Episode 3
Why Your Analytics Aren’t Driving Real Business Impact with Jason Perkowski
Jason Perkowski
He has 15 years of experience in marketing analytics and data operations within B2B marketing. He leads marketing performance measurement and data operations at Equifax and serves as an advisory board member of Customer Data Alliance.
Turning Marketing Data Into Decisions With Jason Perkowski
Introduction
A dashboard full of numbers isn’t the same thing as a dashboard leadership trusts. Jason Perkowski, marketing analytics manager at Equifax, has spent 15 years in data-heavy roles, from finance and BI to software engineering, before landing in marketing operations. He also serves on the advisory board of Customer Data Alliance, an association for practitioners working on the same problems.
On this episode of Ops in Motion, Jason joined Shiv and Kawal to talk through what actually turns raw campaign data into something leadership acts on, why single-touch attribution hides more than it reveals, and why a growing martech stack usually creates more chaos than clarity without the right governance underneath it.
Who should read this: marketing operations and analytics leads building dashboards, attribution models, or a tech stack that needs to hold up under real scrutiny.
Key takeaways
- Clean, governed CRM data has to come before any dashboard. Skip that step and the dashboard just displays the problem faster.
- Single-touch attribution (first touch or last touch) hides the channels actually influencing a deal. Jason’s team only found out webinars and trade shows were driving win rate once they moved to a multi-touch model.
- A bigger martech stack doesn’t mean better marketing operations. Without a defined source of truth and shared process, more tools just means more fragmentation.
- No attribution model captures the full buyer journey. Offline conversations, Slack messages, and LinkedIn touches never make it into the CRM, so the honest move is to treat attribution as directional, not definitive.
Your dashboard is only as trustworthy as the data underneath it
Jason’s process for turning raw campaign data into something leadership actually uses starts well before the dashboard. Clean, governed CRM data comes first. From there, ideally, that data gets ingested into a data warehouse, curated into reporting-ready tables, and only then built out into dashboards designed around what a specific stakeholder actually needs to see.
The step people skip is designing for the stakeholder. A dashboard built for a sales leader and one built for a CMO shouldn’t look the same, because they’re answering different questions. For Jason’s team, attribution is the piece that makes the data real for the business: showing which campaigns actually influenced which sales opportunities, not just reporting activity for its own sake.
“You have to have that stakeholder in mind to understand what they want and need.”
— Jason Perkowski
Single-touch attribution is hiding what’s actually driving your pipeline
One insight changed how Jason’s team approached strategy entirely: mapping out the full buyer journey and how many touches were actually involved in it. Early on, his team looked only at first touch or last touch, a common default. Moving to a multi-touch model, splitting influence across every touchpoint, surfaced something first-touch and last-touch models had been hiding: webinars and trade shows were carrying more weight in driving awareness and influencing opportunities than the simpler model gave them credit for.
That’s the real value of multi-touch attribution. It doesn’t just add more data, it tells you which programs to invest more in and which ones to pull back on, based on actual influence rather than a guess about which touch mattered most.
“Seeing the whole customer journey and the many touches completely changed the insights we’re able to get from our campaigns.”
— Jason Perkowski
A bigger martech stack doesn’t fix broken data. It just spreads it further
Kawal, co-founder of Digital DI Consultants and one of the episode’s hosts, has watched the marketing tech stack grow from a simple CRM and email platform into a sprawl of ABM tools, customer data platforms, intent data providers, and AI enrichment tools. More tools raised expectations too: marketing operations is now expected to connect data across marketing, sales, support, and product, and make all of it measurable.
Her approach to keeping that stack from becoming its own problem starts with strategy, not tool selection. A team doesn’t need fifteen integrations. It needs the right tools connected to a real process, with one clearly defined source of truth, standardized naming conventions and lead lifecycle rules shared across teams, and a recurring audit cadence, quarterly, twice a year, or annually depending on the size of the organization. Ongoing training matters just as much, since a stack nobody fully understands creates the same fragmentation a messy CRM does.
Shiv, the episode’s other host, connected this directly to cost. Bad CRM data doesn’t just create reporting headaches. It wastes budget on invalid contacts outside your actual ICP, lets real opportunities fall through the pipeline, degrades customer service when duplicate or incomplete records slow down response times, breaks alignment between sales, marketing, and customer service, and ultimately leads to decisions made on reporting that isn’t accurate in the first place.
If your martech stack hasn’t been audited in a while, that’s worth fixing before adding another tool on top of it. A martech stack audit is the starting point, not a full migration or integration project, which tends to come after you know what’s actually broken.
Your attribution model will never tell the whole story, and that’s fine
When attribution data doesn’t match what the sales team is seeing on the ground, Kawal’s approach is to name that gap directly instead of pretending the model is complete. No attribution model, first touch, last touch, or multi-touch, captures everything. Offline conversations, Slack introductions, a webinar invite sent over LinkedIn, what she calls dark social, all of it influences a deal without ever showing up in the CRM.
Her fix isn’t to chase a perfect model. It’s to add what qualitative context is available, what sales is actually hearing on calls, what content keeps coming up, then be transparent with stakeholders that the dashboard is directional, not definitive. The purpose of attribution isn’t to prove marketing’s worth. It’s to build confidence in where to invest next.
“Attribution isn’t about proving what marketing value is or how well the marketing is working. It’s about building confidence in where to invest next.”
— Kawal
Action items
Before building a new dashboard, confirm the CRM data feeding it is clean and governed. A polished dashboard on bad data just spreads the problem faster.
Move from single touch to multi-touch attribution if you haven’t already. You may be underrating the channels actually driving pipeline.
Define one source of truth for your data before adding another tool to the stack.
Set a recurring martech audit cadence, and don’t skip it just because the stack seems to be working.
When presenting attribution data, tell stakeholders plainly what the model does and doesn’t capture, rather than presenting it as complete.
FAQs
What’s the difference between single-touch and multi-touch attribution?
Single-touch models credit only the first or last interaction before a deal closes. Multi-touch models split credit across every touchpoint in the buyer journey, which often reveals channels, like webinars or trade shows, that single-touch models undercount entirely.
Why doesn’t attribution data ever tell the complete story?
Because a meaningful share of buyer interactions happen outside the CRM, offline conversations, Slack messages, LinkedIn outreach, that never get logged. Attribution should be treated as directional guidance, not a definitive account of every influence on a deal.
How often should a company audit its martech stack?
It depends on the size of the organization and database, but somewhere between quarterly and annually is typical. The bigger risk is skipping the audit entirely as the stack keeps growing.
Talk to Digital DI Consultants
If your dashboards aren’t earning trust from leadership, or your martech stack has grown faster than your governance has, see how we approach a martech stack audit, or talk to our team about your setup.
