Each story starts with the problem and the result, then shows how I got there: how I framed the problem, got the right people on board, used the data, and made the result stick.

A partner landing page could convert more visitors into applicants, but the improvement had to be proven with a proper test, not a guess.

The average cost to get one customer looked fine, but it hid big differences between partners, states, and funnel stages.

A prominent new partner proposed a payout far above what its traffic was likely worth, and saying the wrong thing could lose the relationship.

HP.com's affiliate program had stopped growing and needed a reset across partners, offers, and how the channel was run.

Plastiq needed the right software platform to run partner relationships on — a decision that had to satisfy several teams and hold up as the program grew.

Two search partners drove most of Norton's affiliate revenue — a risky concentration if either relationship changed.

Partner ads and creative crawled through a scattered review process across 70+ global placements, causing duplicated work and delays.

A useful improvement sat behind a partner's crowded roadmap, and 'more clicks' wasn't going to move it up the queue.

The steps between clicking an ad and buying were confusing enough to cap sales, and fixing them meant coordinating five teams.

Could one person take an AI product from idea to a working, launched platform — and find out whether anyone would pay for it?
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People running many job applications at once lose track of where each one stands — and the AI career tools they try often feel untrustworthy.