.

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Most marketers have a hard time justifying the impact of their work on the business's bottom line. That's why we keep inventing new terms and words like 'demand creation' and 'dark social'—to avoid taking responsibility or ownership of numbers. Yes, I said it!

It doesn't mean it's impossible to measure marketing, or it should not be measured. 

Marketers need to think more like finance and less like marketers to 'have a seat' at the table (meaning executive table). Let's define marketing measurement, its meaning, and why it matters. 

So, What is Marketing Measurement? 

Simply put, it is about measuring the long-term and short-term effects of marketing on the business. I like to break it down into time horizons because that is how marketing works. 

Some activities drive short-term results (sales, leads, SQLs), and others drive long-term results (recall, awareness, market share - though the best way to get more awareness, recall, and market share might be to … sell more stuff to more people). 

Why is this important? 

So you know where to spend your next dollar. If you are making a quarterly or annual plan and own a revenue or opportunity-generated quota, you need to know with a high degree of certainty where you will get what results. 

You must model your funnel to understand what channels bring in what numbers. 

How prospects convert down funnel and past performance is sometimes the best predictor of future results - so place some educated bets and wild card bets. 

Said another way - marketing measurement helps you answer the question, "If you had 100M, where would you spend it'? (maybe the answer is a Superbowl ad).

How do You MEASURE Marketing?
Short answer: with models. 

But let’s first talk about something often overlooked and essential to understanding this topic better - incrementality. 

Let's start with a simple pizza shop analogy that someone used to explain this to me. 

Imagine you own a pizza shop, and you have $5 coupons. You hire 2 people to distribute these flyers and incentivize them with a bonus based on how many customers show up to buy pizza with a coupon in hand. 

Person A goes down the street and hands out flyers to as many people walking around. Meanwhile, person B stands at the pizza shop door and hands flyers to people coming in to buy pizzas.

Which person 'drove more coupons'? 

Person B - a customer walking in to buy pizza anyway will more likely take the coupon for $5 off. But they would have purchased pizza regardless of whether they had the coupon (they had already walked in the door). 

However, Person A drove more people who would not have bought a pizza without the $5 coupon. 

Person A drove more 'new customers' or incremental sales, while person B drove fewer incremental sales. 

So even though person B would get the prize for the most coupons handed out and pizza sold - 90% of those people would have bought the pizza anyway.

Why am I talking about pizza? Because incrementality is a hard concept to solve in marketing. How many of our sales driven by marketing were incremental vs. what they would have bought anyway (without attending that trade show or registering for that webinar, etc.)? We will get into this a little below. 

Alright, to continue from the important concept of incrementality in marketing, let's delve into the different methods used for marketing measurement. These methods aim to quantify the impact of marketing activities, helping businesses allocate their budgets more effectively. 

Understanding these will enable you to approach marketing with a more analytical and financially prudent mindset.

Marketing Mix Modeling (MMM)

Marketing Mix Modeling (MMM) is a statistical analysis technique that evaluates historical data to determine the impact of various marketing tactics on sales and then forecasts the impact of future sets of tactics. 

It considers multiple variables, including economic conditions, competition, marketing spending, etc. 

MMM provides insights into the effectiveness of each marketing channel over long time horizons and helps understand how different marketing mix components contribute to sales. It's useful for allocating budgets across high-level marketing channels and long-term strategic planning. 

For MMM, you need a large dataset to try how different statistical models explain and fit the data and which one has the highest confidence (i.e., explains the data and the fluctuations the best. AKA, it’s a less risky bet). 

For better models, it's ideal to use higher resolution data, e.g., daily data, which would be a better fit than monthly data since there's more data. 

The advantage of MMM is that it goes beyond just online channels and can show the impact of that 100K billboard campaign on the bottom line. 

The problem with MMM is that, like with most B2B businesses, there needs to be more data. For example, a company that sells to an enterprise that only does three deals every year. 

MMM is also better suited for more annual/quarterly analysis and less so for optimizing marketing on a weekly/monthly basis. 

Examples of MMM in action

At 42 Agency, we leverage marketing mix models for our customers and do some advanced analysis to determine insights beyond first-touch / last-touch attribution. 

One of our customers, who spends around $1M monthly in media, spent a significant amount of their budget on Meta Brand Awareness Ads and LinkedIn Reach Ads. We performed an analysis to answer a few questions: 

1. Was Meta & Linkedin Driving MQL/ SQLs? (The answer was yes) 

2. Was spending on Reach and Awareness ads positively impacting the SQL / MQLs? (The answer was not as high as we thought. In some cases, it had a negative effect, i.e., we were likely generating fewer MQLs by spending on Reach / Brand Awareness) 

3. What is the ad stock of different channels, i.e., how long do people remember the ad after seeing it? (FB has the highest AdStock, Adwords the lowest) 

4. What is our point of diminishing returns? i.e., for each additional $$ we spend on different channels - what's the expected conversion and cost/conversion? (Spending beyond a certain point was bringing in fewer conversions at a relatively higher CPA) 

Correlation does not imply causation but makes for an interesting story.

With another customer, we invested heavily in YouTube video ads. 

YouTube has a very low CVR (click-through conversion rate) because 80% of YouTube ads appear on TVs. But they are excellent at telling a brand story that direct response ads like Google Ads or Meta cannot. 

While we could not directly tie YouTube spending to conversions via CVR, there was an evident correlation between YouTube ads flights and conversions from direct/organic. This correlation meant our ads drove prospects to sign up via other channels.

Multi-Touch Attribution (MTA)

Multi-Touch Attribution (MTA) models assign credit to each touchpoint a customer encounters on their journey to conversion. Unlike MMM, which looks at the marketing mix at a macro level, MTA operates at a micro level, analyzing the effectiveness of specific digital marketing efforts—like emails, ads, or SEO / Organic — on a conversion. 

MTA helps marketers understand which touchpoints are most effective at driving conversions, allowing for more granular marketing spend optimization. 

For example, MTA allows us to allocate more spending to LinkedIn Ads next month because the last 30-day report showed it had a higher conversion to opp than Google ads. 

Whereas MMM can pick up enough uptick in performance without direct click-based data - MTA needs clicks & tracking (browser or server side) to tie together touch points and build a customer journey. 

The main issue with MTA is that it relies heavily on UTM tracking, clicks, and cookies, which are increasingly being phased out. 

Important to note - MTA does not rely on 3rd party cookies (which are more so used in advertising) but first-party (1P). But, even with 1P cookies, browsers like Safari delete them after seven days. In a world where prospects are more cross-device, browsers block scripts and strip UTM links. 

In other words, MTA tracking becomes more complex, and its accuracy is blurry.

If you use a cookie banner to opt-in visitors but the visitor chooses not to, you won't be able to track any referral data or other data about how they came to your site. 

In your analytics, it will show as a 'direct visit.' Only 31% of visitors opt-in for cookies, so nearly 70% of your visits will show as 'direct.' 

I can go on for days, but let’s get back on track and dive into some MTA models and usage.

Common Multi-touch Attribution (MTA) Models

Here are the most common MTA models I have come across in my time as a B2B marketer. And how to use them.

1. The First Touch Model

The First Touch Model attributes 100% of the conversion credit to the first touchpoint in the customer journey. 

This model operates on the principle that the initial interaction is the most critical, as it introduces the customer to the brand or product, setting the stage for all subsequent engagement.

Application: This model is helpful for evaluating the effectiveness of top-of-funnel activities, such as awareness campaigns. It helps us understand how we get on people's radars (i.e., awareness) 

How to use it: In Hubspot, this is called Original Source in its out-of-the-box data model. Hubspot / Analytics tools will cookie the first time someone visits your site and connect it to a lead when they convert. 

Typically, if you're using UTMs, this is the UTM of the first time someone visited your website (not just filled out a form). You can track this in Google Analytics (UA) under Model Comparison > First click. GA4 has removed these options. 

2. The Last Touch Model

Conversely, the Last Touch Model assigns all the credit for the conversion to the last touchpoint before the conversion occurs. This model assumes that the final interaction is the most important since it is the closest to conversion.

Application: Ideal for assessing the impact of bottom-of-funnel activities, such as retargeting or search ads. It highlights which channels and messages are most effective in driving the last-mile conversion. 

But this favors high-intent channels like Google Ads vs. TOFU channels like LinkedIn, Meta, or Content. Even though customers might discover you from a Meta Ad on Instagram, Google Ads will take 100% of the credit if you only use a last-touch model, leading you to the false conclusion that Meta Ads simply don't work. 

How to use it: This is what the Latest Source in Hubspot is, or if you use UTMs - the UTM captured on the form (on form submission). While this can be a more extended essay, the Latest Source in Hubspot is a close approximation of the prospect's last touch before they converted. Google Analytics (UA & GA4) supports last-touch modeling in their model comparison tool (last click).

3. The Linear Model

The Linear Model distributes credit equally across all touchpoints in the customer journey. 

This model assumes that each interaction contributes equally to the final decision, from the first engagement to the conversion event. It is easy to implement but may need to be more balanced with the impact of more influential touchpoints.

Application: Best used when you want a simple approach that acknowledges every touchpoint without prioritizing any specific interaction. It's suitable for businesses with a flat, linear customer journey.

How to use it: Lots of 3rd party attribution tools support linear models. It was available as an option in GA UA, but GA4 does not support this. Using Hubspot Pro / Enterprise? You can also build linear attribution reports with Hubspot's Attribution Reporting.

4. The U-Shape (Position-Based) Model

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The U-shape or the Position-Based Model assigns more credit to the first and last touchpoints, typically around 40% each, while distributing the remaining 20% among the middle interactions. This model recognizes that first & last touches have a higher priority.

Application: Useful for marketing strategies emphasizing the significance of first impressions and closing interactions, such as in industries with shorter sales cycles or when launching new products.

How to use it: Lots of 3rd party attribution tools support U models. In the now-sunsetted Universal Analytics, it was available as an option. But GA4 does not support this. 

If you use Hubspot Pro / Enterprise, you can also build U-attribution reports with Hubspot's Attribution Reporting.

5. The W-Shape Model

Expanding on the U-shape, the W-Shape Model incorporates three critical phases of the customer journey: 

• The first touch (introduction) 

• Lead creation (mid-journey engagement) 

• And the opportunity creation (decision). 

Each pivotal touchpoint receives a significant portion of the credit (usually around 30% each), with the remaining 10% distributed among other interactions.

Application: Ideal for businesses with longer sales cycles that involve multiple stages of engagement, such as B2B companies, where initial contact, lead qualification, and final negotiations are all crucial stages.

How to use it: Use 3rd party attribution to model this and understand the three key milestones in a prospect journey (i.e., lead/SQL/Closed Won) to identify what drives TOFU/Conversion and Closed Won. 

6. The Data-Driven (Algorithmic) Model

The Data-Driven Model uses algorithms and machine learning to analyze all touchpoints and assigns credit based on the actual observed impact of each interaction. 

Unlike rule-based models, this approach dynamically adjusts to reflect the varying influence of each touchpoint across different scenarios and customer segments.

Application: Best for organizations with access to large datasets and the analytical capability to process complex models. It provides the most personalized and accurate attribution but requires significant resources.

How to use it: Popularized by Google Ads. I don't think many B2B companies have large enough sets of data for this to be meaningful. 

7. The Time Decay Model

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The Time Decay Model attributes more credit to touchpoints closer in time to the conversion, based on the assumption that later interactions are more influential in the decision-making process. 

The credit diminishes for older touchpoints exponentially or linearly.

Application: Suitable for campaigns focusing on driving quick conversions, where the recency of the interaction is believed to have a more substantial impact on the customer's decision.

How to use it: Use this model with Hubspot Attribution or 3rd party tools to understand deal velocity by channel.

Some Extra Notes on Understanding Multi-Attribution Models 

Looking beyond the models, what matters is that we understand MTA as a way to gain insight into the channels that drive TOFU awareness vs just  BOFU conversions. 

In our work with B2B brands, it's common to see Google Ads driving 80% of conversions on a last-touch model (which is commonly used). But upon some modeling, it becomes evident that paid social, SEO, content, YouTube, etc., are driving TOFU awareness translating into Google Ads and last mile conversions. 

A prospect might hear about a brand on a YouTube or Meta / LinkedIn ad and not convert, but there's a high enough frequency that the same prospect Googles a category or brand keyword, clicks a Google Ad link, and converts. 

Last Touch would give 100% of the credit to Google Ads, First Touch to YouTube or Meta, but the truth is - they both worked together to get the conversion.

But wait. Don't forget about incrementality.

MTA is fantastic, and it tells us what actions/interactions the prospect had that eventually resulted in them converting. 

However, it does not answer the question: Would this person have bought anything anyway? 

One of our customers was heavily indexed on Google Ads (typical for a B2B business), but 70% of their search budget was allocated to branded search (i.e., someone was searching for their brand name). 

We (42 Agency) had a hypothesis that 90% of those converting via branded search would have converted either way on the organic SERP. 

We slowly cut down the branded search budget and re-invested it in category / use case keywords and other channels. While conversions from Google Search decreased, conversions from organic search increased significantly, validating our hypothesis that branded search was not incremental.

Before you take to the streets to kill all branded search campaigns, they have their utility - especially if you are in a crowded market (e.g., CRM, PM tools, ATS, etc). 

It helps you ‘defend’ your brand against competitors and take extra real estate on a page. 

