How to prevent customer churn with AI

Customer churn prevention is a classical business task that can be efficiently addressed using AI. Besides predicting the likelihood of churn, AI can also provide insights into the reasons why a customer is likely to churn. With such information at hand, you have a fantastic tool to improve the customer experience, lower the churn rate, optimize cost and grow revenue.

In our increasingly competitive economy, customers will most often have the option to choose between several similar products and services making it a key task to retain existing customers. Conventional wisdom will tell you that it is much more expensive to acquire new customers than to retain existing ones.

But how do you do that in practices, this is a real challenge for most as the detailed insight is not available. Using AI to predict (and prevent) customer churn could be one of the most important parts to prevent you and your business from churning customers and large amounts of business and income to your competitors.

Distinguishing between different types of churn

Usually, churn is distinguished between voluntary and involuntary churn.

  • Involuntary churn is where customer churn happens due to several factors that are beyond the business’ or the customer’s direct control, such as a credit card being denied for various reasons.
  • Voluntary churn is often the focus area for businesses and the task to predict behavior and get insights into which factors contribute to voluntary customer churn. Having the ability to predict churn and thereby take action to prevent it is what you want to accomplish.

Breaking down the AI prediction model – binary classification

Predicting customer churn with AI is a well-defined task usually known as a binary classification. In binary classification, we are interested in learning the hidden structures in the data that gives us the ability to distinguish between the two possible output classes i.e churn or not-churn.

In this case, we want the AI model to learn how to predict customer churn, given a complete set of customer features, such as age, membership-offer, tenure, etc. This means that for each future customer, the model will give us a probability distribution of the outcome i.e what are the chances of churn/not-churn for the particular customer. Based on this, we may reconsider the customer’s offer in order to lower the probability of churn and retain the customer.

Churn prevention

FIGURE 1. Churn Prevention. After having trained the model we may use it for historical or future customers and obtain valuable business insights in order to lower customer churn. Shown here are the features that impact the model prediction, i.e why is this particular customer most likely to churn.

Building an AI model requires good data science skills that are essential in order to get real value from churn prediction. These skills include:

Data anonymization

  • Data collection: Are we collecting the right data?
  • Data cleaning: What records can we use?
  • Feature engineering: What statistical transformations are required?
  • Model training and evaluation: Which mathematical models perform best and why?
  • Model deployment: What infrastructure and tools do we use to put the best model in production? What CRM system does it have to connect to?
  • Model monitoring and retraining: How do we keep an eye on the model’s performance?

Having all these points in mind leads you directly to a successful data science project that creates real value for your business.


By using AI we are able to address many classical business challenges and create value and insights from data. In churn prediction, implementing an AI model can give businesses an advantage over competitors by having the ability to prevent existing customers from churning and calculating the churn probability for future customers along with the drivers of churn

About the author

Ahmed Zewain

Ahmed Zewain


Ahmed Zewain is a Data Scientist at 2021.AI with an MA in mathematical modeling and computing, and extensive knowledge of several data engineering tools. Ahmed’s skills include building ML POC projects and taking them further into production for a wide variety of clients.

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