Matt Heinz had a post recently asking what the modern CMO should be called. Hundreds of comments debating titles. Meanwhile, most marketing teams can barely use AI beyond asking ChatGPT to write a headline.

I commented something like: “If we as marketers want to be taken seriously, we need to stop inventing new names and focus on the work that needs to be done.”

That’s it. That’s the post.

The work is changing. The titles are irrelevant. And the gap between marketing teams that are actually adopting AI and those just talking about it is getting wider every month.

The Real Problem: Most Teams Are Stuck at L1-L2

Geoff Charles at Ramp posted a thread recently about getting their company “AI-pilled.” 99.5% of employees active on AI tools. Non-engineers shipping code to production. The whole thing.

He included a levelling framework:

- L0: Sometimes uses ChatGPT

- L1: Built custom GPTs, dabbled with coding tools

- L2: Built an app that automates part of their job

- L3: Systems builders who create infrastructure for everyone else

It’s a useful frame. But it’s company-wide and generic. What does this actually look like for marketing teams specifically?

Here’s my version. Five levels, marketing-specific, based on what I’m seeing across our clients and our own team.

The 5-Level AI Adoption Ladder for Marketing

Level 1: AI as Search

ChatGPT as a slightly better Google. Basic queries. “Write me a headline for this ad.” “Give me 5 subject lines.” “What’s the difference between demand gen and lead gen.”

The output is generic. The user doesn’t know why it’s generic. They’re not giving context, not iterating, not building on responses. They type a question, get an answer, move on.

Most marketers are here. They’ve “tried AI” and concluded it’s useful for simple tasks but nothing transformative. They’re not wrong about the output quality. They’re wrong about why.

Level 2: Prompt Engineering

The user realizes the output is mediocre and starts learning why. They write longer prompts. They specify tone, audience, format. They iterate. “Make it shorter.” “More conversational.” “Add a statistic.”

Output improves. Copy is decent. Brainstorms are useful. Research summaries save time. But it’s still generic in a fundamental way — the AI doesn’t know anything about your company, your product, your voice, your customers.

This is where a lot of “AI-savvy” marketers plateau. They’ve gotten good at prompting. They think that’s the skill. It’s not.

Level 3: Context-Aware AI

This is where it gets interesting.

Custom GPTs. Claude Projects. NotebookLM. The user uploads their docs. Brand guidelines. Past campaigns. Competitor research. Customer interviews. Product specs. Pricing pages.

Now the AI isn’t prompting blind. It has context. It knows your voice because it’s read 50 examples of your writing. It understands your positioning because it’s ingested your messaging docs. It can reference actual data from your actual campaigns.

The output shifts from “generic but competent” to “actually useful for my specific situation.”

Most marketing teams haven’t made this jump. They’re still prompting raw ChatGPT with no context, getting mediocre output, and concluding AI is overhyped.

The delta between L2 and L3 is enormous. L3 is where AI starts feeling like a real teammate instead of a parlor trick.

Level 4: Data Analysis

Spreadsheets with AI. Code Interpreter. Actual analytical work, not just words.

Upload your campaign performance data. Ask questions. “Which creative themes are fatiguing fastest?” “What’s the correlation between ad spend and pipeline by channel?” “Identify anomalies in the last 90 days.”

This is beyond copywriting and brainstorming. This is using AI to see patterns in your data that you’d miss or that would take hours to find manually.

For paid media specifically, this is where AI gets powerful. You’re not asking it to write an ad. You’re asking it to analyze 10,000 rows of performance data and surface what matters.

Most marketers don’t even know this is possible. They think AI is for words. It’s also for numbers. And that’s where a lot of the leverage actually lives.

Level 5: Agents and Automation

MCP. APIs. Claude Code. Replit. Building agents. Automating workflows.

At L5, the AI isn’t just answering questions or producing output. It’s doing work autonomously. Monitoring signals and triggering actions. Enriching leads and routing them to the right sequences. Pulling data, transforming it, generating reports, sending them — without human intervention.

This is infrastructure. It’s not “using AI” — it’s building with AI.

Very few marketers are here. The ones who are have effectively become a different kind of operator. They’re not just running campaigns. They’re building systems that run campaigns.

Why L3 Is the Unlock

If I had to pick the single biggest jump, it’s L2 to L3.

L1 and L2 are both “prompting a general-purpose AI.” You get better at it, but you’re still working with a tool that knows nothing about your specific situation.

L3 changes the game. Suddenly, the AI understands your context. It knows your brand voice because it’s read your best-performing content. It knows your competitive positioning because it’s ingested your battlecards. It knows your customer pain points because it’s read the interview transcripts.

The output quality gap is massive. And the use cases expand — you’re not just asking for headlines anymore. You’re asking for strategic recommendations grounded in your actual data.

Here’s the thing, though: L3 requires setup. You have to actually upload your docs. Organize them. Create the project or the custom GPT. Most marketers don’t do this because it feels like extra work upfront. So they stay at L2, prompting blind, getting generic output, and thinking that’s what AI can do.

L3 is maybe 30 minutes of setup. The ROI is hundreds of hours of better output. But humans are bad at upfront investment, so most people skip it.

The Adoption Problem

The issue isn’t capability. The tools exist. L5 is possible today for anyone willing to learn.

The issue is adoption. Getting people to default to AI. Not “can they use it” but “will they.”

This is what Geoff’s Ramp thread actually gets right. He doesn’t talk about tools. He talks about culture.

- Slack channels celebrating AI wins

- Office hours to help people level up

- All-hands demos where people show what they built

- Leaderboards tracking usage

- Manager accountability

He mentions that Ramp considered rewarding token usage before worrying about quality. Just get people using the tools. Build the habit first. Quality comes after.

Meta apparently does something similar — they track and reward AI usage to drive adoption, even before they measure impact.

It sounds dumb. Why would you reward activity over outcomes? Because the activity creates the habit, and the habit creates the skill, and the skill creates the outcomes. You can’t optimize for quality if people aren’t using the tools in the first place.

That’s the line from Geoff’s thread that stuck with me. You can mandate AI usage. Your team will nod and ignore it. But if they see their peers shipping faster, building cool stuff, getting recognized — they’ll want to level up on their own.

What We’re Actually Doing at 42

We’re a 20-person agency. We don’t have Ramp’s resources. We can’t build our own internal AI platform or host 700-person hackathons.

But we’re further along than most.

Claude Code as the operating system.

Not ChatGPT in a browser. Claude Code running locally, hooked into APIs, with custom skills that automate real workflows. Competitive intel. Campaign analysis. Reporting. Content production. The AI doesn’t just answer questions — it does work.

Building Agents & Skills, not just prompts.

We’re packaging workflows into reusable skills that anyone on the team can invoke. One command runs a full competitive analysis. Another generates a campaign brief from a transcript. This is the L5 stuff — building infrastructure that makes everyone faster.

1:1 onboarding.

We’re not sending a Loom and hoping people figure it out. We’re doing individual Claude Code setup sessions. Walking people through configuration. Getting their environment working for their specific role. High-touch, but it’s the only way to actually drive adoption.

GitHub for shared infrastructure.

Team repo with shared API keys, config files, skill definitions. When someone builds something useful, it’s available to everyone. The knowledge compounds instead of staying siloed.

Shipping real things.

Our creative director, Alejandra — not an engineer; is pushing interactive content to Vercel. Production deployments. From someone who six months ago had never touched a terminal. That’s what L4-L5 looks like in practice.

We’re not at 99.5% adoption. Not even close. But the people who’ve levelled up are building things that seemed impossible a year ago. And every week, someone else starts to see what’s possible.

The Point

Stop debating what to call the CMO. The title doesn’t matter.

What matters is whether your team can actually use AI at L3+ or whether they’re stuck at L1-L2, wondering why the output is generic.

The work is changing. Campaign setup, analysis, creative iteration, reporting, enrichment, outbound — all of it can be faster and better with AI. But only if people actually adopt it, build the habits, and level up.

That’s not a tool’s problem. It’s a culture problem.

And if you’re a marketing leader spending time on titles instead of figuring out how to move your team from L2 to L3, you’re focused on the wrong thing.

Where’s your team on the ladder? And what would it take to move them up one level?

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).

Rocksalt released a report last month analyzing 7,000 LinkedIn posts. The finding that caught my attention: mentions of “brand building” are down 79%.

I was surprised. In my bubble on Twitter, everybody talks about brand, brand, brand. The LinkedIn B2B Institute has built an entire content machine around it. Every conference has at least three sessions on “building brand in B2B.” So a 79% drop? It didn’t match what I was seeing at all.

But Anita from Rocksalt had a different read on it. “They’re not calling it brand building,” she said. “The audience is engaging more with content that’s practical and applicable rather than theoretical brand-building discussion, which some people like to pontificate on.”

That word. Pontificate. It captures exactly what’s wrong with most brand conversations in B2B.

The Nike Problem

Here’s my beef with how we talk about brand in this industry: somebody will be waxing on about Nike, and how they’ve built this massive business on brand. Look at the emotional connection. Look at the cultural resonance. Look at Just Do It.

And I’m sitting there thinking: yeah, but they’re Nike. You’re not Nike. You’re a pre-seed startup with 50 people. Nobody knows who you are or what you do. You don’t have cash in the bank. You’re running on runway.

I see this constantly. Someone will point to Salesforce as the example of why brand matters. And I’m like, yeah, but you’re not Salesforce. You’re a startup that needs to find customers.

The brand conversation in B2B has become disconnected from the reality of building a business. We’ve borrowed frameworks from consumer packaged goods (CPG) and Fortune 500 companies and applied them to Series A startups burning cash. It doesn’t work.

Two Shampoos, Same Factory

Let me go back to where this started for me. I worked at Procter & Gamble in Pakistan. Second day of orientation, they sat us down with two bottles of shampoo. Same size. Same type. One branded one way, the other branded differently.

The trainer asked us: “What is the fundamental difference between these two?” We started listing things: packaging, color, price point, positioning.

“No,” he said. “It’s basically the same thing. We just have different branding for them because they’re geared towards different segments.”

That’s when it clicked for me. Commodity products need to differentiate on brand because there’s nothing else to differentiate on. The job of brand is to create that preference when the underlying product is interchangeable.

Technology isn’t soap. At least, that’s what I used to think.

The Differentiation Illusion

My original thesis was simple: technology creates fundamentally differentiated products. What Salesforce does is different on a technological level from what HubSpot does.

But then Anita told me a story that complicated this. She was in cybersecurity. Second week on the job, the CEO comes to her and says: “Anita, what do we do? We came out with this thought leadership thing... and in two weeks, all our competitors are saying the same thing. They just copied it.”

