Case Study

Predicting super detractors

The Business Challenge

The client was struggling to reduce customer churn among non-respondents to customer satisfaction surveys. Identifying super detractors before they churn was critical for the business.

The Solution

We built a machine learning model, based on 1200+ variables analyzed for each customer, detecting potential super detractors based on their behaviors and producing key talking points for the rescue call.

Predicting super detractors 1


Analysis of the in-depth behavior and attitude patterns of identified super detractors

Machine learning model identifying potential Super Detractors based on their behaviours, products, care interactions and usage

Daily list of top 100 customers at risk

Business impact

The model helped our client increase the likelihood of identifying a Super Detractor by 7 times and average NPS given by customers by 2 points.​

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