Customer Segmentation for a Leading Money Exchange Company
Company Size
1,000+
Country
- Worldwide
Product
- RFM (Recency, frequency, monetary) variables
- K-means clustering
Tech Stack
- Data Analysis
- Customer Segmentation
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Customer Satisfaction
- Revenue Growth
Technology Category
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Finance & Insurance
Applicable Functions
- Sales & Marketing
Services
- Data Science Services
About The Customer
The customer is a leading money exchange company operating globally. They are one of the world's top companies in the financial services industry. The company wanted to understand their customers better for targeted promotions and reactivation campaigns. They aimed to identify profitable customers and target relevant ones for their loyalty program. Additionally, they wanted to identify customers performing adversely compared to the cluster average and target them for reactivation timely. The ultimate goal was to perform targeted campaigns based on the behavioral pattern of the customers for revenue maximization.
The Challenge
The company wanted to segment its customers according to their transaction patterns and other behavioral traits in order to identify the profitable customers and target relevant ones for the loyalty program. They also wanted to identify the customers performing adversely as compared to the cluster average and timely target them for reactivation. The goal was to perform targeted campaigns based on the behavioral pattern of the customers for revenue maximization.
The Solution
The solution involved perceiving customer behavior through the analysis of their transactional trends. RFM (Recency, frequency, monetary) variables were used to perform K-means clustering to congregate similar characteristics and segregate dissimilar trends. Two models were built, one at an overall business level and the other at corridor level to have a better understanding of customers in different countries. Profiling of the customers in different clusters was done based on their transaction pattern (salary, festival, campaign period etc.).
Operational Impact
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