View the workshop video – Reduce Customer Churn with Predictive Analytics


The truth is, a lot of analytics are still backward looking; they measure what’s already happened. A rear view mirror may be great if you need to explain why your sales were up or down last month, but it’s NOT adequate for retaining customers BEFORE they leave.

If you monitor task accomplishment, customer satisfaction metrics and customer churn, you may struggle with combining spreadsheets from different analytics systems. But it is all rear view mirror data about the past and customers that may have already left your business.

We’ll focus on using predictive analytics to detect and reduce customer churn, but we’ve also applied it to improving sales margins, and optimizing pricing.

This video covers how to use predictive analytics to:

  • Determine which customers are most likely to churn
  • Pinpoint the factors that indicate churn risk
  • Take targeted actions to retain your most valuable customers

There’s a lot of confusion around Machine Learning and Predictive analytics, so we want to avoid being too technical today. Instead we will focus on how you can use Predictive Analytics. Then we’ll suggest how you can get started with Predictive Analytics.

View the Customer Churn workshop video:


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