Single-day comparisons mostly measure noise. Look at the direction over a period long enough to survive a bad Tuesday, and be explicit about what else changed in that window.
More in Analytics
- 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.