Stop Customer Churn in its Tracks with Predictive Analytics

This article on predicting customer churn is part of our Predictive Frameworks series, which explores the most effective use cases for predictive analytics.

No business wants a customer to walk away, especially when it’s increasingly expensive to obtain them.

But the only constant today, it seems, is change. Industry disruption is the new normal.

Take direct-to-consumer. Countless CPGs are having their flagship products displaced by nimble, data-driven upstarts. The internet has enabled endless choice of products and services, compounded by factors like globalization and multi-device shopping. Tried-and-true customer retention methods, like overloading brick-and-mortar stores or overwhelming TV spots, can do nothing to stymie an onset of customer churn brought about by disruption. In the world of direct-to-consumer alternatives, brand loyalty as a concept is waning. The best experience wins.

Despite sky-high customer acquisition costs, when churn happens many companies can do little but watch. Whatever caused the churn probably happened weeks prior, and your daily dashboard will only show the hit to revenue. Rarely do companies invest as much in their surveying and customer satisfaction teams as they do their sales and marketing.

There can be a litany of reasons a customer churns, and churn can happen in large segments. Factors like seasonality or increased competition can compound an annoying attrition problem into a critical one.

It’s the array of ever-changing reasons and seemingly spontaneous nature of customer churn that make this problem so perfect for predictive analytics. If something seems random, that likely just means you need a machine to find the pattern.

Predicting Customer Churn with Automated Machine Learning

Customer Churn
Dashboard of predicted customer churn, next 14 days

(This blog post assumes the analyst uses a platform to automate parts of the process including: data restructuring and encoding, feature engineering, and ongoing model optimization for data leakage and drift prevention.)

Building predictive data models to predict customer churn will constantly reveal your warning signs, automatically engineering new features to inform who will churn in the future and what to do about it. The spider web of reasons a customer might churn, mixed with the depth of demographic data that can make churn seem random, is a perfect candidate for machine learning to mine for patterns and insights.

Whatever churn means to you—whether a slightly decreased frequency of purchase, or an angry customer breakup email—can be set in the target of the predictive model. Then, neural networks are constructed to identify customers and accounts that will likely churn, with a likelihood risk attached.

The likelihood of churn can be a useful threshold, because at the most basic level you can segment buckets of users into “unlikely to churn”, “likely to churn”, and “will definitely churn” for different treatments.

In the future, every customer should have a churn likelihood score.

Upsell Identification
Machine learning can find churn likelihood for every row of data

Where Business Users Need Control in Predicting Customer Churn

However, when it comes to predicting churn the process matters as much as the result. Indeed, simply having automatic scores generated, however accurate, doesn’t address the new paradigm in analytics that automated predictive analytics brings.

Data people of all kinds—from marketing and sales to data science and analytics—need to affect the model creation process where it counts.

For example, when it comes to churn the detection vs. precision ratio can have a huge impact on treatment effectiveness, and its resulting revenue or savings. In fact, mathematically, there’s an inverse relationship between increasing detection and precision of a churn model. Consequently, marketing teams can use this ratio to fine tune their campaigns, and determine whether a customer needs a heavy discount to retain—or just a light promotion.

Business users need advanced control, such as precision vs. detection threshold

AI and machine learning are quickly changing the way businesses work with data. To save time and ensure efficacy, automation is key. However, organizations should never forget to include the inimitable input of human control and decision making. That’s what makes automated predictive analytics as a platform so powerful. It combines the best of AI and human inputs for a paradigm-shifting analytics experience.


Curious about more opportunities in predictive churn & retention yourself? For more specific stories, read these case studies:

If you want to see how Pecan can identify and prevent customer churn, request a demo and we can help you set up your first predictive model.

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