Your CEO wants to know which campaigns are driving pipeline. Your CFO wants ROI calculations down to the dollar. Your board wants proof that marketing spend is generating returns. And you — well, you’re trying to give them what they want, even though you know the numbers you’re reporting are increasingly fiction.
Here’s the uncomfortable truth: At the exact moment leadership started demanding more precise attribution, digital marketing got objectively messier. Privacy defaults have made tracking harder. Anonymous research has exploded. The “dark funnel” — podcasts, peer conversations, review sites, anonymous web visits — influences decisions you can’t see at all. Meanwhile, B2B buying cycles stretch across months or years, involve committees of 7-12 people, and unfold largely outside your view.
So marketers do what anyone would do when they can’t measure what matters: They measure what they can, and pretend it’s what matters. Bottom-funnel conversions become a proxy for success because at least you can see them, even if they’re telling you almost nothing about what actually drove the decision.
But here’s the thing: We’ve been down this road before. The Mad Men faced the exact same problem — complex buyer journeys they couldn’t track with precision. Their solution? They measured lift. Did awareness increase? Did perception shift? Did the market move?
We dismissed that approach as too vague, too “brand-y,” impossible to prove ROI. But the metric was never the problem. The measurement was the problem. And now, with intent data and predictive analytics, we can finally measure lift with the precision that traditional attribution promised but could never deliver.
The reality of modern B2B buying
Let’s start with what we know about how B2B buyers actually behave, because it should fundamentally change how we think about measurement.
According to 6sense’s B2B Buyer Experience Report, 81% of buyers have already picked a winner before they ever talk to sales. They do roughly 70% of their research anonymously — reading content, consuming demos, checking review sites, asking peers — and make key decisions about who they’ll consider during this invisible phase. By the time they fill out a form or take a sales call, they’re validating a choice they’ve already made.
Think about what that means for your measurement strategy. If buyers have picked a winner after completing 70% of their research anonymously, then optimizing for bottom-funnel metrics means you’re measuring the validation stage. You’re tracking the last 30% of a journey where the outcome is mostly already determined.
And it’s not just that the research happens anonymously. B2B buying has gotten genuinely more complex:
- Enterprise software purchases involve buying committees of 7-12 people, each doing their own research, influenced by different channels, operating on different timelines
- The buying journey isn’t a linear funnel anymore — it’s a sprawling research project happening across months, involving multiple stakeholders, unfolding largely in channels you can’t track
- Traditional attribution was built for a different world: B2C purchases with short cycles, individual buyers, and trackable digital journeys from awareness to conversion
That model doesn’t work for enterprise software with 9-month sales cycles and buying committees comparing vendors across dozens of untrackable touchpoints.
When you can’t measure what matters, you measure what you can
So what do marketers do? They default to metrics they can actually see.
Last-touch attribution. Form fills. MQLs. Demo requests. Content downloads. These metrics feel precise. They generate clean reports. They give you numbers to put in front of leadership. But they’re measuring the wrong thing.
If 70% of the buyer journey happens anonymously, these bottom-funnel metrics are only capturing the final 30% — the validation stage where buyers are confirming decisions they’ve already made.
But here’s the real problem: This doesn’t just fail to give you credit for early-stage work. It actively misleads you about what’s working.
When you can only see bottom-funnel conversions, you start optimizing for them:
- You over-invest in demand harvesting tactics — the ones that capture people who are already ready-to-buy
- You under-invest in demand creation — the work that makes buyers aware you exist and shapes their consideration set during that invisible 70% of the journey
- You end up fighting to be the last vendor they talked to, when the game was really won by being the first one they thought of
And when you report results to leadership, you’re telling them a story about performance that’s fundamentally incomplete. That webinar gets credit for 50 MQLs, but what about the six months of content, brand presence, and category education that made those 50 people even know to register? That last-touch form fill gets 100% attribution, but what moved them from “vaguely aware of the problem” to “actively evaluating solutions?”
You’re not measuring marketing performance. You’re measuring marketing visibility. And there’s a huge difference.
The Mad Men understood something we forgot
This is where we need to take a page from the original performance marketers: the Mad Men era advertisers who had to prove ROI without any of our fancy tracking tools.
They weren’t unsophisticated. They measured lift because direct attribution was impossible. When you run a TV spot that reaches 10 million people, or a print ad in a national magazine, or a billboard campaign across major cities, you can’t track individual conversions. You can’t draw a straight line from exposure to purchase. The buyer journey was already too complex and too invisible.
So they measured lift:
- Did brand awareness increase among the target audience?
- Did perception shift?
- Did purchase intent grow?
- Did market share move?
They ran studies comparing exposed audiences to control groups. They tracked macro-level changes in brand health and market position. They measured influence, not attribution.
For decades, digital marketers looked down on this approach. We called it “brand marketing” — a dismissive term implying it was vague, unaccountable, impossible to prove ROI on. We had pixels and cookies and marketing automation. We could track every click, every download, every form fill. We could prove which campaigns drove which deals.
Except we couldn’t, really. We just pretended we could, because the alternative was admitting that most of what drives B2B purchase decisions happens in places we can’t see.
Meanwhile, we abandoned lift as a metric entirely. It felt too imprecise, too hard to operationalize, too disconnected from the clean attribution data our dashboards could produce. Why measure whether you increased awareness or shifted perception when you could measure exactly which campaign generated which MQL?
But here’s what the Mad Men understood that we forgot: Lift was never a bad metric. We just didn’t have a good way to measure it in the B2B context.