In a way, B2B tech has become commoditized too. When I started in tech, products were genuinely differentiated. Now? Everyone ships features within weeks of each other. AI accelerated this. The gap between “innovative” and “table stakes” has collapsed.

The Brand That Gets Remembered

Anita cited Preston Rutherford: “The brand that’s remembered is the brand that gets bought.”

She also brought up research from Gary at Insight Partners and 6th Sense. One slide stuck with me: on day one of a buyer planning to purchase software, they already know 4.5 out of the 5 vendors they’ll consider.

Key Insight: Buyers know who they’re going to evaluate before they even start evaluating.

How do they know? Brand. Mental availability. The names that come to mind when someone thinks “I need a CRM” or “I need a sales engagement tool.” In an environment where everyone sounds the same, being remembered is a competitive advantage.

Small “b” vs. Big “B”

Let me explain how I actually think about brand now. I call it small b brand.

FeatureBig “B” Brand (Traditional)Small “b” Brand (Practical)FocusHigh-level awareness, “Vision”Tactical execution, being helpfulChannelsTV, Billboards, Generic AdsNewsletter sponsorships, CommunitiesMetricBrand Lift, ImpressionsProximity to the right audienceGoalEmotional resonanceMental availability & Trust

I treat newsletter sponsorships as brand. I don’t care if they click or convert immediately. The primary goal is being in front of the right people consistently. It’s only “mind-blowing” if you’re stuck in traditional categories. If you think brand means big fluffy campaigns and performance means direct response tracking, you’re missing the point. The reality is messier.

The Halo Effect

I was an engineer originally. Analytical. When I got into marketing, I went deep on attribution—Bizible, U-shaped models, W-shaped models.

Then I discovered the halo effect. When you run ads, other things go up. You run ads and your direct organic traffic increases. My attribution software wasn’t tracking the person who saw my ad, didn’t click, but remembered my name and typed it into their browser two weeks later.

The ads work because of everything else. They see you on LinkedIn, they don’t click, but they check you out elsewhere. If you have all the other bits (content, profile, website), the ad succeeds. Performance and organic are two sides of the same coin.

Why I Bought Rippling

We bought Rippling for our payroll and HR. We bought it off a cold email.

Sounds like a direct response win, right? But here is the truth: I had seen Parker Conrad (the founder) in news articles and on podcasts. I had built familiarity with him over time.

• Random cold email? Who are you? Delete.

• Cold email from a company I’ve seen everywhere? Yeah, sure, I’ll take the call.

I knew his story. I knew he’d built Zenefits, had a public flame-out, and came back. I respected that. That’s not rational product evaluation. That’s brand.

The Problem With Marketers

A lot of marketing people don’t get business. They are deep in their “marketing thing”—campaigns, metrics, creative. They are disconnected from the rest of the organization.

• Sales is hard: Looking someone in the eye and asking for $200K takes balls of steel.

• Marketing is safe: You can hide behind metrics or blame the algorithm.

A business can survive without marketing for 12 months. It cannot survive without sales. If marketers don’t speak the language of the business, they’ll always be seen as a cost center rather than a growth engine.

Reddit: SEO for the AI Era

Arjun has a thesis: “If you want to influence customers, you get them before they get to AI (LinkedIn), or you get them when they’re in the AI.”

AI weights Reddit content heavily because humans prefer Reddit for “real” answers. One company drives 10% of their revenue ($10M) just from Reddit by having a team member answer questions and solve problems without being a “shill.”

Reddit is SEO for the AI era.

Conclusion: Stop the Brand Theater

The 79% decline in “brand building” talk doesn’t mean brand matters less. It means the pontificating matters less. The theoretical, aspirational conversations are dying because they never connected to reality.

The Real Question:

Are you building familiarity with the people who might buy from you? Are you being memorable? Are you being useful in the places where your buyers spend time?

That’s brand. Small b. Practical. Tied to business outcomes. ---

Report reference: RockSalt Analysis 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.

Enterprise marketing often feels like this strange paradox:

You’re running the right channels, doing the right things, reaching the right people — and yet the story of what it’s accomplishing never quite comes together.

The impressions are there.

The reach is there.

You see movement everywhere… except in the place the company cares about most: attribution.

It’s not because the work is wrong.

It’s because the model used to interpret the work was never built for an enterprise in the first place.

Most teams still try to measure channels as if each one independently “creates” a pipeline, as if LinkedIn, search, CTV, events, content, and outbound each deserve a neat column in Salesforce labelled Sourced.

But enterprise buying doesn’t move in straight lines.

It behaves more like weather patterns than a funnel, you don’t understand a storm by staring at a single cloud.

Enterprise GTM is a system.

And systems only make sense when you zoom out.

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new release by 42 team

Enterprise GTM Has Three Layers — Not One

Every healthy enterprise GTM motion, whether intentionally designed or accidentally assembled, is built on three layers: exposure, engagement, and activation.

Exposure

This is where marketing creates familiarity.

Buyers see LinkedIn ads during their commute, LinkedIn CTV when they’re streaming, native placements on industry publications, event promotions, POV content, videos in-feed.

Nobody converts here.

But people start recognizing your name.

Exposure sparks the conversation — nothing more, nothing less.

Engagement

This is where interest quietly reveals itself.

Not through forms, but through behavior:

returning website sessions, deeper content consumption, more people from the same account visiting the site, event attendance, 3rd-party intent, upticks in branded search, spikes in direct traffic.

Tools like Syft help you see which accounts are showing up even when individuals stay anonymous.

This is the layer most teams overlook — yet it’s the layer where demand actually forms.

Activation

This is where outbound and sales convert the quiet interest into a real conversation.

Enterprise buyers don’t lack interest — they lack time.

They don’t click “Book a Demo” from an ad because their buying process doesn’t work that way.

A targeted InMail, a consultative gift-card offer, an event invitation, a case study send, a well-timed SDR touch — these aren’t gimmicks.

They’re how you lower the cost of exploration for a buyer who is curious but busy.

Activation is what transforms latent demand into active pipeline.

Without it, exposure and engagement remain inert.

Once you see these layers clearly, the illusion that channels “aren’t working” disappears.

They are working — they’re just doing different jobs.

Where Enterprise Measurement Breaks Down

The frustration most teams feel isn’t that LinkedIn or programmatic or CTV are failing.

It’s that they’re being judged with metrics built for B2C and SMB.

Channels that create demand get measured like channels that capture demand.

And so everything looks underwhelming:

• CTV: “no clicks”

• LinkedIn: “no direct pipeline”

• Events: “no attribution”

• Programmatic: “low CTR”

• Content: “soft”

• Outbound: “separate”

But the truth is simple:

enterprise demand doesn’t show up in attribution — it shows up in behavior.

The question is whether you’re looking in the right place.

What the CTV Data Revealed

We recently analyzed 54 days of LinkedIn CTV data and compared daily impressions with website activity.

By traditional attribution standards, the results were meaningless: zero clicks, zero “conversions,” zero pipeline.

But when we ran correlation analysis — the kind of measurement enterprise actually requires — a very different picture emerged:

• Strong correlation with total traffic (0.69)

• Even stronger correlation with organic search (0.81)

• Notable correlation with direct traffic (0.65)

• Roughly 352 incremental sessions per 1,000 impressions

• Implied cost per session: about eight cents

• Lift pattern: same-day spike + sustained 7-day effect

• Primary behavioral path: CTV → Brand Search → Site Visits

This is exactly how enterprise awareness works.

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It doesn’t generate clicks.

It generates curiosity — and curiosity shows up as search.

Enterprise demand doesn’t emerge in platform.

It emerges around the platform.

Which means if you’re only looking at attribution, you’re measuring CTV (and LinkedIn, and programmatic, and events) through the one lens where they will always look like they’re underperforming.

Why MMM and Correlation Are Better than Attribution in Enterprise

Attribution is good at capturing bottom-funnel demand.

It was never designed to measure stimulus-response patterns in long sales cycles with multiple contacts and anonymous research behavior.

Correlation and MMM are what allow you to see the system:

• whether your spend is increasing brand search

• whether more accounts are showing up on the site

• whether engagement patterns spike during campaigns

• whether buying groups widen

• whether outbound reply rates rise

• whether overall demand becomes more active

Enterprise GTM isn’t a sequence of discrete touchpoints.

It’s a field of influence.

You measure fields through patterns, not click paths.

Attribution captures the moment of harvest.

MMM reveals the conditions that grew the crop.

You need both.

Pipeline Emerges When the System Connects

Once you adopt a systems lens, the pipeline story becomes obvious:

Paid creates familiarity.

Search reveals interest.

Content builds belief.

Events deepen understanding.

Outbound activates the account.

Sales converts the demand.

Pipeline isn’t the output of a channel.

It’s the output of a coordinated system, moving a buying group from unaware → aware → engaged → ready.

Enterprise GTM stops feeling chaotic the moment you stop asking each channel to prove its worth in isolation.

Enterprise GTM Isn’t Broken.

The Way We Look at It Is.

Your channels are working exactly as they should.

They’re simply being evaluated through a model that can’t see their impact.

When you measure the system — not the parts — the whole thing becomes coherent:

• CTV drives search

• Search drives visits

• Visits drive signals

• Signals fuel outbound

• Outbound drives meetings

• Meetings drive pipeline

The work was always working.

It just needed the right lens.

We’re launching LinkedIn Ads Copilot—and it’s available to you.

For years, we’ve gritted our teeth through the LinkedIn Ads interface. Targeting is cryptic. Pricing is opaque. Automation is manual. So we built what we needed: a tool that translates plain-English customer descriptions into precise LinkedIn audiences, shows you real-time CPM/CPC data from their API, and automates the stuff that shouldn’t be manual (budget adjustments, performance rules, notifications).

It’s everything we wished LinkedIn Ads had.

First 10 subscribers get lifetime access for $30. Not a launch price—a founding member price. After that, it goes away.

If you run LinkedIn Ads and spend more than five minutes a week wrestling with targeting or pricing, this saves that time.

https://copilot.42agency.com/

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There is a specific type of anxiety that haunts Enterprise marketing leaders.

It’s not about the creative. It’s not about the budget. It’s the nagging suspicion that if you actually succeed—if you turn on the faucet and drive 1,000 high-intent leads tomorrow—your infrastructure will collapse.

Your sales team will miss the notifications. The data will get messy. The attribution will break. And you will have burned $50,000 to prove that your bucket has a hole in it.

Most agencies ignore this fear. They are paid to spend your media budget, not to fix your Salesforce instance. They launch the ads, generate the leads, and say “Good luck.”

