retention-loop-design
GitHub诊断用户流失原因，设计包含触发、行动、奖励和投资的核心习惯循环。输出留存曲线分析、激活路径、重_engagement_策略及关键指标，旨在通过提升产品契合度与用户粘性实现长期留存。
触发场景
安装
npx skills add mohitagw15856/pm-claude-skills --skill retention-loop-design -g -y
SKILL.md
Frontmatter
{
"name": "retention-loop-design",
"description": "Design retention and engagement loops that bring users back. Use when asked to improve retention, design an engagement\/habit loop, fix a leaky retention curve, or build a re-engagement system. Produces a retention design — the retention curve diagnosis, the core habit loop (trigger→action→reward→investment), the activation→habit path, re-engagement triggers, and the metrics to watch."
}
Retention Loop Design Skill
Acquisition without retention is a leaky bucket — you pay to fill it and it drains. This skill diagnoses where and why users drop, then designs the loop that makes the product habitual: the trigger that brings them back, the value they get, and the investment that makes the next visit more likely. Retention is the truest measure of product-market fit.
Required Inputs
Ask for these only if they aren't already provided:
- The retention curve — how usage decays over time (D1/D7/D30, or weekly cohorts); does it flatten or go to zero?
- The core value & natural frequency — what users come for, and how often they'd genuinely need it.
- Activation definition — the early action that correlates with sticking (or note it's unknown).
- Current loops — any notifications, streaks, or re-engagement already in place.
Output Format
Retention Design: [product]
1. Curve diagnosis — read the retention curve: does it flatten (a retained core exists — good) or decay to zero (no PMF for this segment)? Identify the drop-off point and the cohort that retains best (your beachhead).
2. Activation → habit — the early "setup moment" and the habit milestone (e.g. "3 sessions in week 1"); the shortest path to it, since activation is the strongest lever on long-term retention.
3. The core loop — design the engagement loop explicitly:
- Trigger — external (notification, email) and the internal trigger you want to own (the felt need).
- Action — the simplest behaviour that delivers value.
- Reward — the value/variable reward received.
- Investment — what the user puts in (data, content, social, configuration) that makes the next loop better and raises switching cost.
4. Natural frequency match — align the loop's cadence to how often the job actually recurs; don't manufacture engagement the product doesn't warrant.
5. Re-engagement — triggered winback for users sliding toward churn (behavioural signal → message → return path); pair with lifecycle-crm-plan.
6. Metrics — the retention metric and cohort view to watch, plus the leading indicator (habit-milestone rate) that predicts it.
Quality Checks
- The retention curve is diagnosed as flattening vs. decaying — that determines whether to fix retention or fix fit first
- Activation/habit milestone is defined and tied to long-term retention
- The loop names a trigger, action, reward, AND investment (the investment is what compounds)
- Loop cadence matches the product's natural frequency — no manufactured engagement
- A leading indicator (not just lagging retention) is identified to act on early
Anti-Patterns
- Do not optimise retention before the curve flattens for some segment — if it decays to zero there's no PMF to retain, fix that first
- Do not bolt on streaks/badges without a real reward — gamification on a product with no core value just annoys
- Do not spam notifications to force engagement — manufactured frequency drives uninstalls and erodes trust
- Do not ignore the investment phase — without stored value/data, there's nothing raising the cost of leaving
- Do not report only average retention — cohorts and the best-retaining segment tell you where to aim
Based On
The Hook Model (Nir Eyal) and cohort-retention analysis practice (flattening curve = PMF signal).
版本历史
- a38bc30 当前 2026-07-05 11:21
