Predicting Customer Churn



In this webinar, the BlueGranite team will demonstrate the value of cloud-based technologies for customer churn prediction. Check out our other Azure videos here:

In today’s complex, omnichannel retailing market, maintaining a high customer retention rate is critical to success. Understanding when a customer may be at risk to break ties with your organization could help you take a more targeted approach to relationship management, effectively plan for future financial impact, and even prevent the loss of customers in the first place.

Using the right tools, it is possible to accurately predict customer churn by analyzing historical data from previous and existing clients.

Featuring Azure Databricks – Apache Spark cluster technologies – to create an extremely fast and efficient solution built collaboratively between data scientists and data engineers using mix of product and customer data.

Additionally, we explore how data sets can be enriched to identify root causes of churn so that campaigns and conversations can be created to not only prevent churn, but also to potentially re-acquire dissatisfied customers.

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1 Comment

Emilia Shikeenga

This was very interesting to watch and learned a lot, thank you. A question, why do you think Decision tree performed better than Random forest in this example? Would be interesting to know since we know in most cases RF mostly outperforms DT

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