Retail Site Selection

RetailSiteSelectionSoftware

PopEx retail site selection software finds high-performing retail locations using precise population data, drive-time trade areas, and competitor mapping.

Eliminate the guesswork

Real-estate value follows population.

Retail site selection mapping identifies high-performing trade areas by combining population density, household income, and POI context like competitors and anchors. Map cannibalization risk, compare candidate sites, and forecast store performance with data-driven trade areas instead of guesswork.

Drive-Time Trade Areas

Model 5/10/15-minute catchments that match how people travel, not just how far a circle reaches. Select retail sites using realistic access metrics, and high-resolution population density. Learn more about creating travel-time boundaries, researching global POIs, and finding population hotspots.

Current & Forecasted Population

Use annually updated high-resolution demographics plus forecasts, income layers, and a comprehensive Google database containing all of your competitors and allies to size demand accurately across all of your prospective retail sites. Learn more about using custom shapes or existing administrative boundaries to narrow down your market research.

POIs & Competitive Context

Layer Google POIs against candidate retail sites to see co-tenancy, competitors, and demand drivers that impact sales potential and ramp-up timelines.

Last updated

May 6, 2026

Frequently Asked Questions

How retail site selection works in practice

Retail site selection balances demographics, co-tenancy, accessibility, and competitive context. A strong location can anchor a region; a weak one can drain resources. Population Explorer equips site selection teams with data-driven workflows to compare and prioritize multiple candidate sites.

  1. Define or import trade areas - Draw buffers, create isochrones, or upload existing polygons for review.
  2. Layer demand and access - Use LandScan and WorldPop demographics, income, and overlay Google Places POIs to highlight anchors like malls, grocers, and transit hubs.
  3. Export for operations - Generate reports, shapefiles, and ZIP lists for boards, landlords, or financing partners.

FAQs every retailer asks

How do I measure demand potential?
Combine LandScan and WorldPop with household income and spending power to compare prospective retail sites.

Can I analyze drive-time and walk-time coverage?
Yes. Use isochrones for accurate travel-time boundaries: determine population density within a certain travel time of your candidate retail sites to identify priority locations.

How do I evaluate competition?
Overlay Google Places POIs to identify retailers, anchors, and demand drivers.

What about cannibalization between stores?
Model overlap between trade areas to avoid over-saturation.

How do lease terms and co-tenancy affect decisions?
Map anchors and tenant clusters within range of your prospective retail site.

What about franchise disclosure documents (FDDs)?
Exports from PopEx provide a defensible basis for disclosure.

How do urban vs suburban sites differ?
Urban markets rely on pedestrian and transit access; suburban on vehicles and parking.

How current is the data?
Census tables may lag 5-10 years. PopEx refreshes annually with projections.

How do lease renewals affect long-term viability?
Evaluate whether long-term demographics support renewal decisions.

What if anchor tenants in a mall change?
Anchor churn can dramatically impact performance. POI overlays show current anchors and competitive shifts.

How does omnichannel retailing affect site selection?
Most retail sites must now support buy-online-pickup-in-store (BOPIS) and last-mile delivery.

How do seasonal patterns affect site choice?
Retail performance often spikes around seasonal events or holidays.

Can I test co-tenancy scenarios in PopEx?
Yes. Overlay Google Places POIs to simulate the impact of new anchors or competitor exits.

Why census data can distort retail site decisions

Census-based datasets may miss new malls, suburban growth, or urban infill, creating blind spots for retail site maps. Population Explorer improves accuracy for retail site selectors with annual LandScan and WorldPop updates and Google Places POIs. This combination reflects present-day demand and commercial patterns.

Benefits of a self-serve workflow

Consultant reports are costly and slow to refresh. A self-serve platform empowers retail site selectors and realtors to run scenarios directly.

  • Agility - Compare multiple retail sites quickly.
  • Cost control - Lower recurring consultant expenses.
  • Accuracy - Territories reflect refreshed LandScan, WorldPop, and Google Places data.
  • Transparency - Provide boards and lenders with reproducible evidence.