Experiment & Release Readout
An independent analysis of whether a release changed repeat behaviour, for which users, and with what limits on interpretation.
Independent analytics consultancy · London
Engine Nodecore examines retention curves, acquisition cohorts and in-app behaviour so product teams can separate a genuine habit from a noisy spike.
Flagship engagement
We audit event definitions, reconstruct comparable cohorts, inspect survival and return patterns, and trace the behaviours associated with durable use. Your team receives a concise decision brief, annotated analysis and a live readout.
Typical fit: consumer and B2B apps with live usage data, an established event stream and a retention question that has become difficult to resolve internally.
See scope and preparationThree useful lenses
An independent analysis of whether a release changed repeat behaviour, for which users, and with what limits on interpretation.
A practical audit of event meaning, user identity, property coverage and release continuity before those defects distort a retention decision.
A structured reading of who returns, when usage stabilises, and which early behaviours are associated with durable value.
How the review runs
Agree the user, value event, observation window and decision the analysis must support.
Inspect identity, event semantics, release markers and gaps before drawing a cohort.
Compare acquisition dates, segments and behaviour sequences without hiding uncertainty.
Turn findings into named product questions, measurement repairs and testable follow-up work.
Client note
“The useful part was not another retention chart. It was learning that our apparent week-four drop was largely an identity merge problem, then seeing the remaining behavioural pattern clearly enough to change onboarding.”
— Head of Product, UK subscription app
Start with the knot
Tell us which users, period and decision matter. We will reply within two working days with fit, likely inputs and a sensible first engagement.
Submit an analytics brief