AI Services
Customer Segmentation For Banking
- Objective: To enhance targeted marketing and improve customer retention by developing a detailed segmentation strategy for retail banking customers based on behaviors, needs, and preferences.
- Challenges: Diverse customer base with varying financial needs, behaviors, and demographics. Limited personalization in product recommendations, leading to lower customer engagement. High churn rate among specific customer segments, particularly among younger and high-net-worth individuals.
Solution Approach
Data Collection & Analysis
- Aggregated data on transaction history, product usage, demographic information, online banking interactions, and credit scores.
- Included external data sources, such as lifestyle preferences and socioeconomic indicators, for more holistic insights.
Customer Segmentation Model
- Employed clustering techniques (e.g., K-means, hierarchical clustering) to create customer segments based on behavioral and demographic factors.
- Segments included categories such as “Young Professionals,” “High-Net-Worth Individuals,” “Families with Growing Needs,” and “Retirees.”
Targeted Marketing Strategy
- Developed personalized marketing campaigns with relevant product recommendations (e.g., mortgages for families, investment products for high- net-worth individuals).
- Implemented real-time personalization in digital channels (mobile banking app and website), presenting tailored offers based on customer segment and recent activity