At 42, we take a different approach.

We refuse to optimize within a broken system. Before launching a single campaign, we examine the pipes.

Here is what we typically find.

The “Lifecycle Chaos” (The Customer Case Study)

When we started with a client, a fast-growing Product-Led Sales platform, they had aggressive growth targets. They were ready to scale.

But when we looked under the hood of their HubSpot instance, we found “Lifecycle Chaos.”

• 90% of their contacts were stuck in the “Lead” stage. There was no logic moving them forward.

• 40% of contacts had no “Source” value. Marketing couldn’t prove what was working because the data wasn’t there.

• The “Leaky Bucket”: The most alarming discovery was that 8 high-intent demo requests had been ignored simply because they fell through cracks in the workflow.

Imagine spending $200 to acquire a demo request, only for it to die in a “null” field in your CRM.

We didn’t just patch it. We rebuilt the architecture.

We separated Inbound, Outbound, and ABM logic. We implemented a “Zero Lost Leads” fail-safe workflow with redundancy and Slack alerts.

The result? 100% Funnel Visibility. The team could finally see exactly where leads were stuck. The “lost lead” anxiety vanished.

Only then were they ready to scale.

The “Technical Debt” Trap

The same principle applies to SEO.

We recently audited a security automation platform. They had a great product and were producing content. But they were losing organic visibility.

A standard agency would have pitched them “10 more blog posts a month.” That would have been a waste of money.

Our audit revealed 18 critical technical issues that were actively suppressing their rankings:

• 150+ pages had multiple H1 tags, confusing search engines about what the page was actually about.

• 170 help center pages were missing H1s entirely.

• Broken Canonicals: Tracking parameters were creating duplicate content issues, diluting their ranking power.

You can write the best content in the world, but if Google can’t crawl it or understand its hierarchy, you will not rank.

We didn’t sell them more content. We sold them a Foundation Fix. We cleared the technical debt first, giving their content a clean, crawlable site to live on.

The 42 Standard: The 14-Day Audit

This is why every new engagement at 42 starts with a mandatory Internal Audit (Days 1-14).

We do not launch campaigns on Day 1. We spend the first two weeks digging into your basement.

We check:

1. The Routing: If a VIP account fills out a form, does the VP of Sales get a text message, or does it go into a “General Inquiry” queue?

2. The Tracking: Are we capturing “First Touch” (how they found us) and “Last Touch” (why they converted)?

3. The Data Hygiene: Are we enriching leads with Clearbit/ZoomInfo, or asking them to type their “Company Size” manually?

We identify the gaps. We fix the routing. We clean the data.

Then we turn on the ads.

The Bottom Line

If you are looking for an agency to just “run the ads,” there are plenty of shops that will happily take your money.

But if you want to build a Revenue Engine—one that captures, routes, and measures demand without leaking value—you need to look at the system, not just the channel.

Don’t pour water into a leaky bucket. Patch the hole first.

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.

LinkedIn Ads Aren’t Broken. You Just Haven’t Tested This Yet.

A client asked us recently: “Should we just pause LinkedIn for a bit and double down on Google Ads?”

It wasn’t a flippant question. It was five months into a $10K+/month effort that was driving a handful of leads and a lot of frustration.

So we didn’t say “no.” We said:

“Let’s run a holdout test. Pause it — and track how it affects the rest of your pipeline.”

Because unlike other paid channels, LinkedIn sits in a weird liminal space in most B2B orgs.

It’s not quite brand.
Not quite demand.
Not quite direct response.
And most teams don’t take the time to measure it properly.

But done right? LinkedIn can be one of your most valuable full-funnel channels. You just have to run it differently.

1. Don’t just turn it off. Run a holdout test.

If you’re considering shutting down LinkedIn ads, don’t just flip the switch. Treat it like a real experiment:

• Run a holdout test for 2–3 months. Pause spend on LinkedIn while keeping other channels steady.

• Monitor net-new opportunities, site traffic, and direct traffic. Do they dip without LinkedIn running?

• Check engagement & recall. Have your sales team ask if prospects have “seen your stuff on LinkedIn” during discovery.

This is how you figure out if LinkedIn is actually influencing pipeline — or just eating budget.

2. Treat LinkedIn like a full-funnel channel.

Too many B2B marketers use LinkedIn like a billboard: awareness only.

But that’s not how buyers behave. You need:

• Top-of-funnel reach campaigns to get in front of the right people.

• Mid-funnel engagement with doc ads, testimonials, founder content.

• Bottom-funnel conversion plays like in-feed demo CTAs or InMails.

👉 We recommend allocating budgets across the funnel, not just TOFU:

• 20% for awareness

• 40% for mid-funnel education

• 40% for direct conversion

We've seen this approach work especially well in enterprise and healthcare segments, where buying cycles are long and multiple stakeholders are involved. For one healthcare client, we generate 1 opp/month directly from LinkedIn. In another case, the client over-indexed on TOFU and wasn’t seeing any sourced revenue — we’re now rebuilding their full-funnel structure.

3. Make sure your audience is actually on LinkedIn

Channel fit matters.

One client we worked with sells to IC engineers — they weren’t very active on LinkedIn. But when the company moved upmarket to target engineering leaders, LinkedIn became viable. (Although Twitter, Reddit, and newsletters also played a role.)

Your audience needs to be active and reachable on LinkedIn. If they’re not, no targeting trick will save you.

4. Measure influence the right way

Direct attribution doesn’t tell the full story. But that doesn’t mean you’re flying blind.

Here’s what to do instead:

1. Track account-level engagement — impressions, video views, and clicks from key accounts over time.

2. Correlate ad exposure with CRM activity. Did a deal come in after an account had 20+ impressions in a week? Note the timing and pattern.

3. Use AI to extract qualitative data from discovery calls. Have your reps ask: “Have you come across us before?” or “Did you ever see our stuff on LinkedIn?” Use tools like Grain or Gong to tag those mentions and sync them into the CRM.

4. Run brand recall studies every 3–6 months to check if your audience remembers you and your message.

All of these signals together will tell you if LinkedIn is moving the needle.

5. You don’t need gift cards to generate demos (but they work)

We’ve seen success running in-feed and InMail campaigns without offering a $100 incentive. They can help — especially in crowded categories — but they’re not required.

The key is creative sequencing:

• Video or reach ads to build familiarity

• Doc or carousel ads to educate

• Clear demo CTAs once trust is built

You don’t need to bribe people to book a demo — just build a message they recognize and trust.

6. Community POV

Prompted by Ivars Krutainis 🦦:

“A real world pickle.

Here’s a situation. I have a client that we’ve been running LinkedIn ads for four, almost five months. It’s a technical product and ACV will range depending on the scope of deployment. It could be anywhere from $10k to $250k projects.

We started with target account lists. Adding job titles and member skills on top. It brought in some leads, but it soon dried out.

Then we went with native targeting. Focusing on specific industry verticals. Overlaying with job titles, member skills and company sizes. But we did not see significant uplift, so went back to account based targeting and contact lists (Clay).

We’re running image ads and conversation ads for lead gen. Founder promoted posts and document ads for engagement layer. We’ve tested driving traffic to website vs native lead gen. We’re working on video assets now.

The ad engagement is actually not bad. Image ads get 0.7-8% CTR cold and 1% CTR retargeting layer. Promoted posts get 8-10% and document ads get 3%.

But.

It’s only driving a few direct attribution leads per month. We do see some more influenced deals coming through. It’s always debatable, if the 57 paid impressions on account was the deciding factor for the client to sign up. Or was it all the other interactions they’ve had with the brand.

A lot of effort has gone into running various plays, and monthly budget is around $10-12k for LinkedIn. We’ve been at it for a while, and most brands by this point see more consistent lead flow.

What would you do?

A. Double down on account/contact lists and increase the budget to $20k.

B. Switch to engagement play with focus on founder content and video. Not expecting many direct leads, but building brand mental availability.

C. Pause LinkedIn ads and focus on other channels (paid search).

Practitioners weighed in:

💬 Kamil Newczyński commented:

“You might still get leads even after pausing the ads … In those cases, attribution methods break down … I’d continue with a hybrid approach A + B. Def. not C.”

→ Many warn against completely pulling the plug—LinkedIn may not show up in attribution, but its real value hides in influence and brand presence.

💬 Mo Fawzy suggested:

“Switch to a reach play … focus on founder content, video, social proof … Add a step for SDRs to validate … That’s how I’d do it 👍🏼”

→ Reinforces the idea of running a full‑funnel, keeping budget in awareness but with clear pathways to conversion and sales validation.

💬 Justin Hannemann chimed in:

“Wonder if there’s an option where you split ‘B’ into B1 and B2 where B1 runs 30% of the budget focussed on Brand (founder led/ emotional content) and B2 is offer based … switching offers …”

→ You don’t need to choose between brand or demand—design your funnel to serve both in parallel.

💬 Gabriel Ehrlich emphasized creative/offer fit:

“Good CTR, no conversions = offer or landing page issue. … Use Wynter to test messaging.”

→ If clicks aren’t leading to demos, the bottleneck is messaging or the value prop—not necessarily LinkedIn.

LinkedIn isn’t dead. But lazy LinkedIn is.

If you’re not seeing results, don’t assume the platform doesn’t work—assume your setup might need work.

Run a holdout test. Rebalance the funnel. Check your offer. Ask your sales team what buyers are saying.

And remember: the goal isn’t just impressions—it’s impact.

You don’t need to blow all your budget on reach ads or bribe your way to demos. You need message–market–channel fit. That’s where LinkedIn starts to pay off.

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?”

The Mistake That Can Nuke Your Domain

A few weeks ago, a marketing director reached out to ask if they could use HubSpot Sequences to run a cold ABM campaign targeting banking executives. It’s a question we hear often. HubSpot is already the CRM. The interface is smooth. It integrates directly with Gmail or Outlook. It even lets reps send personalized messages at scale. What’s not to love?

The problem is, on the surface, everything looks like it’s working. Emails are being sent. No bouncebacks. Reps are logging activities. Everyone assumes deliverability is fine.

Until it isn’t.

Until a key account never replies.

Until demo requests mysteriously drop.

Until support emails start hitting spam.

That’s when you realize: the infrastructure beneath your email strategy isn’t just fragile — it’s invisible.

What’s Happening When You Send an Email

Behind every “send” button, there’s a delicate negotiation taking place between your server and the recipient’s inbox provider. It doesn’t matter whether it’s Gmail, Outlook, or something else — they all have one goal: to protect their users from spam.

To make that decision, they look for signals.

