How AI Is Redefining Store Performance: In-store Data

In today’s tech landscape, it’s surprising how limited the insights available to brick-and-mortar retailers still are. Ecommerce runs on precise, data-driven decisions. In-store strategy, from shelf placement to seasonal promotions, still runs largely on guesswork.

With in-store sales representing about 80% of U.S. retail revenue, that’s a lot of the business to be running in the dark.

Why In-Store Analytics Lags Behind Ecommerce

Angie Westbrock, CEO of Standard AI, told attendees that retailers and brands have long relied on incomplete or delayed data sources to make critical business decisions. The usual sources each have a blind spot:

• Point-of-sale transactions tell you what sold, not why
• Manual surveys are slow and small-sample
• Outdated store audits capture a single moment, then go stale

Current metrics like foot traffic and sales data offer only a narrow view of what truly influences shopper behavior. Marketing is often worse, retailers frequently rely on an eight-to-twelve-week lookback analysis that’s only useful after a promotion has ended, once the missed revenue and inventory decisions are already locked in.

The gap is clear. Retailers lack the insight to understand who’s entering their stores, what they’re looking for, and how best to engage them.

What Retail AI Analysis Reveals: The End-Cap Finding

Most retailers assume end caps are prime real estate for impulse buys, but Standard AI’s analysis revealed the opposite.

Traffic Isn’t the Same as Engagement

Customer engagement was actually lower on end caps than in promotional areas farther down the aisles. A spot can get plenty of traffic while driving little real engagement, and only measuring engagement directly shows the difference.

The Best Placement Depends on Context

Moving the promotion into the aisle increased engagement and conversion, but the effect varied by category and store, so no single “best” placement works everywhere.

Sales Data Alone Can’t Show Why

As Westbrock noted, this challenges the conventional wisdom about where to place displays. Sales figures can tell you a promotion underperformed, but not whether shoppers didn’t notice it, didn’t engage, or engaged without converting, which is exactly the gap retail AI analysis fills.

Learn about the Power of AI in Retail with VISION Analytics by Standard AI

How Computer Vision Measures Store Performance

AI, especially computer vision, is redefining how retailers understand shopper behavior. Just as ecommerce measures clicks and conversions, physical stores can now anonymously measure how often products are picked up, how long shoppers engage with a display, and how in-store media affects sales.

These insights let retailers optimize layouts, product placements, and overall strategy with a precision they haven’t had before.

Three areas show the impact most clearly.

1. Revealing the Full Customer Journey

Computer vision measures product impressions, engagement, and shopper interactions continuously, uncovering true performance.

Stores often pull a product that isn’t selling without knowing why. Sometimes the issue isn’t the product, it’s that shoppers never noticed it. Low visibility means they never got the chance to buy. And when engagement is high but conversion is low, the culprit is often confusing packaging or pricing. Computer vision and machine learning help pinpoint which it is, so retailers can adjust placement or promotion and give new products a fair shot.

2. Bridging the Physical and Digital Shopping Experience

Shoppers expect a seamless journey across channels. In-store engagement data can feed personalized digital promotions, creating a unified, responsive omnichannel experience that carries what happens on the floor into the channels a shopper uses next.

3. Improving In-Store Marketing Effectiveness

Retail AI analysis shows not just what sells, but where and why. Instead of leaning on sales data alone, retailers and brands can tell whether a promotion drove incremental engagement and sales, or simply redirected purchases that would have happened anyway. That level of detail replaces guesswork with decisions teams can act on.