Storesight works with brands to provide shelf intelligence data for a variety of uses, including inventory issues.
pilot obstacles
Data obtained from pilots will have limited usefulness until scaled across the enterprise.
“When shelf intelligence remains siled, insights get locked up and value stalls before it reaches the customer,” said Heather Campaign, vice president of growth strategy at Epsilon. “The real impact comes when intelligence connects marketing, merchandising and operations through first-party data and identity foundations, enabling every shelf and shopper interaction to inform the next smarter decision, both in-store and online.”
Siled data can also lead to duplication of investments, and large-scale deployments force organizations to unify decision-making processes. While the necessary level of integration may be a challenge for larger organizations, Patel said it has strengthened Keranova’s position as a category leader and enabled more strategic retailer partnerships and data-driven joint business planning.
“Pricing and promotions are moving from static analysis to dynamic adjustments that reflect shopper behavior and the competitive environment,” Patel said. “Planogram provides a bridge between dynamic digital shelving and fixed in-store environments, using real-time insights to influence future resets, adjust adjacencies, and prioritize spaces where performance and shopper behavior will yield the greatest benefit.”
To scale, you need to build the infrastructure to clean your data in a privacy-secure place and integrate it into systems that can be used for display, media bidding, shelf prioritization, and pricing.
“As shelf intelligence expands, the challenge is not to get more data, but to align the right signals around a shared identity and common language,” Campaign said. “Retail ecosystems are a mix of legacy, first-party, and partner data, and when they are securely connected through interoperable systems, retailers can confidently measure, predict, and optimize performance at scale.”
The complexity increases significantly as shelf intelligence deployments expand, requiring extensive data science and engineering work to integrate the taxonomies, promotional calendars, assortments, and frequency of data updates used by different retailers and digital platforms. But expanding data integration offers businesses new ways to drive growth.
“At Kellanova, we are building a shared data layer so that shelf intelligence, display, and digital shelf data all work in the same ecosystem, creating a single source of truth,” said Patel. “Seamlessly connected allows us to coordinate decision-making across key elements of the shelf. This allows us to see how in-store displays drive online search uptick, and how bid changes on a retailer’s site impact shelf productivity and availability.”
Once that is done, companies will be able to actually prove the value of their technology.
“As shelf intelligence expands, decision-making becomes less passive. Data begins to guide what goes where and why in real time,” Campaign said. “Understanding shoppers and stores based on their identity makes pricing and assortment proactive and personalized, helping retailers balance margin accuracy with a better customer experience at every touchpoint.”
As artificial intelligence significantly increases an organization’s ability to leverage data, organizations should work on implementing AI and shelf intelligence solutions in parallel.
“AI provides superpowers for analysis and insight,” says Kasperbauer. “CPG teams that lean into an AI-first mindset will set an entirely new standard, and the gap between successful implementations and those that don’t will become even wider.”
Many companies are eager to integrate AI into their organizations, but struggle to find use cases that demonstrate a clear ROI that assures stakeholders that the investment is worth it. The combination of AI and shelf intelligence can be an ideal way to maximize the impact of new technology and culture change.
“It’s important to remember that AI is a tool,” Kasperbauer said. “Tools that don’t drive proven business processes have limited value. To reach their full potential, AI applications must be progressively connected to business processes such as category management.”
