Customer Segmentation and Targeting Model

Quantilus designed & developed the segmentation and targeting model to help the supermarket create more personalized and effective marketing campaigns, through: 

• Data collection: The model collects data from multiple sources, including customer transactions, demographics, and behavior, to build a comprehensive view of the customer base. 
• Customer segmentation: The model segments customers based on common characteristics such as demographics, purchase history, and product preferences. 
• Predictive modeling: The model uses predictive modeling techniques to identify which customers are most likely to respond to specific promotions. 
• Targeted marketing campaigns: The model enables the supermarket to create targeted marketing campaigns for specific customer segments, based on their characteristics and predicted response rates. 
• Performance tracking: The model tracks the performance of marketing campaigns, enabling the supermarket to evaluate the effectiveness of the campaigns and make data-driven decisions. 
  
  

The segmentation and targeting model offered several benefits to the supermarket, including: 

• Increased engagement: The supermarket could increase customer engagement and response rates by creating more personalized and relevant marketing campaigns. After implementing the segmentation and targeting model, the supermarket saw a 25% increase in customer engagement, as measured by click-through rates and conversions. 
• Reduced waste: Targeted marketing campaigns reduce wasted resources by focusing on customers most likely to respond to the promotions. By creating targeted marketing campaigns, the supermarket reduced its marketing spend by 30%, while maintaining the same level of customer response. 
• Efficient marketing: The model enabled the supermarket to create and execute marketing campaigns efficiently by identifying specific customer segments. It effectively reduced the time required for campaign planning and execution by 40%. 

• Data-driven decisions: The model provided data-driven insights into customer behavior and preferences, enabling the supermarket to make more informed marketing decisions. The supermarket was able to realize a 20% increase in sales revenue.
  
  

The segmentation and targeting model provided a valuable solution to the supermarket’s challenges in creating effective marketing campaigns. By collecting customer data, segmenting customers, and using predictive modeling to create targeted campaigns, the model enabled the supermarket to engage customers more effectively, reduce waste, and make data-driven decisions.