🔍 How Spotify Uncovers User Retention Cliffs

Hey there,

Welcome back to another edition of Bootstrapped Growth. 👋

Today, we dive into how to identify the ‘retention cliff’ or the user drop-off moment for your product.

Table of Contents

• 🚀 Strategy

• ⭐️ Extra Tips

• 🛠️ Resources

Thanks for reading. Let’s jump in!

🚀 3 Things To Do To Fix Retention Cliffs

1) Find the critical window before users churn forever

Spotify used cohort analysis to change their retention strategy. They discovered that approximately 70% of formerly churned users returned to Spotify within 45 days of leaving.

This temporary loss of users for a 45 day window prompted their redesign of win-back campaigns. Initiatives included personalized playlists and exclusive content recommendations. They also encouraged users to move to premium plans (e.g. Family, Duo) which had higher life time values. These plans sacrifice short-term revenue for significantly lower churn rates.

Takeaway ➡️ Focus your retention efforts and budget on your ‘return window’ or the period when churned users are most likely to come back. Premium family and team plans reduce churn even if they lower ARPU (average revenue per user).

2) Track retention by the hour, not just by day

Calm’s (meditation app) hourly retention analysis revealed that 80% of users dropped off within the first 30 minutes of downloading the app. Identifying that first hour as the ‘retention cliff’ triggered a redesign of the onboarding flow including sending an immediate first email. This simple move saw a four-time increase in revenue from new users.

Takeaway ➡️ Don't just measure the standard Day 1, Day 7 and Day 30 retention points. It may be useful to focus on hours or even minutes after sign-up to find the steepest drop-off point.

3) Compare behavioral cohorts

Calm also conducted behavioral cohort analysis, finding that users who set reminders had 3 times the retention rate of those who didn’t. They discovered that less than 1% of users were finding their ‘Daily Reminder’ feature in settings.

Calm ran an A/B test making the Daily Reminders feature prominent after the first meditation session. The result was that users who set reminders from the new prompt showed the same 3 times retention boost, suggesting a causal link.

Takeaway ➡️ Behavioral cohort analysis can reveal actions that correlate with retention. Ensure you run an A/B test to confirm actual causation versus simple correlation.

⭐️ Best practice tips for identifying retention cliffs

1. Identify behavioural differences. Examine gaps in early engagement, time-to-first value, feature usage and more, for retained users vs churned users.

2. Segment user drop-offs. Break down churned users by channel, geography, intent and plan type.

3. Map retention cliffs to lifecycle moments. Overlay events (end of trials, billing events, seasonality) to find non-product related causes of drop-offs.

4. Test targeted fixes quickly. Use initiatives like better onboarding, nudges and incentives before retention cliff points. Then, measure results.

🛠 Useful Resources

Explore verified revenue from startups at TrustMRR. Useful for research and comparisons.

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