Problem Statement
A digital arm of a bank faces challenges with lead conversions. The primary objective of this division is to increase customer acquisition through digital channels. The division was set up a few years back and the primary focus of the division over these years has been to increase the number of leads getting into the conversion funnel.
They source leads through various channels like search, display, email campaigns and via affiliate partners. As expected, they see differential conversion depending on the sources and the quality of these leads.
They now want to identify the leads' segments having a higher conversion ratio (lead to buying a product) so that they can specifically target these potential customers through additional channels and re-marketing. They have provided a partial data set for salaried customers from the last 3 months. They also capture basic details about customers. We need to identify the segment of customers with a high probability of conversion in the next 30 days.
Link to the competition
Data
Input variables:
ID - Unique ID (can not be used for predictions)
Gender - Sex of the applicant
DOB - Date of Birth of the applicant
Lead_Creation_Date - Date on which Lead was created
City_Code - Anonymised Code for the City
City_Category - Anonymised City Feature
Employer_Code - Anonymised Code for the Employer
Employer_Category1 - Anonymised Employer Feature
Employer_Category2 - Anonymised Employer Feature
Monthly_Income - Monthly Income in Dollars
Customer_Existing_Primary_Bank_Code - Anonymised Customer Bank Code
Primary_Bank_Type - Anonymised Bank Feature
Contacted - Contact Verified (Y/N)
Source - Categorical Variable representing source of lead
Source_Category - Type of Source
Existing_EMI - EMI of Existing Loans in Dollars
Loan_Amount - Loan Amount Requested
Loan_Period - Loan Period (Years)
Interest_Rate - Interest Rate of Submitted Loan Amount
EMI - EMI of Requested Loan Amount in dollars
Var1 - Categorical variable with multiple levels
Approved - (Target) Whether a loan is Approved or not (0/1)
Evaluation Criteria
The Evaluation Criteria for this problem is AUC_ROC .