Which users drive your product.

The problem.

You are optimizing for an average user who does not exist. Effort sprays across everyone equally, when a small group drives most of the value and a large group was never going to stay. Without the segments, every roadmap call is a guess.

What you get.

• Mutually exclusive behavior segments, as SQL you keep. The model, not a slide.
• A day-30 retention curve for each segment, so you see who stays and who never did.
• Activation leak mapping: where each group drops off, and in which session.
• Which segments are worth resurrecting, and which are most at risk of churning, so you know where to act first.
• A ranked experiment backlog, seven or more items, each tagged by effort.
• An event and tracking review, so the data feeding your dashboards and your analytics AI is trustworthy.
• An executive readout your team and your board can follow.

Why not just ask your analytics tool.

The tools answer one question at a time. This is the depth they cannot reach: segments that took ten passes to separate, the exact activation moment, and the tracking underneath that has to be right first. You leave owning all of it.

The proof.

On our own app we segmented 16,531 users into 7 groups. The top 22 percent drive 61 percent of all trips at 80 to 93 percent retention. The largest group retains at 18 percent. We left with 7 ranked experiments and knew exactly who each was for.

At Sygic Road Lords, this kind of work went with a 16.7 point lift in first-day keep-rate and a 20.3 point lift at day 30.