B2B buyer journeys are long, multi-threaded, and involve multiple people from the same account interacting across multiple channels — paid search, email, events, content — over weeks or months. Every one of those interactions plays a role in moving a deal forward. But last-touch attribution erased all of it. Only the final touchpoint before opportunity creation received any credit.
The result was that marketing teams had no reliable way to answer the most important question they faced: which campaigns are actually driving pipeline, and which ones are just noise? Budget decisions were made on instinct. High-performing early-stage programs were invisible. And the relationship between marketing activity and revenue was largely a matter of faith.
This was a first-of-its-kind initiative for the marketing team. There was no prior model, no template, no playbook. The starting point was an abstract business pain and the task was to turn that into something measurable, trustworthy, and usable.
The data challenge was significant. B2B journeys involve multiple people from the same account interacting at different stages across different channels over months. Cleaning, validating, and modeling that data required constant collaboration across Marketing Ops, Digital Tech, Sales, Customer Success, and Data Science teams who each had different definitions of the same things.
The harder challenge was human, not technical. Keeping six cross-functional teams aligned on scope, decisions, and tradeoffs — while resolving data quality conflicts in real time — required as much product leadership as the model itself.
I started by partnering with Marketing Ops leadership to translate the abstract pain into one specific, answerable business question: which campaigns contribute most to pipeline creation? Everything else flowed from there. With the scope locked to one geography, one region, one business unit, and a 5-quarter data window, the project became deliverable.
The shift happened when stakeholders stopped asking "did this campaign work?" and started asking "which combinations worked, and how do we do more of that?" That change in question was the signal that the model had earned trust.
ROI visibility expanded from roughly 30% of campaign spend being tied to outcomes before the model, to over 70% after launch. For the first time, marketing could point to specific interactions and say with confidence: this is what moved the deal forward.
The go-to-market was as important as the model itself. Running demo sessions, presenting at executive reviews, and hosting weekly office hours meant CampaignAttribute IQ was not just built — it was adopted. Teams across Sales and Customer Success began exploring similar data-led approaches off the back of it.
Beyond the numbers, what this project unlocked was organizational confidence. Marketing teams gained a credible voice in revenue conversations. Budget decisions became data-driven. And the model became a springboard for broader analytics initiatives across the organization.
Building a great data product is not just about the math — it is about trust. Trust is built through clarity, collaboration, and relentless communication. My role was not to write the code or build the model myself, but to make sure the right problems were being solved, and that every team involved had the same picture of what success looked like. Leading this from zero to one was a defining moment — it sharpened my ability to think strategically, manage ambiguity, and guide a cross-functional team through uncertainty toward meaningful impact.