How lekker got more insights into their customer churn model with Amazon SageMaker Debugger

 How lekker got more insights into their customer churn model with Amazon SageMaker Debugger

With over 400,000 customers, lekker Energie GmbH is a leading supraregional provider of electricity and gas on the German energy market. lekker is customer and service oriented and regularly scores top marks in comparison tests. As one of the most important suppliers of green electricity to private households, the company, with its 220 employees, stands for environmentally and consumer-friendly products.

Germany’s energy market was liberalized in the 1990s. Since then, customers have free choice of their energy and gas supplier. During the liberalization, the German government standardized the switching processes, so switching your energy or gas supplier is an easy task. However, it’s a challenging task for lekker to hold churn rates low. Preventing existing customers from leaving is several times cheaper than acquiring new ones. The best way to realize low churn rates is to keep their customers satisfied. Knowledge about a customer’s churn risk is helpful information for target-based campaigns, because it allows lekker to focus on customers who are more likely to churn.

This post discusses how lekker used Amazon SageMaker Debugger to get deep insights into their customer churn model. Debugger automatically collects data during model training and provides built-in rules to automatically detect issues in model training.

Data preprocessing

lekker has a wide range of systems with different databases and data structures, and uses Spark and AWS Step Functions to create a data lake on AWS. In preparation of the churn model, lekker creates a Spark processing job that holds customer-specific information like duration, sales channel, consumption, and other information for label creation. lekker make distinctions between active and passive churn. Active churn describes customers canceling their contract. Passive churn describes customers who are no longer in lekker’s delivery area or whose contract was cancelled due to late payment. For the introduced model, lekker uses active churn as a label, which helps better fit marketing expectations for retention campaigns.

Create a customer churn model

Before lekker started with AWS, data came from an Oracle database, which was used as a business intelligence (BI) platform. The BI team and analysts were organized in different departments and had different access rights. Data scientists needed to access data by schema-on-read. Models were trained on local machines or non-scalable servers, and computational restrictions came up quickly. If a model was trained, model monitoring and debugging was hard to perform, while management’s skepticism of potential closed-box models grew. Model deployment was also difficult, caused by missing orchestration tools and limited server availability and capacity.

When lekker decide


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