Is this email authenticated? (SPF, DKIM, DMARC)

Is this sender reputable? (Domain & IP reputation)

Does this message look like spam? (Volume, content, links, formatting)

Each one is a vote either for or against you.

The scary part? If you’re using a shared IP or your core domain for cold outreach, even one campaign gone wrong can tank your entire reputation.

The HubSpot Assumption

Most teams using HubSpot Sequences for outbound are doing so in good faith. The logic makes sense:

• “It sends from my Gmail, so it’s fine.”

• “We’re personalizing everything.”

• “It’s just a small list.”

But here’s the catch: HubSpot Sequences feel like they were built for outbound, but they weren’t.

Sequences were designed for warm sales engagement — people who have filled out a form, booked a call, or interacted in some meaningful way. Not net-new cold outreach.

When you use Sequences to blast a cold list, you’re not just breaking best practices. You’re skating right past the edge of HubSpot’s Terms of Service, and into territory where your emails can:

• Get marked as spam

• Trigger internal compliance reviews

• Harm your entire org’s deliverability

But Doesn’t HubSpot Use My Gmail?

Yes — and that’s part of the illusion. It sends through your connected Gmail or Outlook account, but:

• Tracking links (opens, clicks) can raise spam flags

• The volume and similarity of messages can trigger filters

• HubSpot’s backend routing still touches shared infrastructure

And here’s the kicker: paying for a dedicated IP in HubSpot? That only applies to Marketing Emails, not Sequences.

So no, you’re not shielded. You’re exposed.

The Ripple Effect of a Burned Domain

When your sender reputation takes a hit, it doesn’t just impact one rep. It impacts everything tied to your domain:

• Sales emails stop getting replies

• Support emails get caught in spam

• Billing emails bounce

• Your hiring team gets ghosted

You’ll never see an alert. No dashboard warning. Just silence.

Why Tools Like SmartLead and Instantly Exist

Cold outreach isn’t inherently bad. It’s how most B2B businesses grow. But it requires infrastructure that protects your core brand.

That’s why tools like Instantly, SmartLead, and Lemlist exist. They weren’t built just for automation. They were built for insulation:

• You send from secondary domains

• You rotate across inboxes

• You warm everything up slowly

• You monitor deliverability in real-time

They treat outbound not as a sprint, but a system.

What To Do If You’re Stuck With HubSpot

Sometimes you can’t get new tools approved. Security, procurement, timing. We get it.

Here’s how to run a low-risk, semi-scalable outbound program within your constraints:

1. Buy a secondary domain. Think hello.yourcompany.com or yourcompanyhq.com.

2. Create inboxes for reps (e.g., sarah@hello.yourcompany.com).

3. Configure SPF, DKIM, DMARC, and MX records. You need that technical trust.

4. Connect those inboxes to HubSpot.

5. Warm up slowly. Start with manual sends. Monitor replies.

6. Use Sequences sparingly. Avoid tracking clicks. Keep volume low. Make it human.

This isn’t perfect. But it’s better than risking your core brand.

When Sequences Actually Shine

HubSpot Sequences aren’t the villain here. They’re great at what they’re meant for:

• Following up with inbound leads

• Nudging people after a call

• Staying top-of-mind post-demo

• Managing warm relationships at scale

When the contact already knows who you are, Sequences are magic. Just don’t ask them to do a job they weren’t designed for.

Final Thought

Email is still the backbone of most B2B sales motions. But it’s not just copy and timing that matters — it’s infrastructure. The silent systems under the hood determine whether you ever get seen.

Your domain is a long-term asset. Protect it.

If you’re doing cold outbound, either use the right tools — or isolate the risk.

Don’t let convenience today nuke your entire GTM motion tomorrow.

The $2,500 Question Every Marketer Faces

What would you choose: 100 demo requests or 100 mid-funnel (MOFU) leads?

At first glance, the answer feels obvious. Demo leads are hotter, closer to revenue, and often come with a clear buying signal. Even if they cost 5x more—say, $2,500 vs. $500—you'd still lean toward the demos. After all, we're all trying to make next quarter's number.

MOFU leads, on the other hand, feel squishy. They're labeled as "subscribers" or "content downloads." They take longer to convert, aren't ready for sales, and don't satisfy our craving for quick wins.

But that framing is fundamentally flawed.

The 95% Problem: Why Most B2B Marketing Misses the Mark

Here's the uncomfortable truth: only about 5% of your total addressable market (TAM) is actively in-market at any given time. That 5% is dynamic—it shifts constantly as priorities, budgets, and internal conditions evolve.

Which means the other 95%? They're not shopping for a solution right now.

If your marketing only focuses on the tip of the iceberg—demo-ready prospects—you're leaving the bulk of your future pipeline untouched. You're also fighting for attention in the noisiest, most competitive part of the funnel.

Yes, in-market leads are expensive. But if you want sustainable growth and lower CAC over time, you need to start building trust and awareness with the out-of-market majority—before they raise their hand.

The Demand Generation vs. Lead Generation False Dichotomy

Before we dive deeper, let's clear up some industry confusion that's been muddying the waters.

The Current Narrative:

• Demand Generation = "Educating prospects through ungated content and LinkedIn ads to generate demo requests"

• Lead Generation = "The evil twin that generates leads that never convert because marketers focus on leads, not revenue"

Why This Framing Is Wrong:

We often describe 'demand generation' as pure brand awareness—creating mental associations between your product and specific scenarios. But here's the reality: marketers don't actually create demand. No amount of marketing convinces someone to buy enterprise software out of thin air.

B2B products are considered purchases. Before buying, prospects must evaluate financial circumstances, IT requirements, business processes, integrations, security, and support. Marketing's real job is explaining the value proposition and how it fits into your prospect's world.

The truth? Both approaches have merit when used strategically.

Why Marketers Abandoned the Middle (And Why That's a Mistake)

The MOFU Reputation Problem

MOFU has a bad reputation—and we have ourselves to blame.

For years, marketers flooded the middle of the funnel with:

• Shallow eBooks nobody wanted

• Lifeless webinars that were thinly veiled sales pitches

• Templates that solved nothing

• Content passed directly to sales with zero nurturing

Sales teams, understandably, pushed back: "These people don't even know who we are."

Instead of fixing the approach, we overcorrected. We ditched gated content, stopped scoring leads, and declared MOFU a dead zone.

But here's the truth: the middle didn't stop working—we just stopped doing it well.

The Current "Solution" and Its Problems

Frustrated marketers pivoted to running completely ungated content via paid media, hoping prospects would eventually return as direct traffic ready to buy.

This approach has several critical flaws:

1. Attribution nightmare - Nearly impossible to connect paid media spend to sales conversations

2. Budget uncertainty - No clear understanding of required investment levels

3. Organic vs. paid confusion - Would prospects have found you anyway?

4. Marketing credit games - Stories abound of six-figure social spends with zero results

5. Unpredictable GTM motion - No repeatable system for growth

Think about it: no matter how many ads you see for a product, when the need arises, you likely start Googling. If the brand doesn't show up, you choose the next best option.

The Third-Party Intent Band-Aid

Next came the third-party intent and reverse IP lookup phase. Instead of hoping for the best, marketers began:

• Spending media dollars against specific accounts

• Tracking which companies visited their websites

• Using tools like Bombora or 6sense to flag "intent signals"

• Coupling this data with outbound sales motions

This works better and feels more predictable, but it's becoming increasingly fragile.

The Privacy Problem: Third-party intent relies heavily on cookies and data sharing. Here's how it works: when you browse an article about server security on CIO.com, an intent vendor purchases that data (cookies, device fingerprints) from the publisher, then matches it to your company through IP addresses or previously collected form data.

With GDPR, CCPA, and browsers blocking third-party cookies, this process becomes less accurate and more complex. Plus, you never know if it's the intern reading about CRMs or the CRO making the purchase decision.

The First-Party Data Advantage: Why MOFU Matters More Than Ever

Building Your Own Moat

With increasing privacy restrictions and algorithm changes across search and social platforms, having first-party data on your prospects becomes incredibly valuable. The best way to build this asset? Get prospects to take action on your own web properties.

1P Data in Action:

Imagine you sell logistics software to warehouse companies. Instead of buying a Zoominfo list (which isn't opted-in and may be inaccurate), you:

1. Launch a newsletter and webinar series featuring industry SMEs

2. Create valuable content addressing real logistics challenges

3. Gate some premium content behind simple forms

4. Build an opted-in database of engaged prospects

5. Survey subscribers, share relevant content, and announce events

6. Maintain direct communication without algorithm gatekeepers

The key insight: don't immediately pass these prospects to sales. They're not in-market today but will be in 6-12 months. Use email and content to build mental availability until they're ready.

First-Party Intent Signals

1P data also generates 1P intent signals. When an HR Manager repeatedly reads content about employee handbook creation on your website (and your product helps write handbooks), that's a qualified signal worth acting on.

The Revenue Stacking Framework: A Balanced Approach

Rather than choosing between BOFU and MOFU, successful B2B marketers need a balanced approach:

Layer 1: Capture In-Market Demand (BOFU Focus)

Start here to prove marketing impact quickly:

• High-intent keyword targeting (Google/Bing)

• Bottom-funnel content and remarketing

• Paid social for ready-to-buy prospects

• Strategic partnerships

• Everything measured on revenue and pipeline impact

Layer 2: Build Future Demand (TOFU Investment)

Run parallel programs for out-of-market prospects:

• YouTube and video content

• Social media and community building

• Brand awareness campaigns

• Content that builds mental availability

Subscribe now

Layer 3: Nurture the Middle (MOFU Strategy)

Use MOFU to build first-party databases and intent channels:

• Valuable gated content that solves real problems

• Email nurturing sequences

• Event marketing and webinars

• Direct relationship building without middlemen

Making the Case: 100 MOFU Leads vs. 100 Demo Requests

Given this framework, let's revisit our original question.

100 demo requests give you immediate pipeline and quarterly results. They're essential for proving marketing value and hitting short-term targets.

100 MOFU leads give you:

• A growing first-party database

• Future pipeline development

• Lower long-term CAC

• Reduced dependence on algorithm changes

• Direct communication channels

• Intent signals for timing outreach

The answer isn't either/or—it's both. But if forced to choose and you already have strong BOFU performance, the 100 MOFU leads become your competitive advantage.

The Path Forward: Embracing the Messy Middle

The digital landscape is changing rapidly. Privacy regulations are tightening, third-party cookies are disappearing, and algorithms become less predictable. Companies that build strong first-party relationships will have sustainable competitive advantages.

