App Analytics

App analytics is the practice — and the category of tools — for measuring what users do inside a mobile app. It captures behavioral events (screens viewed, buttons tapped, levels completed, purchases made) and turns them into funnels, [[cohort-analysis]], [[retentionGlossaryRetention (D1 / D7 / D30)The percentage of users from a cohort who return to the app N days after install. D1 = day-1 retention, D7 = 7-day, D30 = 30-day. The single most important non-reven...]] curves, and segments that explain how and why people use the product.

Analytics vs attribution

These are often confused. [[install-attribution]] answers "where did this user come from?" — which ad, campaign, or channel drove the install. App analytics answers "what does this user do once inside?" — the in-app behavior. Acquisition tools ([[mmpGlossaryMobile Measurement Partner (MMP)A third-party attribution provider (AppsFlyer, Adjust, Singular, Branch, Kochava) that adjudicates which ads drove which installs across multiple ad networks, with i...]]s) own the first question; analytics platforms own the second. Mature teams run both and join them, so they can see not just which channels drive installs but which drive engaged, retained, high-LTVGlossaryLifetime Value (LTV)The total revenue (or gross profit) you expect a single user to generate over their entire relationship with the app — the denominator of every UA decision. users.

The building blocks

• Event tracking — the raw record of user actions, the foundation everything else is computed from.
• Funnels — conversion through a defined sequence (e.g. onboardingGlossaryOnboardingThe first-time user experience that guides new installs from app launch to the "aha moment" — where they understand the product's value and become more likely to ret... → activationGlossaryActivationThe percentage of installs who complete a defined "activation event" — the action that converts a passive install into an active user. → purchase), exposing drop-off.
• [[cohort-analysis]] — grouping users by a shared start event to compare behavior over time.
• [[retention]] & engagement — return-rate curves, [[sessionGlossaryApp SessionA single period of continuous user activity in an app, from open to close (or background past an inactivity timeout) — the atomic unit on which most engagement metri...-length]]/[[session-frequency]], [[dauGlossaryDaily Active Users (DAU)The count of unique users who were active in the app on a single calendar day. The most-watched daily engagement signal in mobile apps.-mauGlossaryMonthly Active Users (MAU)The count of unique users active in the app across a 30-day window. The denominator of stickiness and the top-of-funnel reach metric.]] stickinessGlossaryStickiness (DAU / MAU)The ratio DAU ÷ MAU — how often a typical monthly user comes back within the month. 0.5 = average user is active 15 days out of 30..
• Segmentation — slicing all of the above by user properties to find what differentiates good cohorts.

The platform landscape: Amplitude, Mixpanel, Firebase, and Heap are the common product-analytics platforms, integrated via [[sdkGlossarySDK (Software Development Kit)A packaged set of code, libraries, and tools a developer drops into an app to add a capability — the integration unit for attribution, analytics, ads, and monetizati...]] and typically paired with an MMP for the acquisition side. The strategic payoff is closing the loop — connecting acquisition source to downstream behavior so spend flows toward users who actually retain and monetize, not just install.