Not all engagement is equal. Tracking only whether users are active misses the bigger question: how meaningfully? Engagement metrics separate breadth — how many — from depth — how substantively.
▸ Try the interactive toolNot all engagement is equal. PMs who track only whether users are active miss the more important question: how meaningfully are they engaging? Engagement metrics distinguish breadth — how many users engage and across how much of the product — from depth — how substantively they engage with what matters.
The classic breadth metric is the DAU/MAU ratio (daily over monthly active users), a measure of 'stickiness' — how often active users return. But breadth alone can mislead: a user who opens the app daily but does nothing valuable is 'engaged' by activity counts and disengaged in reality. Depth metrics — actions per session, use of core features, meaningful outcomes achieved — capture whether the engagement is substantive. The richest picture comes from pairing the two: are users engaging often (breadth) and meaningfully (depth)?
Breadth — how many engage, how often (e.g. DAU/MAU stickiness).
Depth — how substantively they engage (core actions, meaningful outcomes).
Activity ≠ engagement; pair the two to see if use is frequent and meaningful.
Counting active users treats opening the app as engagement — but frequent shallow activity and rare deep value are very different things the raw count can't tell apart.
| The shortcut | What it costs | What it gives you instead |
|---|---|---|
| Counting activity as engagement | 'Active' users who do nothing valuable look engaged but aren't. | Depth metrics reveal whether activity is meaningful. |
| Breadth without depth | Many users, all shallow — looks healthy, isn't. | Pairing breadth and depth shows the real engagement picture. |
| Depth without breadth | A few power users mask broad disengagement. | Breadth metrics reveal how widely engagement spreads. |
| Vanity engagement | Opens and sessions counted as success. | Meaningful-outcome metrics tie engagement to value. |
Track how many users engage and how often — DAU/MAU stickiness, active-user counts, frequency. This tells you the reach and rhythm of engagement.
Track how substantively users engage — actions per session, use of core value features, meaningful outcomes achieved. This tells you whether the engagement is real or hollow.
Be ruthless about whether a tracked behaviour reflects genuine value or just motion. 'Opened the app' is activity; 'completed the core value action' is engagement.
Read them together: high breadth + low depth means wide but shallow (a retention risk); high depth + low breadth means a small engaged core (an expansion opportunity). The combination is the diagnosis.
Connect engagement metrics to retention and value — deep engagement should predict who stays. Engagement that doesn't predict retention may be vanity (links to leading indicators, Tool 03).
A team celebrated high daily active users — a strong breadth metric — and assumed the product was deeply engaging. But depth metrics told a different story: most of those 'active' users were opening the app, glancing, and leaving without performing the core value action. They were active by the count and disengaged in reality.
Pairing breadth with depth exposed the gap. The high DAU was hollow — wide activity with little substance — and it explained why retention was weaker than the activity numbers suggested. Focusing on driving the depth metric (getting active users to actually complete the core action) turned shallow activity into meaningful engagement, and retention followed.
The deliverable is engagement measured on both axes — breadth and depth — read together to diagnose whether use is frequent and meaningful.
| Low depth | High depth | |
|---|---|---|
| High breadth | Wide but shallow — retention risk | Healthy — frequent & meaningful |
| Low breadth | Disengaged | Small engaged core — expansion opportunity |
Engagement has two dimensions, and measuring only one — usually breadth, because it's easy — produces a flattering, misleading picture. The real signal is in pairing how many with how meaningfully.
The core confusion engagement metrics resolve is activity-versus-value. A raw active-user count rewards a user opening the app and doing nothing exactly as much as a user achieving real value — so a product can look highly engaged while delivering little, which shows up later as weak retention the activity numbers never predicted. Depth metrics fix this by measuring substance: core actions taken, meaningful outcomes achieved. And the breadth×depth grid turns the pair into a diagnosis — wide-but-shallow flags a retention risk, deep-but-narrow flags an expansion opportunity. The ultimate test, echoing leading indicators, is whether engagement predicts retention: engagement that doesn't is probably vanity, and engagement that does is the leading signal of a healthy product.
'Active' can mean 'opened and left.' Measure depth, not just presence.
Wide shallow engagement looks healthy and isn't. Pair with depth.
A deep core can mask broad disengagement. Pair with breadth.
If engagement doesn't predict retention or value, it may be vanity. Tie it to outcomes.
Engagement is one of HEART's five dimensions (Tool 06); this deepens it into breadth and depth.
Deep engagement should be a leading indicator (Tool 03) of retention (Tool 08).
Engagement metrics are often key input metrics driving the North Star (Tool 05).
Activity-as-engagement is a classic vanity-metric trap (Tool 27).
Pick a product. Name one breadth metric (how many/how often users engage) and one depth metric (how meaningfully — a core action or outcome).
Imagine the breadth metric is high but the depth metric is low. What does that tell you, and would the active-user count alone have revealed it?
If high breadth with low depth turns out to be a retention risk hiding behind a healthy-looking activity number, you've seen exactly why engagement needs both dimensions.