Churn (loss of customers to competition) is a problem for telecom companies because it is more expensive to acquire a new customer than to keep your existing one from leaving. This project is about conducting churn reduction using analytics.
Most telecom companies suffer from voluntary churn. Churn rate has strong impact on the life time value of the customer because it affects the length of service and the future revenue of the company. For example, if a company has 25% churn rate than the average customer lifetime is 4 years; similarly a company with a churn rate of 50%, has an average customer lifetime of 2 years. It is estimated that 75 percent of the 17 to 20 million subscribers signing up with a new wireless carrier every year are coming from another wireless provider, which means they are churners. Telecom companies spend hundreds of dollars to acquire a new customer and when that customer leaves, the company not only loses the future revenue from that customer but also the resources spend to acquire that customer. Churn erodes profitability.
ABC Wireless Inc. has hired you to help them with the customers’ churn issue. In this project, you will be working as a part of a team to use historical data from ACB Wireless Inc. to build a model that can predict/identify their customers who are likely to churn. You are free to use any modeling technique/ approach from what we have discussed in our course. Also, you are free to include any set of variable available in their dataset for your model.
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Project report: It includes the following sections
- Project Goal
- Overview of data, including data exploration analysis
- Details of your modeling strategy (i.e. what technique and why)
- Estimation of model’s performance
- Insights and conclusions
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R codes and script
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Prediction’s File
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Presentation Slides
- Nick (@odintech3)
- Alan Smith (@adsmith3)
- Spandana (@spandanasudalagunta)
- Clifton Rebello (@RebellloC)
- Kareem Rogers (@kroger27)