Making every store location a data driven decision
A location intelligence solution that combined store performance, customer demand, demographics, competition, mobility and geographic signals to evaluate new locations, identify cannibalization risk and optimize the retail network.
Store expansion decisions relied too heavily on local knowledge and fragmented market information.
Retail location teams had access to sales and demographic information, but assessing a new site required bringing together multiple datasets and manually comparing potential demand, competition and proximity to existing stores.
What we found
- Location assessments were performed using multiple spreadsheets, reports and external market datasets.
- Store sales were available, but the underlying drivers of local demand were not consistently modeled.
- Competition and proximity to existing stores were considered separately from demand potential.
- Two locations with similar demographics could produce very different results because of accessibility, mobility and surrounding retail activity.
- New site evaluations took significant analyst time and depended on repeated manual analysis.
- Network cannibalization was often assessed after a proposed site had already progressed through the approval process.
- There was no common score to compare candidate locations across markets.
What the business needed
- A repeatable location scoring framework across markets and store formats.
- A single view combining internal performance with external geographic signals.
- Demand potential estimates for candidate locations before significant investment.
- Quantified cannibalization risk against the existing store network.
- Visibility into competition, demographics, mobility and accessibility.
- A decision dashboard that could be used by real estate, strategy, finance and operations.
- A framework that could support new stores, relocations, closures and network optimization.
We built a location intelligence engine that ranked sites using demand, competition and network economics.
The solution combined geospatial analytics with predictive modeling to move location decisions from isolated market assessments to a consistent network level view.
Store location and network optimization platform
The platform created a common analytical foundation for evaluating the current network and future site opportunities.
- Integrated store sales, transactions, customer origin, product mix and store attributes with demographics, competition, mobility and geographic data.
- Built trade areas around stores using distance, drive time and customer behavior rather than relying only on fixed radius assumptions.
- Created location level demand features covering population, income, household composition, traffic, accessibility, nearby businesses and competitor presence.
- Developed location scores that combined demand potential, competitive intensity, network overlap and operating considerations.
- Estimated potential revenue for candidate locations using historical store performance and comparable market characteristics.
- Modeled sales overlap with existing stores to identify potential cannibalization before investment decisions.
- Published interactive location dashboards for site ranking, market comparison and network planning.
A geospatial data foundation that connects market signals with store economics
The architecture creates a reusable location intelligence layer that can support real estate strategy, network planning and store performance decisions.
A six stage approach to turn location data into network decisions
The implementation established a common location score first, then extended the model into revenue potential, cannibalization and network optimization.
Define
Agree on store formats, location objectives, market criteria and commercial measures.
Integrate
Connect store, customer, sales, demographic, competition and geographic datasets.
Map
Create trade areas and geographic features that represent how customers access each market.
Predict
Estimate demand potential, revenue opportunity and cannibalization for candidate sites.
Rank
Score and compare locations using common business and market criteria.
Optimize
Evaluate openings, relocations and closures as a connected network decision.
Location planning became faster, more consistent and more commercially grounded.
Better location selection accuracy
Standardized scoring improved the consistency of candidate site evaluation across markets.
Higher new store revenue potential
Demand based site selection improved the revenue potential of selected locations.
Faster site evaluation
Automated data preparation and location scoring reduced repeated manual analysis.
Lower cannibalization risk
Network overlap analysis identified locations where a new store could shift sales from existing stores.
Faster market comparison
Common location measures allowed teams to compare markets and candidate sites using the same framework.
Reduction in manual location analysis
Reusable data products and dashboards reduced spreadsheet based preparation and repeated calculations.
The retailer could evaluate the network as a connected system instead of treating every store decision separately.
Location decisions became easier to compare, easier to explain and better connected to expected revenue and network economics.
Better Expansion Decisions
Candidate locations were ranked using demand potential, competition, accessibility and expected commercial value.
Lower Network Risk
Cannibalization analysis helped identify locations where new stores could materially overlap with existing store demand.
Faster Planning
Automated location scoring reduced the time required to prepare and compare site evaluations.
One View Across Teams
Real estate, strategy, finance and operations could use a common location score and evidence base for investment decisions.
Build the right store network for the right markets.
From geospatial data and trade area analysis to demand prediction, site scoring and network optimization, a connected location intelligence platform can help retailers make expansion and portfolio decisions with greater confidence.
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