About the Company.
A major Eastern European retail chain operating 200+ hypermarkets and serving millions of customers annually. With 35,000+ employees and a wide geographic footprint, the company relies on accurate location planning, customer insights, and geospatial intelligence to drive profitable expansion.
Solution Summary.
Algoscale built an AI-powered geospatial expansion engine that unified mobility data, competitor presence, demographic layers, and the retailer's first party customer insights to score and prioritize new store locations. The solution delivered an interactive location intelligence dashboard and a standardized analytics framework for expansion, operations, and finance teams.
Customer Challenges.
The client faced significant challenges from fragmented contract management and lack of automation
- Needed ML driven recommendations for new store placements using geospatial & mobility data.
- Existing stores were cannibalizing each other due to overlapping catchments.
- Data existed in silos like sensory traffic data, competitor presence, and demographics were not unified.
- No standardized framework to evaluate store potential or validate expansion decisions.
- Limited ability to incorporate first party transaction data to understand customer value across regions.
Algoscale Solution.
Algoscale engineered a custom AI/NLP-based contract intelligence pipeline to automate translation, extraction, and governance
Built a geospatial ML framework combining mobility patterns, population density, sensory traffic data, competitor distances, CPI/inflation, and category level purchase indicators.- Integrated the retailer’s first party customer & transaction data to identify high LTV clusters and include buying power into store location scoring.
- Mapped financial performance, driven time polygons, market saturation, and catchment heatmaps to highlight high value zones with minimal cannibalization risk.
- Designed a unified analytical protocol with QA checks, standardizing how expansion teams evaluate new sites.
- Delivered an interactive geospatial dashboard for scenario planning, expansion simulations, and profitability scoring.
Algoscale Differentiators.
Deep experience in geospatial analytics, ML-based catchment modeling, and retail planning.- Ability to merge first party data with external mobility, demographic, and competitor datasets.
- Proven frameworks to minimize cannibalization and quantify store expansion ROI.
- Strong expertise in building intuitive location intelligence dashboards for strategic planning.
Values Delivered.
Through this engagement, Algoscale delivered measurable improvements:
- 15% revenue uplift at newly opened stores, even in high competition areas.
- Identified high value catchment pockets using population density and mobility heatmaps.
- Prevented inter-store cannibalization through accurate boundary and market saturation modeling.
- Enabled faster, data backed expansion decisions with a geospatial scenario planning tool.
