Post by Vispera

9,157 followers

Weโ€™re continuing our ๐—ข๐˜‚๐˜-๐—ผ๐—ณ-๐˜๐—ต๐—ฒ-๐—•๐—ผ๐˜… ๐—ฅ๐—ฒ๐˜๐—ฎ๐—ถ๐—น ๐—ฆ๐—ผ๐—น๐˜‚๐˜๐—ถ๐—ผ๐—ป๐˜€ series with another powerful way to understand in-store shelf performance: ๐—ฆ๐—ต๐—ฒ๐—น๐—ณ ๐—›๐—ฒ๐—ฎ๐˜๐—บ๐—ฎ๐—ฝ๐˜€. Retail shelves generate constant product movement, yet most retailers lack a clear, objective view of ๐—ต๐—ผ๐˜„ ๐—ฑ๐—ถ๐—ณ๐—ณ๐—ฒ๐—ฟ๐—ฒ๐—ป๐˜ ๐˜€๐—ต๐—ฒ๐—น๐—ณ ๐˜‡๐—ผ๐—ป๐—ฒ๐˜€ ๐—ฎ๐—ฐ๐˜๐˜‚๐—ฎ๐—น๐—น๐˜† ๐—ฝ๐—ฒ๐—ฟ๐—ณ๐—ผ๐—ฟ๐—บ. High-value areas may be underutilized, slow-moving products can occupy prime positions, and low-activity sections often go unnoticed. Without a standardized way to compare zones, shelf layout decisions often rely on assumptions rather than real inโ€‘store dynamics. ๐—ฆ๐—ต๐—ฒ๐—น๐—ณ ๐—ต๐—ฒ๐—ฎ๐˜๐—บ๐—ฎ๐—ฝ๐˜€ ๐—ฑ๐—ฒ๐—น๐—ถ๐˜ƒ๐—ฒ๐—ฟ ๐—ฎ ๐˜‡๐—ผ๐—ป๐—ฒ-๐—ฏ๐˜†-๐˜‡๐—ผ๐—ป๐—ฒ ๐˜ƒ๐—ถ๐˜€๐˜‚๐—ฎ๐—น ๐—ฟ๐—ฒ๐—ฝ๐—ฟ๐—ฒ๐˜€๐—ฒ๐—ป๐˜๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—ผ๐—ณ ๐˜€๐—ต๐—ฒ๐—น๐—ณ ๐—ฝ๐—ฒ๐—ฟ๐—ณ๐—ผ๐—ฟ๐—บ๐—ฎ๐—ป๐—ฐ๐—ฒ. ๐—ง๐—ต๐—ฒ๐˜† ๐—ต๐—ถ๐—ด๐—ต๐—น๐—ถ๐—ด๐—ต๐˜: - High-movement shelf zones (hot areas) - Low-movement shelf zones (cold areas) - Product interaction intensity across sections - Unexpected product movement or placement deviations ๐—ง๐—ต๐—ฒ ๐—ฟ๐—ฒ๐˜€๐˜‚๐—น๐˜: A clear understanding of which shelf zones truly drive product movement, enabling smarter product placement, stronger planogram optimization, faster identification of underperforming shelf areas, and clear visual justification of shelf value for brands and category teams. ๐—ฆ๐˜๐—ฎ๐—ฟ๐˜ ๐—ณ๐—ฎ๐˜€๐˜ ๐˜„๐—ถ๐˜๐—ต ๐—ข๐˜‚๐˜-๐—ผ๐—ณ-๐˜๐—ต๐—ฒ-๐—•๐—ผ๐˜…. ๐—ฆ๐—ฐ๐—ฎ๐—น๐—ฒ ๐˜€๐—บ๐—ฎ๐—ฟ๐˜ ๐˜„๐—ต๐—ฒ๐—ป ๐˜†๐—ผ๐˜‚โ€™๐—ฟ๐—ฒ ๐—ฟ๐—ฒ๐—ฎ๐—ฑ๐˜†. #Vispera #ImageRecognition #AIinRetail #Retail #ShelfAnalytics

Post content