Stickiness: How Many Users Come Back?

You acquire a user on Monday.

Will they come back?

Day 1 retention: Did they come back Tuesday? (45% do)

Day 7 retention: Did they come back within the first week? (30% do)

Day 30 retention: Did they come back within the first month? (20% do)

These are retention curves, and they tell you if your product is sticky (habit-forming) or one-time experience.

Defining Stickiness Metrics

Day 1 retention (D1): % of users who return 1 day after signup

Day 7 retention (D7): % of users who return at least once in days 2-7

Day 30 retention (D30): % of users who return at least once in days 2-30

Rolling retention: % of users active each day, looking back 30 days

Each tells you something different about stickiness.

Stickiness Benchmarks

Social media (Twitter, Instagram, TikTok):

D1: 40%+ (most users come back next day)
D7: 30%+ (most weekly active)
D30: 20%+ (good monthly retention)

Productivity tools (email, Slack):

D1: 50%+ (habit-forming for work)
D7: 40%+ (strong weekly habit)
D30: 30%+ (strong monthly habit)

Entertainment (gaming, video):

D1: 25-40% (depends on game)
D7: 15%+ (casual games lower, addiction games higher)
D30: 5-10% (long-tail, some keep playing)

SaaS (CRM, project management):

D1: 30-40% (used by teams, not always daily)
D7: 20-30% (weekly active)
D30: 15-25% (monthly active)

E-commerce:

D1: 5-10% (shopping is infrequent)
D7: 10-15%
D30: 20-30% (monthly shoppers)

If your D1 retention is below the benchmark for your category, your product isn’t sticky.

The Retention Curve Shapes

Steep decline (hockey stick shape):

|
|     *
|     *  *
|     *     *
|     *        *
|     *           *
|_____*________________*____
  Day1 Day7 Day30 Day90

Many users come back Day 1, fewer by Day 7, even fewer by Day 30.

This is typical and expected. The goal is how flat the tail is (are some users coming back long-term?).

Gradual decline:

|
|  *
|  *  *
|  *    *
|  *      *
|  *        *
|__*__________*____
  Day1 Day7 Day30 Day90

Similar to steep decline, but flatter. Users stick around a bit longer.

This is better. It means more users are forming habits.

Cliff drop (dangerous shape):

|
|  *
|  *
|  *
|  *
|  
|  (empty)
|__*________________
  Day1   Day7+

Users come back Day 1, but almost none come back by Day 7.

This is a danger sign. Something breaks the habit after Day 1.

Investigate: What happens between Day 1 and Day 7 that makes users leave?

Plateau (ideal shape):

|
|  *
|  *  *
|  *    *
|  *      *
|  *        * * * *
|__*____________*___
  Day1 Day7 Day30 Day90

Initial decline, then stabilizes at 10-15% long-term.

This means you have a “core” of power users who stick around forever, plus many casual users who try and leave.

This is healthy.

Calculating Retention Curves

Method 1: Cohort-based

Track each signup cohort separately:

Cohort	Day 1	Day 7	Day 30	Day 90
Week 1 (100 users)	45 (45%)	30 (30%)	20 (20%)	15 (15%)
Week 2 (120 users)	50 (42%)	33 (27%)	22 (18%)	16 (13%)
Week 3 (110 users)	48 (44%)	32 (29%)	21 (19%)	14 (13%)

Cohort-based is the most accurate because you’re comparing the same users through time.

Method 2: Rolling/aggregated

Count all users active today and today-90 days ago:

Users active today: 500
Users active 90 days ago: 1,000
D90 retention: 500/1,000 = 50%

Rolling retention is faster to calculate but less precise (it mixes cohorts).

Use cohort-based for strategy. Use rolling for dashboard tracking.

Stickiness by Feature

Some features create stickiness more than others.

If your product has a habit-forming feature, users who use it have better retention.

Example:

Slack:

Users who send 1+ messages/day: 95% D7 retention (habit-forming)
Users who send <1 message/week: 10% D7 retention (not a habit)

The feature (direct messaging) creates the habit.

Facebook:

Users who get 5+ notifications/day: 60% D7 retention
Users who get 0 notifications/day: 20% D7 retention

Notifications drive return behavior.

Identify which features drive stickiness in your product. Invest in making those features strong.

The Activation-Stickiness Connection

Activation = getting users to a core action

Stickiness = getting users to come back

These are connected.

If users activate (create their first X, send their first email, complete setup), they’re more likely to come back.

Example:

Users who activate:

D1 retention: 60%
D7 retention: 45%
D30 retention: 30%

Users who don’t activate:

D1 retention: 15%
D7 retention: 5%
D30 retention: 2%

Improving activation from 30% to 50% of new users could double your overall retention.

Improving Stickiness

1. Create habits

Use psychological principles (variable rewards, streaks, social proof) to create habits.

Example: Duolingo gives points for daily streaks. Users come back daily to maintain the streak. High D1 retention.

Example: Slack shows how many unread messages you have. Users come back to catch up. High D1 retention.

2. Frequent value delivery

The more often users get value, the more often they come back.

Example: Email that delivers new content daily = daily habit. Email that delivers monthly = monthly habit.

3. Social reinforcement

If friends are using it, users come back more often.

Example: Snapchat—most value when friends use it too. High D1 retention if friends are active.

4. Notifications

Smart notifications (not spam) remind users to come back.

Push notification that says “You have 5 unread messages” → User comes back.

Notification every 5 minutes → User disables notifications and leaves.

5. Gamification

Points, badges, streaks, leaderboards create loops that pull users back.

Streak: “You’ve logged in 14 days in a row! Don’t break it!”
Badges: “Congratulations, you hit 100 tasks completed!”

The Danger: Over-Optimization for Retention

One warning: You can make a product “sticky” through manipulation (excessive notifications, dark patterns) that users don’t actually like.

Users might come back every day, but they’re frustrated and eventually churn.

Balance: Create real habits through genuine value, not manipulation.

The Takeaway

Stickiness (retention curves) reveals if your product is creating habits or just one-time experiences.

Track D1, D7, D30 retention for each cohort. Compare to benchmarks.

Steep drops in first 7 days = problem (fix onboarding or product clarity).

Plateau after 30 days = healthy (you have core users + casual users, which is normal).

Focus on improving D1 and D7. These are the critical habit-formation windows.

We help you calculate retention curves, identify drops in stickiness, and diagnose what’s preventing users from forming habits.