Start the Enterprise AI Journey with Churn Prediction

Churn prediction is a relatively quick win with machine learning, and its potential value to an organization is staggering, making it a great proof of concept (POC) project.

Given that it costs 5-10 times more to acquire a new customer than to retain an existing one, it seems obvious that all businesses should engage in some level of churn prevention. Because of its business impact and its relative ease in execution, for many types of business, churn prediction is a great first project to tackle with machine learning and AI.

 

In a subscription-based business, even a small rate of monthly/quarterly churn will compound quickly over time. Just 1 percent monthly churn translates to almost 12 percent yearly churn. Given that it’s far more expensive to acquire a new customer than to retain an existing one, businesses with high churn rates will quickly find themselves in a financial hole as they have to devote more and more resources to new customer acquisition.

Michael Redbord, General Manager of Service Hub at HubSpot, Customer Churn Prediction Using Machine Learning: Main Approaches and Models, KDnuggets, 2019

Common Pitfalls of Churn Prediction

When building any machine learning-based model, but especially for churn, one has to be careful that the model is actually learning the right thing. For instance, one of the common pitfalls for a churn modeling project is to train the model on both past and future events. To avoid this common mistake, think about what the model will know once deployed into production:

  • What data will be available?
  • What time frame should be considered for a prediction – probability to churn next week, next month?

Another common pitfall is to produce models that predict people that are obviously going to churn. This is not only a problem with the model, but also with the business implications: the output suggests action when it will be ineffective, since the customers have already decided their intent to churn and thus aren’t sensitive to marketing actions. Instead, models should catch the people who are evaluating leaving and trigger an early warning system.

Finally, the last pitfall is to see a churn model as a one-shot study. Multiple reviews and iterations create a successful strategy to retain and increase customer loyalty. This will likely improve the scalability and reproducibility of the project as well.

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Dataiku for Churn Prediction: From Raw Data to Business Impact

Dataiku is the platform democratizing access to data and enabling companies to build their own path to Enterprise AI. Building a churn prediction model is made easy with Dataiku’s advanced features:

 

  • Accessing and cleaning data: Dataiku’s sophisticated data wrangling features will facilitate the combination of structured (CRM, web analytics…) and unstructured (emails, calls…) data sources to get a 360° view of client actions.
  • Predictive modeling: Dataiku equips users with a broad range of algorithms to use and compare, from simple linear models to complex ensemble methods. Whether they’re implemented in an automated decision system or a churn impact analysis,  this variety equips organizations with the tools to build the best solution every time.
  • Deploying a data product: With Dataiku’s model deployment and data flow automation features, a model can move quickly from a proof of concept to a productionalized business application that drives real value.
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