Define what repeat use looks like
We identify the core value action, its natural frequency, and who should be counted, then test which repeat pattern best predicts retention. Event audits come first, so the metric rests on clean data.
See the agentsA first useful result matters most when it brings someone back. We find the early actions that predict retention, make the first win easier to reach, and test what gives people a reason to return for a second one.
Get startedWe identify the core value action, its natural frequency, and who should be counted, then test which repeat pattern best predicts retention. Event audits come first, so the metric rests on clean data.
See the agentsWe look for the smallest early outcome linked to lasting customer value, mapped to the job the customer came to do. That becomes the target product and onboarding work builds toward.
See the agentsWe flag obstacles between signup and first value, then suggest timely product help, education, notifications, or human support based on where each person is. Experiments confirm which interventions move people forward.
See the agentsWe find what brings customers back after their first win, from templates and defaults to lifecycle prompts. Each intervention has a reason to appear, and the work is ranked against the constraint holding growth back this week.
See the agentsWe connect early and repeat behavior to conversion, retention, and Stripe revenue across cohorts and acquisition sources. You see which habits pay off and which channels bring people who form them.
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