Navigator
Startup
Product & Usage Analytics

Feature Adoption: Are Customers Using the Features You Built?

Tracking which features get used vs. ignored—and what it means for your product roadmap.

• Navigator Team
feature adoption product usage feature tracking product roadmap

You spent 3 months building a feature.

You launched it.

You’re excited.

Then you look at the data: 5% of customers are using it.

95% haven’t even tried it.

This is the feature adoption problem: You built something customers don’t use.

Why Feature Adoption Matters

Feature adoption tells you:

1. Are customers getting value?

  • If 90% of customers use feature A and 10% use feature B, feature A is driving value
  • Feature B might be solving the wrong problem

2. Should you keep building?

  • If you release features and 80% adoption: Keep building more (customers want this direction)
  • If you release features and 10% adoption: Something is wrong (either the feature is broken, or you’re building wrong things)

3. Is onboarding working?

  • If new customers use core feature within week 1: Good onboarding (they understand how to use it)
  • If new customers don’t use core feature: Bad onboarding (they don’t understand the product)

4. Are you creating bloat?

  • If 50 features are all >50% adoption: Clean, focused product
  • If 100 features and 50 have <10% adoption: Bloated, confusing product

High-adoption products are simpler and easier to understand. Low-adoption products have tons of unused complexity.

Measuring Feature Adoption

Track adoption for each major feature:

Metric 1: % of customers who have ever used the feature

  • Feature A: 85% of customers have tried it
  • Feature B: 45% of customers have tried it
  • Feature C: 15% of customers have tried it

This is cumulative (includes all historical users).

Metric 2: % of active customers using the feature monthly

  • Feature A: 70% of monthly active customers use it
  • Feature B: 30% of monthly active customers use it
  • Feature C: 5% of monthly active customers use it

This is point-in-time (current usage).

Metric 3: Frequency of usage

  • Feature A: Used 5x per week on average
  • Feature B: Used 2x per week on average
  • Feature C: Used once per month on average

Frequency matters. Using something once per month is different from using it 5 times per week.

Building a Feature Adoption Matrix

Map adoption against importance:

FeatureImportanceAdoptionStatus
Core searchCritical95%Excellent
FiltersImportant80%Good
Saved searchesImportant30%Problem
APINice-to-have10%Low priority
Custom brandingNice-to-have5%Consider removing

Quadrant 1 (Critical + High Adoption): Your core. Protect it. Make sure it doesn’t break.

Quadrant 2 (Important + Low Adoption): Problem. Why aren’t customers using it?

  • Is it hard to discover (buried in menus)?
  • Is it hard to understand (unclear what it does)?
  • Is it broken (doesn’t work well)?
  • Fix the problem or remove the feature.

Quadrant 3 (Nice-to-have + Low Adoption): Low priority. Don’t invest here. Consider removing to reduce bloat.

Quadrant 4 (Nice-to-have + High Adoption): Unexpected winner. Customers love it. Maybe it should move up in importance.

Reasons for Low Adoption

Reason 1: Discoverability

Customers don’t know the feature exists.

It’s buried in a submenu, or there’s no onboarding flow that shows it.

Solution:

  • Add it to the main UI
  • Create an onboarding tour that highlights it
  • Send email when they should use it (“You just [action], have you tried [feature]?”)

Reason 2: Unclear value

Customers see it but don’t understand why they’d use it.

The feature name is confusing. The description is unclear.

Solution:

  • Rename it to be more intuitive
  • Add inline help (“What is this?”) that explains the value
  • Show examples or use cases

Reason 3: Difficult to use

Customers understand it but can’t figure out how to use it.

UI is confusing. Workflow is unintuitive.

Solution:

  • Simplify the workflow
  • Add a guided tutorial
  • Add error messages that help (“That didn’t work because X, try Y instead”)

Reason 4: Doesn’t solve their problem

You built feature A thinking it would solve problem X.

But customers have problem Y, which feature A doesn’t address.

Solution:

  • Talk to customers to understand their real problem
  • Pivot the feature to address their actual need
  • Or accept that this feature isn’t for them

Reason 5: Broken or buggy

Feature doesn’t work well. It’s slow, crashes, or produces wrong results.

Customers try it once, it fails, they never use it again.

Solution:

  • Fix the bugs
  • Improve performance
  • Test before launch

Adoption by Cohort

Feature adoption varies by customer type:

New customers: Often high adoption of core features, low adoption of advanced features (they haven’t learned them yet)

Power users: Very high adoption of all features

Light users: Low adoption (they only use core feature)

SMB customers: Might only use 20% of features (the essential ones)

Enterprise customers: Might use 80% of features (they need complex workflows)

Track adoption by customer type. It reveals what different segments actually need.

If SMB customers only use 20% of features, maybe you should have a “Starter” product with just those 20 features.

The Feature Graveyard

Every product has features that nobody uses.

  • Dark mode (3% adoption)
  • Advanced filters (8% adoption)
  • API webhooks (2% adoption)
  • Custom report builder (5% adoption)

Question: Should you remove them?

Remove if:

  • <5% adoption for 12+ months
  • Zero growth in adoption (not getting better over time)
  • Costs money to maintain (premium database, support burden)

Keep if:

  • Growing adoption trend (slow but getting better)
  • High-value customers need it (even if small segment)
  • Costs nothing to maintain (dead code, no active support)

Low-adoption features that cost money should be removed. Low-adoption features that cost nothing can stay (they’re not hurting).

Using Adoption to Guide Product Roadmap

Let adoption data guide what you build next:

Data: “80% of users use search, but 40% abandon it and use filter instead” → Action: Improve search experience, don’t build more features

Data: “30% adoption of saved searches, but among power users it’s 90%” → Action: Maybe saved searches isn’t for everyone, but is critical for power users. Improve it for them.

Data: “Users request feature X, but 40% adoption of feature Y (which solves the same problem)” → Action: Don’t build feature X. Improve feature Y so more people discover it.

Data: “Cohort A uses core feature daily, Cohort B uses it weekly” → Action: Cohort A gets value faster. Study what’s different and improve onboarding for Cohort B.

The Adoption Cliff

Some features have high adoption initially, then drop sharply.

Example:

  • Day 1: 70% of new customers try the feature
  • Day 7: 30% of new customers still use it (dropped 57%)
  • Day 30: 10% of new customers still use it (dropped 67%)

This is normal for exploratory features (users try it, decide it’s not for them, move on).

But if adoption of a core feature drops like this, something is wrong:

  • Feature doesn’t deliver promised value
  • Feature is too complex
  • Users found a simpler alternative

Investigate. Fix it or remove it.

The Takeaway

Feature adoption tells you what’s actually valuable to customers vs. what you think is valuable.

Build adoption tracking into your product. Know which features get used and which don’t.

Use this data to:

  • Decide what to improve (high-adoption features need better UX)
  • Decide what to remove (low-adoption features create bloat)
  • Decide what to build next (build more of what customers use)

High-quality products have high feature adoption. Bloated products have low adoption.

We help you track feature adoption, identify low-adoption problems, and use adoption data to guide your product roadmap.