Our retention numbers looked decent. 67% of users were still active after 90 days. But "active" meant they'd logged in at least once in the past 30 days.
When I dug deeper into usage patterns, I found something concerning:
Average usage frequency: 1.8 times per week DAU/MAU ratio: 28%
Users would log in, accomplish a task, log out, and forget about us until they needed that specific task again.
We weren't a habit. We were a tool they remembered occasionally.
The CEO asked a simple question that changed our product strategy: "How do we become something users can't imagine their day without?"
The answer wasn't building more features. It was understanding how habits form and redesigning our product around those principles.
Six months later: Usage frequency: 4.2 times per week DAU/MAU: 51% 90-day retention: 82% (up from 67%)
Here's what we learned about building product stickiness.
The Wake-Up Call: Our Product Was Forgettable
I started by interviewing 25 users with different usage patterns:
- 8 daily users (our dream customers)
- 10 weekly users (majority of our base)
- 7 monthly users (at risk of churning)
I asked everyone the same question: "Walk me through the last time you used our product. What triggered you to open it?"
Daily Users Said:
"I check it every morning before standup. It's part of my routine."
"I have it open all day. I'm constantly referring to it."
"I get a notification about [specific thing], and I click through to check it."
Common thread: Product was integrated into existing workflows or had triggered reminders.
Weekly Users Said:
"I use it when I need to [specific task]. Usually once or twice a week."
"I remember to check it when [specific event happens]."
"I mean to use it more often, but I forget it exists unless I have a specific reason."
Common thread: Product solved a specific problem but wasn't part of daily routine.
Monthly Users Said:
"I signed up because it looked useful, but honestly I forget about it."
"I got an email saying I hadn't logged in recently, so I checked it out."
"I only use it when [very specific, infrequent scenario]."
Common thread: No regular trigger to use product. Relied on occasional reminders.
The insight: Daily users had external triggers (notifications, calendar reminders) or had built the product into existing habits. Weekly and monthly users relied on memory and sporadic needs.
If we wanted to increase stickiness, we needed to create triggers and make the product fit into existing routines.
Understanding the Habit Loop
I read everything I could find about habit formation: BJ Fogg's behavior model, Nir Eyal's Hooked framework, Charles Duhigg's habit loop research.
The common pattern:
Trigger → Action → Reward → Investment → Trigger
Trigger: External cue (notification, email, calendar) or internal cue (feeling, need, routine) Action: User opens product and does something Reward: User gets value from the action Investment: User puts something into the product that makes it more valuable next time Trigger: The investment or reward creates new trigger for next use
Our product had the Action and Reward part down. Users could accomplish tasks and got value.
We were failing at Triggers and Investment.
Users had no regular trigger to open the product, and they weren't investing in ways that made them want to come back.
Redesigning Around Habit Formation
We made four major changes to build habit loops:
Change 1: Created Daily Triggers
Old approach: Users opened product when they remembered it or needed it
New approach: Daily triggers that pulled users back
Triggers we added:
Morning digest: Email sent at 8am with personalized insights
- "Your key metrics from yesterday"
- "3 things that need your attention today"
- "How you're trending vs. last week"
Critical: Email wasn't generic. It showed their specific data, their specific trends. Opening it was valuable even if they didn't click through.
Smart notifications: In-product notifications tied to their workflow
- "New data available for your Monday report" (sent Monday morning)
- "Your weekly goal is 78% complete" (sent Thursday to create urgency)
- "Team member @mentioned you in [project]" (immediate)
Critical: Notifications were relevant and timely, not spam. Users could customize frequency.
Calendar integration: For users who ran regular reports, we added calendar integration
- "Time to run your weekly revenue report" (appeared on their calendar)
- One-click from calendar event to pre-configured report
Results:
- Users with daily triggers: 4.8x usage frequency vs. users without
- DAU/MAU improved from 28% → 41% after trigger implementation
Change 2: Made the Product Fit Existing Routines
Insight from daily users: They didn't build new routines around our product. They inserted our product into existing routines.