Speaking of branded search - many marketers shy away from measuring brands, but that's a mistake. Brands should and can be measured; they need to exist under a unified measurement framework for marketing. 

But how exactly do you do that?

How do you measure brand?

Ironically, the most common way B2B folks measure brand is 'direct traffic,' which is slightly misleading.

At best, direct traffic does not represent someone who is brand-aware. Contrary to popular belief, direct traffic is just any source of traffic (encrypted or otherwise) where web analytics cannot pick up a referral. 

It is not someone typing your website into the address bar. 

A better proxy might be branded search queries (accessed via Google / Bing Search Console), but even then, it’s not the most precise way to measure brand. Prospects who are aware are not necessarily going to your website.

So, what is the golden way to measure a brand? Surveys, Geo, and Holdout testing.

Recall and Aided/Unaided Surveys

Traditionally used by big media agencies and FMCG and CPG brands, surveys (both aided and unaided) are a great way to measure brand campaigns. 

In unaided recall, participants are asked to recall brands, products, or ads without prompts, testing spontaneous brand recall. 

Aided recall, on the other hand, provides prompts or cues to help participants remember. 

These surveys can gauge the impact of marketing activities on brand awareness and recall over time. You might have noticed them pop up on your YouTube or social feeds: 

• 'Which of these brands would you consider buying next time you are looking for X?' 

• 'Which one of these brand ads do you recall seeing in the last seven days?'. 

As your brand gets more share in the market, your aided and unaided awareness should increase. 

Paid Social / Search platforms also offer these as part of their measurement suite. LinkedIn recently introduced brand lift studies measuring recall from a group of people who saw your ad on Linkedin and a group of people who did not. 

Facebook introduced something similar. Previously, these were only available to big advertisers on the platform, but now both are available to self-service advertisers. 

GEO and Holdout Testing

How do you measure an OOH campaign like billboards? When we did it for ourselves and our customers, we looked at a lift in traffic from the city where we did the billboard campaign or brand mentions. 

The principle behind this is Holdout Testing (and done on a geo level, geo testing). 

Holdout Testing, or control group testing, involves withholding certain marketing activities from a target audience segment to compare their behavior against those who did experience the marketing activity. 

This method directly measures incrementality by showing what would happen without certain marketing efforts. For example, if NYC is running a billboard campaign but a comparable city (for argument's sake, Toronto) does not have billboards. There is a comparable difference in key metrics for the two cities.

Note on OOH - buying one billboard for two weeks won't make a big impact - 

Lesson: We rented a mobile billboard for Hubspot's annual conference in Boston for two days. While it got some social media buzz & it made for a fantastic story - we did not get the spike of traffic we wanted. But we got attention for doing something perceived as ‘expensive’ (OOH) & different. 

From Measuring to Acting

Here’s the fundamental idea of this piece: you can and should measure your marketing and brand efforts. Don’t let anyone tell you otherwise.

Even when you do that, you must build an experimental system that allows you to quickly iterate and incrementally advance better ideas based on what you learn from your measurements.

Doing this is a whole other essay. But beyond the tactics, it requires a mindset of clarity, intentionally building a marketing function that moves both fast and slow, and understanding what to measure weekly and quarterly (or yearly). 

Stay tuned for more.

Appendix

Before we talk about ABM campaign templates, let's explore the why behind ABM. If you sell to small/medium businesses, you have a much larger total addressable market. You can sell something to many people (and potentially at a lower price) and build a big business.

But if you sell to an enterprise, you can only sell to a relatively small number of people. So you potentially may need to charge more (and the sales & product tend to be more complex) to build a big business.

Therefore, account-based marketing is essentially GTM for enterprise markets because, by nature, you have a smaller TAM and higher ACV.

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Selling to an enterprise means dealing with complex sales workflows, navigating internal stakeholders, more expensive products, and longer sales cycles. This is also why many enterprise products are not self-service. 

How To Measure Your ABM Campaigns

Enterprise marketing starts at the accounts level. This is a fundamental difference between SMB GTM & Enterprise GTM.

With SMB, you are selling to ‘leads.’ A lead might be a buyer and user; I am a small business owner buying accounting software. I make the buying decision (which software to buy), I am the user (I will be doing accounting or working with accounting form), and the stakeholder (I need this to work so I can make smart decisions about the business). 

With that context, marketing starts with a lead; sometimes, you just work with a single lead. 

In an enterprise scenario, though, a lead is just a person in a ‘buying context,’ and there are multiple people to consider:

• The economic buyer

• Influencer

• Stakeholders

• Procurement

• End-user

In other words, when you sell to an enterprise, you aren’t just selling to one person but to a collection of people with agendas and outcomes.

Navigating this means multithreading different stakeholders, understanding their motivations, and addressing their objections. This is a crucial benefit of an ABM strategy.

It's not always about leads. It's about existing opportunities and moving them through the funnel

So you can measure your ABM campaigns by tracking the following: 

• Lead generation: Although enterprise/account marketing doesn’t always mean ‘generate leads,’ you can generate leads, so measure how many of your inbound leads are coming from your target / named account list. 

• Account Engagement: out of the named account list, how many are currently seeing your targeted content, engaging with your content, coming to your website & in other ways, ‘know you exist’ 

• Coverage: Out of those named accounts, how many decision-makers and stakeholders are in your CRM? How many have an account owner assigned? 

• Paid media: On the Paid Media side (like LinkedIn), how many target accounts are being served impressions, at what frequency, how many of those are watching your video, and what percentage are completing it?

• On the 1P data side, what campaigns, channels, and activities lead to named accounts visiting your website? What is their web engagement, how many pages do they view, and how long do they spend on the site? What content resonates the most?

• Influenced pipeline: Since marketing isn't ‘creating’ net new leads,’ how much do marketing activities influence sales cycles, and what tactics work better? 

• Logo acquisition: What’s my average cost to acquire a new logo 

• Deal Velocity: Are ABM campaigns leading to faster sales cycles? What are the activities that help get named accounts faster? What sales enablement content or material helps? (more on this in the next section)

Regardless of what ABM campaign template you choose to use to measure your ABM strategy, the main point to keep in mind is that ABM is essentially GTM for enterprise markets. With a much smaller TAM and higher ACV, you can’t exclusively rely on software to tell you if your ABM strategy is working.

Get Your ABM Campaign Template

Check out this ABM campaign template

Before we wrap up, let’s explore the idea of deal velocity a bit more.

Formula for Deal Velocity

• Number of Deals: The total number of deals in your pipeline over a specific period.

• Average Deal Value: The average revenue expected from each deal.

• Conversion Rate: The percentage of deals that successfully close.

• Sales Cycle Length: The average time it takes for a deal to move from the initial contact to close, 

Deal velocity is everything in enterprise GTM strategies. You must articulate your teams and finances around the deal velocity and do everything to accelerate this process. More speed = more money in less time.

Important concepts to consider when tackling deal velocity:

• Sales Cycle Length: Does marketing impact the sales cycle meaningfully? How? What can you do to help sales close faster?

• Pipeline & Coverage: Do we have enough pipeline & coverage? If we expect 40% of our pipeline to close, do we have enough pipeline to hit that revenue target? Based on expecting 40% of your pipeline to close, you would need a pipeline of $2,500,000 to achieve a target revenue of $1,000,000.
  
  

That is all for today. Thanks for reading.

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Sample Playbooks & Campaign Ideas:

Here are a couple of playbooks to consider when doing Enterprise Marketing.

Playbook 1

• Use Clay to build the LookAlike of your best customers:

• Enrich the data with LinkedIn URLs/ Company URLs / etc

• Import as a custom audience.

• Reach / Video Campaigns to them with basic creative about what the company is and what it does.

• Identify Decision Makers.

• If they are active on social media (LinkedIn/Twitter), thoughtfully follow their content, add value, and build rapport with them.

• Send them a connect request with a nice message about their content + something that might help them.

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Playbook 2: 

• Copy steps from Playbook One.

• Invite them to co-create content like a podcast or article.

• Invite them to speak at a customer event.

• Host a lunch and learn at their company (you bring the lunch and learning, make it genuinely interesting for them). 

Playbook 3:

• Buy a billboard in the city where your Target Account  is: 

• Run the same creative on social / display and other ad platforms.

• Send them a box of cookies to the office. 

• Design a custom landing page just for that account. 

• Create hyper-personalized content (i.e., X guide for Account Y on how to do ABC). 

• Arm your AE team with sales enablement materials like business cases, one-pagers, and ROI calculators. 

Playbook 4: 

• Use geo-fenced ads to get on their radar, especially during industry events.

• Use SDRs to do outreach and invite them to a wine and dine. 

• Invite customers & critical prospects. 

• Mirror their executives. For example, if you sell to a CISO, bring your own company CISO to build rapport.

Playbook 5: 

• Expand existing customers and departments/business units within large companies like mini-companies.

• Leverage internal case studies, lunch and learns, and other marketing activities to expand from Google's Cloud business to the SMB division or a different regional HQ.

Playbook 6:

• make a magazine featuring your target accounts

• Write a series of content featuring your prospects (interview them)

• CFO Series that features the CFO of your target account with other CFOs.

• Print & ship hard copies to the account & use the content to develop a relationship with the prospects.

Playbook 7:

• Research your target accounts & key decision makers; identify where they went to school

• Buy school alumni swag (like hats/t-shirts) & ship it to them

• Enable tracking notifications so that when they receive the package, you can send them an email + LinkedIn invite.

Playbook 8:

• For Accounts that are with a competitor or were closed lost due to a competitor

• Use Builtwith or other tools to identify when the new ‘tool’ was first detected on their site

• Typical contracts are 1-2 years, so you can either:

  • Time a competitive offer based on some G2 / Capterra reviews

    • i.e., I noticed you have been using the X tool for about eight months now. A lot of users notice these things lacking in the tool (quote reviews; these can be scrapped or pulled via Clay)

    • Offer to buy the rest of the contract out (i.e., you have three months left on the contract - we can buy it out + give you an incentive to switch)

Playbook 9:

• For industry events where you expect your accounts to be in attendance, treat the event as an experiential brand activation versus just tradeshow booth scanners.

• Search ads on the event name + event name/agenda, etc

• Scrape the list of speakers/sponsors to identify if any of your named accounts/prospects are speaking or sponsoring. If yes, build out a custom audience.

• Buy OOH around the event venue or airport:

• Hire a truck billboard to go around the venue

• Print flyers & hand them to the people coming to the event (you can hire someone from Craiglist)

• Have some temp graffiti on the sidewalks

• Sponsor a food truck.

• Host a giveaway for sharing pictures on social media.
  

Playbook 10

• Measure brand recall via LinkedIns native brand lift studies

• Measure brand recall via Dave Rigotti’s Hack

  • Create a list of accounts you want to target. Ours were just our target accounts. Since we did ABM that was the universe of companies we really cared about.

  • Spin up a basic LinkedIn page to run ads against. Ours was called "marketing survey" (we didn't want people to know it was from Bizible as it would sway results)

  • Create and run ads to your brand survey. This is what ours looked like. Super basic, I know, but it worked!
    

  • Run the LinkedIn ads to the account list you created. Sit back and relax for a few weeks. And you’re done!

  • I always asked for a demo at the end. We’d pass them over to sales, who would follow up with them and turn them into the pipeline. It paid for itself.

  • It cost $4,000 for somewhere between 100 and 200 submissions (I don’t remember exactly). This is definitely enough for us to have a pulse on the market.

The following essay will be on Brand vs. brand, echoing Dave Kellog's Advice: “Do you want to build a brand? Go sell some software. Want to improve your brand perception? Go sell some software….” 1

If you enjoyed this, consider sharing it with a friend or on LinkedIn. Thanks for reading. As always - if you have any feedback, reply to this email. If you need help with B2B marketing - check out what we do at 42agency.com (a new website coming soon - send me a message if you want to give some feedback on it)

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The traditional ‘inbound’ playbook that works relatively well for a small and midsize business (SMB) market breaks down when it comes to selling to enterprise markets. The primary reason is that enterprise buyers fundamentally buy and discover solutions differently than an SMB buyer generally does. They are less likely to be on search, querying the problems they’re trying to solve. The buying cycle is inherently longer and more complex. More people are involved, the ticket sizes are significantly bigger, and the risk is exponentially higher. You have to be a ‘line item’ on the profit and loss statement; it's a consultative sales process that is heavily driven by outbound events, networks, and top-down motions, among others.

While Inbound / S.M.B. marketing has always been represented as a funnel (awareness, consideration, purchase) to mean that you have a ‘big top of the funnel,’ enterprise markets work differently. Instead of 100,000 companies that need to be ‘aware,’ only 100 do. Instead of SEO, Ads, and search-oriented content, it needs more sales enablement and direct sales. 

This is precisely what Account-Based Marketing (ABM) platforms understood early on, building an entire industry and narrative around becoming a must-have platform for enterprise sales and marketing. They talked about flipping the funnel, about IP-based advertising (where only specific companies see your ads across the display network with questionable inventory), and they invented the idea of ‘3rd party intent’ using a network of publishers and 3rd party cookies to create ‘intent’ alerts.’ But amongst all this noise around ABM platforms, we have lost the fundamental idea of enterprise Go-To-Market (GTM). Below, we attempt to simplify it and present some playbooks/campaigns/ideas you can implement for your enterprise GTM program. 

