Happy Customer Bank is a mid-sized private bank which deals in all kinds of loans. They have presence across all major cities in India and focus on lending products. They have a digital arm which sources customers from the internet.
Digital arms of banks today face challenges with lead conversion, they source leads through mediums like search, display, email campaigns and via affiliate partners. Here Happy Customer Bank faces same challenge of low conversion ratio. They have given a problem to identify the customers segments having higher conversion ratio for a specific loan product so that they can specifically target these customers, here they have provided a partial data set for salaried customers only from the last 3 months. They also capture basic details about customers like gender, DOB, existing EMI, employer Name, Loan Amount Required, Monthly Income, City, Interaction data and many others. Customer applications can drop majorly at two stages, at login and approval/ rejection by bank. Here we need to identify the segment of customers having higher disbursal rate in next 30 days.
We have train and test data set, train data set has both input and output variable(s). Need to predict probability of disbursal for test data set.
Problem from http://discuss.analyticsvidhya.com/t/hackathon-3-x-predict-customer-worth-for-happy-customer-bank/3802