Your Shelf Data Is Lying to You, and It’s Costing You Money

There’s a version of your business that exists only in your reporting systems. Prices are where you set them. Promotions are executing as planned. Your items are on shelf, in the right stores, at the right time. 

Then there’s what’s actually happening at the shelf. 

These two pictures are rarely identical — and the gap between them is where margin leaks, competitive ground gets lost, and retailer relationships get harder to manage. 

For most commercial teams, the problem isn’t effort. It’s data. Traditional syndicated sources give you a powerful macro view of the market, but they arrive weeks after the fact, aggregated to chain totals, with limited visibility into what’s happening store by store. By the time you see a problem, the promotion window has closed, the competitor has moved, or the reset has already happened without your items in it. 

This is the delayed data problem — and in a retail environment that moves weekly, it’s no longer just an inconvenience. It’s a competitive disadvantage. 

In a recent Category Management Association (CMA) webinar, Mike Sheeter, General Manager of Olds Products Company (home of Koops Mustard), shared how his team is using store-level shelf intelligence — Datasembly’s Compass platform, paired with SPINS — to make faster, sharper commercial decisions across pricing, promotion, and assortment. 

His framing was direct: SPINS told him how the brand was doing. Datasembly showed him what was actually happening on shelf — and why. 

That distinction matters more than it sounds. Volumetric performance data tells you the outcome. Store-level shelf data tells you the conditions that produced it: which promotions actually ran, at what depth, in which stores, against what competitive activity. Without both, you’re managing your business from the scoreboard while the game is still being played. 

One of the most actionable moments in the webinar came when Sheeter described what the data revealed about Koops’ own promotional strategy. 

His team had been promoting at a cadence that felt competitive — matching what they assumed the market was doing. When they looked at actual store-level promotion data week by week, the picture shifted. In several retailers, competitors weren’t promoting as aggressively as assumed. Koops was spending trade dollars to compete with activity that wasn’t there. 

The result: they pulled back promotional investment in specific retailers where the competitive pressure didn’t warrant it, and redeployed those dollars into advertising and store-level programs that moved the needle more. 

This is what real-time shelf intelligence actually delivers — not just better visibility, but better decisions about where to spend and where to hold back. For any team managing trade spend and promotional ROI, the question worth asking is: how much of your current promotional calendar is based on what competitors are actually doing versus what you assume they’re doing? 

Compass is built around the three things that determine how a brand performs at the shelf: price, promotion, and assortment. For each, Koops found specific advantages that changed how they worked. 

On pricing: Retail pricing isn’t uniform — it varies by zone, division, and store format. Koops used Compass to understand where their prices stood relative to competitors not at the chain average, but at the store level. That granularity made pricing recommendations in buyer meetings more defensible and more specific. 

On promotion: Week-by-week visibility into competitive promotional mechanics — depth, timing, vehicle type — let Koops build a promotion plan grounded in market reality rather than assumptions. Two years of historical data meant they could see seasonal patterns, not just point-in-time snapshots. 

On assortment: With 16 flavors across specialty and organic lines, assortment management is complex. Compass let Sheeter walk into retailer conversations knowing exactly what that chain carried, what was missing, and where Koops had a legitimate case for expanded shelf presence. When a new item launched, the team could track distribution week by week — confirming which stores had it, identifying which didn’t, and following up with specifics rather than waiting for the next reporting cycle. 

One capability that came up repeatedly: unmasked private label visibility. Because Datasembly collects from publicly available digital shelf sources, private label items appear without the masking restrictions common in syndicated data. For a brand like Koops competing in a crowded condiment set where private label is a primary competitor, that transparency is meaningful. 

Most commercial teams are working from two separate realities: the volumetric performance story that syndicated data tells, and the shelf conditions that actually produced it. Historically, those two views lived in different systems, with different data cadences, and different sharing rules. 

The combination of SPINS and Datasembly changes that. SPINS brings the category performance depth — sales trends, regional growth, shopper behavior — that brands rely on to build the strategic narrative. Datasembly adds the store-level shelf layer: what prices are actually on shelf, which promotions are running and where, how assortment is shifting week to week. Together, they give commercial teams a single, coherent view of the category that’s both analytically rich and operationally current. 

For Koops, that combination showed up directly in retailer conversations. They could walk into a buyer meeting with the performance story from SPINS and the shelf reality from Datasembly — and because Datasembly’s data is sourced from publicly available digital shelf sources, they could share competitive context across banners freely, without the restrictions that typically limit how syndicated data can be used in customer-facing settings. The result is a more complete, more credible conversation with the retailer. 

The Koops story is one category in one company. But the underlying dynamics — delayed data, promotion assumptions that don’t match reality, assortment gaps that don’t surface until after a reset — show up across CPG in almost every category. 

The brands navigating this most effectively aren’t just buying more data. They’re buying faster, more granular data that connects the shelf reality to the commercial decision — and they’re doing it in a way that can be shared internally and with retail partners without restriction. 

If you want to see what this looks like in your category, using last week’s actual shelf data, that’s exactly what a Datasembly Competitive Product Showcase is built for.