Note: Typically, ABM programs will talk about three flavors of ABM: 

• 1:1 ABM. 

• 1:few ABM. 

• 1:Many ABM. 

It's our P.O.V. that 1:many is not A.B.M. but a narrative invented by ABM vendors to sell display ads. If you are doing 1:many ABM (100K accounts),  you are in fact, doing demand generation / inbound marketing primarily. Enterprise GTM., by nature, is about 1:1 approaches or ‘doing things that don't scale’.

ABM., aka Enterprise Marketing Playbook: 

Step 1: Build an Account list of your ideal prospects.

An excellent place to start is to look at the historical data of your most successful clients and then build lookalike audiences of your best customers or accounts. What are the common patterns among your best customers? Is it a technology / vertical / Geography / Team / Company Size / Revenue (it's probably a mix of the above)? You can use tools like Apollo, Zoominfo, KeyPlay, LinkedIn Sales Navigator, or Clay to build account lists.

Your Enterprise GTM programs will only be as good as your account list. Don’t let sales or leadership list ‘accounts they’d love to close but aren't realistic’—chasing them will waste resources and time. Focus on what you can realistically close. 

Some things to consider when picking accounts: 

• Technographic fit: For example, selling ERP software is great for companies already using SAP.

• Product Usage: (yep, you can do ‘enterprise’ if you have a freemium product (most freemium companies run an enterprise and freemium funnel). My favorite example of this came from a chat I had with Clair Byrd from her time at Twilio - an engineer from Airbnb signed up for a free trial of Twilio and showed an extremely high degree of product usage. You might want to upsell into the org for a ‘top-down’ adoption and ‘bottoms-up’ usage. This means assigning the right resources to the account to get executive buy-in on Twilio as a solution while working with the champion (the engineer who signed up) to navigate the internal complexities.

• Revenue Potential: Enterprise Marketing is not appropriate if you are selling a $20/mo product, given its long sales cycles, complex solutions, and larger ticket sizes. Select target accounts that would see value in your solution and not balk at the enterprise pricing. 

• Expansion: Enterprise, aka Account Marketing, is not just about new logo acquisition. It can also be efficient to sell into the organization, cross-sell into different departments, or upsell into an existing customer base. 

Another thing to consider - using AI and signal-based lookalikes - is what your best customers have in common and how you can find another account like them. For example, Clay.com and Keyplay offer lookalike modeling that takes customer names and finds other closely related accounts. 

Account list building is the most overlooked and critical part of an ABM program's success. It's often overlooked because it's not as exciting as running campaigns, but if you don't have the correct list of accounts, you won’t see much success from any program. 

Step 2: Account Research & Planning

Once you figure out your target accounts, the next step is to understand the market landscape, company priorities, key decision-makers, and other dimensions of the decision-making process. We use CBInsights, Crunchbase, analyst reports, and earning reports to understand each account's priorities and challenges. With LLM / AI models, you can run workflows to generate digestible briefs. 

You can also download our Account Planning Template: Account Plan Social Share

Once you have a more robust understanding of the accounts you want to target, work on categorizing and assigning them to account owners. Also, find a way to segment your accounts into priority/fit. Here’s how we do it: 

1. Tier 1 (T1): These are the accounts with the highest priority that you want to close. Think of them as getting white glove service. You do deep dives into the research, build customized campaigns, and invest time and effort into hyper-personalization.

2. Tier 2 (T2): Accounts in this bucket are those where you are a lower priority. They might be smaller accounts where you assign fewer resources but still do enterprise marketing to close them.

Those in the Great Fit become your Tier 1 accounts, where all your orchestrated efforts should be directed. 

Step 3: Build an Account Plan for T1 Accounts

This is the part where you plan how to communicate your dream-like offer and incentives to the right people through a suitable medium. While this is essentially coming up with a complex set of decisions, knowing precisely what you can do and how is the best way to prioritize realistic actions, you can pursue your ABM strategy. 

This includes a couple of things:

• First, build a master KPI dashboard mapping indicators that make sense for the particular account you're targeting.

• Second, building a campaign calendar that orchestrates Paid Media, Direct mail, Events, Webinars, Customer Experience, Sales activities, and whatever else might be required to close the account. 

We created an ABM template that you can use to understand better how to develop your account plan. Click here to see the template.

The challenge of enterprise sales is that you must become part of the prospect's solution consideration set. Enterprise sales are not impulsive purchases; they can be 18-30 months long.

The consideration set is a set of 3 - 4 products that the prospect has shortlisted. To be in that consideration set, you need to build mental availability, i.e., stay top of mind for the prospect so that you are the first to mind when they want to buy a solution like yours.

This can take different mediums and approaches. 

Step 4: Build a Campaign Plan

Once you have determined the overall target account list and have allocated resources to the program, it’s time for the fun part of building out creative campaigns and content.

This is also what certain folks call “playbooks”.

Here are some ideas. Remember, ABM / Enterprise Marketing is not just about Digital Ads. It is an offline/online/personal/corporate blend to navigate a complex process.

1. Event Playbook: Events are still a big driver for most enterprise buyers and hold much value when done right. There are a few ways to integrate events into your Enterprise Marketing Playbook: 

   1. Owned Events: Host your event for prospects and customers. Selling to a CFO? Have your company's C.F.O. host a round table of industry CFOs and invite some existing customers and target accounts to it. 

   2. Host sales dinners: Book a restaurant and invite your target account executives and your own executives for an invite-only event. Bonus if you have a ‘guest’ who is well-known and influential in the industry (this will make it easier for others to feel inclined to attend).

   3. Roadshow: Intercom used to call these the world tour. Go around cities and countries with a high concentration of prospects and host an event across different cities. Invite key prospects and use it as a brand activation opportunity to build trust.

   4. Attend Industry Trade shows, but make it enjoyable: 

      1. Buy a billboard by the airport (we did this for a customer around the Marketo Conference) 

      2. Make a pre-event Ad (we had a small GIF of the AE’s attending the event that ran as a social ad and people at the event mentioned it) 

      3. Create an ‘event bounty’ (we dressed a customer VP Sales in Orange Sneakers and promised a reward for anyone who found him and took a selfie)

      4. We hired a limo and taxied people to and from hotels to the venue during SaaStr. Everyone called it a ‘moving booth’. 

Also, remember to measure events beyond the apparent meetings and leads to brand activation and experiential marketing. 

Step 5: Create Personalized Campaigns and Content:

If you know who you are selling to, their challenges, and their priorities, wouldn't it make sense to customize your entire approach? That makes enterprise marketing interesting (but also challenging to scale).

Suppose you are selling a P.O.S. solution to Starbucks. They are a publicly traded company, so you can have ChatGPT or Gemini review and summarize earning reports and quarterly priorities. One of the reports talks about the high cost of employee training with existing P.O.S. and how it is a priority for them to find a better solution because it costs them millions in lost productivity. You can then:

• Create tailored messaging for Starbucks aligning to this exact pain point (our POS comes with full-service training + is 5X more straightforward to use and faster than legacy POS)

• Create content for a specific buyer (X department at Starbucks who is responsible for POS rollout) 

• Create content to align the influencers (i.e., multi-threading) like IT / Finance / InStore Exp / etc, who are involved in the decision-making.

Notice that I haven't mentioned ‘6sense or LinkedIn Ads’ yet. Those are channels to get the message to the right people, not the ‘campaign’ itself.

Once you know who and what, you can start producing assets like: 

• Customized Content about why Starbucks needs to switch. How can you help them? What are the business cases? This can be content like videos, a podcast, customer case studies of other coffee retailers, presentations, podcasts, landing pages and web pages tailored for this account (Starbucks) 

• Creative and Copy that you can deploy across LinkedIn / Meta / OOH / Native / and Editorial Advertising to reach them

  • You can research where the VP of purchasing and CFO spend their time, the people they follow, and the blogs they read (hint: use a tool like SparkToro) 

  • You can buy a billboard across the Starbucks HQ in Seattle

  • You can target and re-market to them across Social / Search Ads

  • You can buy a full-page ad in NYT 

  • You can hire a PR firm to talk about you + other coffee chains using your POS and highlight the employee productivity benefits in reputable publications executives read (like WSJ) 

The idea is to create a ‘surround sound effect,’ as Clair Byrd said to me once. 

The outcome is that they see/hear/learn about you, and you build enough trust that either they come to you (aka Inbound), or you can go to them (aka outbound), and they do not need to be told/convinced what you do; they’re already sold on it.

One of our customers who we work with on ABM said to us, “Our AE was talking to the senior executive at enterprise company X and the executive said, ‘I see you guys everywhere, so great job on getting the message out.’ 

Most folks mean ‘we run LinkedIn Ads” when they mean ABM. It's definitely part of it but it's not ABM. 

Note on 6sense / DemandBase etc: For years we’ve been told by Martech vendors that we need tool X or Y to ‘actually do ABM’ and countless times I’ve seen marketing teams buy the tool, sign a 100K contract to run some Display Ads that get placed on questionable inventory and ‘intent’ data based on someone visiting about a broad topic on some industry website. Only to see zero results and no adoption by the sales team. 

Step 6: Measurement

It's not just about leads. It's about existing opportunities and moving them through the funnel.

ABM can be measured in a few ways: 

• Leads: Although enterprise/account marketing doesn’t always mean ‘generate leads,’ you can generate leads, so measure how many of your inbound leads are coming from your target / named account list. 

• Account Engagement: out of the named account list, how many are currently seeing your content, engaging with your content, coming to your website and in other ways, ‘know you exist.’ 

• Coverage: Out of those named accounts, how many decision-makers and stakeholders do you currently have in your CRM? How many have an account owner assigned? 

• Paid media: On the Paid Media side (like LinkedIn), how many target accounts are being served impressions, at what frequency, how many of those are watching your video, and what percentage are completing it?

• On the 1P data side: what campaigns, channels, and activities lead to named accounts visiting your website? What is their web engagement, how many pages do they view, and how long do they spend on the site? What content resonates the most?

• Influenced pipeline: since marketing isn't ‘creating’ net new leads,’ how much do marketing activities influence sales cycles, and what tactics work better? 

• Logo acquisition: What’s my average cost to acquire a new logo? 

• Deal Velocity: Are ABM Campaigns leading to faster sales cycles? What are the activities that help get named accounts faster? What sales enablement material helps? (more on this in the next section)

• Recall: how many of your target accounts can remember you in aided / un-aided awareness? 

  • LinkedIn now offers brand recall as part of their measurement suite. 

• Opps/BDR before ABM and opps/BDR after ABM: Is there a noticeable difference between the Opps/BDR process before and after ABM? If you can get it from 5 to 6 that is a huge increase.

Our friend Dave Rigotti from Inflection.io (ex Marketo / Bizible) also has a great way to measure recall. Here it is (thank you Dave): 

1. Create a list of accounts you want to target. Ours were just our target accounts. Since we did ABM that was the universe of companies we really cared about.

2. Spin up a basic LinkedIn page to run ads against. Ours was called "marketing survey" (we didn't want people to know it was from Bizible as it would sway results)

3. Create and run ads to your brand survey. It doesn’t have to be complicated, it just has to work.

   1. I set it up including unaided awareness, aided awareness, perception, some questions to pivot audience, etc.

4. Run the LinkedIn ads to the account list you created. Sit back and relax for a few weeks. And you’re done!

5. I always asked for a demo at the end. We’d pass them over to sales, who would follow up and turn them into pipeline. It basically paid for itself.

6. It cost $4,000 for somewhere between 100 - 200 submissions (I don’t remember exactly). It was definitely enough for us to have a pulse on the market.

Regardless of how you choose to measure your ABM strategy, the main point to keep in mind is that ABM is essentially GTM for enterprise markets. With a much smaller TAM and higher ACV, you can’t exclusively rely on software to tell you if your ABM is working.

Note: an underrated aspect of Hubspot is their new Target Accounts view. It not only shows you the status of pipeline and target accounts but also activity and engagement against those accounts. All you need for this to work is to have strong data hygiene.

Want to see what an ABM campaign may look like in practice? Check out this template.

Step 7: Tie the dots

The most important yet overlooked aspect of enterprise programs sales.

You must also ensure that the sales and marketing teams work together, inform one another, and promote the same message across multiple touchpoints and stakeholders. When marketing is running multithreading campaigns, make sure Sales is aware and following a similar playbook and messaging.

Enterprise accounts will most likely not just come to talk to sales purely based on marketing campaigns. Sales will have to outbound into these accounts, leverage social selling and develop champions in accounts. 

Pro tip: Salespeople are essential in ABM because they both sell and become front-line researchers. Pay special attention to what sales teams are doing to improve closing rates and inform them of any insights you may find in your marketing campaigns. 

Step 8: Don't forget about the human touch

Remember, these are just people doing their daily work at their organization, and getting pitched solutions isn't their highlight. Be human. Send them cupcakes. Everyone loves cupcakes. 

No matter what you decide, spark their interest and help them notice you exist without entering hard pitch mode. 

Pro-tip: Bad swag gets tossed away. Try to do something that genuinely brightens their day. 

Beyond the ABM Steps

As you probably noticed by now, with clear examples, the strategies that succeed in SMB translate only indirectly to enterprise markets (at best). While ABM strategies and playbooks are abundant and helpful, beyond the steps above, what matters is to think about how to become part of the consideration set of products an enterprise client may buy to solve a problem.

