Brands invest significant resources in merchandising, often relying on third-party interventions to ensure proper stocking and presentation of products in stores.
To accomplish this, brand representatives are deployed to stores on scheduled route visits or to conduct checks on available products and rectify any issues.
However, this process frequently fails to yield meaningful insights beyond surface-level evaluations, according to David Gottlieb, chief revenue officer at Trax, leaving brands with limited understanding of in-store conditions.
“[Manufacturers] understand what they sell to the retailer, which often gets sold to and shipped to a distribution center,” Gottlieb told PYMNTS in an interview. “But when it comes down to the individual stores that make up a chain, they have very little visibility in terms of what the actual conditions are that shoppers are experiencing.”
Signal-based merchandising addresses this gap, he said, pointing to Trax’s solution, powered by proprietary image recognition and machine learning algorithms.
The solution collects and analyzes real-time, in-store data points — termed signals — from high-traffic shoppers via a mobile app. These signals are gathered from various store environments, including coolers, displays and grocery aisles.
“Whether it’s in Walmart or Target, we have more than one shopper in every one of those outlets every single day so we’re essentially [getting] a sense of what products are available for sale to the shopper on a day-by-day basis. That is not something that exists in the industry today,” Gottlieb noted.
Moreover, with thousands of Walmart supercenters across the U.S., it’s obvious that not all stores yield equal sales figures. Consequently, utilizing data directly from specific Walmart stores gives manufacturers granular insights into product performance at each store, facilitating informed decisions on resource deployment.
Ultimately, businesses get precise insights into store conditions, optimizing product placement and availability as well as allocating merchandising resources to stores with the highest potential for value creation and sales impact.
“We have our own score that we’re generating from our signals […] to develop our hit list of which stores we want to visit every single week,” he remarked.
Similar to how smartphones gradually became an indispensable part of consumers’ daily lives, Gottlieb said signal-based merchandising, while in its early stages, holds significant promise.
In fact, he said he already envisions a future where manufacturers are not blindly allocating merchandising resources but rely on continuously updated data assets to inform retail investment decisions.
“We imagine a future, not that distant, where no manufacturer would plan a route or invest in merchandising without first understanding, ‘where can I have the most impact?’ or ‘where can I invest my dollars to get the ROI that my company expects from me?’,” he said.
However, a key challenge lies in scaling operations effectively, one that requires maintaining a robust shopper base that is reliably going into stores where information needs to be captured. That, and committing to regular technology upgrades will be crucial to enriching the shopping experience, Gottlieb noted.
This evolution will involve integrating Trax’s technology into shoppers’ routines, thereby providing augmented reality shopping experiences that enhance convenience and choice.
It’s “a really exciting flywheel for us because we get the benefit of both understanding what’s happening in those stores and also creating more value for those shoppers,” he said. “And being able to achieve the right balance is really critical for us as we think about scale.”
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