Big Data Predictive Analytics of the Churn Rate
Expert: Michael Scharpf
Industry: Automotive & Engineering
Area: Marketing & Sales
Increase customer loyalty and reduce costs with our Big Data Predictive Analytics to identify switching candidates.
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In the automotive industry, it is expensive to win a new customer. Therefore, it is crucial to identify customers with switching intentions at an early stage and retain them through appropriate measures. Our client, a leading car manufacturer, was faced with the challenge of identifying customers with a high risk of switching and understanding the reasons for their churn behaviour.
To predict customer switching intentions, we used a generalised linear model (GLM). The GLM uses various data sources, including customer, vehicle and social media data, to create a holistic customer history. Using this data, we performed modelling to determine the likelihood of customer churn and identify the drivers of it.
Thanks to our method, we were able to identify exactly those customers who had the highest risk of switching. By targeting these customers and developing appropriate customer retention measures, our client was able to significantly reduce churn rates. Resource allocation in the company became more effective and the company was able to both save costs and increase customer loyalty. The method has a hit rate of 90%, which means that our model was able to correctly predict churn in most cases.
Our project shows how Big Data Predictive Analytics can help companies better understand their customers and develop effective marketing strategies. The use of machine learning methods such as GLM and the use of social media data enables companies to develop a deep understanding of their customers and better understand their needs and wants. This can help businesses not only retain customers, but also attract new ones and increase their sales.
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Michael Scharpf | Sr. Principal Key Account Manager | Alexander Thamm GmbH