Using Location Data to Fix Underperforming Stores (2026)

The Tool You Already Have, Used Backwards

Every brand I work with uses location data to evaluate new sites. Trade area demographics, foot traffic, competition density, drive-time polygons. They run the analysis, score the opportunity, and make a decision.

Then I ask: "Have you run the same analysis on your existing stores?"

The answer, almost always, is no. The store opened three or five or ten years ago. The trade area analysis that justified the lease is sitting in a folder nobody has opened since. The market around the store has changed, but the data the team looks at hasn't.

This is the gap. The same location intelligence that helps you pick new sites can diagnose why existing stores underperform and whether same-store sales optimization comes from operational fixes, lease renegotiation, relocation, or closure.

Portfolio optimization is the same discipline as site selection, pointed at stores you already operate.

Why Stores Underperform (And How to Tell)

A store that misses revenue targets has a problem. But the nature of the problem determines the solution. There are three categories.

The location changed. The trade area that supported the store when it opened looks different today. A new competitor entered. The anchor tenant in the same center closed.

A road was rerouted. The residential population shifted. The store is the same, but the market around it isn't.

The location was wrong from the start. The original site analysis missed something. The trade area was smaller than projected. The customer demographics didn't match the concept. The visibility from the road was worse than it appeared on paper. The store never had the potential anyone assumed it had.

The location is fine; operations are the problem. The trade area has enough demand. The demographics match. The traffic is there. But the store isn't capturing it. Staffing issues, poor merchandising, bad hours, weak marketing. The location data looks healthy; the P&L doesn't.

These three categories demand different responses. Diagnosing which one applies to each underperforming store is the first step, and it requires current location data, not the data from the year the store opened.

The Portfolio Optimization Audit: Five Steps

Here's how to use location data to diagnose your existing portfolio. This process works whether you have 10 stores or 500 (though larger portfolios benefit from automation to score at scale).

Step 1: Re-Score Every Location

Run the same site analysis on your existing stores that you'd run on a new opportunity. Current demographics. Current foot traffic. Current competition. Current trade area boundaries, measured rather than assumed.

This creates an apples-to-apples comparison. Your existing store at 123 Main Street gets the same score and breakdown that a prospective site at 456 Oak Avenue would receive. If 456 Oak scores an 82 and 123 Main scores a 61, you have a starting point for diagnosis.

One thing to watch: if your scoring platform includes a competition lens, your own store will appear in the competitive set for that location. That pulls the competition score down because the model sees a direct competitor at zero distance — which is you. When scoring existing locations, account for this by either excluding your own brand from the competitive analysis or interpreting the competition score with that context. The demographics, foot traffic, and visibility lenses are unaffected.

When we work with brands at GrowthFactor, we score entire portfolios using the same five-lens framework applied to new sites. The result is a ranked list of every location by current opportunity, not historical performance.

Step 2: Compare Predicted Performance to Actual

This is where the diagnosis happens. Once you have a current score for each location, compare it to the store's actual revenue.

Two patterns emerge.

High score, low actual revenue. The location data says this site should perform well, but it doesn't. This points to an operations problem. The demand exists. The customers are nearby. The traffic is there. Something inside the four walls is the issue: staffing, hours, format, marketing, cannibalization from your own nearby stores.

Low score, low actual revenue. The location data confirms what the P&L shows. The site doesn't have the trade area support it needs. The question becomes: did it ever have it, or did the market change?

A third, rarer pattern: low score, high actual revenue. This store is outperforming what the data suggests. Investigate why. It may have a local advantage the data doesn't capture (a loyal customer base, a unique format, an operational leader). Or it may be riding momentum that will fade as the trade area continues to weaken.

One frozen dessert brand we worked with discovered their assumed trade area of 16 minutes was actually 23 minutes when measured from customer transaction data. Stores they thought were underperforming relative to their trade area were actually pulling customers from farther away than anyone expected. The stores weren't underperforming. The performance expectation was based on the wrong geography. Once the team adjusted their trade area assumptions, two stores moved from the "close" discussion to the "invest" discussion.

Step 3: Check for Self-Cannibalization

Before blaming the market, check whether you're competing with yourself. When a brand has multiple locations in a metro, each store's trade area overlaps with others in the system.

A store that opened with a $1.2 million run rate and gradually declined to $900,000 may not have a market problem. It may have a newer store 12 minutes away that captured $300,000 of the original store's customers. The trade area shrunk not because demand left, but because your own brand redirected it.

