{"id":6315,"date":"2025-07-25T10:42:37","date_gmt":"2025-07-25T10:42:37","guid":{"rendered":"https:\/\/www.footfallcam.com\/blog\/?p=6315"},"modified":"2025-10-30T09:02:41","modified_gmt":"2025-10-30T09:02:41","slug":"staff-exclusion-methodology","status":"publish","type":"post","link":"https:\/\/www.footfallcam.com\/blog\/2025\/07\/staff-exclusion-methodology\/","title":{"rendered":"Staff Exclusion Methodology"},"content":{"rendered":"\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" loading=\"lazy\" width=\"1024\" height=\"576\" src=\"https:\/\/www.footfallcam.com\/blog\/wp-content\/uploads\/2025\/07\/Staff-Exclusion-methodology-1024x576.png\" alt=\"\" class=\"wp-image-6323\" srcset=\"https:\/\/www.footfallcam.com\/blog\/wp-content\/uploads\/2025\/07\/Staff-Exclusion-methodology-1024x576.png 1024w, https:\/\/www.footfallcam.com\/blog\/wp-content\/uploads\/2025\/07\/Staff-Exclusion-methodology-300x169.png 300w, https:\/\/www.footfallcam.com\/blog\/wp-content\/uploads\/2025\/07\/Staff-Exclusion-methodology-768x432.png 768w, https:\/\/www.footfallcam.com\/blog\/wp-content\/uploads\/2025\/07\/Staff-Exclusion-methodology.png 1080w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p><span style=\"font-family: archivo, sans-serif; color: #666666;\">In the world of retail analytics, accuracy starts with one key principle: filtering out staff from customer footfall. At FootfallCam, we\u2019ve taken a privacy-first, AI-driven approach to solving this challenge \u2014 using\u00a0<strong>Re-Identification (Re-ID) technology<\/strong> to exclude staff without the need for badges, wearables, or manual interaction.<\/span><\/p>\n<p>\u00a0<\/p>\n\n\n\n<p><span style=\"font-family: archivo, sans-serif; color: #666666;\">This post explains the rationale behind this method, how we train our models, and the proven accuracy behind our solution \u2014 already deployed in thousands of stores worldwide.<\/span><\/p>\n<p>\u00a0<\/p>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p><span style=\"font-family: archivo, sans-serif;\"><strong>Why Staff Exclusion Matters<\/strong><\/span><\/p>\n<p>\u00a0<\/p>\n\n\n\n<p><span style=\"font-family: archivo, sans-serif; color: #666666;\">In any store, a significant portion of daily footfall is from staff \u2014 not customers. Store associates walking in and out, merchandisers doing planogram checks, cleaners, and security personnel all show up in traditional people counting systems. Without proper exclusion, this skews your key retail KPIs:<\/span><\/p>\n<ul>\n<li><span style=\"font-family: archivo, sans-serif; color: #666666;\">Inflated conversion rates<\/span><\/li>\n<li><span style=\"font-family: archivo, sans-serif; color: #666666;\">Misleading zone-level dwell time<\/span><\/li>\n<li><span style=\"font-family: archivo, sans-serif; color: #666666;\">Incorrect peak hour analysis<\/span><\/li>\n<li><span style=\"font-family: archivo, sans-serif; color: #666666;\">Ineffective heatmaps and journey paths<\/span><\/li>\n<\/ul>\n<p>\u00a0<\/p>\n\n\n\n<p><span style=\"font-family: archivo, sans-serif; color: #666666;\">The result? Bad data leads to bad decisions.<\/span><\/p>\n<p>\u00a0<\/p>\n\n\n\n<p><span style=\"font-family: archivo, sans-serif; color: #666666;\">That\u2019s why FootfallCam uses Re-ID \u2014 a computer vision model that recognises repeating movement patterns over time to distinguish staff from customers, passively and anonymously.<\/span><\/p>\n<p>\u00a0<\/p>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p><span style=\"font-family: archivo, sans-serif;\"><strong>How Re-ID Works in Retail<\/strong><\/span><\/p>\n<p>\u00a0<\/p>\n\n\n\n<p><span style=\"font-family: archivo, sans-serif; color: #666666;\">Re-ID doesn\u2019t rely on facial recognition or personal identifiers. Instead, our system uses non-PII visual features \u2014 body silhouette, outfit consistency, walking gait \u2014 to build a unique movement signature.<\/span><\/p>\n<p>\u00a0<\/p>\n\n\n\n<p><span style=\"font-family: archivo, sans-serif; color: #666666;\">When someone reappears across different zones or re-enters the store multiple times in a day, the system flags them as a likely staff member. Over time, these signatures become more reliable, enabling automated exclusion from analytics dashboards and exports.<\/span><\/p>\n<p>\u00a0<\/p>\n\n\n\n<p><span style=\"font-family: archivo, sans-serif; color: #666666;\">And because this is fully edge-processed, no sensitive footage leaves the store, and all identities remain anonymous.