Shoptalk U.S. Solution Spotlight: Pathr.ai - RETHINK Retail
Shoptalk U.S. 2022 Solution Spotlight

Shoptalk U.S. Solution Spotlight: Pathr.ai

RETHINK Retail’s Solution Spotlight shines a light on industry vendors and what makes them distinct.

This vendor spotlight is on Pathr, the industry’s first AI-powered Spatial Intelligence platform that uses anonymous location data to drive actionable business insights in real time. Pathr helps businesses better understand how the movement of people and objects in and around an organization’s physical space can impact its success.

To learn more about Pathr, visit: https://pathr.ai

 

 

 

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TRANSCRIPTION:

George Shaw:

Hi, I’m George Shaw, founder and CEO of Pathr. Pathr is the world’s first real time spatial intelligence platform. That means as we take existing infrastructure, use that to anonymously track people as they move through physical spaces, and then we apply machine learning to extract business value from that tracking. Thinking about the challenges that retailers are facing today, when we start with the macro level, retailers operate a lot of real estate and they do that so that consumers can come and shop physically in-person. Some of the things that we’re seeing now are an increase in loss and fraud. Shoplifting is on the rise. Staffing is more challenging than it ever was. Staff dollars are more expensive than they’ve ever been. So, retailers are hungry to optimize their staffing. And then there’s a lot of competition for consumers’ eyeballs and for consumers’ attention, and so retailers are working on that as well. They’re spending a lot of time thinking about their marketing, a lot of time thinking about their merchandising, and how they arrange the physical space so that they can please consumers better.

The way Pathr addresses these various issues that are facing retailers is by understanding human behavior. Everything we do is about understanding human behavior in different contexts and in different ways. Those different human behaviors that we understand will vary by the particular problem you’re trying to solve. For example, if you want to solve an operational challenge around checkouts, you want to understand cuing behavior. How are people standing in line? How long are they in line? How long are those lines? Are they leaving that line because the line is too long and then they don’t make the transaction? So, these are some of the behaviors that’ll affect the operational challenges.

Whereas if we’re talking about loss prevention or loss type challenges, the behaviors are a little bit more obvious. We want to be able to see shoplifting. We want be able to understand that shoplifting behavior in real time as it unfolds with no risk of any bias. Our system turns people into a dot anonymously. We don’t collect any demographics, no PII whatsoever, but just based on how that dot moves, we get a view into that behavior. That behavior might be shoplifting, and if we can understand that in real time, then the retailer can go and take action on that as it’s unfolding.

The technology that we bring to bear is really in two parts. One is about the tracking and how do you do this tracking. We have a solution that’s very scalable so that we can track people anonymously using existing infrastructure. Every retailer, one with most physical spaces, have some camera infrastructure already and so we leverage that, and that’s really a function of the sort of sophisticated AI that we’re able to bring to bear, to be able to do that tracking anonymously. And then once you have that tracking, you want to be able to understand it more deeply. A lot of this tech came from sports analytics where they understand every play that’s happening in a game so that they can give that information back to the coach. We understand every play that’s happening on the sales floor of a retail store. And so if you really think about those two components, that’s the big categories of technology that we’ve built and that we bring to bear.

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