AI Store Segmentation and Assortment

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

Challenge

The client wanted to maximize revenue in Western Europe as part of its global expansion strategy. External factors like weather, demographics, and nearby points of interest, along with traffic, attendance, and inventory data, made manual assortment decisions difficult. Without a systematic approach, revenue potential remained untapped, and expansion planning was uncertain.

Solution

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.