I recently spoke with Mike Spindler, the CEO of ShelfSnap, about the company’s entry into the “shelf-level collaboration” arena.
(Quick aside: As I mentioned in a previous posting, I prefer the term “Shelf-level Collaboration Solutions” over Demand Signal Repository (DSR) products because I believe it’s both more accurate and evocative.)
ShelfSnap is in start-up mode, and although the company has conducted some interesting pilots for large consumer goods companies, it has not yet been selected as a long-term partner by these companies. Nevertheless, I think ShelfSnap offers an interesting solution that fills a void when it comes to shelf-level collaboration.
Right now, DSRs are populated mainly with POS, syndicated data, store-level inventory data, and shipments from the retail DC. One form of shelf-level collaboration occurs when a DSR merchandising application sends out an alert saying that an SKU at a particular store is out of stock at the shelf, or that it is selling at a much slower rate than expected. These alerts are based on inference. The application does not know that a store has not restocked the shelf; it makes that prediction based on math. The math can be very simple: this product is jumping off the shelves at other stores but it’s not moving at this store; we know that inventory was shipped to the store, so this must mean that stockers have not moved the product out of the backroom yet. The math gets a lot more complex when it comes to predicting things like inaccurate perpetual inventory levels.
Mike’s point is that these are inferences. If you tell a Walmart store manager that a shelf needs to be restocked in the hope that he will assign someone to do the job, you better be right. You don’t want to generate false positives and send labor to the shelf to fix a problem that does not exist. Even if only 10 percent of your alerts are false positives, you could become “the boy who cried wolf” too often. So, some consumer goods companies use the merchandising alerts to reroute their third party merchandisers to visit a particular store to make sure the alert is correct.
But Mike points out that this does not fully solve the problem. Mike began his career at Nielsen, where he used to ask a group of his managers to separately go to a store and check to see if the planogram was correct for a particular category. In this store, Product A should have three facings and be in the middle of the top shelf, Product B should have one facing and be on the far right hand side of the bottom shelf, and so on. Mike said the managers never came back with the same data.
The ShelfSnap solution is based on workers using a digital camera to take a picture of the store shelf, and then the solution’s patent-pending Image Recognition Technology analyzes color, package size, and label design to identify the correct product. These pictures are fed into the ShelfSnap Analytical Engine that then generates quantitative analytical results: What shelf was the product on? How many linear feet did the facing have? Was the product out of stock? This data can then be turned into prioritized merchandising exceptions. They also provide analysis of new product cut-ins, displays, and point-of-purchase display reporting.
I basically see the ShelfSnap solution as providing a practical way for manufacturers and retailers to use planogram data for shelf-level collaboration, or for retailers themselves to have more effective task management at the store. I also see it as improving the alerts generated by existing DSR solutions. Say a DSR is predicting that the perpetual inventory level at a particular store is incorrect (the store says it has 12 of the item, but the DSR application thinks it only has 9). This math is based on lower sales than expected for that item at that store. But if the product is supposed to have three facings on the prime shelf but it only has one facing on the bottom, the product location could explain the variance in sales rather than an incorrect inventory level.
In a recent posting, Adrian Gonzalez quoted Jim Stengel, the former global marketing officer at Procter & Gamble, on the greatest challenges facing marketers in the consumer goods industry. To paraphrase slightly, Jim asked how consumer goods manufacturers could add “digital” to “their branding model?” How can they “work with retailers to have maximum impact on value creation?” This is an application that contributes in both of those areas.
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