Deep reference for measuring feature adoption throughout the lifecycle - from launch through maturity or deprecation. Covers the adoption funnel, scorecards, la
This skill provides a structured framework for measuring feature adoption across its entire lifecycle—from launch through maturity or potential deprecation. It breaks down adoption into stages such as awareness, trial (activation), repeat use (engagement), and habitual use, each with clear definitions and metrics like trial rate, repeat rate, and habitual rate. The skill also includes scorecards to visualize adoption health, launch metrics for checkpoint evaluations, and kill criteria to help decide when a feature should be retired.
By quantifying adoption at each stage, marketers and product teams can diagnose bottlenecks, prioritize interventions, and track progress against targets over days, weeks, and months. This approach helps align feature success with user behavior and business goals rather than relying on vague usage signals.
This skill is ideal for product marketing managers launching new features who need to validate adoption against defined success criteria. It also serves growth leads responsible for optimizing user engagement by identifying drop-off points in feature usage. Additionally, agency strategists running product analytics for clients will benefit from its clear metrics and decision frameworks to support feature lifecycle management and ROI justification.
Whether you’re tracking adoption during onboarding for new users or driving discovery among existing users, this skill helps tailor interventions based on real adoption patterns and trade-offs.
A typical workflow begins with defining the eligible user population and key events for awareness and core feature actions to correctly segment trial users. Next, practitioners set targets for adoption metrics at checkpoints such as Day 7, Day 30, and Day 90 to evaluate early activation and sustained engagement. The third step involves analyzing the adoption funnel via scorecards to pinpoint bottlenecks—such as low trial or repeat rates—and implementing targeted interventions like reducing friction or improving onboarding copy. Finally, users apply kill criteria to underperforming features based on adoption thresholds and support signals, guiding decisions to iterate, reposition, or deprecate.
Throughout, segmentation between new users and existing users informs tailored strategies for feature discovery and habitual use.
How do you define a "trial" event accurately? Trial requires the user to complete the core feature action, not just open or hover over it. For example, applying a filter instead of opening the filter panel counts as a trial.
What is the rationale behind using “3+ uses in 14 days” for repeat rate? This threshold balances filtering accidental clicks and capturing meaningful repeated engagement, but it can be adjusted based on feature frequency.
When should a feature be considered for deprecation? If overall adoption at Day 90 is less than half the target, or if trial or habitual rates fall below defined thresholds without clear fixable causes, the feature should be evaluated for kill or redesign.
Attach this skill to a Metaflow agent task focused on product analytics or feature performance evaluation to automatically guide adoption measurement based on standardized metrics and checkpoints. Expect structured outputs like adoption scorecards and diagnostic insights that highlight bottlenecks and potential kill signals. This enables data-driven decisions about feature lifecycle management and targeted interventions. We recommend combining this skill with user segmentation and event instrumentation tasks to ensure comprehensive adoption analysis and actionable recommendations.
For broader context, see our roundup of claude marketing skills, and read Claude Code reporting workflows for marketing agencies for related setup guidance.