That’s the job. To become an easy and effective product to choose for multiple stakeholders who need to show positive results in a profit and losses statement. Should you decide to go for a platform or to look at it as an account vs. lead-focused effort between sales and marketing, the job isn’t done until you become a challenger product in an established category. 

It is an uphill battle, but let’s not kid ourselves; ABM is just marketing for enterprise leagues. 

If you enjoyed this post, please share it with friends and on social media. Be sure to tag us @get42agency on Twitter or 42 Agency on LinkedIn!

HubSpot is a powerful CRM for attracting customers through inbound marketing, but that’s not all. It can also integrate all the data you need to keep track of contacts, nurture leads, and drive real results. But with so many tools and capabilities, it can be challenging to know where to start and how to make the most of your CRM.

To help set you up for success and keep you on the right path, here are a few of our favorite best practices. Win big with customers by checking out these eleven tried and true plays from the 42 Agency team.

1. Define your business goals and map out your processes.

Before jumping head-first into the deep end, make sure you establish your goals and qualify what success looks like. Ask yourself: what are you hoping to get out of HubSpot? What pain points are you trying to solve?

Then, define your go-to-market setup and how your business operates. It’s all about understanding your core market model. That’s because your core markets define what activities you need to measure. This will also help you integrate the CRM with your entire business and growth strategy.

Start by looking at your system of record for sales and marketing analytics. If it's top-down, that might be leading by SQL. If it’s bottoms-up, your focus might be on activated users.

Map out your customer journey. Break down your process and consider your sales process. What happens when somebody comes to your site and requests a demo? Does a lead go straight to a BDR or an AE? Do you have a qualification call first? How do you create opportunities from leads and how do you define and progress the stages of the lifecycle?

2. Integrate your CRM with your entire growth operation.

Once you map out your process, understand where your data lives and how it flows together. Map out your system architecture to see where all the data is coming from and where it needs to go. You might have sales data in Salesforce, blog data in WordPress, automation for the sales team, upgrades in Google. You need to make sure that it’s all connected to HubSpot. 

Also take a look at your primary points of contact for HubSpot. Who are your stakeholders? These are most likely your VPs of Sales and Marketing, your founders etc. What systems are they using and what do they want to get out of them? For example, your VP of Sales might want a weekly report on active VPR. It’s critical to understand your end goals for outputs, so you know what kinds of reports you need to build. Based on these insights, define the data to be captured and the account/opportunity objects to use.

If you’re looking at HubSpot for marketing purposes, the good news is that HubSpot has a native integration with Salesforce. The bad news is that setting it by yourself can be a hassle. Use tools like HockeyStack to automatically combine data into a single, unified and transformative view — so you won’t miss out on any meaningful insights.

3. Keep it lean to make the most of your time and effort.

From contacts to demos and letters, most businesses have too many forms.The problem is that form management can become an administrative nightmare. That’s because if you have ten different forms, you have to make every update across all ten forms. 

Merge your forms — so you don’t lose precious time managing them. Minimize your number of form types, by using the same form across all verticals. In other words, create a single point of entry with the same form across different places. Standardize your day-to-day data inputs.

Consider what fields you want to be tracking, so you're not creating a new contact every time somebody submit a form. Instead, use HubSpot to track and capture ATM fields out-of-the-box. Our friends at HockeyStack can help you automatically track all form submissions that come through HubSpot. 

HubSpot lists are the best way to identify and organize who lives in your CRM. These lists can help you segment for emails, rank leads, and find upsell opportunities. They can also support you in spotting disengaged leads who could use some extra love.

When it comes to naming conventions, include year/day/ month and the purpose of the list. Set up lists for customers, opportunities, and deals. And then use these lists across email marketing, workflows, and reports.

4. Take advantage of HubSpot for marketing and ads.

Hubspot natively integrates with Facebook ads to capture key data. That means you can segment by campaign for ad creative, and report on it all in the CRM.

Also be sure to take advantage of Hubspot “ad interactions.” Somebody may not have converted off an ad, but they likely clicked elsewhere. By capturing these interactions, you can send first-party audiences to your ad platforms. You can even tell Hubspot to list your customers by look-alikes, and save the headache of exporting and importing CSV files.

5. Optimize your landing pages and A/B testing.

When it comes to landing pages, create three or four templates so you can easily plug and play. Once you know the jump rate, you can clone them as many times as you like. Then all you need to focus on is copy edits and images. That’s so much simpler and faster than creating a new landing page each time.

Now, you're ready to do A/B testing with HubSpot (or else use Google Optimize). You can even create "spot content," so that if somebody lands on a page many times, they’ll be directed to diverse content.

6.  Publish blogs and emails for full visibility.

The advantage of publishing blogs with HubSpot is full end-to-end visibility. It's easy to run analytics and capture data on HubSpot-hosted blogs. You can natively segment and report on your blog content performance.

That empowers you to tell who converted off specific blog posts. There are even attribution tools to see which blogs generated the most revenue. HubSpot makes these kinds of invaluable connections for you.

The only disadvantage is from a SEO perspective. Hubspot's blogs are out-of-the-box on a "subdomain" rather than on a unique directory. For example, your blog url will read as "<<name of blog>> @Hubspot.com or <<name of blog>>/hubspotblog. But while that’s worse for SEO, the benefits of full visibility can be well worth it. 

When it comes to emails, the first step is understanding your different use cases. For regular communication (as well as internal emails), plain text is usually the way to go. But for marketing emails or newsletters you'll likely need more design. Then you're ready to set up templates. You can buy them off the HubSpot template marketplace or create them yourself.

Keep in mind that HubSpot overrides your GTM sources. For example, if you send out an email and forget the GTM properties and bank email, you don't have to worry. HubSpot will automatically add them in. But if you post on social media and want a custom video data setting, remember to turn the feature off. 

HubSpot emails also default to send to everybody. This includes people who haven't been engaging. Consider turning off the default, so you don't send out cold emails to disengaged prospects and get a high bounce rate.

7. Stay on top of website activity.

HubSpot has its own tracker for your website. While it may not be as powerful as an actual analytics tool, it still gets the job done for uncovering important website touches from your key contacts. This can be a meaningful starting point for more sophisticated analytics tools, because you can connect historical website data directly through HubSpot. Just keep in mind that HubSpot only tracks pageviews and sometimes tracks them as duplicates.

8. Connect all the dots with campaigns.

With HubSpot, you can see how leads convert to customers and use that data to inform your platforms and engagement channels. It’s essential to view your marketing and sales activities all together to understand what works (and what doesn’t), so you can optimize moments that matter and grow conversion. 

From influence to revenue, HubSpot campaigns can help you understand every customer journey. It enables you to see your return on investment from trade shows to blogs, landing pages, forms, offers, ads, emails, and more.

Let's take a look at trade shows. Some of your visitors may have been existing opportunities, while others were new to your CRM. You can associate these lists to a Hubspot campaign. As deals progress and close through the funnel, HubSpot can report if a trade show had an influence and how much. Use HubSpot campaigns to get a unified view of every single marketing piece you deliver. Though it’s important to note that HubSpot’s own reporting may not be enough, so think of it as a supplemental tool for drilling down on insights. 

9. Empower prospects and customers to book meetings directly.

HubSpot has its own meeting scheduler that syncs with Google Calendar and Office 365 Calendar to show your most current availability. This can become an important tool in your buyer journey, because it enables prospects and customers to easily book a meeting with you or multiple people in your company. Plus, it saves all of the back-and-forth emails trying to find a time that works for everyone. The HubSpot meeting scheduler is also connected to your HubSpot contacts database — so every time a prospect books a meeting your database automatically grows and stays up-to-date. 

10. Keep your data clean and connected.

It's essential to keep your data tidy, starting with your context objects. Go to your "new contacts" settings. Set new contacts to automatically associate with the right account and contact owner. That will save you the big headache of ending up with partial contacts. 

You’ll also want to map the lifecycle state between accounts and contacts (so you don't send an email meant for a lead to a prospect). The main lifecycle stages are: “Lead > MQL > SQL > Opportunity > Customer.” You don't have to use all of these, but mapping at least three (usually MQL >  SQL > Customer) can offer a lot more clarity to your operation. Take advantage of HubSpot to automatically set, update, and sync lifecycle stages. For example, you can automatically turn a website form submission into an MQL. 

Above all, make sure your core triangle between contacts, deals, and companies all tie together. Missing these associations is the root of most reporting and operational issues. Check if you have any deals without contacts or companies associated with them. We also recommend  associating with companies instead of contacts (though this may not work for every customer). And make sure you don't have contacts without associated companies. You’ll want to check and fix these kinds of disconnects on a regular basis. There’s also a toggle to see the same financial state between contacts and companies. 

Always ensure there are no duplicates in your database. And check that all contacts are up-to-date for lifecycle stage and current status. Are they new to the funnel, in active sales conversation, or disqualified? 

If you're using custom lead sources, make sure you keep these tidy too. HubSpot is impressive at capturing online resources for lead sources. But an uploaded disc or anything from an integration will automatically track offline. So, set up a custom resource field to identify all sources. Create worksheets to map out online ads, social media posts, emails, and offline trade sources.

With HubSpot, there's a dedicated place to set up target accounts for companies. This enables you to get a singular view of all target accounts. You’ll be able to see everything from opportunity values to API output notions.

With "companies," HubSpot also offers automatically attributed insights. You can even look at third-party resources like industry value and annual revenue. Then, you can create a list of all companies including contacts within a segment. That makes it easy to segment your database and push them to specific ad platforms. Just take these insights with a grain of salt, because they’re not always 100% accurate. 

11. Discover HubSpot's lesser-known gems.

Don't miss out on HubSpot's overlooked treasures. The CRM recently rolled out “time to close.” This new feature enables you to see your sales velocity. You can determine how much time it took for a contact to go from open to close. Or even how long it took for a contact to move from late stage to close won. Plus, you can get these insights all out of the box, so you don’t have to create custom data (like in Salesforce). 

Another powerful but under-used tool is original source. It can capture valuable attribution data and campaign insights for keywords and Adwords. And don't underestimate the power of "offline factoring." This feature can help you integrate your HubSpot ad platforms. In traditional marketing, a conversion is marked when a user submits a form. But with offline factoring, you can track if a user who submits a form actually becomes an opportunity. Then, you can send that data back to your ad platforms, and track how your campaigns perform on down funnel metrics.

When it comes to making it easier for customers to fill forms, HubSpot form fields automatically populate previously submitted visitor data by default. HubSpot checks a visitor's browser for tracking cookies. If this cookie is associated with a contact, it will pre-populate fields previously submitted on that device.

Hubspot also recently launched two new tools powered by artificial intelligence (AI): Content Assistant and ChatSpot.ai. You can use these newly published AI features to help save you and your team precious time and also create stronger connections with your audiences.

Content Assistant (in beta) helps you ideate, create, and share quality sales and marketing content in mere minutes. This tool can suggest blog titles (and create outlines) related to a product or service, and even write content for blog posts, knowledge-based articles, landing pages, website pages, sales and marketing emails.

ChatSpot.ai (in alpha) offers a chat-based experience to help you use HubSpot’s software. The tool can assist you with a variety of tasks from adding contacts and companies to the HubSpot CRM, to creating custom reports for marketing, sales, and customer service — as well as drafting personalized sales emails.

After all, your smartest HubSpot plays are about building deeper connections between your data, pipeline, and customers.

Need help with your HubSpot CRM? Let's chat!

We hope you found these best practices a good starting point for your own playbook. At 42 Agency, we know the ins and outs of HubSpot. We can help you set up a high-performing CRM, so you can drive a more connected customer experience and transform how your customers engage and buy.

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We are prone to believe that the further along from conversion your ICP is, the less they need to know about the product (they are just discovering they have a problem). While that is partially true, some of the best products out there create different content because it has a precise product/brand angle, even before people know they are ready to buy. 

In this piece, we will discuss why content marketing and product marketing should work in sync to create highly engaging and unique content. 

As always, this is a way to think about a problem, not an ultimate answer, but feel free to digress in the comment section.

Content in Marketing

There isn't a single corporate hierarchy, but according to what we have seen, content areas are usually allocated in the marketing department. It makes sense. To do marketing, you need content to do the following:

• Inbound SEO type of content

• Websites/landing pages

• Emails

• Newsletter

• In-product comms

• Social

• Paid

• Events

• 3rd party 

Indeed there are more, but you get the point. Content traditionally belongs in marketing because to do marketing, in many ways, is a storytelling function. Plus, it makes sense to identify the themes customers are actively looking for and create content around their awareness levels to drive pipeline and sales. 

Product Marketing, on the other hand, often sits within Product and sometimes within Marketing. They’re often the ones who create positioning & messaging documents, sales battlecards, write product launch emails, in-app communication, and other launch related assets. Still, this person sits in a hybrid place closer to the product than marketing. 

However, just because we have traditionally structured things this way doesn't necessarily mean it is the most effective way. Product marketing and content marketing should be more aligned so that the entire funnel is covered with the creativity of content marketing and the detail and product expertise product marketing has to offer.

Content Marketing and Product Marketing: Two sides of the same coin.