<\/span><\/p>\n<p>\u00a0<\/p>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p><span style=\"font-family: archivo, sans-serif;\"><strong>Model Training and Accuracy<\/strong><\/span><\/p>\n<p>\u00a0<\/p>\n\n\n\n<p><span style=\"font-family: archivo, sans-serif; color: #666666;\">At FootfallCam, we\u2019ve trained our Re-ID engine using over 2 million retail video segments across supermarkets, fashion chains, convenience stores, and QSRs \u2014 accounting for variations in lighting, layout, ceiling height, and uniform policies.<\/span><\/p>\n<p>\u00a0<\/p>\n\n\n\n<p><span style=\"font-family: archivo, sans-serif; color: #666666;\">It took 3-6 months of iterative training and in-field validation to tune the model across different store formats. Today, we benchmark accuracy at:<\/span><\/p>\n<ul>\n<li><span style=\"font-family: archivo, sans-serif; color: #666666;\">94\u201397% precision in correctly excluding known staff<\/span><\/li>\n<li><span style=\"font-family: archivo, sans-serif; color: #666666;\">Less than 2% false exclusion of actual shoppers (often staff shopping off-shift)<\/span><\/li>\n<\/ul>\n<p>\u00a0<\/p>\n\n\n\n<p><span style=\"font-family: archivo, sans-serif; color: #666666;\">The system improves over time through continuous learning, adjusting to new uniforms, seasonal outfit changes, and store-specific routines.<\/span><\/p>\n<p>\u00a0<\/p>\n\n\n\n<p><span style=\"font-family: archivo, sans-serif; color: #666666;\">Re-ID is the only method that scales effortlessly across a retail chain, requires no behavioural change, and works silently in the background.<\/span><\/p>\n<p>\u00a0<\/p>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p><span style=\"font-family: archivo, sans-serif;\"><strong>Privacy, Always<\/strong><\/span><\/p>\n<p>\u00a0<\/p>\n\n\n\n<p><span style=\"font-family: archivo, sans-serif; color: #666666;\">Our Re-ID system is fully GDPR-compliant. No images are stored. No person is identifiable. Just pattern-matching of an anonymous movement \u2014 a clean, ethical solution to a long-standing problem.<\/span><\/p>\n<p>\u00a0<\/p>\n\n\n\n<p>\u00a0<\/p>\n<p>\u00a0<\/p>\n<p><span style=\"font-family: archivo, sans-serif; color: #666666; font-size: 16px;\"><a href=\"#footfallcam\" target=\"_blank\" rel=\"noopener\">#footfallcam<\/a> <a href=\"#peoplecounter\" target=\"_blank\" rel=\"noopener\">#peoplecounter<\/a> <a href=\"#peoplecounting\" target=\"_blank\" rel=\"noopener\">#peoplecounting<\/a> <a href=\"#staffplanning\" target=\"_blank\" rel=\"noopener\">#staffplanning<\/a> <a href=\"#staffmanagement\" target=\"_blank\" rel=\"noopener\">#staffmanagement<\/a> <a href=\"#retail\" target=\"_blank\" rel=\"noopener\">#retail<\/a> <a href=\"#smartretail\" target=\"_blank\" rel=\"noopener\">#smartretail<\/a> <a href=\"#aianalytics\" target=\"_blank\" rel=\"noopener\">#aianalytics<\/a> <a href=\"#reidentification\" target=\"_blank\" rel=\"noopener\">#reidentification<\/a> <a href=\"#reiidtechnology\" target=\"_blank\" rel=\"noopener\">#reiidtechnology<\/a><\/span><\/p>\n<p>\u00a0<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In the world of retail analytics, accuracy starts with one key principle: filtering out staff from customer footfall. At FootfallCam, we\u2019ve taken a privacy-first, AI-driven approach to solving this challenge &#8230;<\/p>\n","protected":false},"author":1,"featured_media":6323,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[716,719,188,186],"tags":[574,28,16,15,614,795,12,530,794,788],"_links":{"self":[{"href":"https:\/\/www.footfallcam.com\/blog\/wp-json\/wp\/v2\/posts\/6315"}],"collection":[{"href":"https:\/\/www.footfallcam.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.footfallcam.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.footfallcam.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.footfallcam.com\/blog\/wp-json\/wp\/v2\/comments?post=6315"}],"version-history":[{"count":8,"href":"https:\/\/www.footfallcam.com\/blog\/wp-json\/wp\/v2\/posts\/6315\/revisions"}],"predecessor-version":[{"id":6803,"href":"https:\/\/www.footfallcam.com\/blog\/wp-json\/wp\/v2\/posts\/6315\/revisions\/6803"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.footfallcam.com\/blog\/wp-json\/wp\/v2\/media\/6323"}],"wp:attachment":[{"href":"https:\/\/www.footfallcam.com\/blog\/wp-json\/wp\/v2\/media?parent=6315"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.footfallcam.com\/blog\/wp-json\/wp\/v2\/categories?post=6315"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.footfallcam.com\/blog\/wp-json\/wp\/v2\/tags?post=6315"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}