Why Some Retail Locations Outperform Others: Data-Driven Insights

Walk around any major retail corridor and the pattern shows up quickly. Two stores from the same brand, often only a few miles apart, can have completely different trajectories. One builds a reliable base of customers and becomes a top performer. The other never quite finds its footing.

Operators like to chalk these differences up to execution or staffing, but in most cases the real story starts long before opening day. The biggest variable is the information used in the site-selection process. And more of that process is quietly shifting toward third-party credit and debit card data because it captures what economic development reports, census tables, and traffic counters miss: how people actually spend.

Below are the signals that repeatedly separate strong sites from the ones that underperform — and how retailers are using data to improve their odds.

1. Market Share Maps Reveal Where a New Store Can Actually Compete

Retailers used to rely on rough estimates of competitor presence. Now they can see actual spend in a trade area by brand. When market share is mapped from card transactions, the picture is much clearer: some neighborhoods are effectively locked up by one or two players, while others are wide open.

The strong performers tend to land in areas where the spend landscape is still fluid — places where no brand has fully captured the local customer. A lot of struggling locations, by contrast, opened in ZIP codes where a competitor already had deep loyalty. It is difficult to unwind that kind of habit once it’s entrenched.

This is why many retailers now treat market share mapping as a non-negotiable step. Without it, they’re guessing at competitive pressure instead of measuring it.

2. Competitor Distance Isn’t Nearly as Important as Competitor Influence

Two stores can sit across the street from each other and both succeed — or fail. The map alone doesn’t explain much. What matters is whether the competitor is attracting the same type of shopper you need.

Card data helps break down that influence:

• Who is visiting
• How often
• How much they spend
• Whether they already cross-shop between concepts

Retailers use this to understand whether they’re stepping into a direct head-to-head scenario or entering a neighborhood where the competitor is essentially serving a different demographic.

The biggest misses often happen when teams assume “being nearby” means “being in competition.” The data shows that’s not always the case.

3. Psychographic Fit Is Emerging as One of the Strongest Predictors of Store Success

Demographics still matter, but they aren’t telling the full story anymore. Two ZIP codes with the same income and population numbers can behave very differently once you look at actual spend.

Card data exposes the patterns that matter at the store level: the frequency of small-ticket purchases, comfort with higher basket sizes, preference for national brands versus independents, and willingness to experiment with new concepts.

This is where a lot of site-selection surprises come from. Retailers open in a seemingly “perfect” neighborhood, only to discover that the local spending style does not match what the brand needs to succeed.

Psychographics used to be a luxury. They’re quietly becoming table stakes.

4. The Online Share of Spend Can Make or Break a Brick-and-Mortar Bet

In some categories, entire neighborhoods have migrated online. In others, shoppers still strongly prefer to buy in person. The only way to see that split clearly is through card-level data.

The strongest sites tend to be in areas where in-store spend still makes up a meaningful share of the category. These are the ZIP codes where customers want the physical experience, whether that means trying products, picking up on the way home, or just sticking with old habits.

Many of the underperforming locations were built in areas where online spend had already swallowed a large part of the category. Traditional site-selection tools rarely catch that shift until it’s too late.

5. Category Momentum Matters More Than Most Teams Realize

Before signing a lease, it helps to know whether people in the neighborhood are spending more or less in your category than they were a year ago. Card data gives a near-real-time read on this.

When category spend is rising locally, new stores benefit from built-in tailwinds. When it’s falling, even strong brands feel the drag. Plenty of operators have been surprised to learn that a “healthy” ZIP code, based on population and income alone, is actually cooling off in their category.

Momentum will not save a bad site, but it will accelerate a good one. And it’s one of the easiest signals to check.

Stacking the Signals Raises the Probability of Choosing a Winning Site

Retailers like to talk about site selection as if it’s equal parts art and science. That may still be true, but the science side is getting sharper. When teams combine market share maps, competitor influence, psychographic behavior, online versus in-store ratios, and local category momentum, the blind spots shrink.

The top-performing stores typically score well across several of these areas at once. They open in neighborhoods with room to win, customers that match the brand’s behavior profile, and a category that is headed in the right direction.

The bottom performers usually miss on one or two of these signals — often ones that were never measured in the first place.