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Standard AI’s Westbrock Looks At How AI Is Redefining Store Performance

In today’s advanced tech landscape, it’s surprising how outdated and limited the insights available to brick-and-mortar retailers can be. Unlike ecommerce where decisions are data-driven and precise, most in-store strategies — from shelf placement to seasonal promotions — are often based on guesswork. With in-store sales representing 80 percent of retail revenue, why are we still operating in the dark?

Yesterday, Angie Westbrock, CEO of Standard AI, told attendees that retailers and brands have long relied on incomplete or delayed data sources — point-of-sale transactions, manual surveys, or outdated store audits — to make critical business decisions. Current metrics, like foot traffic and sales data, offer only a narrow view of what truly influences shopper behavior

When it comes to marketing efforts, retailers often rely on an eight to 12 week lookback analyses, which are only useful after a given promotion has ended. By the time insights are gathered, missed revenue opportunities and uninformed inventory decisions have already taken place.

The gap is clear: retailers lack the insights needed to understand who is entering their stores, what they’re looking for, and how best to engage them. One common assumption is that end caps are ideal for driving impulse purchases. However, Standard AI’s analysis revealed a surprising trend: customer engagement was lower on end caps compared to promotional areas further within the aisles.

In this case, the more strategic use of space within the aisle led to a significant increase in engagement and conversion. This highlights that the effectiveness of end caps can vary depending on the category and store context, challenging the conventional wisdom on where to place promotional displays.

AI, especially when powered by computer vision, is redefining the way retailers understand shopper behavior. In the same way ecommerce tracks clicks and conversions, physical stores can now track how often products are picked up, how long shoppers engage with displays, and even in-store media impact on sales.

These insights empower retailers and brands to optimize their store layouts, product placements, and overall strategy with unforeseen precision, according to Westbrock.

Here are some ways AI and computer vision are driving a measurable impact in retail:

Revealing The Full Customer Journey:

Computer vision technology allows retailers to track product impressions, engagement, and shopper interactions in real-time, uncovering true performance.

In practice, stores often discontinue a product if it isn’t selling well without fully understanding the reason behind its poor performance. The problem isn’t always whether people like the product — it’s whether they even notice it in the store. Sometimes, low visibility means shoppers never get the chance to buy it.

Conversely, if engagement is high but conversion is low, there might be an issue with confusing packaging or pricing. Computer vision and machine learning can help uncover whether it’s worth experimenting with promotional strategies, such as adjusting the product’s placement, and provide retailers with data to ensure new products reach their full potential in the market.

Bridging The Physical & Digital Shopping Experience:

As consumers expect a more seamless shopping journey across channels, AI is helping retailers integrate in-store engagement metrics with their digital strategies. For example, in-store data can be used to inform personalized digital promotions, creating a unified and responsive omnichannel experience.

Improving In-Store Marketing Effectiveness:

AI-driven insights enable a deeper understanding of promotional success by showing not just what sells but where and why. Rather than relying on sales data alone, retailers and brands can assess whether marketing investments yield increased engagement and incremental sales — or simply redirect purchases. This level of detail replaces guesswork with actionable data, helping marketers optimize their strategies and collaborate more effectively to achieve meaningful results