Lift, reimagined: How modern tech makes the old metric actionable
Here’s the breakthrough: Intent data and predictive analytics give you visibility into the anonymous research phase. You can finally see what’s happening during that invisible 70% of the buyer journey — which accounts are researching, what they’re looking at, how their behavior is changing, and whether they’re progressing toward a purchase decision.
That means you can measure lift with actual precision. Not “Did our brand awareness increase in a survey?” but “Did this campaign reach our target accounts? Did those accounts show increased research activity? Did they progress to more active buying stages?”
Let’s break down what that actually looks like:
Reach lift: Which accounts in your TAM are you actually influencing?
Intent data reveals which accounts are actively researching in your category, even when they’re doing it anonymously. You’re not waiting for someone to fill out a form or visit your website. You’re tracking research behavior across the broader web — what topics they’re consuming content about, what solutions they’re investigating, what signals indicate they’re in a buying cycle.
This lets you measure reach in a way that matters: Are your campaigns getting in front of accounts that fit your ideal customer profile and show buying intent? Are you reaching the right 500 companies, or just generating clicks from anyone with a pulse?
No form fill required. You’re measuring influence at the account level, during the phase where buying decisions are actually being made.
Engagement lift: Are those accounts showing increased research activity after exposure?
Once you know which accounts you’re reaching, you can track whether your campaigns are actually moving the needle:
- Are accounts showing more intense research behavior after exposure to your content, events, or ads?
- Are they consuming more information? Expanding their research to adjacent topics?
- Are they showing signals that they’re taking the problem more seriously?
This is where you start to see which channels and campaigns are genuinely creating demand versus just harvesting it. If accounts are showing increased research intensity after engaging with your content, you’re influencing their journey. If they were already deep in research and your campaign just happened to be the last thing they saw before converting, that’s a very different story.
Buying stage progression lift: Are accounts moving through their journey?
This is where predictive analytics comes in. By comparing current account behavior to patterns from 12 years of buying cycle data, you can identify where accounts are in their journey and whether they’re progressing:
- Are accounts moving from early-stage awareness signals to mid-stage consideration behavior?
- Are they going from passive research to active evaluation?
- Are the accounts you influenced last quarter now entering sales conversations?
You’re not waiting for a hand-raise to know if your marketing is working. You’re watching the journey unfold in real-time, seeing which accounts are moving forward and which campaigns are correlated with progression.
This is lift with the precision that traditional attribution promised but could never deliver. You’re measuring influence at the account level, tracking reach and engagement and progression, in the part of the journey that actually determines winners and losers. You’re seeing the 70% that used to be invisible.
What changes when you optimize for lift
When you shift from optimizing for bottom-funnel attribution to optimizing for lift across the entire buyer journey, everything changes.
First, you spot opportunities earlier — while you can still actually influence them. Instead of waiting for accounts to raise their hand when they’re 70% done with their research, you’re identifying accounts when they first enter a buying cycle. You can shape their consideration set instead of just fighting to get into it.
Second, you invest in the right channels. Not the ones that get last-touch credit, but the ones that are actually reaching target accounts and moving them forward. Maybe that podcast sponsorship never generates a single form fill, but you can see that accounts exposed to it show increased research intensity and faster stage progression. Maybe that content hub drives tons of traffic but none of it is from target accounts showing buying intent. Lift metrics let you optimize for real influence, not just visible conversions.
Third, you can finally have honest conversations with leadership about what marketing actually does. Instead of pretending that last-touch attribution tells the whole story, you can show:
- “Here are the 500 high-fit accounts we reached this quarter”
- “Here are the 200 that showed increased research activity”
- “Here are the 75 that progressed to active evaluation stage”
- “Here are the 12 that entered sales conversations”
That’s a narrative about building pipeline, not just capturing it. It shows marketing’s role in creating demand and shaping buying decisions, not just harvesting people who were already ready to buy.
And finally, you stop fighting over the last 30% of the journey and start dominating the first 70%. You align with how buyers actually buy — doing extensive research, building consensus across a committee, picking a winner before they ever talk to sales — instead of optimizing for how your CRM wishes they would buy.
You’re measuring what actually matters: Are we reaching the right accounts? Are we influencing their research? Are we moving them toward a decision? Those are the questions that determine revenue outcomes. Bottom-funnel conversions are just the visible manifestation of work that happened much earlier.
The future is actually the past
The Mad Men had the right metric all along. They understood that when buyer journeys are complex and largely invisible, you need to measure influence and lift, not just attribution and conversion.
We spent two decades dismissing that approach because we thought we’d solved the measurement problem with digital tracking. But privacy defaults, anonymous research, and genuinely complex B2B buying cycles have brought us full circle. Direct attribution was always a bit of a fantasy in enterprise sales. We just had really good tracking tools that let us pretend otherwise.
The difference now is that we can measure lift with precision:
- We can see which accounts we’re reaching
- We can track their research behavior
- We can identify buying stage progression
- We can prove that early-stage influence drives late-stage revenue, with actual data at the account level
We can stop pretending we know exactly which touchpoint “caused” a conversion — as if complex buying decisions work like that. We can start measuring what actually matters: Are we reaching the right accounts during the phase where decisions are made? Are we shaping their thinking? Are we moving them forward?
That’s the performance cue we need to take from the Mad Men era. Not the three-martini lunches (well, maybe those too), but the understanding that influence precedes action, and you need to measure both.
The tools finally exist to do it right. Time to bring lift back.
