Churn Prevention

Stop losing users before it happens. This guide shows you how to identify at-risk users, understand why they’re leaving, and take action to bring them back.

What you’ll learn

Define and measure churn for onchain apps
Identify at-risk users before they churn
Understand why users leave using behavioral analysis
Create re-engagement segments and campaigns
Track the effectiveness of retention efforts

Prerequisites

Formo SDK installed (Installation Guide)
At least 30 days of data for meaningful churn analysis
Defined what “active” means for your app

Part 1: How to Define Churn for Your App

What is churn?

Churn is when a user stops engaging with your app. For onchain apps, this typically means:

App Type	Churn Definition
DeFi protocol	No transactions for 30+ days
Gaming	No gameplay sessions for 14+ days
Bridge	No bridge transactions for 60+ days

Step 1: Check your retention baseline

Go to Retention in the Formo Dashboard

Set the starting event to connect (wallet connection)

View the cohort table

A typical retention pattern looks like:

Cohort	Week 0	Week 1	Week 2	Week 4	Week 8
Week 1	100%	25%	18%	12%	8%
Week 2	100%	28%	20%	14%	-

If Week 4 retention is 12%, then ~88% of users have churned by Week 4.

Step 2: Calculate your churn rate

Monthly churn rate = (Users at start - Users at end) / Users at start × 100

Or use the Explorer with this query:

WITH monthly_active AS (
  SELECT
    toStartOfMonth(timestamp) AS month,
    countDistinct(address) AS active_users
  FROM events
  WHERE type = 'connect'
  GROUP BY month
)
SELECT
  month,
  active_users,
  lagInFrame(active_users) OVER (ORDER BY month) AS prev_month,
  round((lagInFrame(active_users) OVER (ORDER BY month) - active_users)
    / lagInFrame(active_users) OVER (ORDER BY month) * 100, 1) AS churn_rate
FROM monthly_active
ORDER BY month

Part 2: How to Identify At-Risk Users

User lifecycle stages

Formo automatically assigns lifecycle stages to users:

Stage	Description	Churn Risk
New	First session	Medium - haven’t proven value yet
Returning	Multiple sessions	Low - showing engagement
Power User	5+ active days in 30 days	Very Low - highly engaged
Churned	30+ days inactive	Already churned
Resurrected	Returned after churning	Medium - may churn again

Step 1: Create at-risk segments

Go to Users and create these Segments: Segment: At-Risk Power Users

Lifecycle = Power User
Last seen between 14 and 30 days ago

These are your most valuable users who are showing signs of disengagement. Segment: New Users Not Returning

Lifecycle = New
First seen more than 7 days ago
Last seen = First seen (only one session)

These users tried your app once and never came back. Segment: Declining Activity

Was active 3+ times in previous 30 days
Active only 1 time in last 30 days

These users are reducing their engagement.

Step 2: Monitor segment sizes

Track how many users are in each at-risk segment over time. Use Ask AI to generate monitoring charts:

“Show me the count of users who were last seen between 14 and 30 days ago and have more than 5 sessions”

“How many users have only had 1 session and haven’t been seen in 7 days?”

Save these charts to a Dashboard to monitor segment sizes over time.

Part 3: How to Understand Why Users Churn

Analyze churned user behavior

Use Flows to see what churned users did in their last session:

Go to Flows

Filter by users who are now Churned

Set the starting event to their last session

Look for patterns in exit behavior

Common churn signals

Signal	What it means	How to detect
Failed transaction	User hit an error	Filter Activity by `status = failed`
Abandoned flow	Confusion or friction	Flows showing exits before completion
Only viewed prices	Window shopping, no intent	Funnel drop-off at connect step
Single feature use	Didn’t discover value	Flows showing limited exploration

Compare churned vs. retained users

Create two cohorts and compare their behavior: Churned cohort:

Users who were active 60+ days ago
Have not returned in 30+ days

Retained cohort:

Users who were active 60+ days ago
Have returned in the last 30 days

Compare:

Number of sessions in first week
Features used
Transaction volume
Entry referrer

This reveals what retained users do differently.

Part 4: How to Set Up Churn Alerts

Real-time monitoring

Create Alerts to catch churn risks early: Alert 1: Whale going inactive

Trigger: User with net worth > $100k
Condition: No activity for 7 days
Action: Notify sales/BD team

Alert 2: Power user declining

Trigger: User was power user
Condition: Activity dropped 50% week-over-week
Action: Add to re-engagement campaign

Alert 3: Failed transaction spike

Trigger: Failed transactions
Condition: Count > 2× daily average
Action: Notify engineering (may be a bug causing churn)

Weekly churn review

Set up a weekly dashboard review:

Check at-risk segment sizes
Review failed transactions
Analyze retention by cohort
Identify any anomalies

Part 5: How to Run Re-Engagement Campaigns

Export at-risk segments

Go to Users > select your at-risk segment

Click Export CSV

Download wallet addresses

Use these for targeted campaigns on:

Twitter/X: Target ads to wallet owners
Farcaster: Direct outreach
Email: If you have email addresses linked
In-app: Show personalized messages on return

Re-engagement strategies by segment

Segment	Strategy	Message
Power users going quiet	VIP outreach	”We miss you! Here’s what’s new…”
One-and-done users	Education	”Complete your first transaction. Here’s how”
Failed transaction users	Support	”We noticed an issue. Let us help”
Feature-limited users	Discovery	”Have you tried [feature]?”

Track re-engagement success

After running campaigns:

Create a segment of users targeted

Track how many returned (Resurrected lifecycle)

Measure their subsequent retention

To measure reactivation rate, filter your Users list by the Resurrected lifecycle and compare to the number of users you targeted in your campaign.

Part 6: How to Build a Churn Prevention Dashboard

Recommended metrics

Metric	Chart Type	Purpose
Monthly churn rate	Line	Trend over time
At-risk user count	Number	Current risk level
Retention by cohort	Retention	Long-term patterns
Days since last seen distribution	Bar	Inactivity spread
Churned users by referrer	Table	Which sources have worst retention
Reactivation rate	Number	Campaign effectiveness

Key metrics to track

Leading indicators (predict future churn):

At-risk segment size
Week 1 retention rate
Failed transaction rate
Average sessions per user

Lagging indicators (measure past churn):

Monthly churn rate
Churned user count
Lifetime value of churned users

Summary

You’ve learned how to:

Define churn appropriately for your app type
Identify at-risk users before they leave
Understand why users churn through behavioral analysis
Set up alerts for early warning signals
Run re-engagement campaigns with exported segments
Track effectiveness of retention efforts

Getting started

Features

Use Cases