This doesn't mean abandoning bottom-funnel tactics—those remain crucial for immediate results. But it does mean investing in the messy middle with better content, clearer intent signals, and more sophisticated nurturing.

Key Takeaways:

• Only 5% of your TAM is in-market at any time

• First-party data becomes more valuable as privacy restrictions increase

• MOFU isn't broken—it was just executed poorly

• Balance immediate results (BOFU) with future pipeline (MOFU)

• Build direct relationships to reduce platform dependency

It's time to unlearn that BOFU is all there is. The companies that master the messy middle will own the future of B2B marketing.

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.

Marketing is not about marketing, it’s about sales.

This may not always be the case for more significant enterprise deals with companies post-PMF, but the spirit of the insight remains unquestionable: selling matters.

Doing, figuring out what people buy, and building the product with that in mind (plus a beta version of the brand that works) beats expensive and expansive branding projects, especially before you even know if the market wants your product.

Marketing is not just about creating a memorable brand but also a risk-averse, profitable one.

After all, not all of us have the budget or the confidence of Jaguar to create a new memory imprint that disrupts legacy for the search for new buyers.

But let’s go back to B2B.

According to HockeyStack, it takes 266 touchpoints and 2.879 impressions to close a deal in 2024. This topic was hot, and tons of opposing views got people talking about the report from all angles.

Whether the data makes sense is irrelevant to this entry, but beyond the data it shows that marketers are under tremendous pressure to connect their actions to their results.

It kind of seems like we're the ones in charge of sales.

Shocking.

If you were traditionally a more brand-oriented marketer, now that you had fewer dollars, you probably had to justify your investments and show tangible revenue results beyond brand awareness or A+ copy. If you were a performance-oriented marketer, you had to find better ways to fine-tune your work, possibly exploring better creative and copy than you usually did.

In a way, both types of marketers had to explore each other's realms, recognizing the importance of "brand" and "performance" efforts (not one or the other).

The tension between performance vs. brand didn't matter half as much as actually being able to sell the product. Embracing brand with a b, not with a B, and focusing on tactical, marketing-driven initiatives directly impacting how prospects and customers view the company became the way forward.

Beyond the upper-lower case distinction, however, there is an important problem to consider when we think about brand and performance in B2B: Halfassing your creative or copy will no longer work (marketing got harder), and ignoring that you have to make money, not just awareness won't serve you either (Sales ➡ brand, not the other way around).

You must prove your marketing system works, and it translates to revenue.

So how can we do that? by being creative?

Almost.

Creativity Alone Won’t Save You

You know how you find the perfect copy, a beautiful creative, perfect targeting and execution, and ultimately, you get nothing but crickets?

B2B marketing is hard. Unfortunately, we were spoiled (post-pandemic tech boom) to think that creating products that startups would sell and buy from other startups was the norm.

But bubbles are always meant to burst. Bull markets, as many of us painfully came to learn, don’t last forever.

We had envious budgets (we see you Monday), too many products, and often the luxury to pay bank for apps we barely touched and people with fuzzy roles.

Joining the right communities, running “the playbooks,” and generally operating according to the status quo became less effective over time.

Best practices became an exercise of diminishing returns.

We did more and accomplished less.

VCs hated it.

The consequence? Focusing all our creative energy on finding the next playbook that could save us.

As Obaid puts it, however, B2B marketers have a limited set of ideas that don’t necessarily solve the problem of increasing efficiency. We love talking about creativity, but actually creating new experiments is a rare skill when being a copycat “apparently” helps us solve the problem.

What are we doing as marketers to work better with fewer resources?

Is marketing creativity leading to an authentic marketing system the missing ingredient to win? is it marketing effectiveness that translates into sales?

Is it both?

The Brand + Performance Romance

The fragility of our identities as performance or brand marketers became evident to everyone paying attention in the space. Over-indexing in a single vision of marketing and its function became a harmful mentality that pushed us, in many cases with tons of pain and regret, to accept that we need both yin and yang to create a whole functional process.

Silos became the genuine enemy.

Marketers had to accept that creating a product people want to buy matters as much as making a brand experience they can measure.

Econometrics became a trend, reconciling marketing’s impact on sales today and measuring its contribution to future sales in a less biased way (last-touch).

As with most cultural changes, many marketing professionals found a way to achieve their goals, but they paid a heavy price.

For those obsessed with brand and meaning, it meant doubling down on short-term sales acceleration, and for those obsessed with performance and data, it meant developing a sense of taste.

While these seem like superficial observations, there are genuine implications you probably had to endure personally.

As a brand marketer, you probably had to let go of a couple of designers to hire a paid specialist (or learn performance). As a performance marketer, you probably had to think of creative new experiments to meet your goals (lean into creativity and brand or hire a creative role).

Whatever your journey was, you probably learned that neither performance nor brand would save you.

In fact, the one thing that saves B2B marketing is sales.

Or, to be more specific, paving the way for sales to actually be able to make a call a decision maker will gladly take.

In the end, you’re really working in sales. It may not suck as much as the constant rejection BDRs face, but it isn’t a “creative” job either.

This perception of marketing as a different entity from sales must change.

Need some proof? Everyone knows of Jaguar, but without sales, you gotta make bold moves.

The Sales Marketer

I often joke that performance marketers are failed engineers and brand marketers are failed artists. But my joke has been conceptually wrong all along.

We're failed sales reps.

Not all of us, probably not you reading this post, but many marketers despise picking up the phone and calling to close a deal. Our worst experience probably was being an SDR or BDR and having to face rejection day in and day out.

Unfortunately, the pain of selling is precisely what we must endure to become better marketers.

Sure, we don't make the call or visit their offices, but we must undergo a metamorphosis and obsess over the entire process and the pain it takes to close an enterprise deal.

We have to learn that dealing with professionals requires being a professional rather than a creative superstar or a data nerd.

Focus on being a solid vendor first, then worry about marketing.

That may mean learning that the deal closes while playing golf, attending an F1 race, or at a concert. Plus, acknowledging that your fantastic brand doesn't matter if decision-makers don't find it helpful for their internal needs, even if your ads are beautiful and algorithmically clean.

It's time for marketers to learn to show up and ensure your tech product becomes the preferred choice to solve an organization's process. It is not just about being the go-to product for your key stakeholders but about learning to influence a complex web of opinions and bureaucratic decisions.

That simple and that complex.

How? By running 0 to 1 marketing experiments that pay off with minimal risk (this is where your creative energy must go). Once your experiments work and you find the real product-market fit, you'll see how fun and creative B2B marketing can be.

But, most importantly, as marketers we must learn to think beyond our marketing bubble and focus on showing up and doing the hard things first. That generally isn’t building the perfect attribution model, or a rebrand, but rather, convincing others to pay for your product.

Whether you are performance or brand marketer matters a lot less than helping your team close sales and do whatever it takes (brand + performance) to close bigger deals faster.

You’re the future sales of your company.

It is time to let go of our inner identity struggles as X or Y marketers, or as playbook A vs. playbook B fans. It’s time to adapt, and become relentless and professional about winning.

It’s a 0 to 1 game. Sold vs. not sold. And that, is also on your shoulders as a sales marketer.

Creativity alone won't save B2B marketing, but being creative about selling and showing up by doing the repetitive 0-1 experiments, will.

You’re neither a brand or performance marketer.

You’re a salesman. Act accordingly.

We’ve always been early adopters of tech. It’s part of our identity — both as people and as a company.

So it’s no surprise that when GPT, Claude, and LLMs broke onto the scene in a big way, we dived right in.

At first, we were skeptical — they seemed like “autocorrect on steroids.” But over the last 12–18 months, we’ve been drinking the Kool-Aid and have become fully convinced: the knowledge work of sitting at a chair and pressing buttons on a computer (or “computer work,” as my mom calls it) won’t be the same.

Just as the Industrial Revolution upended manufacturing and created mass production, AI and LLMS will create mass production of knowledge.

And I don’t think that’s necessarily a bad thing.

AI enables small teams to augment themselves and unlock higher levels of productivity. Most knowledge work today boils down to some combination of Excel, email, and meetings. If you can delegate the mundane to AI and tools, you free people up to focus on the things humans are better at — decisions, context, vision, strategy, QA, and creativity.

Why Most Marketers Struggle With AI

When I talk to other marketing folks, the most common use case for AI is “writing” — blog posts, emails, copywriting.

But often, the results are disappointing. Marketers either stop using AI, or they resign themselves to accepting mediocre output.

The real issue isn’t that AI is bad — it’s that most people don’t use it properly.

The way we think about AI is as a flexible general assistant.

It’s like a junior generalist — eager to help, but easily lost without enough direction.

Context matters — for humans and machines alike.

You can’t expect an AI to generate magical output if you don’t give it enough context.

Telling Claude, “write 500 words on Demand Generation,” will probably give you mediocre word vomit.

But if you tell Claude:

• Read a bunch of my past writing,

• Take the POV of a VP of Marketing,

• Emphasize how inbound marketing has changed over the last 15 years,

• Match this tone and voice,

  Then the output improves 10x.

Just like a human can’t function without understanding the task, the background, and what “good” looks like, AI and LLMS need better instructions to deliver.

Who We Are (In Case You’re New)

Before diving into examples, here’s a quick overview — written, fittingly, by our AI friend:

42 Agency helps B2B companies build growth engines for the AI age. We combine demand generation, marketing operations, and automation to drive revenue — faster and more intelligently. We’re not just experimenting with AI — we’re actively embedding it into how we work, how we execute, and how we help our clients scale.

What we are building @ 42

Sales & Operations

One of the first areas where we started using AI seriously was sales. Some of the ways we use it today:

• Account Research

  When a new inbound comes in, we use GPT to summarize what the company does, recent news, technologies they use, and the market dynamics.

  → Saves time and gives our team richer context before the first call.

• Sales Call Follow-Ups

  We use Sybill for sales calls. It doesn’t just record — it provides context on buying intent from discovery calls and generates follow-up emails.

  → Output is much better than general-purpose call recorders because Sybill is optimized for sales.

• Structuring Sales Proposals

  We have a custom GPT trained on our previous proposals and project plans.

  → It helps draft proposal structures, ensuring we touch on key pain points and best practices.

  Especially useful to keep messaging consistent across the team.

What We’re Building (Right Now)

• Sales Coach Agent

  We’re working on an AI Sales Agent that acts like a virtual coach for discovery and proposal calls.

  It’s built using sales frameworks from Liz (our sales coach) and other best practices.

  Instead of focusing just on technicalities, we’re training it to know what “good” looks like — context, qualification, rapport, clarity.

  → Built with Zapier and Replit in a lightweight way so it’s customizable as we iterate.

