An energy company in Europe sought to stop its increasing churn rates, driven by an undifferentiated sales and retention approach.
The company was operating with only a basic customer segmentation strategy and assuming a one-size-fits-all method for its customers.
It was critical for the client to reduce its churn rates. Our MyBase platform became a valuable tool to conduct a comprehensive churn profiling project for our client.
We implemented a holistic customer base management system, including GDPR-compliant data storage and usage, and elevated the pricing approach to a new level.
To overcome this challenge, we implemented an extensive ETL (extract, transform, load) process, along with several predictive churn models and an interactive dashboard.
We used a three-step approach to develop a CLTV (customer lifetime value) model, a predictive churn model, and a customer segmentation model.
Step 1: We created a database of variables to accurately predict churn and price acceptance. This database was used to analyze when in the pricing timeline customers were most likely to churn (around ten weeks after renewal).
Step 2: Next, we applied machine learning models to understand customers’ price elasticity curves, which we used to build price acceptance models.
Step 3: We incorporated all the components into an interactive dashboard, enabling the pricing manager to adjust models and product prices while seeing the effects of pricing decisions at a portfolio level.
With stronger tools, the pricing manager achieved more detailed product pricing, faster price setting, and a deeper understanding of customer price elasticity.
More importantly, the interactive dashboard helped the steering committee assess the long-term impact of pricing decisions on portfolio margin.
The churn rate decreased by more than 10 percent with our MyBase solutions. Our client also reported a margin increase in excess of 10 million euros within the first year of implementation.
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