Churn risk & prevention

Sid Chaudhary

Sid Chaudhary

Founder & CEO

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Utilize predictive analytics to identify users at risk of disengaging or churning. Implement tailored retention strategies to maintain their interest and loyalty.

About the Growth Play

Churn risk and prevention is crucial for SaaS companies aiming to retain their user base and maintain steady revenue growth. Churn, or customer attrition, represents the percentage of users who stop using a service over a given period. High churn rates can significantly impact the sustainability and profitability of a subscription-based model, making it essential to implement effective strategies to mitigate this risk.

This use case effectively leverages Intempt's lifecycle agent and automated engagement tools to address the challenges of churn management.

Benefits

  • Early identification of at-risk users: Predict which users will likely churn before they disengage completely.
  • Targeted retention strategies: Implement personalized campaigns to re-engage at-risk users.
  • Increased customer lifetime value: Retaining users longer by addressing their needs and concerns promptly.
  • Improved user experience: Enhance satisfaction by providing timely and relevant interactions.
  • Data-driven decision making: Utilize insights from predictive models to make informed marketing and product decisions.

How it works

Step 1: Set up data collection

Ensure you have at least 21 days of data collection with a minimum of 10,000 daily events.

Verify that the goal event (e.g., churn) has at least 200 true and 200 false occurrences over 30 days.

Examples of events that can represent churn:

Step 2: Create a lifecycle agent

Navigate to the Agents section in Intempt

Select "Create agent" and choose the target goal event representing user churn.

Configure the Agent.

Intempt will create an output attribute that stores prediction values for each user.

Step 2 output attribute

Step 3: Segment users based on lifecycle scores

Step 4: Design and implement retention strategies

For high-risk users:

Create a journey:

Create journey

Set a trigger:

Set trigger

Add actions:

Email body example

Schedule follow-up calls:

Follow-up calls

Add conditions:

Check email engagement
Email engagement condition

Check follow-up call status:

Implement personalizations:

Dashboard message

For medium-risk users:

Create a journey:

Medium-risk journey

Set a trigger:

Medium-risk trigger

Add actions:

Feature promotion email

Offer loyalty rewards:

Loyalty rewards email

Add conditions:

Monitor email engagement
Email engagement follow-up

Implement personalizations:

Feature highlight banners

For low-risk users:

Create a journey:

Low-risk journey

Set a trigger:

Low-risk trigger

Add actions:

Regular engagement email

Promote advanced features:

Advanced features email

Add conditions:

Track engagement

Implement personalizations:

Onboarding checklist

Step 5: Monitor and adjust strategies

Regularly review the effectiveness of your retention campaigns using Intempt's journey analytics.

Track user responses and adjust strategies based on the analytics data.

Continuously retrain your model with new data to maintain accuracy and relevance.

Frequently asked questions. Answered.

You'll need at least 21 days of data collection with a minimum of 10,000 daily events. The goal event (like churn) should have at least 200 true and 200 false occurrences over 30 days. This gives the model enough signal to make accurate predictions.

It depends on your business, but common examples include: user cancels subscription, user hasn't logged in for 30 days, usage drops significantly (fewer logins, fewer features used), or user submits negative feedback or a support ticket showing dissatisfaction. You define what churn means for you.

Once trained, the model segments users into groups like Champion, Regulars, Promising, Needs Attention, and At Risk. These categories are based on predicted churn likelihood, so you can treat each group differently instead of blasting everyone with the same message.

Yes. When configuring the agent, you can filter out users based on things like login date, feature usage frequency, subscription status, or support ticket history. This keeps your predictions focused on the users who actually matter for retention.

You've got options. Send personalized re-engagement emails with discounts or special offers, trigger SMS or webhook notifications to alert your customer success team to make follow-up calls, or display personalized messages on their dashboard when they log in. The idea is to reach them before they're fully gone.

You can add conditions in your journey to check email engagement and whether they open it. Click anything? If not, send a follow-up email after a few days. If they're still unresponsive after a call attempt, you can escalate to a higher support tier. The journey adapts based on what they do (or don't do).

Yes. You can create personalizations that display different messages depending on the user's risk level. High-risk users might see a "We miss you, here's 20% off" message when they log in, while low-risk users see tips about advanced features. Same dashboard, different experience.

Use Intempt's journey analytics to track user responses, opens, clicks, conversions, drop-offs at each step. Review regularly, adjust your strategies based on what the data shows, and keep retraining your model with new data so it stays accurate as user behavior evolves.

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