Decrease customer churn rate through predicting who are the most likely churners​

Challenge

The client was struggling to simultaneously identify the customers, who are the most critical churners while also sustaining the highest return on investment from marketing campaigns.​

Solution

We first applied a logit model to understand what were the most impactful drivers of churn. Additionally, the effect of the drivers was quantified. ​Next, we used extreme gradient boosted trees as the approach for predicting the churn on an individual level. ​

Reduce churn and increase customer satisfaction through predicting super detractors​

Result

We helped our client to efficiently spend the marketing budget and target efforts only at customers that were classified as part of the top percentile churners, reducing churn by 20% among the likely churners.​

Deliverables:

  1. A list of significant differences between the drivers of churn.​
  2. A list of suggestions for effective marketing campaign targeting. ​
  3. A churn score for each of the active customers of the client, allowing them to identify the ones that are most likely to churn in the upcoming months​.