The Business Problem
Traditional retailer segmentation is often one-dimensional and static, grouping stores by last year’s sales volume alone over longer range data. This simplistic approach fails to capture the true nature of a retail partner, especially as it goes through several demand side patterns. It cannot distinguish between a large, stagnant store and a smaller, high-growth store that is rapidly adopting new product trends.
The AI Solution
This project builds a sophisticated, multi-layered segmentation engine that analyzes retailers through three independent strategic lenses:
- Trend Adoption: Using sales data for “New Era” vs. “Traditional” products, this model identifies which retailers are Early Adopters of new trends versus Traditional Strongholds.
- Business Value: Using revenue and growth metrics, this model segments retailers into personas like “High-Growth Stars” and “Stable Cash Cows.”
- Product Mix: This model analyzes the product assortment to find “Chocolate Specialists,” “Ice Cream Destinations,” or “Balanced Confectioneries.”
The system uses Unsupervised Machine Learning (K-Means Clustering) to discover these segments and then leverages a Generative AI (Google’s Gemini) to automatically create strategic personas and actionable recommendations for each group. What makes this stand out further is the aspect that this solution directly integrates back to the CRM solution to update the retailer segmentation automatically, and even further maintains the historical changes in segmentation so that sales strategists can understand the shifts even better.
Coming soon