Retail Expansion · Site Analysis
Every failed retail location started with a site evaluation that looked good on paper. The trade area circle said 80,000 people. The demographics said median income $72K. The lease was signed. The store underperformed. What the circle missed: a rail yard on the west side cutting off half the population, and two competitor locations that the radius tool didn't flag because their polygons only slightly overlapped. DriveZone shows you what the circle doesn't.
Three failure modes
A 3-mile radius from a candidate site on the north bank of a river counts all the residents on the south bank — even if there's only one bridge, five miles away. In dense markets, this type of barrier error produces population overcounts of 30–50%. Retailers who base lease decisions on those counts sign leases for locations that can never reach the projected revenue.
ZIP code demographic analysis is still standard at most retail chains. It's also systematically wrong: ZIP codes cross income boundaries, split neighborhoods, and rarely align with actual trade areas. A site that spans two ZIP codes may pull demographic data from neither one accurately. Block-group-level data inside a real drive-time polygon is the correct method — and the one this tool uses.
Retail chains consistently report that their lowest-performing locations are ones that looked viable in isolation but were opened too close to existing units. Radius analysis misses this because it can't accurately model which customers in an overlap zone will choose which location. Drive-time polygon overlap analysis identifies the shared trade area before the lease is signed.
Feature breakdown
Every trade area boundary is calculated from actual road network routing — not a Euclidean circle. Set any threshold from 5 to 60 minutes. Toggle drive, walk, or bike modes. The resulting polygon reflects geographic barriers, road density, and traffic patterns.
Population, household count, median household income, and median age — all measured inside your actual drive-time polygon using Census block-group data. Not a ZIP code average. Not a radius estimate. The real demographic profile of your real trade area.
Count competitor locations within your trade area polygon. Visualize their positions relative to your candidate site. Measure competitive density ratios (population per competitor) against category benchmarks to assess market saturation.
Load your candidate site and existing units simultaneously. Measure polygon overlap as a precise percentage. The Business plan supports N-way comparison — evaluate an entire market footprint before committing to the next location.
Evaluation framework
Generate the 10-minute drive-time isochrone (primary trade area) and the 20-minute isochrone (secondary). Choose the threshold that matches your category's customer willingness-to-travel. Resist the temptation to use the same threshold for every market — urban and suburban sites of the same concept often have significantly different real trade area sizes.
Read the population, household count, and median household income inside the polygon. Compare against your category's minimum demographic thresholds — the figures that your brand's existing high-performing locations hit in their primary trade areas. If the candidate site doesn't meet the demographic floor, the location is already at risk.
Count competitors within the polygon. Calculate the population-per-competitor ratio and compare it against your category benchmark. A ratio well above benchmark signals an under-served market. A ratio below benchmark signals saturation — the question then becomes whether your concept is differentiated enough to capture share from existing operators.
Apply your category's penetration rate and average annual spend per customer against the primary zone population to build a revenue range. This is not a prediction — it's a structured comparison tool. Its value is comparing sites against each other on the same methodology, not in hitting a specific number.
Load the candidate site's polygon alongside each nearby existing unit. Measure trade area overlap percentage. A 15% overlap is generally acceptable. Above 30%, model the revenue transfer before you greenlight the site. Above 40%, the cannibalization risk is high enough to reconsider the location or renegotiate the protected territory of the existing unit.
FAQ · Retail site selection
Expansion markets
Related reading
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