Feature Adoption Tracker

You just shipped a new feature. The dashboard shows some adoption numbers, but you don't know which segments are actually using it, which ones tried it once and never returned, or which features are collecting digital dust. Deciding where to invest the next sprint feels like guesswork.

Quick scan

• Best for: SaaS product managers who need to prioritize feature improvements and onboarding tweaks.
• Output: A feature adoption report with heatmaps, stickiness scores, and a list of under-adopted features with suggested onboarding fixes.
• Inputs: User‑segment data, feature‑usage logs (dates, actions, session counts).
• Time saved: Cuts manual data pulling and cross‑referencing from hours to minutes.
• Main caveat: The report’s quality depends on the accuracy and granularity of your usage data.

What it does

The Feature Adoption Tracker takes raw usage data—such as event counts, session durations, and feature‑access timestamps—and structures it into an adoption heatmap across user segments. It calculates stickiness scores (daily active usage / total users in the segment) and flags features that fall below a configurable threshold. For each under‑adopted feature, it suggests onboarding improvements based on common patterns: in‑app tutorials, tooltips, or re‑engagement emails. The output is a ready‑to‑present report, not a dashboard you still have to interpret.

When to use it

• Right after a feature launch, to measure initial traction.
• During quarterly planning to decide which features need investment.
• When preparing a product review with the leadership team.
• To compare adoption across different user cohorts (e.g., new signups vs. power users).

When not to use it

• If you lack even basic event tracking (the skill cannot invent data).
• For one‑time feature experiments that lack a baseline.
• When the analysis requires statistical significance forecasting beyond simple adoption trends.

How to install and use

1. Download the ZIP and extract it into your local skills folder.
2. Open Claude Code and load the skill via the command provided in your skills UI.
3. Prepare your usage data in the format described in the skill (CSV or JSON with columns: user ID, segment, feature ID, action, timestamp).
4. Paste the data into the session and run the command: Analyze feature adoption for my latest feature release, segment users by activation, and flag under‑adopted functionalities for improvement.
5. Review the generated report; you may refine parameters like the stickiness threshold or the segments to include.

A concrete example

You’re the PM at a mid‑size SaaS company that just launched a collaborative editing feature. You load last month’s usage logs: 5,000 users across three segments (free, pro, enterprise). The skill first produces a segmented heatmap showing that enterprise users adopted the feature heavily (stickiness 65%), while free‑tier users barely touched it (stickiness 12%). It then flags the feature in the free segment and suggests an in‑app guided tour during the second login, when free users typically engage with core features. The report is 3 pages, with tables and a priority list. You use it to brief the onboarding team that afternoon.

Limitations

• Data‑dependency: The skill cannot correct incomplete or mis‑labeled event data. If your tracking omits key actions, the adoption picture will be skewed.
• Suggestion generality: The onboarding improvements are templated and may not cover complex multi‑product setups or deeply customized user journeys.
• No predictive modeling: It gives you a snapshot; it does not forecast future adoption curves or churn probabilities.

Download the skill

If you run feature adoption analyses more than once a quarter, this skill structures the entire workflow—from raw data to actionable recommendations—so you can move from question to decision in one session. Download Feature Adoption Tracker

Related skills

• SaaS Churn Risk Briefing Studio – Once you’ve identified low‑adoption features, pinpoint the churn risk they create across accounts.
• Product Handoff Brief Builder – Turn the top‑priority improvement into a structured design‑to‑engineering brief.

Related skills

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Install

Start with the ZIP package, then choose the AI tool workflow that fits your setup.

1. Download feature-adoption-tracker.zip (6 KB).
2. Open the package and read SKILL.md, plus any included references, templates, or scripts.
3. Use the instructions directly in any AI tool that supports reusable instructions, project knowledge, custom agents, or uploaded reference files.

In Claude, open the command picker with / and select this skill by name when you want to use it.

For Claude Code, unzip into ~/.claude/skills/ so the folder lands at ~/.claude/skills/feature-adoption-tracker/, then reload Claude Code. For Claude.ai, upload the same ZIP from Customize → Skills.

Use with other AI tools

This package is not locked to one vendor. If your AI tool does not support Claude-style skills, copy the core instructions from SKILL.md into the tool's custom instructions, project prompt, agent setup, or reusable prompt library.

• Upload or paste any included reference files as project knowledge where your tool supports it.
• Keep the output format from SKILL.md intact so results stay predictable.
• Run a small test with your own data before using the workflow in production.

Compatibility depends on the features your AI tool provides. Treat scripts as optional local helpers unless your environment can run them safely.

Claude installation reference

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