Customer Churn Prediction and Prevention Using AI

Customers are at the heart of any business. Growth of a company depends heavily on the company’s ability to acquire new customers as well as its ability to retain existing customers. According to Bain & Company article Retaining customers is the real challenge, a 5% increase in customer retention can increase company profits by as much as 95%. According to an American Express article on retaining and acquiring customers , it can cost between 6 to 7 times more to get a new customer, when compared with retaining an existing customer.

Since losing existing clients can be hugely damaging to organisations, customer churn prediction and prevention becomes a crucial aspect of sustainable business growth. There are different ways in which AI-based solutions can be used for customer churn prediction and prevention.

Churn prediction and prevention

What is customer churn?

Customer churn is a metric that measures the number of customers who stop using a company’s services or products in a given time period. (This Investopedia article gives further details on customer churn rate calculation.) Organisations can lose clients due to a variety of reasons: Insufficient value-add of the product/service, difficulties in accessing the product, better quality or price of competitor products. A churn analysis can determine a company’s churn rate as well as reasons as to why customers are abandoning the product or service. In contrary, customer churn prediction and prevention looks at ways of retaining customers who are most at risk of leaving a company.

What is the use of AI in customer churn prediction and prevention?

Organisations can develop AI-based solutions to derive and implement suitable strategies for churn prevention.

A customer churn prediction model can be used to understand the likelihood of a customer leaving a product or a service. Using data on customer behaviour, a churn prediction model can evaluate how likely it is for each customer to leave the product or service. Organisations can then use targeted marketing to encourage clients most at risk of leaving to continue using the product or service.

What’s the process to build a churn prediction model?

Machine learning technologies and data on customer behavioural patterns can be used to build churn prediction models. The usual machine learning model building pipeline involves gathering and pre-processing customer data, identifying and improving suitable prediction features, developing and training the model, evaluating the model, and deploying the model.

How convenient is it to build and deploy a model?

This is the tricky bit.

Gather the data, build the model, deploy – easier said than done. Building a churn prediction model from scratch can be hugely costly, in terms of money, time, and resources. Further, traditional churn prediction models are often built with dated data mining and statistical methods, leading to outputs that are not entirely accurate. So developing a model from scratch is hard, building a robust one is even harder.

This issue is compounded by the lack of suitable talent. Customer churn prediction using machine learning requires well-trained and experienced AI experts, who are often hard to find and costly to recruit. This complicates the process and drives up the costs of implementing AI-based churn prediction models.

What’s the solution?


evoML is a state of the art AI optimisation platform built by TurinTech. evoML automates the complete data science cycle and brings the entire process into a single platform. This enables organisations to use AI-based solutions easily for customer churn prediction and churn prevention.

However, the biggest value-add evoML brings is in its ability to optimise machine learning models. With embedded code optimisation, evoML ensures that models are efficient and fast, cutting down the time taken for the model-building process. This also allows the platform to use real-time data to provide more timely and accurate insights.

evoML is a code-free platform, which means that AI subject-matter expertise is not always required to build and deploy a machine learning model. The platform provides the necessary tool kit for both business and tech teams in the organisation to derive valuable client-retention insights using the data they have, without having to rely on the input of a data science team. So, if an organisation’s marketing team is working on customer churn prediction and prevention, then they can directly use evoML to find an AI-based solution to suit their marketing strategy .