Project Name: Reducing Customer Churn
Timeline: 06th June 2018 - 05th July 2018
Project Description
Churn (loss of customers to competition) is a problem for companies because it is more expensive to acquire a new customer than to keep your existing one from leaving. This problem statement is targeted at enabling churn reduction using analytics concepts.
Given DataSets
- Test_data.csv
- Train_data.csv
Problem statement The objective of this Case is to predict customer behaviour. We are providing you a public dataset that has customer usage pattern and if the customer has moved or not. We expect you to develop an algorithm to predict the churn score based on usage pattern. The predictors provided are as follows:
- account length
- international plan
- voicemail plan
- number of voicemail messages
- total day minutes used
- day calls made
- total day charge
- total evening minutes
- total evening calls
- total evening charge
- total night minutes
- total night calls
- total night charge
- total international minutes used
- total international calls made
- total international charge
- number of customer service calls made
Target Variable
- move: if the customer has moved (1=yes; 0 = no)
Deliverables
- Code written in both R and Python
- Project output with the given datasets in an .ipynb notebook which can be accessed https://sangeetm.github.io/Churn-Reduction/Churn-Reduction.html