Why I'm Joining the Board of Dreamdata

I spend a lot of time thinking about go-to-market efficiency — how companies decide where to invest their marketing dollars, how they measure what's working, and how they improve over time.

For years, much of marketing measurement has revolved around attribution — trying to determine which programs, campaigns, channels, or touches deserve credit for an opportunity or sale. While attribution works well when dealing with short, linear funnels (e.g., selling toothbrushes online), it gets exponentially harder in B2B environments, where companies sell complex products, engage scores of contacts, and generate hundreds of touchpoints over long sales cycles. This is known as multi-touch attribution (MTA).

It's easy to get cynical about attribution. I've periodically recommended that CMOs drink their coffee from this mug during quarterly business reviews (QBRs).

But if we did indeed "make it up," the question is why. The answer is simple: because "the business" — the CEO, CFO, and board — won't stop asking fundamental questions. Which marketing programs work? Which don't? What should we do more of? What should we do less?

Ever since John Wanamaker famously said that he knew half his marketing budget was wasted but didn't know which half, businesses have been trying to answer those questions. And as marketers, we have to answer them.

We can't say that it's too hard to measure, that tracking has gotten more difficult, or that part of the process happens offline and isn't trackable at all. The CEO, CFO, and board don't care. Saying "it's complicated" instead of answering will get you an express ticket to the unemployment line.

So attribution ends up a bit like the way Churchill described democracy: the worst system, except for all the others that have been tried.

The mistake is expecting attribution to answer every question. Like a hiker lost in the woods, we should triangulate to determine our position. No single bearing is sufficient. Instead, we should combine multiple techniques, including attribution, incrementality testing, marketing mix modeling, journey analytics, and intent signals. No single approach provides the answer, but together they can help us make better decisions.

But the opportunity doesn't stop at measurement.

Reporting and analytics are table stakes – important ones with career-limiting consequences if you get them wrong. But once you've built the data foundation required to understand customer journeys, attribution, engagement, and pipeline, you can use it for much more than reporting.

Historically, marketing systems have been divided into two camps: systems of insight and systems of execution. One set of tools tells you what happened. Another helps you do something about it. There is an opportunity to bring those worlds closer together. The same data used to understand buyer behavior can also be used to influence it.

Suppose your analysis reveals a set of accounts demonstrating strong buying signals. Or a segment of prospects who consistently engage with a particular type of content. Or opportunities that share characteristics with your most successful deals. Why should those insights remain trapped inside reports and dashboards? They can be transformed directly into audiences for advertising, targets for outbound campaigns, priorities for sales development teams, and signals for customer success organizations.

This is where marketing measurement evolves into demand activation. Instead of explaining performance after the fact, the data can be used to improve future outcomes. Marketing teams can build more relevant audiences. Campaigns can be targeted more precisely. Sales teams can focus on the accounts most likely to engage. Marketing and sales can operate from a common view of buyer activity and intent.

The goal is no longer just to understand demand. It's to help create it. The same data foundation that powers analytics can improve execution.

And this is where things get even more interesting. That data foundation can also become the platform on which AI agents operate. As we're rapidly learning in the age of AI, success is often less about having the best model than having the best data. Agents need context. They need memory. They need a rich understanding of accounts, contacts, buying groups, campaigns, engagement, pipeline, and revenue.

Put it together and you get this. Marketing analytics are table stakes. Critical, but table stakes nevertheless. Demand activation is where things get interesting because we're no longer just watching the game — we're playing it. Marketing agents are where things get really interesting because we're putting more players on the field. Dreamdata is well positioned across all three.

That's what excites me about the company. I get the chance to work in a domain I care a lot about. I get to work with a product that is well positioned for the industry's evolution. I get to be at the forefront of marketing agents. And I get to work with Nick, Lars, Steffen, and the rest of the team.

I'm looking forward to the journey. Thanks to Roberto for introducing me to it.