Customer Service

Why data partnerships are the future of retail

Learn how real-time visibility into shelf conditions can help retailers and suppliers improve execution, boost sales and deliver a better in-store experience.

Photo: Adobe Stock

September 25, 2025 by Ali Moosani — CEO, GoSpotCheck by FORM

In today's hyper-competitive retail landscape, data collaboration between retailers and suppliers is emerging as a strategy for driving performance where it matters most: the store shelf.

As brands face continued pressure to execute at retail, access to store-level data — including out-of-stocks, display compliance, and the activity of competitors — has become critical. Data partnerships and collaboration can help both sides move beyond slow, often flawed, manual reports toward real-time, practical intelligence. These data collaborations result in faster decision-making, fewer missed opportunities, and a better experience for consumers.

The partnership role

Consumer packaged goods brands have long wanted access to in-store execution data, but those without their own field or direct store delivery teams lacked a way to get it. That's where modern data partnerships come into the picture: They're a "bridge" that allows suppliers to correlate sales performance with real-time store conditions.Technology has improved the quality of that data, and data collaborations are now making access to those metrics possible.

At the ground level, retail execution is anything but orderly. Displays get repurposed, brands compete for space, and store staff may improvise merchandising despite corporate direction. In this environment, even well-planned campaigns can go awry, and brands often don't realize things have gone off course until it's too late. Access to real-time data provides clarity and enables suppliers to detect issues, diagnose causes, and take action.

These insights include critical information such as on-shelf availability and whether or not agreed-upon merchandising plans are being followed. Just as important is the visibility into competitive presence — how rival products are positioned and promoted in the same environment — as well as how much self space a brand occupies relative to competitors.

Insights necessary

Surfacing this kind of granular data creates a real-time snapshot of how well retail strategy is being translated into execution on the shelf. For suppliers, it means the ability to quickly identify problems, such as out-of-stocks or poor product placement, and take action. For example, an item that is selling below expectations might initially appear to be a product issue. But real-time data can reveal that it was placed outside the "strike zone" where visibility and sales are highest.

Consider an Oktoberfest beer that's released in early fall. The sales window for this product is narrow; by mid-November, consumer demand shifts to winter seasonals. If retail execution data shows that the beer isn't properly merchandised in mid-October, the brand still has time to act. Field teams can be sent to correct placement issues and ensure the product is visible and accessible before the sales window closes.

Conversely, if brands don't have these kinds of real-time, or near-real-time, insights, the product could be shelved in the wrong place, or might not even be shelved at all, leaving the brand with unsold product. Scale that scenario across a beer company that distributes to thousands of locations, and it's easy to see how margins will be negatively affected. With timely insights from data partnerships, brands are able to maximize sell-through and protect ROI.

The challenge of data accuracy

Although the benefits of in-store execution data are clear, there are still challenges. Data accuracy is crucial for the success of these collaborations; partners must ensure that both human data collection is precise and technological accuracy is consistent. If the quality and reliability of the data is not there, the consequences can ripple across the business.

Further, large CPGs with national field teams and substantial budgets are often best positioned to take advantage of these tools. Smaller or emerging brands may find it difficult to afford the same level of insight, putting them at a disadvantage in competitive retail environments.

Retailers understand this and are motivated by the opportunity to create new revenue streams and top line sales. As such, the industry could move toward a modular, marketplace-style approach to in-store execution data. Instead of committing to broad, all-encompassing data sets, brands would be able to subscribe to SKU-level data from specific categories — like, say, yogurt or ketchup. This would allow suppliers to invest in the insights most relevant to their business, making participation more flexible and cost-effective. For retailers, it creates a new, scalable revenue stream. For brands, especially smaller ones, it lowers the barrier to entry.

Ultimately, both retailers and suppliers want the same outcome: better on-shelf availability, stronger execution of merchandising plans, and an improved customer experience. By working together, retailers and suppliers can unlock a shared view of the shelf, one that enables faster adaptation, smarter execution, and stronger performance. As the technology behind these efforts continues to mature, and more brands adopt this approach, it's likely that data-driven partnerships will shift from a competitive advantage to a strategic necessity — and in the process become a cornerstone of modern retail strategy.