Yulu Retention & Growth Strategy - Complete Case Study Solution | Notion

PART 1: USER SEGMENTATION & COHORT STRATEGY

Data Foundation

Data Sources:

https://docs.google.com/spreadsheets/d/1ocymDJNnksC6MO0QkxZK39TVtDD6Dkou_eJBmTI5UEE/edit?usp=drive_web

5-TIER TRACKING FRAMEWORK

TIER 1: REAL-TIME SIGNALS (Trigger Immediate Action - 0-24 hours)

Metric	Threshold	Delay	Why Track	Action
Help Clicks >5	Per rental	0 hrs	Direct frustration signal; highest correlation with 70% churn	Auto-SMS + telesales alert
Battery Swap Failures	≥1 failure	0 hrs	Blocks user from continuing delivery work; immediate opportunity loss	Auto-reroute to alternate station
Payment Failures	Any failure	15 mins	Booking blocked; user can't proceed; happens before "lapsed plan"	SMS with split payment option
App Uninstall	Any uninstall	2 hrs	Explicit exit signal (~90% churn probability)	Winback SMS/WhatsApp

Why These Signals:

Direct observation (not inferred)
Real-time = intervention during active moment
Combined, they account for 80% of same-week churn

TIER 2: LEADING INDICATORS (Predicted Churn 5-7 Days in Advance)

Metric	Threshold	Delay	Why Track	Action
Days Since Last Rental	7 days	2 hrs	Breaking weekly habit = habit formation loss	SMS nudge + ₹50 bonus
Days Since Last Rental	14 days	4 hrs	Plan expired; 60% churn if not intervened	Execute Cohort 1A campaign
Booking Gap vs Historical	Gap +50%	2 hrs	User consciously changing behavior (not external)	Check-in SMS
Utilization Ratio <30%	8.9% of plan used	4 hrs	User overpaying (₹27/km vs ₹5/km optimal); drives price sensitivity	Value communication SMS + 5-day plan
Help Clicks Escalating	+3 clicks vs prev	2 hrs	Issues compounding = churn trajectory	Service recovery + credit

Why These Signals:

Predict churn 5-7 days before it happens
Enable proactive intervention (not reactive)