Definition
Product adoption measures how deeply and broadly customers use your product (features, frequency, breadth of teams). It is a driver of retention and expansion.
Example
An account uses three core features weekly across two teams, signaling high adoption.
How to use it
- Track adoption by cohort and by segment; blended adoption hides weak cohorts.
- Tie adoption metrics to retention outcomes to avoid vanity usage metrics.
- Separate breadth (how many features) from depth (how often) to see gaps.
- Monitor adoption drop-offs after releases to catch regressions.
Common mistakes
- Using logins as a proxy for adoption without feature usage context.
- Aggregating adoption across very different personas.
Measured as
Measure Product Adoption on the same customer segment, time window, and revenue basis each time you review it.
Misused when
- Using logins as a proxy for adoption without feature usage context.
- Aggregating adoption across very different personas.
Operator takeaway
- Track adoption by cohort and by segment; blended adoption hides weak cohorts.
- Tie adoption metrics to retention outcomes to avoid vanity usage metrics.
- Separate breadth (how many features) from depth (how often) to see gaps.
- Keep Product Adoption consistent by cohort, segment, and period before you use it as a decision signal in planning or reporting.
- Interpret the metric alongside retention, margin, or payback so one ratio does not hide the real operating trade-off.
Next decision
- Read PLG metrics hub: activation, trial conversion, stickiness, and adoption if the decision depends on interpretation, policy, or trade-offs beyond the raw formula.
- Decide whether Product Adoption is a growth, retention, or efficiency signal before you set targets around it.
Where to use this on MetricKit
Guides
- PLG metrics hub: activation, trial conversion, stickiness, and adoption: A practical hub for product-led growth metrics: activation rate, trial-to-paid, DAU/MAU and WAU/MAU stickiness, feature adoption, and PQL-to-paid conversion.
- Cohort analysis playbook: retention curves, LTV forecasting, and payback: A practical cohort analysis workflow: build retention curves, forecast LTV, and translate retention quality into payback and growth decisions.