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 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.
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.
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.
Data-driven retention analytics reduces churn and retains customers cost-effectively.
We are happy to answer any questions regarding our services, your data, and how we could work together.
This project has indirectly received funding from the European Union’s Horizon 2020 research and innovation programme under project REACH Incubator (Grant Agreement no. 951981).
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