PROBLEM STATEMENT The focus on customer churn is to determinate the customers who are at risk of leaving and if possible on the analysis whether those customers are worth retaining. The churn analysis is highly dependent on the definition of the customer churn. The business sector and customer relationship affects the outcome how churning customers are detected. Example in credit card business customers can easily start using another credit card, so the only indicator for the previous card company is declining transactions. So project goal is, given a data of spend and count per account we are creating supervised learning binary classification model which will predict if given account is at risk of churn of not.
CHURNING DEFINITION: For this project I am defining churning by three factors if there is not activity for last 4 months and if amount of transaction and count of truncations are decreasing over the period then those account are at the risk of churning.
DATASETS AND INPUTS: • Spend ( https://www.dropbox.com/s/qia89hd3i2j2nl6/spend.csv.gz?dl=0 )
• Counts ( https://www.dropbox.com/s/jbnzgk6shgnz5hb/counts.csv.gz?dl=0 )
The variables between the two files are: • date • account id • amount of spend for a time period • number of transactions for a time period
- Prajakta Gujarathi LinkedIn