Predicting customer churn with AI – how can you know what your customers are thinking?
Portrait of author Jon Taylor
Jon Taylor

Head of Brand & Content

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How can AI predict customer churn?

By Jon Taylor on January 24, 2020

I’m writing this article on January 24, and if you’re reading it with all of those ambitious New Year’s resolutions still in tact, congratulations – you’ve made it further than most people!

 January 19 is officially known as “Quitter’s day”, and is the day that most people are likely to cave on those “I’ll definitely go to the gym every night after work this year”-type promises. 

Yes, it’s true, human behavior can be predictable – but only sometimes. For businesses looking to build brand loyalty and maintain high customer lifetime values, trying to anticipate customer churn and knowing what people are going to do before they actually do it has always been a key challenge.

While you can probably have a good idea of when someone will ditch that New Year’s resolution, how can you predict when someone is going to quit all year round?

The answer to this can be found in your business’ data –  regardless of whether you’re a retailer, wholesaler, a gym, or even a subscription service. More and more consumer-facing companies are starting to perform rigerous data analysis and use the findings to spot patterns and trends which can indicate how likely a customer is to churn.

With this knowledge, you can then take appropriate action to reduce customer churn – whether that’s through special offers, some personalized recommendations, or by providing them with more of the products and content that they’re interested in via email or social media comms. 

Naturally, artificial intelligence (AI) and machine learning (ML) are very, very handy when it comes to understanding and predicting your customer churn rate.

With a Predictive Customer View of your data – data which could include customer purchases, feedback, website usage, or even social media activity – it’s possible to predict, to a high degree of accuracy, when any given customer is likely to leave, and gain a clearer understanding of why this might be the case.

Businesses leveraging their data with Peak’s Customer Intelligence solution, for instance, can get these insights delivered back to them via customer churn dashboards, APIs, or delivered straight back into their own business systems – allowing marketing teams to take prompt action in order to protect and retain their customers.

For the vast majority of industries, acquiring new customers is considerably more expensive than retaining your existing ones – so make sure that an AI-powered customer retention and churn prevention strategy is on your agenda for the year ahead.



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