Feature Adoption Rate Calculator
Track try rate, stickiness, depth, and retention lift across up to 20 features. Detect shelfware, rank onboarding opportunities, and grade your feature portfolio. No signup.
Last reviewed: April 2026
How many users are currently active in your product.
Average revenue per user per month. Used for dev-cost ROI analysis.
Feature Portfolio
Hover a row to highlight in chart.
| Feature | Type | Eligible | Tried | Sticky (30d) | Uses/wk | Retention Lift | Dev wks | Grade | |
|---|---|---|---|---|---|---|---|---|---|
| Power | % | B | |||||||
| Power | % | C | |||||||
% | F | ||||||||
% | F | ||||||||
% | D | ||||||||
% | F | ||||||||
% | F |
Adoption vs Retention
Bubble size = depth (uses/week). Top-right = Power Features. Bottom-right = Shelfware.
Feature Portfolio Report Card
Six dimensions of a healthy adoption portfolio.
Mid-range breadth. Run onboarding experiments on the 2 lowest-adoption core features.
Users engage but not habitually. Look for triggers that create weekly rituals.
Some features move the needle. Ruthlessly onboard users into the top-lift features first.
Half your core features are missed on first pass. Add guided tour or checklist.
Low shelfware — your roadmap is efficient.
One or two power features. Identify what they share and replicate.
Try Rate vs Adoption Rate
Large gap between try and adoption = sticky-leak. Small gap but low values = discovery-leak.
Shelfware Detector
Onboarding Opportunity Rank
Ranked by: (target try rate − current try rate) × retention lift. The top feature gives the biggest return on a guided tour or onboarding tooltip.
What-If Simulator
Simulate a product-led experiment that raises every feature's retention lift.
Simulate an onboarding improvement that keeps more users past day 30.
Changes are live-previewed in all panels above.
Reverse Calculator
Scenario Compare
The Feature Adoption Rate Formula
Feature adoption is the single most actionable per-feature metric for product managers. It answers the question every stakeholder actually cares about: "did anyone use the thing we built?"
Try Rate
Try Rate = Tried / Eligible Users
Captures discovery — did users find the feature at all?
Sticky Rate
Sticky Rate = Sticky (30d) / Tried
Captures retention past first touch — did the feature deliver on its promise?
Adoption Rate
Adoption Rate = Sticky / Eligible
The north star: combines discovery and stickiness into a single number.
A feature with 40% try rate and 80% sticky rate is a winner at 32% adoption. A feature with 80% try rate and 20% sticky rate is shelfware — even though adoption is 16%, the high try rate means users know about it but abandoned it. These are very different product problems.
Feature Types: Core, Power, and Niche
Not every feature should be used by every user. Tagging features by type produces honest benchmarks — comparing Dashboard adoption to an Advanced Permissions adoption will always make the latter look bad unless you account for intent.
The portfolio weighted score in this calculator weights Core features at 1.0, Power at 0.6, and Niche at 0.3. A Core feature at 40% adoption drags the score more than a Niche feature at 40%, because Core should be higher by definition.
The Adoption vs Retention Quadrant
Plotting features on adoption (X) against retention lift (Y) produces four quadrants that map directly to product decisions. This quadrant is the single most useful visualization in feature analytics — one glance tells you what to cut, promote, or invest in.
Shelfware: The Hidden Tax on Your Roadmap
Every shelfware feature costs more than its initial dev cost. It adds UI complexity that slows onboarding, requires ongoing maintenance, shows up in documentation, and splits engineering focus on bug fixes. A shelfware feature rarely dies — it just bleeds.
This calculator flags a feature as shelfware when try rate exceeds 20% but stickiness is below 35%. The threshold is deliberate: a low-try feature is a discovery problem, not shelfware. Shelfware specifically means users found it, tried it, and walked away.
The Shelfware Drag dimension in the report card quantifies what percentage of your total dev investment is locked up in shelfware. When that number exceeds 45% of dev capacity, the tool flags the portfolio as "Shelfware Heavy" — the most actionable zone in the entire analysis, because cutting shelfware unlocks immediate roadmap capacity.
