Pensa Systems, a leading innovator in automated retail shelf intelligence, today announced the launch of its facings optimization solution that will help Consumer Packaged Goods (CPG) brands grow revenue through improved retail shelf facings optimization.
The term “shelf facings” refers to the number of units of a product that are visible from the front of a retail store shelf. In grocery retail, competition between CPG brands for facings is fierce given its direct translation to profitable revenue growth.
Traditionally, facings allocation has been determined based on rough estimates of relative sales volume and velocity derived from backwards looking point-of-sale (POS) data. Brands often couple POS data with artificial shelf scenario simulations to estimate shelf performance.
POS data, however, is notoriously “noisy”. For example, slow product sales reflected in POS data might be an indication that a particular product is not selling, or it might be an indication that the product is often out-of-stock on the shelf and not available for sale.
Pensa closes this gap for CPG brands and retailers by providing a continuous view of actual products on the retail shelf across a wide range of stores and deployed shelf configurations. By correlating real on-shelf product availability with the number of facings per product, Pensa applies the concept of pricing elasticity to facings to determine the optimal number of facings by category and by product to maximize revenue without allocating space for one product at the expense of other products.
“Optimizing the shelf is now possible in a new way leveraging the practical power of computer vision and AI,” says Richard Schwartz, President and CEO of Pensa Systems. “Basing facings analysis on a highly-accurate and granular view of what’s on the shelf and available for sale heralds a new era of brands and retailers working closely together to optimize facings for mutual benefit”.
Pensa’s Artificial Intelligence (AI) is the first fully automated shelf intelligence solution. To automatically deliver accurate, near real-time continuous shelf data, Pensa’s AI learns to visually recognize and distinguish between products on the shelf much as a human does versus scanning a barcode on the back of a package or comparing a single product image against a product image database.
Pensa captures then analyzes a video stream of hundreds of images, taken from numerous angles, of each individual product on the shelf to accurately identify and distinguish between products in near real-time to calculate a highly accurate view of shelf inventory.