Three structural phases stand out whenever content marketing teams plan how to create new pieces. The first is to understand the problem/pain and break it down according to the customer's awareness. The second is the way SEO can be used to further add value to the piece (matching keywords with intent). And finally, some more advanced marketing teams also develop an integrated strategy on how to distribute content, how to repurpose it, and how to make the most out of it for the brand (however, this is not always the case). 

After you have these components in place, you can finally create your piece. And if you get it right, in theory, you will resonate with your ideal customers. 

While there are many metaphors or mental models to see content marketing, most content marketing uses those three steps. If not, brands tend to produce more critical "thought leadership" content that aims to be noticed not due to its SEO potential but its unique point of view and contribution to any particular field. 

Product marketing, on the other hand, traditionally focuses on marketing the product at the lower end of the funnel. That means, on some occasions, product marketing may collaborate with content marketing teams, but in many other cases, they can get in each other's way given that the content production process is fractured between TOFU (content marketing) and BOFU (product marketing).

We think instead of getting in each other's way, both areas could learn from each other and help the product elevate content (more on this in the next section).

We recently came across this post from Zach Leete, who explained how having product marketing and content marketing collaborating is a bit more complex than one anticipates. While there are many reasons why this may be, we can narrow them down to a couple of them. First, content marketing is focused on intent, meaning generating awareness around the category and problem the ICP has. And second, product marketing leverages selling points and angles that may be way too oriented on conversion rather than awareness (you can also see this as two sides of the same coin).

What if we could find a way to reconcile these content areas? What if product and content marketing are underperforming, given their lack of collaboration?

Content marketing and Product marketing in sync.

This is going to be a cliché thing to say, but knowing your customers' deepest needs is at the heart of everything. To do so, we suggest beginning the process by identifying the ZMOT (zero moment of truth essentially that tells you what led your prospects to realize that they have a certain problem), and analyzing the ICP needs to solve three fundamental questions:

• Why buy anything? (Awareness)

• Why buy now? (Awareness + Consideration)

• Why buy us? (Consideration + Conversion)

We have also observed a pattern in terms of the type of employees that help the organization answer those questions, as you will see in the following image:

While this is not 100% accurate, thinking about organizations buying your product with this lens allows us to understand how product marketing and content marketing should be more aligned than in most companies today. Let's dive a little deeper and rest our case.

Let's start with the roles.

Executives: These roles usually have a primary objective for any organization: to make the best decisions possible to help the company grow. They are the ultimate decision-makers who delegate some of the decisions to their managers but are almost always gatekeepers regarding crucial determinations. 

They may not need to read the SEO-focused content marketing blogs, and a thought leadership piece would be more appealing. Their job is to decide and strategize. And, to make better decisions, you need to understand as many mental models and angles as possible. 

They need to be aware of your problem and the product that solves that pain, and then they need to think about how it improves their strategy. Once they have clarity on that probably, one of their top-level managers will do thorough research about the types of solutions they could buy, and finally, they will sign off on the decision. 

They need to know enough about the product so it is considered valuable, they need to understand how the product matches their strategy and make the decision. 

Managers: Their job is to make sure all gears are in place. They may not be the ones who make crucial decisions, but they still call the shots on already established systems to make sure they are efficient and profitable. In parallel, they are also the ones who can influence executives to acquire new tools and technologies to improve operations further. 

Managers may not have the time to research and figure out the right tool for their business, but they know they need to innovate to improve processes. Usually, they set a task for their staff members to research to facilitate decision-making. Finally, the manager may call the shots if the decision is not crucial to the business (or if the budget does not exceed a certain amount). 

Staff: Staff members are the ones who execute whatever is needed to make sure organizations are profitable. Their primary job is not to call the shots, although they often participate in decision-making (at least in healthy and balanced organizations), but to get things done. 

When it comes to buying new products that foster organizational innovation efficiency, they usually have to do the research required. 

Most of the content we put out there for all three players in the buying process is created exclusively by content marketing. Indeed they will find content that matches their intent, but the nuances and selling points product marketing can leverage may be missing. 

Likewise, suppose product marketing builds the entire content strategy. In that case, it may not be optimized for keywords for Google to rank because the PMM team might not have matched awareness keywords with crucial features in the product. 

We believe we could find the sweet spot in the balance between (product marketing and content marketing). This combination generates a different organizational culture that fosters collaboration and innovation. 

Instead of running two parallel strategies (content marketing and product marketing), we could run an integrated system that provides detailed product selling points + intent and awareness topics. Content marketing gains insight into how to position the product, and the product team learns more about how their ICP builds awareness around the category. It becomes a symbiotic movement that generates value instead of causing friction. New features can be created based on intent, and a better product description helps executives, managers, and staff members in the decision process. 

Imagine doing that without adding stress to content marketing areas and having to write selling points they may not know about (they are not product experts). Plus, without expecting product marketing to be aware of larger contextual trends (they are not SEO/intent experts). 

Content marketing and product marketing are similar even though their objectives are different. But what if they weren't? What if the objective is a larger shared goal of adding as much value to your ICP plus timely product information that highlights your solutions' importance?

We created a table just to highlight some of the ways in which we think both product and content marketing can elevate any product content below:

It is time to think about internal partnerships across departments. If you aren't, chances are your competitor is and will win simply because they know how to create the right content, for the right person, with the right product feature.  

Content should report to product marketing, and product marketing should report to content marketing. Through mutual accountability and collaboration, your content production and delivery will stand out and help you win. 

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Community Plugs

Are you hiring for - revenue operations, SEO, SFDC admin, demand generation talent? Anati Zubia has some incredibly brilliant connections looking for new opportunities.

Also, be sure to fill out our hiring form to find the best talent for your organization. We want to use this newsletter as a channel for connecting the best talent with great companies out there.

Depending on how you operate your financial calendar, you’re probably either wrapping up your annual growth plan now or just getting started. And If you’re using Objectives and Key Results (OKRs) to manage your business, you may be either setting OKRs for the year or next quarter. 

Regardless, SaaS industry benchmarks are great barometers for setting goals in the short and long-term.

Plus, if you’re like most, you’re probably asking your team, “How does our SaaS company compare to similar companies in this regard?” on a daily basis. 

The next time that question comes up and you want to keep your team honest about where you may be falling short or realistic about what’s possible, you should turn to benchmarks. 

Finding solid benchmarks is another challenge, though. 

Most benchmark reports are static or done annually. These are still helpful and we’ve included a bunch of these in our list below.

But, it’s also really nice to know how your most recent month’s performance compares to other companies. Especially during these turbulent times of inflation, the great resignation and the SaaS layoff spree we’re currently experiencing. So, some of these benchmarks are real-time or real close to real-time. 

Without further ado, here’s the list we compiled: 

Real-time SaaS Sales & Marketing Benchmarks

We’ve partnered with Databox to build out our own Benchmark Group using their amazing, new tool. It has allowed us to build a proprietary benchmark just for SaaS companies. We’ve chosen to restrict our group just to SaaS companies over $5M in Annual Recurring Revenue (ARR) and who sell to enterprise-sized accounts. In other words, if you’re a startup or selling exclusively to SMBs, this isn’t really for you. 

Here’s a sneak peak of what the data will look like once you join.

The only difference is that once you join the group, you’ll see a vertical line that shows how your performance compares to the group and your charts will be red or green depending on whether you’re above or below the median. Plus, there’s significantly more benchmarks available for other metrics. 

It’s perfect for internal presentations if you want to show off where you’re excelling or if you’re battling for budget to improve certain Key Performance Indicators (KPIs).

If you’re interested in joining, just reach out to us by email or fill out this form: https://www.42agency.com/lets-talk

Assuming you meet the criteria, we’ll send you an invitation to join, you’ll connect some of the tools you use and voila!  –  your own custom benchmark report. 

You’ll be able to join our group for free. You — and only you — will be able to see your performance relative to the aggregate of the group.

Of course, we’re happy to chat with you about how you can improve your performance, but we can’t see your data and there’s no obligation to talk to us. In fact, we appreciate you being willing to join the group since having more members helps us build a better benchmark. 

And in 2023, we’ll be running quick surveys to help all our members identify the best practices that lead to high performance. 

Real-time SaaS Financial Benchmarks. 

Profitwell’s free analytics tool has amazing benchmarks for SaaS business. Just like our marketing benchmark, they provide free access too! You can see how your growth rate, churn, lifetime value (LTV), average revenue per customer (ARPC) and more – all compare. But, the best part is that you can compare your company to cohorts of companies just like yours. For example, if you are doing $5M ARR with a $10k ACV, you can compare to just companies in that range if you want. 

Check it out here: https://www.profitwell.com/benchmarks

Not to be outdone, ChartMogul has very similar benchmarks available in their product too: https://chartmogul.com/saas-benchmarks-report/

Annual SaaS Spending Benchmark

For 10 years in a row, SaaS Capital has done a survey in Q1 of what SaaS companies spend their $$$ on. Here’s a great graph showing a high-level breakdown

Don’t just rely on this chart, though. Check out their report for a full run down to see which of your expenses may be out of line. 

https://www.saas-capital.com/blog-posts/spending-benchmarks-for-private-b2b-saas-companies

Annual SaaS Growth Rate Benchmarks

The best benchmarks are up-to-date and also give historical context. OpenViews’s 2022 Benchmark survey was run between July and August 2022 and over many years. So, they effectively caught what  happened in SaaS this year (vs the previous years): the fact that most SaaS companies switched from a growth-focus to an efficiency focus. They even nicknamed it the “Whiplash effect”  to capture how companies went from investing aggressively in growth to focusing on reducing payback and cash burn.

For example, here’s a great benchmark on CAC payback: 

Check out their full report here: https://openviewpartners.com/2022-saas-benchmarks-report/#key-takeaways

OpenView doesn’t have a monopoly on SaaS growth rate benchmarks. Other very thorough and noteworthy growth rate benchmarks include: 

1. Revops Squared B2B SaaS Benchmark Report https://www.revopssquared.com/benchmarks 

2. The CapChase SaaS Benchmark Report https://www.capchase.com/lp/benchmark

3. Nathan Latka’s SaaS Benchmarks data including revenue, growth rate, valuation and customer count/acv data from interviews from with 3k+ SaaS founders: https://getlatka.com/
   

Hopefully, this list of benchmarks helps you in setting your strategy and goals for 2023. If you’d like to chat with us about how we can help or just to join our group, contact us here. https://www.42agency.com/lets-talk 

We’re big believers in Benchmarks and we’ll be building out lots of benchmark data in 2023 in our group for everything from Google Analytics and Search Console data to HubSpot marketing and CRM metrics, as well as data from all of the major ad platforms. 

I hope you’ll join our group. When it comes to growing a SaaS business and benchmarks, there’s power in numbers! 

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It is a great piece that helped me understand lots of DTC concepts, and where he articulated many thoughts I had with a lot of clarity and tactical depth. I am super excited to have him join us on 42/ as a guest author today.

Illustrations by Alejandra Céspedes.

"Ad platforms are just tip of the iceberg" - Michael Lorenzos.

As marketers, we overcomplicate concepts and pat ourselves on the back once we can 'connect' A to B. In most cases, our ROI with the customer journey in the funnel gives meaning to our existence. And now, this malady has become a pandemic with increased reliance on just data (without much context). 

Due to this, we marketers have developed myopia, as we are doing the same thing repeatedly, trusting the data, and forgetting to step back or zoom out in Michael's words, to look at the bigger picture.

In our chat with Michael Lorenzos, a B2B marketing rockstar (he would say a learner), we discovered a different way to look at marketing that might be helpful for your organization. This perspective focuses on understanding not only the details of the ad platforms' strategy in performance marketing but also zooming out to analyze them under the lens of classical marketing philosophies, lean development, and customer behavioral patterns.

We call it Zooming in and out.

Before we continue, and as always, remember this is just a "way" to see and understand marketing efforts, not a maxim that is set in stone. 

The Zooming in and out paradigm.

First thing first, how would we define Performance marketing to a marketer from decades ago? Simply put, it is a way for businesses or marketers to have the ability only to pay when your prospect does a specific action (sale, lead or click) generate business outcomes or revenue, etc. 

We perform; therefore, we are.

The ability to analyze ad spend has been a godsend, or 'tech people' send for new-age marketers with a myriad of platform tracking all types of digital behaviors. Tracking  our customer footprints has become so much easier. The old saying by John Wanamaker, "Half the money I spend on advertising is wasted; the trouble is I don't know which half," is no longer a reality.

Unlike in previous decades, a CMO can confidently assign X amount for your ad spend budget and tell you to spend it wisely, knowing you now have the tools to tracking your spending according to the buyer journey or position in the funnel.

And it is through performance, through results, that we define whether a particular marketing decision is worth it.

While focusing obsessively on making sure the marketing machine you are building runs, it is also crucial to zoom out. Looking at external forces and marketing fundamentals around your algorithm-driven motions helps push your performance efforts further. 

This "Zooming in and out", in other words, is simply an attempt to reconcile marketing fundamentals with performance marketing data-driven outcomes. 

Zooming in allows you to figure out what works and how. And zooming out lets you figure out why something works (or doesn't) and how to create more of it.

Zooming in

In this paradigm, the zoom-in section refers to training the different ad platforms to ensure they behave as we want them to. This requires a constant loop in which we make sure everything is connected in the backend, we experiment and launch/optimize campaigns, and most importantly, we feed the ad platforms the right data points so they can perform.