Onboarding Opportunity Ranking
Guided tours, tooltips, and in-app checklists are expensive to build and maintain — you can only afford a few. The Onboarding Opportunity Rank solves the prioritization question with a simple formula:
Opportunity Score = (Target Try Rate − Current Try Rate) × Retention Lift
Features with a large try-rate gap AND high retention lift rise to the top. A high-lift feature already at 80% try rate has no opportunity — everyone finds it. A low-lift feature with huge discovery potential is also low opportunity — gaining adoption on it doesn't retain anyone. The top of the list is the sweet spot: features users are missing that would actually keep them.
Feature Depth: The Habit Signal
Depth (uses per week) is the habit formation signal. A feature used once a month is a destination — users go to it when they need something specific. A feature used daily is a habit — users check it without thinking.
Habits drive retention more than any other metric. Per Nir Eyal's Hook model, features must reach 2–3× weekly use to cross into habit territory. This calculator flags features with 8+ uses/week as Power Features in the scatter chart and counts them in the Power Signal dimension of the report card.
If your portfolio has zero features above 8 uses/week, you have an activation problem that no onboarding tour will fix — the features themselves don't create recurring value. This is the hardest failure mode to diagnose without explicit depth measurement, because DAU/MAU hides it.
Six Dimensions of Feature Portfolio Health
The weighted adoption score rolls up six dimensions that each capture a distinct aspect of portfolio health. A strong portfolio scores well across all six; a weak portfolio has one or two that drag the entire grade down.
The weighting reflects real-world feature investment priority: breadth and retention lift matter most because they directly compound ARR, while Power Signal gets the smallest weight because it's binary (either you have habit features or you don't).
Feature Adoption vs DAU/MAU: Why They Diverge
Strong DAU/MAU can mask a shelfware problem. Many products have 40–50% DAU/MAU driven almost entirely by one or two core features, while the remaining 80% of the feature set is shelfware. Users are engaged with the app, but not with most of what the team has built.
Feature adoption cuts through this. Instead of aggregate engagement, it asks: what is the adoption curve on every distinct feature we shipped? The answer is usually sobering — and it is the single best input for a product-led growth strategy, because it tells you which features are worth the onboarding investment and which are dragging you down.
Frequently Asked Questions
What is feature adoption rate?
Feature adoption rate is the percentage of eligible users who actively use a feature 30 days after first touch. Formula: Sticky ÷ Eligible × 100. It combines discovery (try rate) and stickiness (retention past first touch) into one number.
What is the difference between try rate and adoption rate?
Try rate is the percentage of eligible users who touched the feature at least once. Adoption rate is the percentage still active 30 days later. A big gap between them is a stickiness problem; a low try rate is a discovery problem.
What is a good feature adoption rate?
Core features should reach 50%+ adoption (70%+ is elite). Power features are healthy at 25–40%. Niche features are expected at 10–20%. Benchmarks shift by product category — consumer apps see higher core adoption than B2B tools.
What is shelfware in SaaS?
Shelfware is a feature users found but abandoned. The calculator flags shelfware when try rate is above 20% but stickiness is below 35%. Shelfware taxes the roadmap — consider sunset, resurrection campaign, or re-onboarding.
How does feature adoption predict retention?
Retention lift measures how much a feature improves retention vs users who never touched it. Users adopting 3+ features retain 2–3× better than single-feature users. The tool weights retention lift by feature type (Core matters most) to find the best onboarding investment.
Is feature adoption the same as DAU/MAU?
No. DAU/MAU measures aggregate engagement. Feature adoption is per-feature. A product can have strong DAU/MAU while 80% of features are shelfware — all engagement concentrates in one or two features. Feature adoption reveals which parts of the product earn their place.
What is feature stickiness?
Feature stickiness = users still active at 30 days ÷ users who tried. It isolates retention past first touch from discovery. A feature with 40% try rate and 80% stickiness is a winner even at 32% adoption. Stickiness is the purest signal of feature-level product-market fit.
How do I measure feature ROI?
Use the reverse calculator "Justify Dev Cost" mode: enter dev weeks invested and the tool computes how many retained users the feature must produce to break even. Below that threshold, the feature is net-negative ROI. Weight by retention lift for LTV impact.