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.
Case Study
Predicting super detractors
The Business Challenge
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.
Deliverables
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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Retention Analytics
Data-driven retention analytics reduces churn and retains customers cost-effectively.
