Customer Clustering Analytics
For a Large Asset Management Company
CLIENT & PROBLEM STATEMENT
- The Client is large Asset Management Company based in India with a large customer retail base.
- The client wanted to leverage customer clustering analytics to gain insights into the investment behavior of clients and subsequently classify them based on demographics, relationship value (e.g., Assets under Management (AUM) and transaction behavior (e.g., gross sales, net sales).
APPROACH
- We collated comprehensive data on customer demographics, relationship value, and transaction behavior from various sources within the AMC’s database.
- We employed the K-means clustering algorithm as a central component of our approach.
- Factors such as investment frequency, preferred fund types, and investment amounts were considered during the clustering process.
- By analyzing which customers invested in specific types of funds, we enabled the AMC to tailor its product offerings to better align with the needs and preferences of each segment.
SOLUTION & OUTPUT
- The classification of customers based on demographics, relationship value, and transaction behavior facilitated targeted marketing efforts and personalized recommendations.
- The AMC could recommend specific schemes to each customer cluster based on their investment preferences, thereby enhancing customer satisfaction and loyalty.
