How do I see which features drive stickiness in a SaaS product?

To see which features drive stickiness in a SaaS product, you want to link feature usage to retention behavior. The goal is to identify features that are strongly associated with users coming back over time.

Here’s a practical way to do it:

1) Define “stickiness”

Pick a retention metric that matches your product:

• DAU/WAU/MAU for engagement
• Week-1 / month-1 retention
• Repeat usage within N days
• Logo retention vs user retention
• Expansion / renewal rate if you’re looking at customer-level stickiness

A common approach is:

• Sticky users = users active in 3+ of the last 4 weeks
• Or retained users = users who return in the next 7/30 days

2) Instrument feature events

Track actions at the feature level, not just page views:

• feature_used
• feature_completed
• feature_saved
• feature_shared
• report_exported
• workflow_automated

Include:

• user ID
• account/org ID
• timestamp
• feature name
• context (plan, persona, lifecycle stage, device, etc.)

3) Compare retained vs non-retained users

For each feature, compute:

• Adoption rate among users who were retained
• Adoption rate among users who churned
• Frequency of use
• Time to first use
• Depth of use and whether it’s used repeatedly

A simple signal:

• If users who use Feature X have much higher retention than those who don’t, Feature X may be sticky.

Example:

• Users who used “automation rules” had 65% 30-day retention
• Users who never used it had 22% retention

4) Rank features by correlation with retention

Use a few lenses:

• Lift: retention among feature users vs non-users
• Predictive power: how well feature usage predicts retention
• Repeat usage: features used multiple times are often stronger stickiness drivers than one-off features
• Sequence: features used early in onboarding may be especially important

You can start with simple stats:

• retention by feature usage
• chi-square tests / proportion tests
• logistic regression

5) Control for confounders

Some features look sticky because power users already use them more. Control for:

• customer size
• plan tier
• role/persona
• onboarding completion
• tenure
• industry/use case

Better methods:

• logistic regression
• survival analysis
• matched cohorts
• propensity scoring

6) Look for “power features” and “habit features”

Different feature types matter:

• Power features: create measurable value quickly (e.g. automation, collaboration, reporting)
• Habit features: encourage recurring use (e.g. alerts, dashboards, scheduled reports)

Habit features often drive stickiness more than flashy but rare features.

7) Use cohorts and funnels

Create cohorts based on first feature used:

• cohort A: used feature X in first 7 days
• cohort B: didn’t use feature X

Then compare:

• week 1, 4, 8 retention
• activation to retained conversion
• expansion/upsell later

Also look at feature funnels:

• feature discovery → first use → repeat use → retention

8) Qualitative validation

Quantitative data tells you “what correlates,” but not always “why.” Talk to customers:

• Which features are indispensable?
• Which features would they miss if removed?
• Which features are part of their weekly workflow?

This helps separate:

• “nice-to-have” features
• true retention drivers

9) Watch out for common traps

• Vanity correlations: popular features may not cause retention
• Selection bias: advanced users use more features and also retain better
• Multicollinearity: features often cluster together
• Seasonality / usage cycles: some products are inherently periodic

10) A simple analysis starter

If you want a quick first pass:

1. Define retention window, e.g. 30-day retention.
2. For each feature, split users into:
   • used feature at least once
   • never used feature
3. Compute retention rate in each group.
4. Rank by retention lift.
5. Validate top candidates with regression or cohorts.

Example table

Feature	Retention among users who used it	Retention among users who didn’t	Lift
Automation rules	68%	25%	+43 pts
Dashboards	55%	30%	+25 pts
Comments / collaboration	52%	33%	+19 pts

That gives you a shortlist of likely stickiness drivers.

If you want, I can also help you:

• design an event taxonomy for tracking feature stickiness,
• write the SQL to measure it,
• or show you how to do it in Ampli/Segment/GA4/Mixpanel/Amplitude.