Adoption Metrics are the family of product-usage metrics that measure how successfully users take up a product and its features — from first meaningful action through habitual, recurring use. Rather than a single number, adoption is tracked as a dashboard of complementary metrics: activation rate (did the user reach first value), feature adoption rate (are specific capabilities being used), DAU/MAU stickiness (how frequently users return), and time to value (how quickly users reach their aha moment). Together they answer whether a product is actually being used the way it was designed to be.
A useful mental model splits adoption into three stages: activation (first successful use), habit formation (recurring use integrated into a workflow), and expansion (breadth of features adopted). A healthy adoption dashboard shows all three, because a product can activate users well yet fail to build habit, or build habit on a narrow slice of features while the rest go unused.
Targets vary by metric: activation rate commonly targets 30%–40%+ for self-serve SaaS; core-workflow feature adoption should target 60%+ among eligible users; a DAU/MAU ratio above 20% indicates a sticky product, and above 50% is best-in-class for daily-use tools.
Each function reads Adoption Metrics through a different lens and takes different actions when it changes.
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Metrics that are commonly analyzed alongside Adoption Metrics.
askotter capabilities and guides that act on this metric.
See how each role uses Adoption Metrics in context with the full set of metrics they own.
askotter connects your data sources and applies causal analysis to tell you exactly why your metrics are changing, not just that they changed.
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