Mobile adds constraints the web does not have: offline sessions that arrive hours later, app store release cycles, crash and performance data that belong in the same store as the behavior. Analyzing them separately is how teams miss that a retention drop was a crash.
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- Active usersPeople who performed at least one qualifying action in a window. The window and the action are your definition, not an industry constant.
- AggregationRolling raw events into summaries. Fast, but only answers questions you asked in advance, which is why Countly keeps the raw events too.
- AI overviewA written summary of what changed in the data and why, generated from live events rather than from a monthly export.
- Anomaly detectionWatching metrics for values outside their normal range and raising them before someone notices in a weekly review.
- Behavioral analyticsAnalysis of what people actually did in the product (events in sequence) rather than of who they are on paper.
- Behavioral cohortA group defined by actions taken, such as everyone who used a feature twice in their first week. Recomputed as the data arrives.