How to zoom in:

1. Make sure your tracking is spotless: As we thoroughly discussed with Michael, it is surprising how many performance marketers jump directly to experimentation without ensuring everything works on the back end.
   
   In performance marketing, measuring is vital. Even the best experiment without the correct measurements is bound to fail. So before you spend a cent, be sure you have set up suitable systems to understand how that investment adds value.
   

2. Use first principles to train the algorithm: We already have algorithms that do everything, but the reality is that there isn’t a single one-size-fits-all playbook when it comes to setting up ad platforms.
   
   As mentioned in this HBR article, we must be cautious about algorithm marketing because they lack context, they may paint a picture of customers that isn’t accurate, and aren’t sensitive enough to context.
   
   The job, when it comes to algorithms, is to train them to work for us. We need to own and design the logic behind its “if-then” nature. No other marketer will understand your product as well as you; no other person will appreciate your ICP as well as you. 
   
   It is up to you. After all, as Michael mentioned, ad platforms are simply “dumb machines that need anchor points to optimize to”.
   

3. Make sure you experiment and think critically about the impact/value - not the metrics: As performance marketers, we tend to focus on specific KPIs that will allow us to demonstrate to our stakeholders why our work matters. While this is fundamentally true, it is also essential to go beyond the metric and consider the impact/value our actions have on the marketing system we are building. 
   
   In that sense, you can do the right things without having immediate metrics to back it up. Sometimes, bold moves take a while to show results, but just because it can't be proven immediately does not mean it is not essential. 
   
   Remember, marketing has short and long-term goals. Moving the needle today is important, but so is having the capacity to plan for the future.
   
   

4. Optimize for signals: Optimizing for signals means selecting suitable conversion events for your campaign type, i.e., registering for a webinar or email signup. Once optimized for the right trigger, you can feed the ad platform algorithm with the correct data.
   
   To do this successfully you must:

   1. Figure out something you can track - This is particularly important because every company is unique, and as a marketer, you are the soul of the organization that truly understands how to connect your offer with a problem your customer faces.
      
      We are aware that tracking can be a bit more challenging when you are making bets based on value (rather than metrics). But, exclusively sticking to CAC (one of the more popular metrics), might not be enough.
      

   2. Figure out how to maximize the actions you are tracking
      
      NOTE: keep in mind that if you have yet to do your homework right, i.e., have optimized your tracking right, you would be selecting the wrong signals, which would ultimately lead to feeding irrelevant data into the algorithm, thus skewing the whole of your campaign efforts and money. 

Zooming out

Marketing 101 relies primarily on a contextual understanding of the world that surrounds your product. It is an invitation to zoom out, exploring the outward forces that can affect the marketing efforts around our product and adapting processes to match such troops. 

However, this is an afterthought in performance marketing for B2B SaaS. Partly because we think the algorithm and testing, learning, and adapting will help us find success (which we already discussed as insufficient) and partly because connecting external patterns to our B2B SaaS products is difficult. 

Within the sphere of D2C, it is easy to see how trends influence your campaign plans. For instance, the No-Sugar (a social factor) pushed Coca-Cola to create and market a no-sugar coke.

But when we talk in the context of B2B SaaS, it can get a bit tricky (being the marketing lead of a business selling to businesses servicing other businesses that are working with businesses is quite a conundrum). 

Yes, yes, we know how in this scenario identifying trends and their impact can get hard. Still, it is vital to be aware of uncontrollable external factors, as you might be wasting money pushing marketing tactics on an unwilling audience to embark into the journey.  

So whether these trends are political/economic/social /technological-ly influenced, any marketer (B2B or DTC) will tell you it can make or break your digital campaign. That's why stepping back from ad platform tactics is critical to find clarity in your campaign strategies. 

Here are a couple of things you can do to zoom out and gain clarity of the context around your solution:

1. Understand your market trends: While there are many ways to do this, we recommend the following: 

   1. Do customer research: Talk to your current customers, and find out what they like about the product and what they need but has yet to be there (new features). Another often ignored way to do this is to use your product and be critical about it. 

   2. Do some review mining: Find out how your solution is perceived out there. Learn what some of the positives and negatives are and how your product fits in the category. 

   3. Be curious about the world in general, not just the industry: Another way to be 100% myopic is to consume content exclusively related to your industry. Plenty of exciting problems, products, and initiatives can shed some light on the challenges you are facing. We're not saying you must lose focus, quite the opposite; you must focus but also remember that inspiration and creative solutions are rarely where we think they are.

2. Explore value-add touchpoints for your ICP: The funnel is a myth that allows us to categorize where our potential customers are and what needs to be done for them to become customers. However, it needs to be improved when mapping out value-add touchpoints, primarily because we focus more on the structure of the funnel than on the customer's journey with our solution.
   
   The Forget The Funnel team has thoroughly discussed this perspective on several occasions with tremendous success. Yet, we keep doubling down on funnels, standard KPIs, and metrics that are algorithm-friendly (rather than customer-friendly).
   
   Let go of this structure and talk to your customers to discover the nuances of your product's journey. Only then will you find what is valuable, what needs to happen for more people to want to buy this product, and what needs to be measured.

3. Ensure you have an organized and connected marketing operations system: We already discussed in the zoom-in section that you must ensure your tracking is spotless. Yet, even if the monitoring is spotless, you must also make sure that marketing operations make sense for the organization's overall health and support other functions of the business (especially sales and customer support).
   
   All businesses share an objective: to increase revenue. And, as a marketer, your job is directly linked to that revenue stream required for the company to thrive. You may not be the one closing, but your efforts directly influence whatever happens on the revenue front. 

   
   Given this massive responsibility, having a connected marketing operations system is fundamental. Therefore, it is best to ensure all your efforts and tracking systems are spotless and build a predictable marketing operations system for other business areas.
   

4. Apply general marketing principles to the scientific marketing zoom-in process: Being caught up in the algorithm game can lead us to build experiments and iterations to ensure an indicator matches our expectations (but not to grant the company’s success). However, by zooming out and gaining insight, we must remember that our marketing maxim should be to design experiments that have an impact (even if the needle does not move right away).
   
   Remember that marketing is a marathon, not a sprint. Having the ability to zoom out to gain perspective and zoom in to swiftly and thoroughly execute whatever you think will work is critical.
   
   Obsess about adding value to your ICP, even if the measurement (performance) is unconventional.
   
   In a competitive marketing world, bold bets and understanding marketing as simply bridging the gap between a solution and a user is much more powerful than you can imagine. 

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Editors note. This has been a long process. Back in 2019, I started writing a course on Facebook Ads for B2B, but it never saw the light of day (if you want to take a peek, I can send you a copy on Google Docs; just hit reply).

I’ve wanted to write this for a long time. Facebook is often misunderstood (privacy & elections aside), so I started hammering out an essay and ended up writing a brain dump of 20 pages that was disconnected & didn’t quite make sense. Rabia & Sebastian then took my mess on a page & edited it down to what you see below. A lot has changed with Facebook, aka Meta post iOS16, with Apple building its ad network under the disguise of privacy & hampering the effectiveness of Facebook & other ad platforms. However, we still believe it has enormous potential, primarily if you sell to more SMB folks & have a larger TAM.

Facebook has a billion users - odds are your buyers are on Facebook or Instagram browsing newsfeeds just like the rest of us.

Illustrations by Alejandra Céspedes.

Facebook often gets a bad rep as something that does not work for B2B marketing. But after generating 7 figures in pipeline, and qualified leads from Facebook - we often see folks write off Facebook as ‘something that does not work’ because of a few common mistakes (we’ll talk about this later on). 

Let's address the elephant in the room first. With over a billion people using Facebook & Instagram, it's highly likely your buyers are there, you just need to find the right levers to reach them. This is why together with the team, we decided to write a post on how we run B2B SaaS campaigns, with seven-digit budgets, that meet revenue goals. 

We hope you’ll find it helpful, and as always, if you have any comments, feel free to tweet us or reply to this email. 

First things first. We are a B2B SaaS marketing agency focusing on Marketing Operations, Demand Generation, and account-based marketing. The following is how we approach Facebook Ads for B2B SaaS companies and isn’t applicable to ecommerce or DTC brands. 

Facebook Ads - Right fit for you?

So the first question that you should be asking yourself is “do i need to do Facebook marketing?” and our answer is - FB might not be great for all B2B SaaS. That's why we have created following checks for ourselves, that helps us in determining, its suitability for our clients.So let's dive into them;

FB is great if:

✅ You have a mid-funnel strategy - where you want to get case studies, blog posts, podcasts, webinars, white papers, and other great content in front of the right audience.

✅ You are looking to build brand awareness and brand affinity.

✅ You are exploring a low-touch approach to starting a conversation with your customer (i.e., free trials, webinars, content distribution, videos, etc.).

✅ You are selling to a broad audience (Ex. By industry), or if you are an SMB or have a freemium product.

✅ You want to reach people “MOST likely” interested in what you do, keeping in mind that these buyers haven't reached the point of realization and are not actively searching for a solution .

Note:

═ Lead-gen campaigns can be improved with offline conversion tracking. However, they can be hit or miss, 40-60% junk rate if FB auto-fills form information. Try to avoid that.

═ It is a low effort, low friction channel if you optimize for lead quality, not lead volume.

FB is not so great if:

❌ You are looking for a pure BOFU approach (i.e., people who are actively searching in the market for your solution.) You try to use it as an intent-based channel (for this we suggest Google a much better option for you)

❌ You expect leads to turn into sales right away - it is highly unlikely though it might be possible in few cases. Remember that on FB you are interrupting people with your ad, contrary to Google where they are actively searching for a solution like yours.

❌ You expect the same quality of demos for enterprise / mid market buyers as other channels. Quality might be lower since FB skews more towards SMB.

❌ If your CRM integration is not in place, you’re not feeding the Facebook Algorithm quality data to optimize against. 

❌ If offline conversion in place via Conversion API or Zapier / Segmentation, etc. is not in place, it will also not work as well as it can.

Now that we have determined whether or not FB is the right channel for you, let's understand how FB ads should be approached for targeting the right crowd.

Facebook Ads - An Untapped B2B Channel:

We believe that Facebook is a ‘prospecting’ channel, given that the users there aren't explicitly looking to buy. Unlike Google, where intent and problem statements are much more evident, FB is a marketing channel that helps us to understand people's preferences. 

Perhaps this is why it is a forgotten channel among the b2b crowd.

So in other words, it's not exactly a ‘high-intent' channel, but rather a channel that will help you reach the right audience (albeit they might not be in the purchasing mood). 

|The second reason why b2b marketers ignore FB, in our experience, is because oftentimes companies don't have the right campaign strategy devised and are not measuring the right metrics. We believe that it is not the “type of Ad” that leads straight to a demo but rather a mid-market solid play to getting quality content & building a brand affinity in front of your key audiences, based on demographics and interests (and yes you can generate quality SQLs as well) 

Side Note:

Remember, if you're selling to a true enterprise company (7 figures in ACV, smaller TAM), then Facebook might be more of a re-marketing+awareness channel for you (and you should focus more spend on LinkedIn / Google) But if you're selling to SMB, Facebook might be a spend of 50%+ of your total. We’ll dive into this a little more below.

42s FB Playbook:  

For an effective campaign strategy it is essential, in our opinion, to nail these 6 elements in any fb ad campaign.These are the aspects and the processes we take into consideration when planning our campaigns for our projects. Let's go step by step and get into the nitty gritty of them all. 

The first thing to do is to understand the offer. You could directly start with a BOFU offer (like a Demo), but in our opinion, you would be leaving ‘money’ on the table for those folks (80% of the market) who are not ready to buy. Try diversifying offers with videos/ checklists or other helpful content / artifacts. 

Audiences: 

‘Know thy audience’ or as we say at the 42, know who might be looking for you. Facebook offers 3 main audiences that you can leverage to reach, so as to convert them into potential buyers: 

• Interest Audiences: 

Based on declared and inferred interests from Facebook and off platform behaviour & profiles - Facebook can put you in different interest groups. For example here is mine: 

Advertisers can then choose to reach folks based on these interest categories. If I am trying to reach B2B Marketers - I can use Hubspot / Salesforce / Optimizely as an interest category to show my ads to those folks likely to be interested in B2B Marketing (based on the interests above, they do reflect my interest in B2B Marketing) 

• Custom Audiences - 

This is the first party data onboarding, that I mentioned briefly above. Any data that is owned by you (i.e. customer data in your CDP & DB or CRM) can be uploaded & matched to profiles and people on the Facebook platform. This can get very powerful because you can create omni channel campaigns to show your ads to folks on Facebook while also running email campaigns, or other channels. 

You’ll notice Facebook now also has a column for LTV providing more data points on ‘who your highest value customers are’ and is trying to build models to reach more of those (vs lower value customers).

A common objection on B2B marketing on Facebook tends to be ‘but my prospects use business emails and they won't match to custom audiences on Facebook’, fair argument. But the thing you may not be taking into account (if you have this objection) is that email is *one* data point that Facebook uses. It also uses location, phone, name, etc. So, even though the match rates might be lower (before FB stopped showing match rates we saw 60-70% match rates) - it's still significant enough. 

The second most common question when thinking about B2B marketing on Facebook is: how many people do I need to create a custom audience? In theory you can create a custom audience with 10 people but it won't be as effective. We recommend 1000 rows to start.