Inbound Lead Enrichment (partially live). Not a big focus, but we use Zapier integrated with webflow to capture inbound leads to the website & enrich & push the information into Slack on GTM, challenges & ICP to help qualify the lead. The ‘AI piece’ generates background on the company/person & GTM notes.

Client Onboarding & Internal Knowledge

• ClickUp AI for Inbound Applications
  We use ClickUp’s native AI to help rate and summarize inbound job applications. We’ve set up a custom AI field that pulls from specific application questions, like “What’s your experience with ClickUp?” — and compares them against the requirements for the role. It then gives each candidate a 1–5 rating based on fit. This helps us prioritize initial screenings (though yes, we still read every single one). If we’re hiring a PM who needs to be fluent in ClickUp, the AI flags whether their experience matches what we’re looking for — before a human even touches the application.

• Notebook LLM for Client Onboarding
  When we onboard new clients, we like to get deep—market landscape, ICPs, product features, brand guidelines, messaging, positioning, sales process, funnel definitions, sales calls, and closed-won/lost deal context—all of it. Why? The more context we have, the more effective we are—whether we’re running Demand Gen or tightening up RevOps.

  Instead of dumping this into a Notion doc no one reads, we use Notebook LLM to turn it into a dynamic, searchable study guide. It becomes a living knowledge base — updated with new call transcripts, docs, campaign results — that we can query, prompt, and use to generate campaign briefs or train Custom GPTS. It’s like onboarding a new team member, but faster, more accurate, and less likely to forget stuff.

• Briefs → Custom GPT Training
  We’re big fans of custom GPTS and reusable artifacts. Inside our Chatgpt Team workspace, we’ve got a library of internal GPTS we’ve built, especially for copywriting. The briefs we create in Notebook LLM get uploaded as source material for these GPTS, turning them into mini-experts on each client.

• This means we don’t start from a blank page every time we need ad copy, landing page content, or email sequences. We just prompt the GPT (usually using templates from our internal prompt library), and it spits out something directionally solid — already in the right voice, style, and strategy lane..

• Loom + GPT for SOPS
  We don’t love writing SOPS. (Shocking!) So instead of documenting everything manually, we just hit record on Loom, walk through the task, and let GPT do the rest. It reads the transcript, pulls out steps, and drafts a usable SOP — which we then polish up before sharing with the team.

  It’s not perfect, but it gets us 70–80% of the way there. Way faster than writing from scratch, and honestly, good enough that we actually document things instead of “leaving it for later” (aka never).

• Avoma → GPT → Handbook
  Our best thinking happens out loud — in strategy calls, internal debates, or onboarding sessions. Instead of letting that context disappear into the void (or worse, unsearchable Zoom recordings), we use Avoma to transcribe key meetings.

  Then GPT steps in.

  We feed the transcripts into GPT to turn those conversations into usable internal documentation—things like “how we think about attribution” or “why this campaign structure worked.” These get added to the 42 Handbook, so future team members don’t have to start from zero. It’s like turning every meeting into a chapter in our internal playbook without making someone take notes.

Creative & Content

MidJourney + Visual Electric
We use AI image tools like MidJourney and Visual Electric as part of our creative workflow — especially for campaign ideation, ad creative, and design support when clients don’t have existing visual assets (which is… often the case).

This isn’t about making everything look “AI-generated.” It’s about creating fast, brand-aligned visuals that tell a story:

• Visual metaphors to support a campaign concept

• On-brand hero images for landing pages

• Scroll-stopping visuals for LinkedIn and display ads

• Mockups when the product doesn’t photograph well (or doesn’t exist yet)

We treat AI image generation like a creative prototyping tool — using it to explore direction, mood, and composition quickly. Then we refine with design. Sometimes it’s the final asset. Sometimes it’s a sketch for a designer to run with. Either way, it saves time and unlocks more experimentation.

We’ve built a prompt library and internal guide (based on our deck) to help the team get consistent results.

We also:

• Use blend mode to mash up styles and references

• Apply brand guidelines by describing colour palettes and referencing ICP visuals

• Avoid group shots and complex human poses (because, well, MidJourney’s fingers still need therapy)

• Use –v 6.1 and –q 2 for quality control and the latest generation outputs

• Pull from historical and cinematic references to add emotion, weight, and texture

The real win? It lets us go from concept → creative much faster, especially when we’re working with tech clients who lack brand photography or want to stand out from generic stock.

→ We even created an internal guide on how to prompt better because, yes, prompting is half the job.

Gia for Social Content is a new platform by a friend (Tukan, who exited LeadSift). It is a call recorder, social selling platform, and content generator all in one. As an agency, we have a lot of great conversations, nuggets, ideas, and wins in internal and client calls, but they rarely get surfaced. The Gia bot joins all our calls and generates social content, which can then be tweaked and posted to LinkedIn. They also help you keep track of new connections, LinkedIn network & influencers in the space.

Join the waitlist (and the testimonial on the home page is not paid for, but I am an advisor & hold a small amount of outstanding shares)

RevOps, Data & Operations

Beyond the copy, creative & onboarding use cases, we use AI extensively in our RevOps work. RevOps especially lends itself well to an AI-first approach because of the structured data, logical progression & similarities to programming (CRMS are essentially a database), From internal tooling or custom ,GPTS here is how we use AI in the RevOps function.

• GPT for Data Hygiene
  Every RevOps team eventually hits the same wall: your CRM becomes a dumpster fire of inconsistent naming, weird lifecycle stages, mis-tagged UTMS, and duplicate contacts. Instead of fixing it manually or pretending it’s not happening, we use GPT to generate scripts and logic flows that automate cleanup. It saves hours of grunt work, reduces human error, and lets us standardize processes without needing a full-time admin.

• Formatting Raw Lists
  How often have you uploaded LinkedIn or Meta custom audiences only to get errors or low match rates because of formatting issues or incorrect headers? If I had a penny each time that happened, I would write this on a Malibu beach. We have a library of custom GPTS, one of them being formatting company or contact lists into platform-friendly custom audience lists.

• Lead Validation:

  Use GPT or Claude to quickly verify inbound leads (e.g., real website, business email). You can do this at scale with Clay. If you're interested in Clay, here are a few videos we did (more coming).

• Break-Even Calculator GPTS
  We’ve trained GPTS to calculate break-even metrics like CPC, CPA, cmql, and csql based on ACVS, funnel conversion rates, and close rates. This gives the Demand team real benchmarks to work with — not just “gut feel” targets. It’s especially useful for forecasting performance, setting channel goals, and making sure we’re not overspending to win leads that won’t pay off.

• GPT as CFO
  Forecasts revenue, margin, burn, team expansion, and profitability. It helps us (particularly me) forecast better & plan hiring, ramping & sales pipeline. Agency is a constant balance of ‘not enough work & too much work, not enough talent’.

Mini AI-enabled tooling.

• One unexpected place where AI and automation have been game-changers for us is in cleaning up HubSpot data, especially regarding forms. Whenever we run a HubSpot audit, we find the same issue: too many forms, non-standardized field usage, and hidden fields overwriting key properties. It creates chaos — messed up lead sources, broken lifecycle stages, and inconsistent reporting. To solve this, we built a lightweight web app called Formly. It connects to your HubSpot instance, fetches all your forms and the fields they use (including hidden ones), maps them out, and even suggests which forms can be consolidated. It’s saved us hours of manual work and helped us spot issues that would otherwise slip through the cracks.

• We’re also building a tool to make forecasting and ICP scoring more accurate and less reliant on gut feel. One of the challenges with lead scoring is that it’s often based on surface-level signals like email opens or pageviews, rather than real conversion data. So we started codifying our ICP methodology into a web app (built on Replit) that does a few things: First, it includes an AI-powered ICP builder — you can write freely about your ideal customer (or paste in a Notebook LLM summary). It translates that into a structured ICP definition. Then it connects to your HubSpot instance, pulls Contacts and Deals, and lets you define your “converted” stages — like SQL or Opportunity. From there, it analyzes which attributes correlate most strongly with actual conversion, giving you data-backed insight into your accurate ICP. Instead of scoring leads based on arbitrary web activity, you can build forecasts and prioritization models grounded in real CRM performance. This is currently an early prototype.
  

• Another internal tool we’ve been experimenting with is Regressr — a lightweight app we built to help with performance analysis and budget forecasting. Most teams rely on spreadsheets or platform dashboards, which are fine for reporting but don’t give you directional insight. Regressr connects to ad platform data (like Google Ads or LinkedIn), lets you input historical performance metrics, and runs simple linear regressions to help project future performance. You can use it to model how spending changes affect conversions or CPL, identify diminishing returns, and visualize performance trends over time. It’s beneficial for quick, back-of-the-napkin forecasting without spinning up full models in Excel or BI tools. It’s still early, but it’s already helping us (and some clients) make smarter media planning decisions with less guesswork.

Client Solutions (We Build These Too)

We don’t just use AI for us — we build with it for clients:

• Zapier + GPT will extract structured data from email threads. Alot of times with partners marketing, registrations etc come via email. We use AI to extract the structured data from emails, parse it into a table & then create (or update) contacts in Hubspot.

• Spam classifiers: An AI-enabled workflow that grades form submissions on the likelihood of it being spam. It looks for common spam patterns (we train it spam submissions + give it standard identifiers)

• Clay Workflows: We use Clay & Clay Agents extensively with customers to scrape & intelligently deploy crawlers to determine non trivially identifiable data. For example, understanding how many locations a company has listed on the website & location addresses or which technology they use on the backend (that doesn’t have a web script that BuiltWith can pick up)

• Competitive Intelligence: We are prototyping some early internal / client tooling that will help us stay updated about competitor web changes, ads, and messaging.

AI isn’t a department. It’s a layer.

We’re not “an AI agency.” We’re an agency built with AI across ops, creative, strategy, sales, and delivery.

It doesn’t replace thinking. It replaces tedium.

And it lets us stay lean, weird, and effective.

P.S. Yes, this essay was written with Claude & GPT as our co-pilot.

BONUS: We use Rally to test copy concepts against sythentic audiences & calibrate it against audiences on Wynter

Get the ABM Blueprint

A plug-and-play Figma file we use to plan and execute full-funnel ABM campaigns.

✅ Persona-level messaging

✅ Ad + LP mapping by funnel stage

✅ Built for paid, email, and more

Get it here.

Want to see what AI-native execution looks like across sales, marketing, and RevOps?

We’ve built it — and we can help you do the same.