"I check it during my morning coffee while reviewing yesterday's metrics." "I have it open during team standups." "I look at it right before my 1:1 with my boss."
We made it easier to fit into these existing moments:
Slack integration:
- Daily digest posted to Slack channel at standup time
- Quick commands: "/report revenue" generated report in Slack
- Alerts for key events posted to relevant channels
Mobile app optimization:
- Fast load times (<2 seconds)
- Most common actions accessible in 2 taps
- Offline mode so commute time was productive
Browser extension:
- Quick access from any page
- Glanceable metrics in toolbar
- One-click to common reports
Results:
- Slack integration users: 62% DAU (vs. 28% overall)
- Mobile users: 3.2x more daily sessions
- Browser extension users: Daily usage increased 2.4x
Change 3: Built Investment Mechanisms
The habit loop research was clear: The more users invest in a product, the more likely they are to return.
Investments we encouraged:
Customization:
- Custom dashboards (took 10-15 minutes to set up)
- Saved reports and analyses
- Personalized alerts and thresholds
Users who customized dashboards had 71% higher retention because they'd invested time and didn't want to recreate that elsewhere.
Data accumulation:
- Historical trend analysis (more data = more valuable insights)
- Year-over-year comparisons
- Predictive analytics based on their historical patterns
The longer users stayed, the more historical data they had, the more valuable the product became. Switching would mean losing that history.
Social connections:
- Team workspaces
- Shared dashboards
- Collaborative annotations
Users who invited teammates had 89% retention because churning meant disrupting team workflow.
Learned preferences:
- Product learned which reports they viewed most
- Predicted what they'd want to see based on past behavior
- Auto-generated insights tailored to their interests
Results:
- Users with 3+ investments: 84% retention at 90 days
- Users with 0 investments: 41% retention at 90 days
Change 4: Created Variable Rewards
Habit research shows: Variable rewards (unpredictable positive outcomes) are more engaging than predictable rewards.
We added elements of discovery:
Insight surfacing:
- "We noticed something unusual in your data..." notifications
- Anomaly detection: "Sales in Region X up 40% vs. last week"
- Opportunity alerts: "You're trending toward your best month ever"
Users opened these because they were curious what we'd found. And the insights were genuinely valuable, not manufactured engagement bait.
Comparative benchmarks:
- "You're in top 10% of users for [metric]"
- "Your growth rate is 2.3x industry average"
- "Similar companies are seeing [trend]"
Progress celebration:
- Milestone achievements: "You've tracked 1,000 customers!"
- Streak tracking: "7-day streak checking your dashboard"
- Goal progress: "You're on track to hit your quarterly target"
Results:
- Users who received insight notifications: 2.1x more likely to return next day
- Variable reward engagement: 38% higher than static reports
The Results: From Tool to Habit
After 6 months of habit-focused redesign:
Engagement metrics:
- DAU/MAU: 28% → 51%
- Average usage frequency: 1.8x/week → 4.2x/week
- Session length: Increased 18% (users spending more time per visit)
Retention metrics:
- 30-day retention: 74% → 85%
- 90-day retention: 67% → 82%
- 12-month retention: 51% → 68%
Business metrics:
- Churn rate: -37% reduction
- Expansion revenue: +42% (sticky users upgraded more)
- NPS: 42 → 61
The product became indispensable instead of occasionally useful.
What We Learned About Building Habits
Lesson 1: Triggers Are More Important Than Features
We used to prioritize feature development over engagement features.
This was backwards.
The best product in the world won't succeed if users forget it exists.
Triggers (notifications, emails, integrations) became our highest-priority roadmap items because they determined whether users came back.
Lesson 2: Fit Into Existing Habits, Don't Create New Ones
We initially tried to create new habits: "Check our dashboard every morning!"
That failed. Creating new habits is hard.
What worked: Fitting into existing habits. Users already checked Slack every morning. We put our content there. Users already had standup meetings. We made it easy to pull our data into those meetings.
Don't ask users to change their routines. Insert your product into routines they already have.
Lesson 3: Investment Creates Stickiness
The more users customized, configured, and added data to our product, the harder it became to leave.