• Lookalike Audiences - Building off Custom Audiences, a powerful feature of Facebook is Lookalike modeling. In plain english it means - you give Facebook your best match customers list (as a custom audience), and facebook then uses the different data points to find commonalities b/w your customers and all of facebook users, and builds audiences that are the ‘quite similar to your customers’, essentially extending your reach beyond just custom audiences (that you own). 

Lookalike audiences are great when: 

• Creating lookalikes from Customers - potentially reaching new audiences with similar technographic patterns.

• Creating Lookalikes off Opp / SQL / MQL (if there’s not enough customers) 

• Creating lookalikes off a conference list (folks who attend Dreamforce are highly likely to be buyers of CRM software)

• And you can go on.

Negative lookalikes: Another powerful feature is the idea of negative lookalikes (h/t to Micheal Taylor for sharing). In a nutshell if you can use lookalikes to find more ‘people like my customers’ you can also use it to suppress ‘people who are not a best fit’. So upload a list of your bad fit leads / closed lost opps (because of bad fit) and suppress those lookalike audiences from your campaigns.

You can create lookalike audiences from custom audiences, website visitors, engagement offline conversion data (if you're passing back value with the offline events), etc. But remember, if your ‘seed source’ is bad data, the lookalike model will also result in bad data.

Beyond CRM lists - Facebook can build custom and lookalike off video views, ad engagement lead forms etc.

Tip: Take advantage of website remarketing audiences - they are the low-hanging fruit. 

Ads and Creative.

Perhaps the most common question we get regarding creatives is: ‘what kind of creative works best?”. As always, it depends. But here's a general rule: creatives that break a pattern work better. 

On the other hand, videos generally work better than ‘static’ adverts for TOFU but for mid/bottom funnel - In our experience, we find static works better. 

Some of our creatives here

Leverage Facebook's Dynamic Creative and Ads to quickly test different creatives and copy variations vs trying to manually create 10 different ads. Use Facebook's dynamic URL builder to build specific tracking parameters for different creatives / placements and ads vs trying to hardcode UTMS (or not using them at all) 

The advantage is that facebook will dynamically insert placement / ad name etc when someone clicks (similar to tracking URLS) and you can also add custom URLS.

We cannot stress this enough - creative uniqueness and quality makes a difference. And, of course, customizing by creative is a baseline requirement for good performance. 

We’ve audited B2B Brands spending 100K / mo on Facebook that don't have vertical / stories sizes for creative or they use 1200x628 for Instagram (use 1080 X 1080). 

It seems obvious, but it is not. It is adamant to get the creatives right if you want to win. 

Budget/Bidding

By default Facebook offers lowest cost bids. But in some cases (with higher ACV products) it might make sense to have a different bid strategy

You can use Cost Caps (i.e. I want to generate a result as long as it is under $X) but with Bid / Cost Caps - you want to make sure the daily spend is at least 2X the cost cap. 

For example  - I know that I want to keep my CPA under $500 so I set up a cost cap bid for 500. But if my daily budget is $200, it's unlikely I'll get any conversions (because FB can only spend 200 a day). In this case we’ll update our daily budget to be 1000 (2x our cost cap) to give Facebook enough room to spend on a daily basis to get that conversion.

Our preferences

• Adset budget over campaign budget because it allows more control to manipulate different Adsets based on budget. But Facebook’s Campaign Budget Optimization is getting better over time. Increasingly we’re using less ad-sets / campaigns, and favouring single or 2 ad-sets.

• Creating a budget strategy: Collect baseline data before experimenting with different bid strategies. In *most* cases going for the lowest cost is the best way to go. But, you can test different strategies to see what works.

Timing to keep in mind

• Pay attention to increased competition around the holidays. (Ex. eComm on Black Friday.)

• FBs learning phase requires one week to optimize for your goals. Each change (Adset, audience, etc.) must run for a minimum of one week before analyzing the results. 

Optimize Lead Forms

Facebook Lead Forms are god-send for B2B Marketers however they can be a hit or miss on quality. Here’s some tips to improve their performance.

• Add a work email field. This requires users to fill it out by hand, helping to improve the quality of data collected. Otherwise it auto-fills with the email associated with their Facebook Account (hello sleepy_hollow_85@hotmail.com)

• Make sure the custom fields map correctly to CRM - a lot of the fields will map out of box but if you add custom fields (like dropdowns) make sure they are mapped correctly. We do add some fields to qualify a prospect up-front (i.e. Current TechStack etc).

• Review - Add an extra step to review the info before it's submitted to allow them to review.

Unlike LinkedIn - where Landing Page CPA’s are incredibly high, Facebook's LP Conversions can be more favourable in terms of quality and CPA. That way you can avoid using Lead Forms and instead set up Landing Page conversion campaigns.

Copy

We generally get a long copy (400-600 range) and a short copy (200-300 range) and adapt. But here are the specifics you need to keep in mind:

• Primary Text: 125 characters

• Headline: 40 characters

• Description: 30 characters

Our Playbook in a Nutshell:

Let's take a review of the basics of a campaign setup for a Facebook Campaign. Our objective is a mix of brand affinity + conversions. So here are our suggestions:

Things to do !

✅Know your audience - Be sure you have an understanding of your brand's Ideal Customer Profile. This can include things like what they read / who they follow + what the jobs to be done with your product is. For example if I am selling to accountants & bookkeepers, I know they likely pay attention to popular accounting blogs / vendors (like QuickBooks). 

✅Dot your i’s and cross your p’s - Know your Setup tracking, pixel, structure, objectives, spend, events, and CRM integrations. 90% of the time (when we review Facebook Accounts) they don't have the Facebook Pixel installed, and even if they do, the setup is incorrect.

✅Learn to Leverage your campaigns! – leverage appropriate campaign types (video vs conversion vs traffic) based on the business objectives. 99% of the time we leverage conversions / landing page views / video views.

✅Pick the right intent data type - Onboard any first or third party data. This can be CRM lists (existing customers) or Third Party audiences from places like LiveRamp / Bamboora or G2.

Campaign (the overarching offer)

✅Ad Set (Audience Variations) - Bundle your Similar Audiences Together. This is especially important post iOS14. Facebook has less visibility on iOS devices so larger audiences tend to perform better (Facebook needs 50 ‘events’ a week to optimize) vs running smaller discrete audiences.

✅Create more Versions – make 3-4 Copy / Creative Versions.

P.S: We offer full service B2B Creative - ask us for details.

Finally, a few common mistakes that we have observed folks doing , over the course of running B2B SaaS campaigns with seven-digit budgets.

Things Not-To-Do:

❌ Poor campaign structure - i.e. too many overlapping ad-sets and not enough creatives.

❌ Optimizing for the wrong conversion - Ex. focusing on Likes or Link Clicks instead of Conversions.

❌Issues with the pixel setup.

❌ No FB pixel on website.

❌ Not tracking campaigns down to opportunities and sales.

❌ No CRM integration.

❌Not leveraging 3rd party or first party audiences.

Special Note: We often get asked if you should use MetaData or Clearbit Ads. We love both products (and we’re partners) but it doesn’t make sense to be spending upwards of 4K/mo on these products, unless your Facebook Spend is 50K+. You can still experiment with different creatives and copy using Facebook's dynamic creative (it's not as robust as Metadata but it's good enough). 

Those products shine when you're spending 50K+ on media spend. Clearbit in particular is an audience tool we use together with LiveRamp for a lot of 3rd party audiences. Once you are scaling beyond your custom / first party / third party and native audiences , we encourage you to explore Clearbit / Metadata’s product offer (and we can offer you 20% discounts via our partnership with both platforms + help you choose the best one).

Also check out Primer for building B2B audiences ( disclosure we are partners).

Finally , If you want to learn more or want us to help you run your campaigns, be sure to reach out to hello@fourtytwo.agency or join our 42/ newsletter to keep up with B2B SaaS related updates. 

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Ask ten folks what attribution means, and you'll likely get ten answers. We don't believe or advocate for attribution, so marketing & sales can get credit or fight over it. But instead, we seek to understand the direct & indirect ways folks learn about your brand & end up being a customer - so you can prioritize investing in whatever works.

Attribution is also often viewed as 'click-based' activity (i.e., someone clicked an ad that led them to take action). This understanding is heavily based on cookies & as cookies get scrutinized & regulated for privacy, this will become less effective. We see attribution as more than *click-based*, and we build data models that consider the holistic mix of marketing channels (online & offline). Finally, we explore how mediums interact & support each other, measure Adstock, & contribution, as well as map out the ceiling/limits.

You can even do geo testing/measure lift & get directional attribution. It's less about tying a specific action to generate revenue than understanding the comprehensive actions that lead to the right outcomes.

In other words, attribution is fundamentally not new.

FMCG brands have measured their media spend on TV and offline for decades. Before we had cookies to tie a journey, we would ask folks how they found us; then, technology got better at stitching together the data so we could understand the journey in aggregate.

Now it seems like we're back in the debate of 'attribution is useless; you can't measure XYZ' to 'we need to be hyper data-driven, and we need to measure every single click'.

The truth is always somewhere in the middle, but if we don't measure our outcomes in marketing, how will we ever improve with time?


Community Shoutouts:

Be sure to check out the following job openings by some of our friends. We love working with them; indeed, you will too.

- Float: They are looking for Account execs, Content Marketing Managers, Sales Managers, etc.
- Afresh: Several openings across most fields (Operations, engineering, sales, etc.)
- Opsera: Openings in Palo Alto and Chennai.
- Mathison: Openings in business development, finance, and marketing.


Quick favor - Before you start reading, please fill out the audience survey so we can write better essays for you (and we’d like to know who you all are) - it takes 30 secs

Attributing marketing spending and channels to actual opportunities and revenue has always been the struggle of marketers. After all, the results are an existential matter; they spell whether they’ve done their role as a B2B marketer. 

Even so, attribution is an old problem. In fact, back in 1976, a British statistician named George Box wrote the famous line, “All models are wrong, some are useful.”

That usefulness concept still holds true. What has changed, and not for the better, is the perception that attribution models are true given they are data-based.

But they aren’t.

Let’s start from the beginning…

The Harsh Truth: You Can't Trust Data

Before the internet, marketing was a matter of trial and error (and luck). No one knew for certain what would work for a marketing campaign, and you still had these dopey creative types joining C-level execs pitching their exotic ideas, hoping they would help the brand differentiate and grow. 

Some of it worked. "Just do it," inspired by Gary Gilmore's execution and the highly successful Air Strip campaign by Braniff, highly regarded back then, were massive hits for the brands. 

Today, probably neither of them would ever see the light of day. The first would be too morbid for superficially woke western societies. And the second would never fly, given the hyper-sexualization of flight attendants.

Back then, marketing was different. You had to spend a lot of money trying out things and trust a team of researchers (qualitative and quantitative) and creatives to create the perfect ads to get in front of the right people at the right time. 

Today, anyone with a computer can do the same from scratch, knowing nothing, by watching a couple of youtube videos and having access to a credit card. 

The thing is, no one knows how these massive platforms work. We (marketers) run playbooks and specific tactical steps because they have previously worked. But no one can tell you exactly how AdWords or FB ads work with 100% accuracy.

That is their game. And our disadvantage as marketers.

We (marketers) are uncomfortable with the idea of ambiguity, and we try everything to remove chance from the equation. Our ads either work or don't; it is a black or white scenario, no in-betweens. 

We have reached a point where ambiguity is now lost; as Thomas Bauer argues, complexity, plurality, and diversity are no longer valuable. 

With behavioral economics and massive datasets combined, we have never been closer to cracking the motivations and reasoning behind purchases. Yet, it seems like Daniel Kahneman and Dan Ariely are nowhere in the average marketer's mind. 

We also happen to use data to build stories around our actions, to use data to prove our vision and to tell investors or clients their money is well spent. 

We often do not want to face the fact that we can argue several perspectives with the same datasets.

Sometimes, data is unbiased, and we use it to understand and improve our campaigns. And on other occasions, we use data to prove to a client that something is not executed correctly with their current vendor to bring them to our client pool.

Our perspective and interpretation matter. Whether biased or unbiased, we interpret the data in our favor. 

We trust data above all else; we think it is true and parallelly ignore that our interpretations influence what the numbers say.

But the reality is that there isn’t a single truth, even with the right data. 

Knowing that, here's how you can start using attribution models, while at the same time knowing their limits

Why Are You Measuring Attribution

The most important thing to get attribution “right” is to understand why you are doing it in the first place. 

Generally, attribution projects start with a core question you want to be answered. This decides how you gather and measure your marketing data. 

Common questions are: 

• Where did our customers last quarter come from? 

• Which of the channels last quarter contributed to opportunities?

• Which channel should we spend less on?

And so, to understand attribution we will discuss some of the most common models used by marketers today.

Types of Attribution Models

Fig. 2 - Linear First touch and last touch attribution

Most of the time, you can just look at the original source and last source columns in your CRM data to find out which channel or campaign the contact or account first interacted with, and which channel or campaign they interacted with before they became an opportunity. 

This is usually enough when you’re starting out, but also presents some issues you’ve already encountered.

1. Contact level data vs account level data – Opportunities aren’t created with just one contact, but with multiple people in a buying team. Can you really say that marketing brought those contacts in? 

2. Not every interaction can be tracked with cookies –  Whether it’s trade shows, “dark social” or a simple offline conversation, there are plenty of ways customers are discovering and learning about your product that are probably not going to be in your “source of truth”/CRM.

