Post by Vispera
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Weโre continuing our ๐ข๐๐-๐ผ๐ณ-๐๐ต๐ฒ-๐๐ผ๐ ๐ฅ๐ฒ๐๐ฎ๐ถ๐น ๐ฆ๐ผ๐น๐๐๐ถ๐ผ๐ป๐ series with another powerful way to understand in-store shelf performance: ๐ฆ๐ต๐ฒ๐น๐ณ ๐๐ฒ๐ฎ๐๐บ๐ฎ๐ฝ๐. Retail shelves generate constant product movement, yet most retailers lack a clear, objective view of ๐ต๐ผ๐ ๐ฑ๐ถ๐ณ๐ณ๐ฒ๐ฟ๐ฒ๐ป๐ ๐๐ต๐ฒ๐น๐ณ ๐๐ผ๐ป๐ฒ๐ ๐ฎ๐ฐ๐๐๐ฎ๐น๐น๐ ๐ฝ๐ฒ๐ฟ๐ณ๐ผ๐ฟ๐บ. 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 inโstore dynamics. ๐ฆ๐ต๐ฒ๐น๐ณ ๐ต๐ฒ๐ฎ๐๐บ๐ฎ๐ฝ๐ ๐ฑ๐ฒ๐น๐ถ๐๐ฒ๐ฟ ๐ฎ ๐๐ผ๐ป๐ฒ-๐ฏ๐-๐๐ผ๐ป๐ฒ ๐๐ถ๐๐๐ฎ๐น ๐ฟ๐ฒ๐ฝ๐ฟ๐ฒ๐๐ฒ๐ป๐๐ฎ๐๐ถ๐ผ๐ป ๐ผ๐ณ ๐๐ต๐ฒ๐น๐ณ ๐ฝ๐ฒ๐ฟ๐ณ๐ผ๐ฟ๐บ๐ฎ๐ป๐ฐ๐ฒ. ๐ง๐ต๐ฒ๐ ๐ต๐ถ๐ด๐ต๐น๐ถ๐ด๐ต๐: - High-movement shelf zones (hot areas) - Low-movement shelf zones (cold areas) - Product interaction intensity across sections - Unexpected product movement or placement deviations ๐ง๐ต๐ฒ ๐ฟ๐ฒ๐๐๐น๐: 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. ๐ฆ๐๐ฎ๐ฟ๐ ๐ณ๐ฎ๐๐ ๐๐ถ๐๐ต ๐ข๐๐-๐ผ๐ณ-๐๐ต๐ฒ-๐๐ผ๐ . ๐ฆ๐ฐ๐ฎ๐น๐ฒ ๐๐บ๐ฎ๐ฟ๐ ๐๐ต๐ฒ๐ป ๐๐ผ๐โ๐ฟ๐ฒ ๐ฟ๐ฒ๐ฎ๐ฑ๐. #Vispera #ImageRecognition #AIinRetail #Retail #ShelfAnalytics