Retention

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name: Retention description: User retention strategy, cohort analysis, churn prevention, and reactivation campaigns metadata: category: product skills: ["retention", "churn", "cohorts", "engagement", "lifecycle"]

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Core Metrics

| Metric | Formula | Healthy Range | |--------|---------|---------------| | Day 1 retention | Users active day 1 / signups | 40-60% | | Day 7 retention | Users active day 7 / signups | 20-35% | | Day 30 retention | Users active day 30 / signups | 10-20% | | Weekly retention | WAU this week / WAU last week | 85-95% | | Churn rate | Lost customers / start customers | <5%/month | | NRR (Net Revenue Retention) | (Start MRR + expansion - churn) / Start MRR | >100% |

Cohort Analysis

Track by signup week, not calendar week:

Horizontal axis: weeks since signup (0, 1, 2, 3...)

Vertical axis: signup cohort (Jan W1, Jan W2...)

Cell value: % of cohort still active

Identify:

Which cohorts retain better (product changes, marketing source)

At which week users drop off (week 2 cliff = aha moment too late)

Seasonal patterns (holiday signups retain worse)

Churn Signals

Early warning indicators (flag before churn):

Login frequency drops 50%+ from baseline

Core feature usage stops

Support tickets spike then go silent

Billing page visits without upgrade

Team member removals

Data export requests

Engagement Loops

Retention requires habit formation:

| Loop Type | Trigger | Action | Reward | |-----------|---------|--------|--------| | Personal | Email digest | Review updates | Progress visible | | Social | Notification | Respond to team | Recognition | | Content | New content alert | Consume | Knowledge gained | | Progress | Streak reminder | Complete task | Streak maintained |

Design for variable rewards - predictable = boring.

Lifecycle Stages

| Stage | Timeframe | Goal | Tactics | |-------|-----------|------|---------| | Activation | Day 0-3 | Reach aha moment | Onboarding, setup wizard | | Engagement | Week 1-4 | Build habit | Usage nudges, tips | | Retention | Month 1+ | Maintain value | Feature discovery, check-ins | | Expansion | Ongoing | Increase usage | Upsell, team invites | | Reactivation | After churn | Win back | Campaigns, incentives |

Reactivation Campaigns

Timing matters:

7 days inactive: Soft nudge ("We miss you")

14 days inactive: Value reminder + what's new

30 days inactive: Incentive offer (discount, extended trial)

90 days inactive: Last chance + feedback ask

Message formula:

[Acknowledge absence] + [New value added] + [Easy re-entry CTA]
"Your dashboard is waiting. We added [feature]. One click to resume →"

Feature Stickiness

Measure which features predict retention:

Usage correlation: Users of feature X retain 2x better

Time to feature: Users who reach feature X in day 1 retain 3x

Feature breadth: Users of 3+ features retain 5x vs 1 feature

Double down on sticky features in onboarding.

Churn Prevention

When churn signal detected: 1. Immediate: In-app message acknowledging drop ("Need help?") 2. Day 3: Email from founder (personal, not marketing) 3. Day 7: Offer call or live support 4. Before renewal: Proactive outreach with usage summary

Cancel flow optimization:

Ask reason (required, 4-5 options)

Offer pause instead of cancel

Show what they'll lose (data, history, price lock)

Easy return policy ("reactivate anytime, data saved 90 days")

Retention Benchmarks by Model

| Business Model | Good D30 | Good Monthly Churn | |----------------|----------|-------------------| | B2C freemium | 10-15% | N/A (free) | | B2C subscription | 8-12% | 5-7% | | B2B SMB | 15-25% | 3-5% | | B2B Enterprise | 25-40% | 1-2% |

Common Mistakes

Measuring retention from signup, not activation

Treating all churned users the same (voluntary vs involuntary)

Reactivation emails without new value proposition

Ignoring payment failures as churn (30-40% of churn is involuntary)

No segmentation in cohort analysis (power users mask problems)