3. User error and bias – CRM inputs are occasionally done manually. It can’t be avoided that someone is more likely to assign a source for a contact that is directly related to the one that they’re working on (especially if it’s a 1:1 interaction… or you love the campaign). 

4. Self-reported attribution – related to #3, if you were to ask your customer where they first found out about you, they wouldn't always give you an accurate answer. It’s not intentional most of the time – it’s just how memory works. 

Multi-touch attribution

Multi-touch attribution (MTA) is the better choice for B2B marketers. You can map out all the touchpoints that led to an opportunity being created for that account (you must keep in mind all the relevant contacts too). Commonly used methods are Linear, Time Decay, and the Position Based Model. 

Fig. 3 - The Linear Model assigns equal weight to each touchpoint

Fig. 4 -The Time Decay model assigns more weight to the more recent touch points

Fig. 5 - The Position Based model assigns more weight to the first and last touch points. 

Fig. 5.1 - Model Comparison

You can also use these percentage weights to get an estimated value to the contribution based on the deal size of this opportunity or contract. 

Fig. 6 - Percentage Weights

This is helpful if you want to look at contributions for particular channels or campaigns across your entire customer base or pipeline (e.g. Ads to download the ebook contributed $50,000 this quarter out of a total pipeline size of $200,000)

MTA is preferable for B2B marketing because

1. It’s holistic 

2. It’s dependent on how marketing plays into your business

Which model works for you is entirely dependent on the customer journey patterns at your company and you can adjust based on that.

For example, if you use one of these basic models, and it’s saying that your webinar isn’t to be given much weight, even if you know it’s really been the game-changer for your demand gen. (And the attendees do end up becoming customers!)

In this case, then you consider first, how many customers have this campaign as part of their customer journey, and at which stage? If a fair amount of them (>50%) are viewing a certain piece of content in the middle of the journey, then it means you’d have to adjust the weights. It’s either you’d want to add more percentage weight to the mid-funnel, or more percentage weight to webinars.

(If you’re statistically inclined and have a lot of data, you can look into logistic regression analysis if you want to start modeling which marketing events can tell you the likelihood of someone becoming a customer or converting).

Of course, attribution models can’t solve inherent gaps in data, such as human error and bias, and assigning weights to different actions is more an “experiment” than “truth”, but it can describe the state of reality to some degree.

An Alternative to Attribution Models

Attribution models answer immediate questions but fall short in terms of helping you with other types of decision-making.

For example, you already know every customer has interacted with a Facebook campaign in their journey, but does that mean you can spend as much of your budget as you can on it?

On the flip side, what if you have campaigns that are great for other aspects of marketing, but don’t seem to play a major part in helping a customer move down the funnel. Can you justify spending on them? And are you better off running these than not?

If you have a lot of spend and impression data for your performance marketing campaigns, you can try Marketing Mix Modeling (MMM) to help you answer whether these campaigns and channels contribute to opportunities (to a statistically significant degree), even if they aren’t all direct response campaigns. Then you can figure out where to reallocate spend, if needed.

Fig. 7.1, 7.2 - Marketing Spend and Contribution to Leads

Start with your question

As always, to start organizing your data, you need the question you want to be answered. This is something like, “I want to know if my marketing spend on these particular channels over the last 6 months is contributing to the pipeline and if so, how much?”

Gather your data

You can also segment the spend by individual campaigns within those channels if you have enough data. At bare minimum, the data you want are spend (or impressions) and your KPI you want to measure against.

You should also have decided whether you want to just measure based on events, new contacts, or an actual opportunity (per account), so you can avoid double counting.

Fig. 8 - Channels.

Run the analysis (on a statistical program)

The results of a marketing mix model show you whether a channel or campaign does contribute to your KPI (compared to just not spending on it at all). And if it does contribute to your KPIs, to what degree? 

Marketing Mix Models are statistical analyses, so the results would look something like this, where the coefficient is the measure of the contribution. The way to read them is, for each dollar (or 100 or 1000 dollars) I spend, how many leads would I get?”

In simple terms, the larger the number of the coefficient, the higher its contribution to your KPI you’re comparing it to (in this case, opportunities in the pipeline). And measures of statistical significance (like P-value) tell you whether this variable does or doesn’t have significance at all.

Fig. 9 - Coefficients.

You get the contribution number (like below) if you multiply spending on channels with the coefficient you got in the statistical model.

In the example below, Facebook has 0 incremental contribution to leads, despite having the same spend as Linkedin.

Fig. 10 - Marketing spend reimagined.

The cool thing about marketing mix models is that they also consider the carryover effect of your ad campaign, based on patterns within your own data. You can basically understand if Ads in a certain channel or campaign tend to have better recall than others (you can get better spending efficiency knowing this).

Fig. 11.1, 11.2, 11.3, - Decay per channel

And you can also use them to find out at which point in spending the contribution starts to flatten out.

Fig. 12 - Spend vs. Lead Contribution

Lastly, the MMM answers the important questions on how to properly allocate marketing spending for your business goals without needing cookies-based attribution. 

Final thoughts

Attribution is a tricky subject that requires more thoughtfulness than your average marketing activity. 

Models are nothing more than models. You can have infinite models to share your truth, but in the end, beyond attribution, your job is to make sure your product is successful (not to prove how your role or your department is doing well).

In essence, all data is subjective. Therefore, we must aim to explain the chaos to the best of our abilities, but even if we do, we must be aware we can never access the whole “true” picture.

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Before I watched an F1 race, I couldn't help but think "wouldn't the team with the most resources win every time?". With each team free to build their racing car as they see fit, my thinking was that the person in the driver's seat can only make so much of a difference. Obviously, that's not the case in Formula One racing; the magic is in the constraints. Both in terms of regulations on how F1 cars are built as well as in-race rules defining how many pit stops each driver must take.

As my interest in F1 grew, and especially after watching the Drive to Survive series, my curiosity pointed me to what happens off the track. I was surprised by how heavily systemized and data-driven the Formula 1 teams were in supporting drivers before, during, and after a race. 

I've spent a long time in marketing operations, and so I found myself nodding silently reading the start of Vikash Koushik's essay in this same publication:

"Most organizations view marketing operations as plumbers who you can call to fix the broken pipe. But not enough companies look at them like an architect."

The "off-track" members of Formula 1 teams have mastered the art of being a supporting cast to their drivers and there are lessons here for marketing ops practitioners in becoming a strategic partner to the functions you serve.

So buckle up and let's see how far we can take this analogy. Here’s a roadmap for marketing ops practitioners to become more architect, less plumber.

Make Your Tech Stack Resilient 

I couldn't help but feel second-hand embarrassment watching an episode of Netflix's "Drive to Survive" series. A driver calls in to the crew, letting them know his breaks aren't working and his engine is low on power. "We can't fix it", the engineer responds as the driver quits the race.

Post-race, an interviewer asks the McLaren team's Race Engineer whether he'll accept responsibility for what transpired. Laughing nervously, he tries to strike a balance between saving his job and acknowledging his responsibility.

Anyone in a role that involves building things for other team members to use can relate to this scenario, or hopefully not. From leads from a certain campaign not syncing correctly (looking at you LinkedIn Lead Gen forms) to automation breaking under increased scale, marketing ops failures often impact other functions, leading to a breakdown in trust.

Fickle marketing tech stacks built on bandaids and quick hacks will break constantly, draining time from work that can position marketing operations as a strategic partner. And so step one in building an ops function that is strategic is to build a resilient marketing tech stack that effectively serves end-users.

Much of what this entails requires marketing operations managers to act more like product managers or DevOps engineers, with the broader marketing team as the customer. 

The greatest failure of that Race Engineer from "Drive to Survive" was that he and his team only knew that something was wrong when the driver ran into problems and reported them. This shows a lack of a good quality assurance (QA) and diagnostics process. If you only know something is broken when the end-user tells you so, something is wrong

By building monitoring and alerting into your tech stack, you can avoid hairy situations like this by knowing when things go wrong and fixing them before more damage is done. Common alerts I set up include:

• Leads where lead source is null

• Leads without a campaign membership

• Missing UTM parameters

• Automated website QA with Ghost Inspector 

Focus on Continuous Improvement

F1 drivers race across 190 miles of track, with that distance split up into laps determined by the specifics of the course. With most races comprising 50-70 laps, each iteration after the first is an opportunity for the driver to improve upon the last lap.

The teams that can learn from each lap and pass those learnings on to the driver quickly and effectively are at an advantage. In fact, "evidence suggests... that the key determinants of a Formula One race are the internal efficiency and expertise of the teams" 

Marketing operations managers need to stand up systems and processes that help their team improve with every campaign.

For marketers in B2B orgs, the ultimate goal is to achieve a measure of predictability and repeatability with how they go-to-market.  What channels and messaging are most effective at accelerating stalled deals? When is the right moment to have an SDR reach out? 

The only way to get even close to that level of calling your shot is to build the systems and processes necessary to retrospect campaigns, learn from their performance, and most importantly roll those learnings into the next campaign.

As with most things in marketing ops, this starts with clean data. Without a clean and well-maintained Marketing Automation Platform (MAP), you'll never be able to reconstruct a customer's journey and understand the campaigns that led to key inflection points.

Going even further, I believe that having a data warehouse like Snowflake is table-stakes for a mature B2B marketing team and one big reason is being able to take snapshots of your CRM and MAP to then create cohort reports. While most commonly used for measuring in-product metrics like Daily Active Users, cohort reports can also be used to measure the impact of a campaign over time. By creating a cohort of all accounts and leads touched by a campaign, you can visualize how they move through different funnel stages. 

Improve Your Team’s Learning Rate 

A key part of continuous improvement is learning, and specifically how fast your organization learns. As a marketing ops practitioner, you have two main levers to improve your team’s learning rate; speed and process.

Setting a goal for how long it takes your team to go from campaign kick-off to launch is important now only because it will help identify inefficiencies in your processes, but also because rapid learning requires rapid iteration. The more campaigns you can launch with a defined audience and clean data, the faster you can learn. The more at-bats your team has, the more assumptions can be tested. 

The easiest way to speed up campaign creation is to templatize the common elements of a campaign. From having a standard library of creative templates to pre-built workflows that can be cloned and adjusted for different campaigns, marketing ops practitioners should be looking for areas where templates can reduce duplicated efforts. 

The second lever, process, may seem boring but it is arguably even more important than speed. A lot of marketing teams “do A/B testing”, but much fewer actually have an experimentation program. An experimentation program goes beyond simply running A/B tests. It includes defining how A/B test ideas are generated and prioritized, the process for rolling out successful tests once a hypothesis is validated, and knowledge capture to document the tests run, the hypothesis, and the result. 

A/B testing sporadically may lead to the occasional win, but consistent results require a process. A good place to start is to think through how ideas are generated and captured on your team. Good test ideas can come from anywhere. Each team member should know how and where to submit their ideas for experiments, and experiment results should be easily available along with the different variations and their results. It’s often a good idea to archive your test results in a spreadsheet as it can be difficult to filter down to specific tests in some A/B testing tools like Google Optimize.

Know When to Experiment and When to Build for Scale

One of my favorite books on marketing is Hacking Marketing: Rethinking Marketing Management in a Software World by Scott Brinker, editor at chiefmartec.com. A key lesson from the book is the distinction between building for scalability and building for innovation. 

Building for innovation means building just what you need to validate or invalidate your assumptions. Something built for innovation should be expected to work, but is clearly not expected to scale. These are quick experiments that have a purposefully short shelf-life. On the other hand, building for scalability means building systems that are standardized and documented. 

While many marketing ops practitioners may already bifurcate their work in this manner, it is critical that this distinction is made clear to your team. Make sure you specifically call out and make known what is an experiment built for quick iteration and learning and what’s an established system. For example, when building for innovation, highlight potential points of failure, like a workflow that can only handle a certain number of leads concurrently. 

Build with the User in Mind

F1 drivers have different driving styles and this can impact how their cars are built. Some drivers perform best with an unstable car that allows them to oversteer when needed, while others prefer a stable rear. Throughout Drive to Survive, race engineers analyze drivers’ styles to adjust their vehicles for maximum performance, often asking the driver how certain parts of the track feel. 

For marketing operations practitioners, it’s important to have the same level of clarity around who you’re building for and what kind of solution works best. If there’s one skill that separates world-class marketing operations practitioners from the rest, it’s requirements gathering. Early career marketing ops practitioners will often jump quickly to building out a solution and I still fall into this trap myself sometimes. It takes patience and trust to take a step back, ask the right questions and only then move on to building. 

A simple question like “will this be used just for this campaign, or are you planning to roll out this workflow to more campaigns?” can completely change the kind of solution you build. 

Get Comfortable with Being a Background Actor

I’ve yet to see a post-mortem of a successful startup where clean data, spiffy automations, and good operational practices were credited for that success. 

Marketing operations is by its nature a supporting role and it’s the product, performance, growth, and content marketers that will get credit for success and so marketing operations practitioners should be comfortable with that supporting role and aim to make themselves a force multiplier for their team. 

By building a resilient martech stack and creating systems and process that help your team learn and improve with every campaign, you can free yourself from constantly fixing bugs and taking in requests and use that time to do what marketing ops is best positioned to do in a marketing organization; see both the whole picture and the small details in order to identify areas for improvement and automation.

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