| name | retention-strategy |
| description | Use when the user wants to reduce churn, improve user retention, increase engagement, or understand why users stop using their product. Also use when the user mentions 'churn,' 'retention,' 'why users leave,' 'engagement,' 'DAU/MAU,' 'stickiness,' 're-engagement,' or 'keeping users.' |
Retention Strategy
Retention is not a feature. It is the result of users repeatedly getting value from your product. Retention strategy is the set of decisions that make that value delivery reliable and habitual.
The Retention Curve
Plot retained users (%) over time (days/weeks) for a cohort:
- Flat curve at a non-zero value — product has a retained audience; optimize to lift the floor
- Curve approaching zero — product has not found its retained audience; fix value delivery before any other growth work
- Bump at day 1, drop by day 7 — activation problem: users try it, don't get value fast enough
- Gradual decline — engagement problem: users activate but habit doesn't form
Rule: Fix the shape of the retention curve before investing in acquisition. Pouring users into a leaky bucket is waste.
Retention Levers by Stage
Day 0-1: Activation
- Reduce time to first value
- Guide user to the aha moment immediately
- Minimize setup friction
Day 1-7: Habit Formation
- Trigger return visits with genuine value (not just notifications)
- Show progress made since last visit
- Surface the next obvious action
Day 7-30: Depth
- Introduce more of the product's value surface
- Connect user to social/team elements (multiplies retention)
- Personalize based on usage patterns
Day 30+: Expansion
- Identify power users and expand their usage
- Introduce adjacent jobs the product can do
- Create switching costs through integrations, data, or network effects
Churn Investigation Protocol
When churn spikes or sits above acceptable levels:
- Segment churned users — by acquisition channel, cohort, plan, company size
- Find the pattern — which segment churns fastest? In what timeframe?
- Interview churned users — 5-10 interviews. Question: "Walk me through the moment you decided to stop using [product]."
- Audit activation — did churned users reach the aha moment before leaving?
- Check usage before churn — did usage drop before churn? (leading indicator for at-risk users)
The Habit Loop (Nir Eyal — Hooked)
Trigger → Action → Variable Reward → Investment
- Trigger: What brings the user back? (external: notification; internal: emotion/habit)
- Action: Simplest behavior in anticipation of reward
- Variable reward: Unpredictable, relevant reward that satisfies and creates curiosity
- Investment: User puts in effort/data/relationships that increase future value
Products with high retention have internal triggers (user thinks of the product unprompted). Products with low retention rely entirely on external triggers (notifications users ignore).
Re-engagement Campaigns
For users who've gone dormant (no activity in 7-30 days):
| Day | Trigger | Message focus |
|---|
| Day 7 | First absence | What they're missing / progress update |
| Day 14 | Extended absence | Social proof / new feature |
| Day 30 | At-risk | Direct outreach if high-value account |
Stop re-engagement after 3 attempts. Continued outreach trains users to ignore your emails.
Common Rationalizations
| Rationalization | Reality |
|---|
| "Retention will improve when we add more features" | Features add complexity. Retention improves when the core value is delivered faster and more reliably. |
| "Our churn is normal for the category" | Normal churn is a floor, not a target. The companies winning your category have below-normal churn. |
| "We need more users to see the retention pattern" | You can see the shape of the retention curve with 100 users. Wait for 10,000 and you've wasted months. |
| "Notifications will bring users back" | Notifications remind users the product exists. Value brings them back. |
Verification
