Product Segmentation
Challenge
Varied inventory levels, with high and low product turnovers made management complex.
Products were not prioritized by high velocity and profitability.
Out-of-stock events couldn’t be avoided due to lack of data.
Solution
Built a clustering algorithm to categorize products by recency of purchase, purchase frequency, and profit margin, for each store.
Categorized products into four classes (A, B, C and D) to focus on best performers
Results
Increased overall margin by 2% by focusing on high-margin product categories.
Reduced slow velocity product inventory and increased best-selling inventory.
Improved inventory management by treating different product categories differently.
Avoided more out-of-stock events using product categories.