Improving Traditional Models of Churn Prediction

There is little doubt that customer churn is a significant issue in the telecom industry, particularly in mature markets where product penetration is very high and there is a declining pool of available customers who are new to the technology.

Over the past decade or so, companies experiencing the pain of churn have begun to deploy systems and processes that identify and communicate proactively with at-risk customers.

Most of these processes are driven by sophisticated data mining and analytics, and it must be said that a great deal of progress has been made in customer behaviour modeling and predictive analytics since the early 2000s. However, plenty of opportunity for improvement still remains as brands begin to explore retention strategies arising from the analysis of unstructured external data.

Generally speaking, most existing data-based retention systems for communications service providers follow a very simple process: they apply sophisticated mathematical models to structured data from their own internal network and customer data systems, and analyze for patterns and attributes which they can correlate with probable customer outcomes. Then they rank or score customers who fall within the churn risk groups they have identified. The final step is to reach out to the customers in those groups and offer some sort of retention incentive (discount, upgrade, personalized communications, etc…) with the goal of keeping customers in the fold.

There is a fairly long list of statistical and mathematical constructs to choose from when deciding which predictive model to deploy. These include logistic regression, stochastic gradient boosting, various Bayesian techniques, decision trees, neural net, random forest, and discriminant analysis. Each has its advocates and detractors, and apparent strengths and weaknesses, depending on the types of data available and the assumptions being used to work up the models. A detailed analysis or evaluation of each of the models is far beyond the scope of this article, but suffice it to say that choosing one model over the other probably only offers small incremental performance gains. And although each has its own nuances and subtle differences, all of these techniques together can be characterized as leveraging internally-generated input data, to identify churners vs non-churners in the customer dataset as correctly as possible.

The fact remains that a company’s internal data drives almost all current predictive analytic processes. To us, that indicates that there is a lot of room for improvement, because there exists a wealth of analyzable data that lives externally to the organization… data that is rich enough to stand on its own, or that can be streamed and blended with internally-derived data. The external data we’re referring to is real churn measurement and detailed device information.

There are some compelling reasons to look at external churn measurement as an important source of data for a retention solution:

  • No longer limited by company records, churn activity among competitors is readily accessible and provides a direct comparison to a brand’s own customers.
  • Identifying mobile devices sets the stage perfectly for intelligent and accurate segments to understand who churns and why.
  • Remarketing campaigns can be honed to your existing customers, new prospects, and potential churners.

Mining big data for churn signals holds great promise for communications service providers and other service industries that are susceptible to high customer churn, and here at ThinkCX, we’re building a business around the detection and delivery of churn events for companies looking for a customer retention edge. It’s true that not all your customers can be found externally, but the insights the data provides is direct, real, individual, and actionable. Reliable external churn data just might be the best thing to happen to your churn modeling!