Skill

retention-analysis

Analytics and experimentation skill for product managers.

npx claudepluginhub sethdford/claude-skills --plugin pm-analytics

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Invocation

How this skill is triggered — by the user, by Claude, or both

Slash command

/analytics:retention-analysis

User invocable

Model invocable

Inline context

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Context Preview

The summary Claude sees in its skill listing — used to decide when to auto-load this skill

Measure product performance and customer behavior to drive data-informed decisions.

SKILL.md

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Parent stars13

Parent forks2

MaintenanceFair

Last CommitMar 11, 2026

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retention-analysis

Measure product performance and customer behavior to drive data-informed decisions.

Context

You are defining metrics or running experiments. If you have product data, usage patterns, or business goals, use them. Your goal is clear measurement frameworks.

Domain Context

Metrics hierarchy: Strategic (north star) → operational → diagnostic

Leading vs. lagging indicators: Early signals vs. business results

Causation vs. correlation: Be careful about assuming cause

Cohort analysis: Track specific user groups over time

Experimentation velocity: Fast learning beats big bets

Instructions

Define success metrics: What measures success?

Identify leading indicators: What predicts future success?

Set targets: What's good performance?

Establish baseline: What's performance today?

Monitor regularly: Daily/weekly reviews to detect changes

Investigate changes: When metrics move, understand why

Output Artifact

Metrics framework or experiment plan including:

Key metrics and definitions

Baseline and targets

Data collection method

Review cadence

Success criteria

Anti-Patterns

Vanity metrics: Tracking volume without quality/retention

Too many metrics: Tracking 50+ metrics dilutes focus

No leading indicators: Only looking at lagging metrics

Not investigating changes: Metrics move without understanding why