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.
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.
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.
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.
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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