The antidote to aggregate metrics that flatter. A product can look healthy in total while every recent cohort is worse than the one before, because the older cohorts are large enough to hide it.
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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.