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CRM Hack: The Customer Churn Factor

Watch and learn how to use a simple, smart formula that could help you predict customer churn before it’s too late

The challenge of communicating with each customer at the right time is only getting harder. Your customers are constantly exposed to different messages from different brands and different products – all the time – and behavior quickly changes.

Marketers must find a way to break through that noise and leave customers with a message that they will actually act upon. That requires personalization – and perfect timing.

To find the most appropriate time to send a message to each customer and have that message be the most relevant one at that time – you need a robust yet simple and personalized way to engage with each customer in your database.

It’s especially true with customers who are at a higher risk of churn.

That’s why we created the Churn Factor formula. Watch the mini-workshop below, or read the transcript under it to learn more:

Get your guide on Re-engaging Your Churned Customers >>


To start predicting churn, first, you must find the frequency of how much time elapses, on average, between two consecutive activities of a single customer using the following Optimove calculation:

Churn Factor

There are many different methodologies to predict churn, but in many of them, we have to pay some price of personalization. The Churn Factor aims to solve that problem. Calculating customer’s individual Churn Factor can flag risk-of-churn customers based on their own personal activity frequency.


Two weeks have passed for both Customer A and Customer B since their last activity. However, Customer A purchases every month while Customer B purchases every week. The key difference between the two is their Churn Factor. Using Optimove’s calculation, you can see it is completely different:

From a churn perspective, it is clear that Customer B is more at risk of churn as they have missed their own purchasing cycle.

Though this is a fairly simple example, it illustrates nicely how simple it can be to predict and prevent churn by first figuring out who the customer is and then sending out the right campaign to prevent them from churning.

Concluding Churn

Using a customer’s personal frequency to understand when they are at risk of churn is a better methodology than setting a single, distinct threshold to the entire customer base.

Want to know more? Reach out to us:

Strategic Services Team: [email protected]

Sales Team: [email protected]

Don’t miss out on our previous CRM hacks from Optimove experts:

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Yoni Barzilay

Yoni Barzilay is Optimove’s Director of Data Science, North America. He has a knack for finding creative solutions for extreme data challenges, and has led some of Optimove's biggest e-commerce onboarding projects. Yoni holds a BSc in Industrial Engineering and Management, specializing in Information Systems.