Switching costs aren't just about migration effort. They're about losing:
- Custom configurations
- Historical data and trends
- Team collaboration context
- Learned preferences
Build features that make users invest in your product, and they'll become sticky naturally.
Lesson 4: Habit Formation Takes Time
We measured habit formation by tracking "% of users with 7-day usage streak."
Week 1: 12% had 7-day streak Week 4: 23% had 7-day streak Week 12: 34% had 7-day streak
Habits don't form overnight. Our triggers and rewards needed to consistently deliver value for weeks before behavior became automatic.
Patience is required. Don't give up on habit-building features after two weeks.
Lesson 5: Not Every Product Needs Daily Usage
Some products don't need daily habits. A contract management tool might be monthly. An HR platform might be quarterly.
The question isn't "how do we get daily usage?" It's "what's the natural frequency of the problem we solve?"
For us, the problems users solved were daily (checking metrics, tracking performance). So daily habits made sense.
Match your stickiness strategy to your product's natural usage frequency.
How to Build Stickiness Into Your Product
Here's the framework:
Step 1: Understand Current Usage Patterns
Pull usage data for past 90 days. For each user:
- How often do they use the product?
- What triggers their usage? (Scheduled? Need-based? Random?)
- What actions do they take?
- How long do they stay?
Segment users by frequency:
- Daily users: X%
- Weekly users: X%
- Monthly users: X%
- Inactive: X%
Step 2: Interview Each Segment
Ask:
- "When was the last time you used [product]?"
- "What triggered you to open it?"
- "What made you come back the next time?"
- "Have you integrated it into any routines?"
Look for patterns in daily users that are absent in weekly/monthly users.
Step 3: Identify Trigger Opportunities
External triggers:
- Email digests at optimal times
- Smart notifications based on user behavior
- Calendar integrations
- Slack/Teams integrations
- Mobile push notifications
Internal triggers:
- What emotions or needs drive usage?
- What existing routines can you fit into?
Step 4: Build Investment Mechanisms
Make users invest in ways that increase switching costs:
- Customization (dashboards, reports, settings)
- Data accumulation (more data = more value)
- Team collaboration (multi-user switching cost)
- Learned preferences (product gets smarter over time)
Step 5: Create Variable Rewards
Don't just show users what they expect. Surprise them with:
- Unexpected insights in their data
- Achievements and milestones
- Comparative benchmarks
- Opportunity alerts
Variable rewards keep users coming back out of curiosity.
Step 6: Measure Habit Formation
Track:
- DAU/MAU ratio (industry benchmark: 20-50%)
- % of users with 7-day usage streak
- Time between sessions (decreasing = habit forming)
- % of users who customize/invest
Goal: See these metrics trend upward over 90 days.
The Uncomfortable Truth About Stickiness
Most product teams focus on acquisition and activation while ignoring retention and stickiness.
This is backwards.
A sticky product with modest acquisition beats a leaky product with great acquisition every time.
Example math:
Product A (high acquisition, low stickiness):
- 1,000 signups/month
- 50% activate
- 30% retain at 90 days
- Result: 150 retained customers per cohort
Product B (modest acquisition, high stickiness):
- 500 signups/month
- 60% activate
- 70% retain at 90 days
- Result: 210 retained customers per cohort
Product B grows faster despite 50% fewer signups because stickiness compounds.
The best product teams:
- Prioritize triggers and engagement features
- Build investment mechanisms that increase switching costs
- Fit into existing user routines instead of creating new ones
- Measure DAU/MAU and habit formation metrics
- Accept that habit formation takes months, not weeks
The teams with low stickiness:
- Focus only on acquisition and activation
- Build features without considering engagement triggers
- Assume users will remember to use the product
- Don't measure usage frequency or patterns
- Give up on engagement features after a few weeks
I was on the second team until our decent retention masked low engagement and we realized users weren't actually addicted to our product.
Now I design every product around habit loops from day one.
Because features get users to try your product once. Habits get them to use it every day.
And products that become daily habits don't churn.