How ITG Brands Is Rethinking Shelf Intelligence

Shelf optimization has been a priority for consumer goods companies for decades, but the tools available to manage execution have changed significantly. Advances in AI and computer vision are giving brands a more objective, scalable and timely view of what is happening at the shelf, while helping field teams act on issues faster.ITG Brands is putting those capabilities to work, saving field representatives eight to 10 minutes per store by automating routine tasks. Learn how the company is translating shelf data into action and what it takes to make the technology part of field teams’ everyday work.During a recent CGT Expert Exchange, Michael Abhulimen, SVP for insights and intelligence at ITG Brands, and Praveen Gopalakrishnan, CEO and co-founder of Snap2Insight, discussed how shelf intelligence is moving beyond periodic audits toward near-real-time execution management.One of the biggest shifts is the move away from manual observation. In the past, field representatives might count facings, complete audit forms and record compliance individually, creating inconsistencies depending on who conducted the visit, noted Abhulimen. AI-enabled image recognition can now capture the shelf as it actually appears and convert that visual information into structured data at scale, he said. This means every store visit can effectively become a shelf audit, providing a much broader view of execution across the retail network.That data can also be delivered quickly enough to influence what happens during the store visit itself. "Rather than waiting for days or weeks for reports, teams will already know our out-of-stocks, planogram issues, pricing errors, missing point-of-sale materials and then compliance gaps," he said. At ITG Brands, automating tasks such as counting facings and recording compliance has saved field representatives eight to 10 minutes per store while helping increase the number of calls each rep can make, according to Abhulimen. For field teams, the value goes beyond faster data collection. Shelf images can be translated into information about products, promotions and pricing, then used to recommend next-best actions while the representative is still in the store. That makes the technology more useful operationally and helps turn raw data into specific actions.The technology is also changing how brands and retailers work together. Historically, disagreements over whether a display was executed correctly or a promotion was in compliance could be difficult to resolve. Shared image-based data creates a common source of evidence, helping both sides address issues more quickly and reducing disputes around execution, trade investments and contractual commitments. Retailers can also use the same data to gain greater visibility into category conditions across large store networks.Still, successful AI deployments depend on more than technical accuracy. Gopalakrishnan emphasized the importance of beginning with a measurable business problem rather than launching a broad visibility initiative. A successful pilot should demonstrate business value, incorporate feedback from field users and show how the resulting data leads to better decisions. "That might mean you pick a category where there is the biggest opportunity to be had. You have one clear decision to make, and you have a baseline that you can compare against," said Gopalakrishnan. Looking ahead, shelf intelligence is likely to become more continuous and predictive. Instead of checking shelves only during scheduled visits, companies may increasingly rely on connected devices, computer vision and richer data integration to monitor conditions continuously. The long-term direction is toward a shelf environment that functions less like something to be periodically audited and more like a live source of operational intelligence.

How ITG Brands Is Rethinking Shelf Intelligence

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