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.

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

Result

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.​

Deliverables:​​

1. Analysis of the in-depth behavior and attitude patterns of identified super detractors. ​
2. Machine learning model identifying potential Super Detractors based on their behaviours, products, care interactions and usage.​ ​
3. Daily list of top 100 customers at risk.​

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.

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

Result

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.​

Deliverables:​​

1. Analysis of the in-depth behavior and attitude patterns of identified super detractors. ​
2. Machine learning model identifying potential Super Detractors based on their behaviours, products, care interactions and usage.​ ​
3. Daily list of top 100 customers at risk.​