You know marketing is working. You just can't prove it.

Possibly — and it depends on what's underneath. Attribution tools work best when the signals they read are complete, governed, and accurate. If your UTMs are inconsistent, your CRM sync has gaps, or Marketing and Sales are using different definitions for the same metrics, a tool will surface those problems faster and more expensively than fixing them first. The diagnostic tells you exactly where you stand — and whether a tool will work for you right now or create a false sense of progress.

Usually it didn't stick because the fix was technical without being organizational. Someone configured the workflows but Marketing and Sales never agreed in writing on what the definitions actually mean — so the moment a new campaign launched or a new sales rep joined, the governance broke down. The Phase 2 engagement starts with a formal alignment session where Marketing, Sales, and RevOps agree on lifecycle definitions, MQL thresholds, handoff rules, and what counts — before a single workflow is built. Written sign-off from all three functions is what makes the infrastructure stick. When the rules are owned by the team and not by a consultant who leaves, they survive.

RevOps is built around the sales motion — pipeline visibility, forecasting, and CRM hygiene for Sales. Marketing attribution infrastructure — UTM governance, lead scoring logic, lifecycle stage definitions, first and last touch mapping, and the reporting that connects marketing spend to closed revenue — sits within RevOps in theory. In practice, it's almost always deprioritized against sales-facing work. The marketing attribution layer ends up ownerless, incomplete, or built just well enough to report activity without connecting it to revenue. That's the gap I fill.

Yes — and it's often the best time. Building a marketing ops function is time-consuming, and the foundational work — attribution infrastructure, governance, lifecycle definitions, system configuration — almost always gets deprioritized against day-to-day demands. I work alongside the team to build the foundation they're already planning, get it done faster, and free them to focus on execution instead of being pulled in every direction at once. I also act as another set of eyes — helping establish the infrastructure, governance, and operating model that gives a new function something solid to build on, rather than spending the first year untangling what was set up quickly.

That's worth exploring. "Covered" usually means GA4 is installed and there's a dashboard somewhere. What it rarely means is a complete chain — UTM parameters governed consistently across every channel, CRM fields mapped correctly, lead scoring tied to lifecycle triggers, first and last touch reconciled across systems, and executive reporting that traces a closed deal back to its original source. There's almost always a gap between what marketing believes is tracked and what Sales and leadership can actually see. I'm not trying to replace what's working — I'm trying to find where the chain breaks.

AI is a pattern matcher. It works with the data it's given — it can't tell you what's missing, what's being tracked incorrectly, or where your systems are siloed. In most B2B marketing stacks, the data is incomplete before AI ever touches it. Inconsistent UTMs, broken CRM sync, untracked activities, ungoverned fields — AI builds confidently on top of all of it without flagging any of it. The result looks like an answer but reflects your gaps back at you. The foundation has to be right first. That's the work I do — and it's what determines whether AI gives you signal or noise.

I implement. The diagnostic produces a scored assessment of what's broken, what it's costing, and a prioritized roadmap. From there, most clients bring me in to build it — every workflow, every integration, every dashboard configured and tested, not handed off to your internal team to figure out. Some clients take the roadmap and implement internally. Both are valid. But if you want it built, I build it.