A Cross-Selling Model Framework using Machine Learning for a leading US Retail Bank to identify deposit holders/customers who are most likely to accept a personal loan
Client : Leading US Retail Bank
Objective
To help a leading U.S. retail bank increase cross-sales and reduce marketing expenses by creating a machine-learning-based model to identify deposit holders/customers most likely to accept a personal loan.
CRISIL's Solution
- Implemented various data mining techniques to identify customers who most likely to accept a personal loan
- Developed Exploratory Data Analysis (EDA) to find the outliers, trends and patterns in customer data
- Developed Tree-Based classification models (classification and regression trees, unbiased recursive partitioning: a conditional Inference and random forests) to analyze the relationship between the target variable and covariates
- Developed a support vector machine (SVM) algorithm to classify customers
- Developed a k-means clustering approach to group customers to study their grouping properties
- Employed open-source software R was for this data-intensive implementation
Client Impact
- Increased revenues by enhancing cross-selling success rates. New model improved immediate response rate by 21 percent and the long-term response rate by 34 percent
- Allowed bank to reduce marketing expenses through effective sales targeting
- Model integrated into the client’s systems and is now a key driver of sales & marketing strategies