What are user engagement metrics?
User engagement metrics are the measures a product team uses to track how often, how deeply and how consistently people use a product, such as active users, stickiness, session depth, feature adoption and retention. Read together, they show whether users keep getting value.
Which user engagement metrics should you track?
Track a small set that covers frequency, depth, breadth and staying power. These are the user engagement metrics most product teams rely on:
| Metric | How to calculate it | What it tells you |
|---|---|---|
| Active users (DAU, WAU, MAU) | Unique users who did a key action in the day, week or month | How many people get value in a period |
| Stickiness (DAU/MAU) | Average daily actives ÷ monthly actives | How often monthly users come back |
| Session frequency | Sessions per active user per week | How habitual use is |
| Session depth | Key actions per session | Whether a visit gets work done |
| Feature adoption rate | Users of a feature ÷ active users | Which parts of the product people rely on |
| Cohort retention | Users still active in week N ÷ users who started in that cohort | Whether value lasts |
Worked example for feature adoption: a product has 2,000 monthly active users, and 500 of them used saved reports this month. Feature adoption for saved reports is 500 ÷ 2,000 × 100 = 25% of active users. If the feature exists to save weekly users time, 25% is worth a closer look.
"Active" should mean a key action that delivers value (created, sent, shared, approved), not a login or a page view. Most bad engagement dashboards start there.
How do you choose the right engagement metrics?
Match them to how the product is meant to be used:
- Cadence. A daily tool gets DAU and DAU/MAU. A weekly or monthly tool (payroll, reporting, planning) gets WAU or MAU and retention by cohort, or it will always look weak.
- Role. Admins, everyday users and occasional approvers need separate cuts. Blend them and every number drifts toward the middle.
- Outcomes. Time in app and page views go up when users are stuck. Prefer counts of completed work.
- Comparison. Outside benchmarks rarely transfer, because definitions of "active" and "key action" vary so much. Compare each cohort with your own earlier cohorts on the same definition.
Pick one or two to report weekly and keep the rest for diagnosis.
What do user engagement metrics leave out?
Every metric in the table counts actions. None of them records what the user was trying to do, or what they wanted and couldn't find. A rise in session depth can mean users are getting more done or clicking around looking for something.
Intent fills that gap. When users can ask an in-product agent for what they want in plain language, each request records the goal. Foldspace's product analytics (funnels, retention and session replay) counts the actions, and Conversational Analytics adds what the table leaves out: the intent behind each request to the Product Agent and whether each conversation ended resolved, unresolved or escalated.