Retention

User retention strategy, cohort analysis, churn prevention, and reactivation campaigns

Iván@ivangdavila

Install

openclaw skills install @ivangdavila/retention

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:

text

[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:

Immediate: In-app message acknowledging drop ("Need help?")
Day 3: Email from founder (personal, not marketing)
Day 7: Offer call or live support
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)