Analytics & optimization
Measurement, experimentation, and iteration cycles that make every launch smarter than the last one. Analytics treated as a design surface, so the team can see what to change and why.
Most analytics setups are broken in ways nobody wants to admit. Events are duplicated, source tracking is inconsistent between platforms, and the dashboard everyone looks at is quietly built on numbers that never reconciled to begin with. Before we optimize anything, we audit what is there and rebuild the parts that cannot be trusted, because running experiments on top of broken tracking just produces expensive confusion and a slide deck no one wants to defend in a meeting.
Once the foundation is honest, experimentation makes sense. Tests are scoped tightly, with a written hypothesis, a defined primary metric, and a stop condition agreed in advance. We prioritize against real business impact instead of curiosity, and we resist running five things at once when one careful test will actually change a decision. Reporting is short, opinionated, and ends with a call: keep, kill, or double down. Over a few cycles the team stops arguing about the numbers and starts using them, and the site, the funnels, and the creative all sharpen together.
What it solves.
- Analytics is set up but no one is looking at it, or acting on it.
- Site and funnel performance is unclear because tracking is broken or duplicated.
- There is no experimentation cadence, so improvements are anecdotal.
- Executive reporting takes days and still doesn't answer the important questions.
What ships.
- Analytics and event tracking audit and setup
- Attribution model and dashboarding
- Experimentation plan and prioritization framework
- Conversion audits on primary funnels
- Ongoing optimization cycle with monthly reporting
Who it is for.
- — Teams running paid, content, and product growth in parallel
- — Companies preparing for a raise, launch, or acquisition
- — Founders who want an honest picture of what is working
The method.
Trust the numbers first
We start with a tracking audit so the data everyone is looking at is actually correct before we build anything on top of it.
Small, honest experiments
We run tightly scoped tests with clear hypotheses and clear stop conditions, not vanity dashboards.
Report the decision, not the chart
Every reporting cycle ends with a decision: keep, kill, or double down. Charts serve the decision, not the other way around.