Hire 42 Agency

Fix your RevOps with 42

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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We made a set of cards for YouTube Tactics. You can download the full set here

Succeeding with YouTube in B2B marketing requires a marketing reset. It’s not just about immediate clicks and conversions, as we have been taught, but about playing the long game and recognizing sales cycles are longer and more complex and customers are actual people. 

In this entry we break down how to incorporate YouTube into your marketing mix, focusing on brand building to improve performance, creative strategy, and the right metrics for success.

Why YouTube Matters for B2B

At its core, marketing is about capturing attention and building trust. Yet, with more competition and noise than ever before, traditional B2B tactics such as relying solely on traditional channels may not always be enough to stand out. That’s where YouTube comes in.

YouTube’s strength lies in its ability to blend storytelling and reach. It’s an ideal platform for engaging potential buyers who may not be in-market yet but are open to learning about your brand and offerings. Whether you’re using it for thought leadership, product demos, or customer success stories, YouTube is a platform where your brand can shine in three seconds in ways that go beyond traditional text-based ads.

Note: We recommend treating YouTube as an incremental channel, not as a direct conversion engine. It is especially useful in the middle funnel, where potential buyers are not yet ready to pull the trigger but open to hear you out.

Shifting from Performance Marketing to brand Building (with a b)

One of the key differences when using YouTube is that it shifts the focus away from purely bottom-of-funnel performance marketing that can be 100% measurable. B2B marketers often make the mistake of measuring success based solely on immediate conversions and are obsessed with attribution. While conversion metrics are important, and attribution is a must, YouTube excels as a brand-building tool and a great addition to your marketing mix.

This shift in focus is essential because B2B buying cycles are long and complex and the chances of someone seeing an ad and buying your product right away are close to 0. But by consistently showing up with creative, engaging content, you’re positioning your brand as the go-to solution when they are ready to act.

Keep it simple. You don’t need to overcomplicate yourself and think this is a Brand initiative that requires a significant investment or complex systems. The brand building we are discussing is more about creating awareness and showing up on multiple channels where your potential customers live online. It’s a marketing surround system process, not a channel effort to direct conversion linear sequence. 

Note: Let go of the 95-5 attention rule and embrace the fact that your buyer might be watching Bluey with their kid on YouTube. Sure, they may not be in the “buying” state of mind, but a great impression does not do any harm.

Creative: The Heart of YouTube Success

It’s no secret that creative quality can make or break your success on YouTube. In fact, the quality of your creative is just as important as your targeting and strategy. But, unlike most people believe, quality does not refer to super expensive production, but rather, genuinely thoughtful content for your audience. 

In other words, there’s no excuse to skip YouTube (even a simple Loom that is well done can do the trick). 

But to make it a bit more concrete, we have listed some of the essential elements we take into account to make sure our client’s creative resonates:

Hook Them Early: With YouTube’s skip feature, you’ve got only a few seconds to grab attention. Make sure your brand message and core visual elements are front-loaded in the first 3 to 5 seconds (ideally 3).

Experiment with Formats: Different formats work for different audiences. Test product demos, thought leadership videos, customer testimonials, or behind-the-scenes content to see what resonates most. Don’t be afraid to go beyond traditional talking-head videos, stop-motion graphics, animation, or even podcast-style content can all be effective.

Remember that quality doesn’t mean hi-fi expensive production, it means relevance to your audience.

More on formats in the next section.

Keep the Narrative Simple: Clear, concise messaging works best. Focus on delivering value in a way that’s easy to digest. Clear is better than clever.

Leverage Brand Recognition Early: Integrating your branding and core messaging early in the ad is critical to avoid losing viewers to the skip button.

Your ads don’t need to be fancy or overly produced to be effective, but they do need to make an immediate impact and tell a story. The right creative can help you stand out in a crowded marketplace and build a lasting impression with potential buyers.

Check out this video we ran for one of our clients. It is simple, showcases the product, positions the brand, and engages the audience 

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Smart Targeting on YouTube

One of the most powerful aspects of YouTube as a platform is its integration with the Google Ads ecosystem, which provides a wide range of targeting options. But B2B marketers need to be strategic about how they approach it.

Start with Remarketing: The most efficient way to begin is by remarketing to users who’ve already interacted with your website or engaged with your other ads. These audiences are warm, so your messaging should focus on reinforcing trust and moving them closer to conversion.

Leverage Custom Intent Audiences: These are audiences based on specific search queries that have a strong likelihood of conversion. For B2B, these custom intent audiences allow you to target users who are researching topics relevant to your products and services.

Avoid Broad Targeting: In-market audiences can work for large-scale awareness campaigns, but they’re often too broad for B2B, leading to wasted spend. Stick to more refined audiences that are closely aligned with your buyer personas.

Use Channel and Competitor Placements: Another often underused tactic is to target specific YouTube channels or content that aligns with your industry. Placing your ads on competitor or industry-specific videos can be a highly effective way to reach your target audience where they’re already spending their time

Best YouTube Ad Formats for B2B Marketing

YouTube offers a variety of ad formats, each with its own strengths. The key is knowing which format to use based on your campaign goals, your audience, and where they are in the buyer’s journey.

Here’s a breakdown of the most effective YouTube ad formats for B2B marketing and how to leverage them:

1. TrueView In-Stream Ads (Skippable)

What it is: These are the classic skippable ads that play before, during, or after a YouTube video. Viewers have the option to skip the ad after five seconds, so you only pay when someone watches at least 30 seconds, the entire ad (if shorter than 30 seconds), or engages with the ad.

When to use it:

• Best for brand awareness and top-of-funnel engagement.

• Ideal for storytelling, where you want to convey a narrative or build trust over time.

• Use these ads when you want to offer longer content (30 seconds to a few minutes), such as product demos, thought leadership pieces, or case studies.

Tips for Success:

• Front-load your key message: Since viewers can skip after five seconds, your brand, key message, or hook must appear immediately.

• Keep it engaging: The content must be engaging enough to hold viewers’ attention. Use compelling visuals or emotional appeals to keep viewers watching past the five-second mark. That doesn't mean it has to be a hi-fi video, but it has to be interesting.

• Call to action (CTA): Make your CTA clear and actionable, whether it’s to visit your website, sign up for a demo, or download a white paper.

Check out another example of an ad we used for one of our clients that led to great results. Andy O'Reilly, Senior Manager of Finance and Technology, Forma.ai

2. TrueView Discovery Ads (In-Feed Video Ads)

What it is: Discovery ads appear in the YouTube search results or as recommended videos. They blend into the organic content and look like a regular video thumbnail, enticing viewers to click and watch on their own.

When to use it:

• Perfect for mid-funnel engagement, where viewers are more likely to be in a research phase and exploring related content.

• Use when you want to drive traffic to a specific video or content series related to your brand, product, or expertise.

Tips for Success:

• Optimize your thumbnail: Since this ad looks like a regular video, your thumbnail needs to be visually compelling and reflective of your video’s value.

• Catchy headline: Write a clear, attention-grabbing headline that promises value to your target audience.

• Use in-depth, educational content: Since people are more likely to click if they’re actively searching for information, ensure your video delivers substantive content, such as how-to guides, product tutorials, or deep-dive interviews with industry experts.

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3. Bumper Ads (Non-Skippable, 6-Second Ads)

What it is: Bumper ads are short, non-skippable videos that last six seconds. While these ads are short, they can be extremely powerful for quick brand messages and reinforcement.

When to use it:

• Best for brand recall and message reinforcement.

• Great for retargeting campaigns or as follow-up ads for viewers who have already engaged with your longer content.

• Ideal for event promotions, product launches, or brief CTA-driven messages.

Tips for Success:

• Be concise and memorable: With only six seconds, your message needs to be laser-focused. Use strong visuals and a simple, impactful message.

• Use them in series: Bumper ads are effective when used as part of a broader campaign, complementing longer TrueView ads to reinforce key messages and increase recall.

4. Non-Skippable In-Stream Ads (15-20 Seconds)

What it is: These ads are non-skippable and force the viewer to watch for the full 15 to 20 seconds. They appear before, during, or after the main content.

When to use it:

• Great for mid-funnel or bottom-of-funnel engagement, where you want to deliver a focused, clear message to viewers who are more likely to be considering your offer.

• Useful for announcements, special promotions, or case studies that can be delivered within a short time frame.

Tips for Success:

• Focus on one key message: Since the time is limited, don’t try to cover too many points. Choose one core message and deliver it with clarity.

• Strong CTA: As with skippable ads, ensure your call to action is clear and easily accessible, whether it’s to visit your site, book a demo, or download a resource.

• Highly polished content: Since viewers can’t skip, make the ad as visually appealing and engaging as possible to maintain attention.

5. YouTube Masthead Ads

What it is: These are premium placements that appear at the top of the YouTube homepage. Masthead ads are highly visible, offering massive exposure for your brand.

When to use it:

• Ideal for large-scale brand awareness campaigns, such as major product launches, rebranding efforts, or event promotions.

• Best used for broad reach when you want to make a big impact in a short amount of time.

Tips for Success:

• Massive reach, broad appeal: Your message should cater to a wide audience, as the masthead reaches millions of viewers.

• High-quality visuals: Since this is prime real estate, your visuals and messaging should be polished and aligned with your broader campaign goals.

• Perfect for limited-time promotions: Use masthead ads for urgent, time-sensitive campaigns where you need maximum exposure.

Here’s an example of another ad we used for one of our clients that led to positive results.

6. YouTube Shorts Ads

What it is: YouTube Shorts are short, vertical videos (less than 60 seconds) similar to content found on TikTok and Instagram Reels. These ads can be placed between Shorts content, and they are optimized for mobile viewing.

When to use it:

• Best for engaging mobile-first audiences and driving quick engagement.

• Ideal for quick product showcases, behind-the-scenes content, or user-generated content to appeal to a more casual, mobile-centric audience.

Tips for Success:

• Keep it vertical: Since Shorts are designed for mobile viewing, ensure your content is vertical and formatted to look native to the platform.

• Fast-paced content: Shorts work best with snappy, dynamic content that grabs attention quickly and keeps the audience engaged throughout.

• Test organic first: Try experimenting with organic Shorts content before turning them into ads to see what resonates best with your audience.

Choosing the Right Format for Your Goals

Selecting the right ad format depends on where your audience is in the buyer’s journey and what you want to achieve. Here’s a quick guide:

• Top of Funnel (Awareness): TrueView In-Stream Ads, Masthead Ads, YouTube Shorts

• Middle of Funnel (Engagement/Consideration): TrueView Discovery Ads, Non-Skippable In-Stream Ads

• Bottom of Funnel (Conversions/Action): Non-Skippable Ads, Bumper Ads

By aligning your format choice with your objectives and audience’s place in the funnel, you’ll maximize the impact of your YouTube campaigns and ensure your message is both seen and remembered.

