Retail Assortment Optimization with Machine Learning | Improve Retail Sales | Sigmoid Case Study
Watch this case study to know how Sigmoid helped a global F&B major with a ML solution to align their brand strategy with business rules and promotion guidelines. Sigmoid developed an ML-based assortment lifecycle solution that sorted products across different categories based on growth rate and relative market share of the products to identify product profitability. This led to an improvement in market share by 0.8% and 3% improvement in contribution margin.
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