Assortment Optimization
Assortment Optimization leverage machine learning algorithms to determine the ideal mix of products to offer in a store or online, considering factors like sales data, customer preferences, profit margins, and shelf space constraints. The AI analyzes granular data to identify which products are complementary, which are substitutes, and how changes in one product's availability affect sales of others. This helps retailers maximize sales and minimize dead stock.
Imagine a brilliant chef who knows exactly which ingredients (products) to stock to create the most popular dishes (sales) given their kitchen's size and customer tastes.
Optimizing product assortment directly impacts profitability by ensuring shelves are stocked with what customers want, reducing waste from unpopular items.
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