Data Analytics + Insights

Most marketing analytics exists to survive meetings. Dashboards nobody opens between reviews, attribution models everybody quietly distrusts, and quarterly readouts engineered so no decision is ever forced. I build the opposite: measurement that would survive being explained out loud, to your CFO or to the customer being measured.

The stack of habits is simple and mostly unfashionable. Tracking audited before it is trusted, because the setup nobody checks is where numbers go wrong first. Attribution treated as a set of useful lies to be triangulated, not a truth machine, with phone calls and offline outcomes pulled back into the picture your dashboard currently ignores. Incrementality proven with holdout tests any marketing team can run, no data science department required; I validated a 342-to-1 return this way when the dashboard's version of events was too good to sign my name to. Marketing mix modeling where spend and channel spread justify it, and a clear-eyed no where it does not. And governance, the deeply boring UTM and naming discipline that decides whether next year's questions are answerable at all.

The deliverable standard is one I stole from the best client I ever had: every analysis ends with a decision. Not an insight, not a consideration, a decision, with the number that would change it stated in advance. Dashboards get rebuilt until the people they are for actually open them, which usually means fewer charts, plainer language, and one page that says what to do Monday.

Privacy is treated as a design constraint, not an obstacle. Consent-gated measurement, first-party data used as a verb, clean-room realism instead of clean-room theater, and regulated-category setups that have already survived HIPAA scrutiny. If your numbers feel confident and your decisions do not, the gap between them is the engagement. It is usually shorter than the last three vendor pitches you sat through, and it ends with numbers you will defend without flinching.