2-5% higher revenue with AI-driven store segmentation and assortment optimization.
The client runs a global network of stores. To maximize revenue and optimize inventory in the new markets, they needed a tailored store-level assortment strategy that would take into account multiple market factors and historical demand. Hiflylabs designed an AI-driven solution to cluster stores, forecast demand, and optimize product selection, resulting in projected revenue growth and data-driven expansion recommendations.
2-5%
expected annual revenue growth per store
10
new store locations recommended
Hiflylabs implemented an AI-driven solution in three phases. First, we clustered stores based on weather, demographics, and POI data. Next, we built store-month-product demand models using inventory and transactional data. Finally, we introduced linear integer programming to optimize future assortments considering predicted demand and capacity limits.
This structured approach created a data-driven basis for new store openings and allowed us to suggest 10 new store locations, promising to maximize revenue across the network.
AI
Data
Retail
Python
Dataiku
Google OR-Tools
Python
Dataiku
Google OR-Tools
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