The Metrics that Matter (and Those That Don’t)

While running YouTube ads, the temptation to measure success by direct conversions alone is hard to resist. But in B2B, indirect metrics are often more telling of long-term success. 

Here’s a breakdown of what you should be tracking, and what to avoid:

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Key Metrics to Focus On:

View-Through Rate (VTR): This tells you how long users are staying engaged with your video. A high VTR indicates that your content is resonating with your audience, even if they don’t take action immediately.

Skip Rate: If viewers are skipping your ads too quickly, it’s a sign that your creative isn’t hooking them early enough. Adjust your messaging or format to capture attention within the first few seconds.

Cost Per View (CPV): This is a key metric for understanding the cost-efficiency of your campaigns. YouTube can offer a lower CPV compared to other channels like LinkedIn, making it a cost-effective way to drive awareness. 

Impact on Other Channels: YouTube rarely works in isolation. Use tools like Google Analytics to track how your YouTube ads are impacting traffic on other channels. If you see an uplift in organic, direct, or branded search traffic, it’s a good indicator that your ads are driving brand awareness.

Channel Placements and Device Breakdown: Analyze where your ads are performing best both in terms of specific YouTube channels and the devices they’re being viewed on. This can give you insights into which platforms and contexts work best for your messaging.

Metrics to Avoid Over-Focusing On:

Click-Through Rate (CTR): While CTR is a useful metric for other types of ads, it’s less important on YouTube where engagement and brand awareness are the primary goals. A low CTR doesn’t necessarily mean your campaign isn’t working.

Immediate Conversions: YouTube is a long-game channel, especially for B2B. Don’t judge the success of your campaigns purely on immediate conversions. It’s more about the indirect impact on overall traffic and eventual leads.

Google’s “Optimization” Metrics: Relying solely on Google’s automated optimization can lead to inflated performance metrics that don’t truly reflect the effectiveness of your campaigns. Always dig deeper to ensure you’re looking at the right indicators of success. 

Avoid Common Mistakes

Marketers often stumble when first experimenting with YouTube ads. While there are many things that could go wrong, here are the top three mistakes to avoid:

1. Trusting Google’s Automation: While it’s tempting to let Google handle everything, over-reliance on automated recommendations can lead to wasted budget or misplaced targeting. Customize your campaigns to fit your business objectives.

2. Neglecting Your First Few Seconds: If your branding and core message aren’t visible in the first 3 to 5 seconds, expect high skip rates. Make your hook strong and immediate. People expect everything right away in video. 

3. Expecting Immediate Results: YouTube ads won’t typically generate leads overnight, especially in B2B. Use it as part of a longer-term strategy and multi-channel mix to build awareness and trust before asking for the sale.

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Final Thoughts: YouTube as an Additional Surface for B2B

If you haven’t started in other channels, perhaps this is not the channel to prioritize. If, however, you have already built a performance muscle across social media and search, focusing on creative content that cuts through the noise, targeting the right audiences, and measuring success beyond direct conversions via YouTube can be a great addition to your marketing mix. 

Incorporating YouTube doesn’t mean abandoning performance marketing; it means expanding your strategy to include a channel that excels at building brand equity, engaging audiences, and driving results over the long term. Now is the time to add YouTube to your B2B marketing playbook and start reaping the rewards.

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

Note: We’ve published a 100+ point Hubspot Audit checklist (that we use ourselves). You can grab a copy here.

You're joining a new company at an earlier stage, and you don't quite have your team or structure nailed yet. Or, you're inheriting a team that may be a little junior and green around the ears. The CEO has set aggressive goals for the first 90 days, and you want to make sure you hit the ground running. 

Rubber meets the road. You need to deliver. 

The bad news? It's going to be rough. 

The good news is that you don't have to do it alone.

Regarding fractional/part-time help for marketing, there are many options. Especially with the recent economic downturn, many marketers are striking out on their own and going down the self-employment route. 

But - here's the kicker: there are too many options, and many marketing leaders we talk to don't know how to orient themselves or start somewhere. 

We'll help you navigate this better. 

But first, let's break down what the options are: 

• Fractional CMOS / Advisors 

• Freelancers

• Consultants

• Agencies

• Contractors. 

So how do you know which kind of hired help to bring & what can you expect to pay? 

The following are general ballparks based on my experience of being or working with folks across the spectrum. There are some similarities, but there are also some differences. 

Let's start with the 'new' kid on the block.

Fractional CMOS / Advisors 

First, I'd advise to be wary of anyone calling themselves a fractional CMO. I've come across too many people who were content marketing managers at a startup who now bill themselves as fractional CMOs. It sounds better than calling yourself a freelancer. But a true 'fractional' CMO must actually be qualified enough to be a CMO (for the business you are trying to hire them for). 

Fractional CMOS has become a new 'trend'; buyers beware. 

Ironically, most actually good CMOS who end up doing fractional work don't call themselves fractional CMOs; they bill themselves as advisors. 

So what is an advisor / fCMO, and when do you need to hire one? In a nutshell, if you need more hands-off, one call a week plus high-level strategy and team structure help. Advisors are great at advising but typically won't be hands-on with keyboard execution. 

Advisors can help answer questions like: 

• What should my GTM strategy be?

• Who is my next hire? 

• How can I build a marketing strategy to go upmarket?

• What is the right time to start a field marketing program?

• How can I expand into a new market?

• How can I launch this new product line without cannibalizing my existing product line?

• What's the best structure for my team?

• How can I measure marketing performance and team?

Advisors also help unblock founders and marketing leaders. They act like sounding boards and help you think through more challenging high-level problems foundational to your org. Like Amazon likes to call them, these are one-way doors. Typically, these are strategic decisions involving a lot of investment and are difficult to reverse (or, sometimes, quite impossible).

Cost / Investment wise, it varies since there are so many folks billing themselves as advisors or factional CMOS. But the average is $300/hr for advisory work. If this sounds high, it's because these are highly experienced folks who can probably help more in an hour (if you ask the right questions) than you fumbling your way through the dark. 

Typically, engagements with advisors only last for a short term. They come and solve your problems in theory, and it is up to you and your team to deliver.

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Consultants 

Generally, consultants are a bit more hands-on than advisors/ fCMOs. Unlike advisors with a breadth of experience, consultants are more hands-on but also focused on specific areas of marketing. While consultants are less strategic, broadly speaking, they can help with specialized areas like Revops, Demand Gen, Content, or Product marketing. 

A consultant is typically a mix of strategic & hands-on. As a Demand Consultant, I would advise companies on their GTM strategy or marketing mix while also working with the team to build marketing programs, align with goals & help shape the strategy and execution. 

Hiring the correct type of consultants is important because consultants can be specific to a business area. If you have a content marketing problem, hire a content marketing consultant (as opposed to a 'marketing consultant'). 

Cost/Investment for Consultants:

Consultants' rates can vary widely depending on their expertise and the complexity of their tasks. Generally, you can expect to pay anywhere from $180 to $450 per hour. 

A consultant is ideal if you have an in-house team but need help improving efficiency or expertise in a particular business area. Unlike fCMOs or Advisors, consultants aren't entirely hands-off; they balance execution and strategy. 

Freelancers

Freelancers are a flexible option for businesses looking for a more hands-on approach in specific areas of marketing. Unlike consultants who may provide a mix of strategic and execution support, freelancers usually specialize in executing tasks within particular areas. 

A common place where companies get freelancer help is writing or content marketing. This happens because, for the most part, it's very easy to scope out a chunk of work, i.e., write about topic X vs trying to provide them context on the whole business. 

Freelancers are ideal for short-term projects or augmenting your team's capabilities without the commitment of an FT hire when you need less strategic insights and more manpower to execute against a specific scope. 

Freelancers typically need more hand-holding to get into a working rhythm. Finally, also remember that freelancers will stick closely to the scope of work, so you must be clear about your expectations for them to meet them. 

Cost/Investment for Freelancers:

Freelancer rates are generally project-based or hourly, ranging from $50 to $120 per hour. 

Agencies

((GOOD)) Agencies combine the strategic input of consultants with a wide variety of skills and operational talent. Agencies are ideal when you need to solve complex problems with minimal oversight. They excel at bringing together different expertise and orchestrating them for effective solutions. 

Suppose freelancers must define the problem, solution, and steps to get there (and have the freelancer execute). In that case, advisors help them think through issues, and consultants figure out problems and perform in a specific area - agencies are, in some ways, the best of all. They figure out the problem (why is my revenue down this Q compared to last) - come up with a solution (because we didn't invest enough in top of the funnel three months ago) and deliver against the solution (we should diversify our marketing mix and measure our average sales cycle so we can invest before we need revenue to mature) with minimal oversight. Agencies combine different expertise from strategy to execution to help organizations do more.

Cost/Investment for Agencies:

Typically, good agencies start at 10K+ (monthly retainer), but I've also seen many terrible agencies charge 15-20K without delivering meaningful work or results. Many agencies will charge % of spend (on the paid side), but we prefer charging a flat retainer and a variable cost of creative on top. 

42 Agency helps B2B companies with RevOps / Demand Generation and Creative. We help our clients across the spectrum of these services because Demand Gen needs Creative + Content (Paid / SEO / Affiliate / Organic / Field), a robust measurement infrastructure (RevOps) and Full Funnel Marketing to work. 

Contractors

Contractors are similar to freelancers but often work on a longer-term basis. They can fill specific roles within your marketing team, bringing specialized skills for the duration of a project or a set period.

Contractors more closely work with your team, typically not working across multiple clients (like freelancers) but being " without being on payroll (which can have tax benefits).

Cost/Investment for Contractors:

Contractor rates are typically hourly (billed at longer-term contracts) or fixed-term contract-based. Rates can range from $80 to $130 per hour. 

Which one should you choose?

As with most things in marketing, there isn't a one-size-fits-all answer to building your dream team and meeting your goals. An excellent place to start is to consider how much runway and velocity you need to deliver and then plan accordingly.

Agencies typically deliver faster for strategic+executive work; freelancers are the best for very specialized and defined tasks; fractional CMOs and Consultants are typically the best at helping you understand the challenge you're facing; and contractors are like having an in-house employee and may take some time to ramp up but are a lot more helpful when it comes to long-term execution.

What you may need also changes according to the maturity of your organization, the type of product you sell, the existing talent on your team, etc. 

Earlier-stage companies may benefit from a mix of contractors and freelancers, while more mature companies, for example, may benefit from an agency.

Whatever it is, we hope this piece brings a bit more clarity.  

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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