boost-retention-engagement

name	Boost Retention & Engagement
description	Diagnose and fix churn by measuring cohort retention curves, identifying activation metrics, and designing re-engagement campaigns. What it does: analyzes retention patterns by user segment and builds targeted strategies to keep users coming back. When to use it: when CAC is rising relative to retention, you need to understand why users leave, or you're designing engagement features. Trigger on: churn rate, DAU declining, retention curve, activation metric, engagement funnel, re-engagement campaign, cohort analysis, dead zone.
dependencies	[]
connectors	[{"name":"Google Analytics","reason":"Track cohort retention curves, DAU/MAU trends, and identify exactly where users drop off"},{"name":"Slack","reason":"Analyze support tickets and user feedback to identify churn reasons and common frustration points"},{"name":"HubSpot","reason":"Measure lifecycle stage transitions and design win-back campaigns for at-risk or churned accounts"},{"name":"Intercom","reason":"Segment users for targeted re-engagement messaging based on engagement depth and last-seen data"}]
author	Harmony
version	1.0.0
icon	sparkles
cover
screenshots	[]
category	Customer Success

Purpose

Retention is the only metric that matters in the long term. A product that acquires 1,000 users but keeps none of them is a waste of money. A product that acquires 100 users but keeps 50 forever wins.

The key insight: your retention curve tells the story of your product-market fit. A healthy curve dips sharply in the first week (inevitable churn), then flattens—meaning you've found your real core users. A curve that decays to zero is a broken product masquerading as growth.

Your job is to find the single behavior in week 1 that predicts whether a user will still be active in week 8. This is your activation metric. Users who hit it stick around. Users who don't hit it leave.

Then, your job is to re-engage users who haven't hit activation. And finally, to segment and resurrect users who've already churned.

When to Use This Skill

• Your DAU/MAU ratio is below 30% (most users don't return monthly)
• You're acquiring users cheaply but they leave before paying you
• You don't have a clear hypothesis about which behaviors predict long-term retention
• A cohort from 6 months ago is gone; you need to understand why
• You're designing an engagement feature and want to measure whether it moves retention
• Support tickets or user interviews reveal churn reasons you need to address systematically
• You want to resurrect churned users but don't know which segments are worth targeting

Instructions

1. Build cohort retention curves and identify your baseline

   • Define cohorts: group users by signup week or month
   • Track % of cohort active at days 1, 7, 14, 30, 60, 90
   • A healthy curve: 50%+ survive week 1, 30-40% survive month 1, flat after 60 days
   • Plot curves by user segment (freemium vs. paid, self-serve vs. sales-assisted, persona)
   • Sub-bullets: Compare current curves to competitor benchmarks; identify which segments have healthiest curves; flag cohorts with abnormal drop-off

2. Identify your activation metric through correlation analysis

   • Ask: What single behavior in week 1 predicts month 2 retention?
   • Common activations: Facebook (7 friends in 10 days), Slack (2K messages sent), Twitter (following 30 accounts), Duolingo (7 days in a row)
   • Test multiple candidates: completion of onboarding, creation of first artifact, time spent, social connection made
   • Segment activation by persona—power users and casual users may have different activation metrics
   • Sub-bullets: Pull user data; correlate specific actions with 60-day retention; identify the single highest-predictive action; validate across 3+ cohorts