Self-cannibalization is the most common hidden cause of same-store sales decline in growing brands. It's also the easiest to diagnose with data: overlay the trade area polygons of every store in a metro and look for overlap exceeding 20 to 25 percent. We covered the mechanics of this analysis in our market saturation guide.

Step 4: Segment Into Action Categories

Once diagnosis is complete, sort every underperforming store into one of four buckets.

The "close" category requires careful analysis. Research from ICSC's Halo Effect III study shows that closing a store suppresses a retailer's digital sales by 11.5 percent. A store that loses $50,000 per year on the P&L might be generating $200,000 in local digital sales that disappear when the physical presence goes away. The math changes when you account for the halo effect.

Step 5: Build the Benchmark for "Good"

After scoring the full portfolio, you know what a healthy location looks like in your system. Maybe it's a foot traffic score above 70, demographics fit above 65, and competition score below 40. Whatever the pattern is, it becomes your benchmark for both existing stores and new opportunities.

As Kevin Hawk at TNT Fireworks put it: "It may not be so much about opening the winning one as it is eliminating the losers. If you can just increase your batting average by not opening bad stores, that's super important." His team used their portfolio benchmarks to tighten the filter on new sites, reducing the rate of first-year closures.

The portfolio audit creates the benchmark that makes that elimination possible, and it applies retroactively to stores you've already opened.

What Changes When You Look at Existing Stores With Fresh Data

Most brands evaluate sites once, at signing, and never look again. The world changes around the store while the analysis stays frozen. McKinsey's research on physical store strategy confirms that trade areas are dynamic, not static, and that the most resilient retailers re-evaluate their portfolios continuously.

Trade areas shift. New residential development moves the center of population. A highway interchange opens and changes drive-time access. A competitor opens three miles away and shrinks your effective reach.

Co-tenancy changes. Your anchor tenant leaves. A complementary business closes and gets replaced by something irrelevant.

The center's occupancy drops from 95 percent to 75 percent. We detailed how to evaluate co-tenancy impact in a separate guide.

Demographics evolve. The neighborhood gentrifies and your value-oriented concept no longer fits the income profile. Or the opposite: incomes decline and your premium pricing loses its market. Census ACS data from five years ago doesn't capture what happened last year.

Competition enters. A direct competitor opens nearby. A category disruptor enters the market. An online-first brand opens a showroom in your trade area. Your competitive position changes even if nothing about your store does.

Running updated location analysis annually, or whenever a portfolio-level performance question arises, catches these shifts before they become crises. The data to do this already exists. Most teams just don't point it at their own stores.

The Relocation Decision Framework

Relocating a store is expensive: build-out costs, moving expenses, transition period with reduced revenue, marketing to announce the new location. Data should drive the decision, not frustration with current performance.

Three conditions that justify relocation:

1. The current location scores below your portfolio benchmark, and a higher-scoring site exists within the same trade area. You're not leaving the market. You're moving to a better position within it. The customer base is already familiar with your brand. The transition period is shorter because you're relocating, not entering.

2. The trade area has changed in ways that won't reverse. A highway bypass redirected traffic permanently. The anchor tenant's space is being converted to non-retail use. The demographic shift is structural (long-term population loss, not a temporary dip). If the change is permanent, staying doesn't help.

3. The lease is expiring and renewal economics don't support the location. The landlord wants a rent increase that the location's performance can't support. You have 12 to 18 months to find a better site in the market. This is the lowest-risk relocation scenario because you're not breaking a lease.

Two conditions where relocation is usually wrong:

The underperformance is operational, not locational. Moving a store with staffing problems to a better site creates a better-located store with staffing problems. Fix the operations first. If performance improves, the relocation question may disappear.

The brand is exiting the market anyway. If there's no viable site in the metro, relocating to a slightly better location in a market you shouldn't be in wastes capital. Close and redeploy the investment to a market with actual demand.

Putting It Into Practice

Portfolio optimization sounds like a project. It doesn't have to be. Start with three things.

Run your lowest-performing stores through a current site analysis. Take your bottom five stores by same-store sales growth (or your bottom 10 percent if you have a larger portfolio). Score them using the same framework you use for new sites. Compare current location scores to actual performance. The gap tells you whether the problem is the location or something else.

Check for self-cannibalization in your densest metros. If you have three or more locations within 15 minutes of each other, overlay the trade area polygons. Measure the overlap. If any two stores share more than 25 percent of their trade area, one of them is pulling revenue from the other. That's information you need for lease renewal decisions.

Build your benchmark. Take your top 10 performing stores and look at their location data. What do they have in common? High foot traffic scores? Low competition density? Strong demographic fit? Whatever the pattern is, that's your template for both new site selection and existing store evaluation.

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