{"id":6161,"date":"2025-07-16T09:58:26","date_gmt":"2025-07-16T09:58:26","guid":{"rendered":"https:\/\/www.footfallcam.com\/blog\/?p=6161"},"modified":"2025-10-30T09:48:30","modified_gmt":"2025-10-30T09:48:30","slug":"workshop-staff-planning-app","status":"publish","type":"post","link":"https:\/\/www.footfallcam.com\/blog\/2025\/07\/workshop-staff-planning-app\/","title":{"rendered":"Workshop: Staff Planning App"},"content":{"rendered":"\n<p><span style=\"font-family: archivo, sans-serif; color: #666666;\">FootfallCam\u2019s <strong>Staff Planning App<\/strong> empowers retailers to optimise labour allocation using real-time data and AI-driven demand forecasting. Moving beyond outdated staffing methods based on sales or raw footfall, the app identifies high-intent shopper groups and predicts when and where staffing is truly needed. It integrates with existing WFM systems to provide data-backed roster suggestions, while post-operation reviews quantify unmet demand and missed sales. By aligning staff supply with actual customer demand, retailers can maximise conversion, reduce wasted hours, and improve in-store experience\u2014without increasing payroll. It\u2019s data turned into action\u2014simple, scalable, and immediately impactful.<\/span><\/p>\n<p>\u00a0<\/p>\n\n\n\n<p><span style=\"font-family: archivo, sans-serif;\"><span style=\"color: #666666;\">Watch the Webinar Recording Here:<\/span> <span style=\"color: #166bff;\"><a style=\"color: #166bff;\" href=\"https:\/\/www.youtube.com\/watch?v=ka7Ns8dWd70\" target=\"_blank\" rel=\"noopener\">FootfallCam Webinar &#8211; Staff Planning App<\/a><\/span><\/span><br \/><span style=\"font-family: archivo, sans-serif;\"><span style=\"color: #666666;\">Download the Slides Here:<\/span> <span style=\"color: #166bff;\"><a style=\"color: #166bff;\" href=\"https:\/\/www.footfallcam.com\/Content\/data\/documents\/Internal-Use\/Software-App-Workshop-Staff-Planning-App.pdf\" target=\"_blank\" rel=\"noopener\">Software App Workshop &#8211; Staff Planning App<\/a><\/span><\/span><\/p>\n<p>\u00a0<\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"FootfallCam Webinar - Staff Planning App\" width=\"800\" height=\"450\" src=\"https:\/\/www.youtube.com\/embed\/ka7Ns8dWd70?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n\n\n\n<p>\u00a0<\/p>\n<p><span style=\"font-family: archivo, sans-serif;\"><strong>The Problem: Missed Sales = Missed Demand<\/strong><\/span><\/p>\n<p>\u00a0<\/p>\n\n\n\n<p><span style=\"font-family: archivo, sans-serif; color: #666666;\">Retailers often lose sales quietly\u2014not because their product was wrong, but because no one was available to serve a willing customer. These missed sales are invisible in most traditional metrics. Sales figures only show fulfilled demand. They don\u2019t reveal what could have been sold had more staff been available.<\/span><\/p>\n<p>\u00a0<\/p>\n\n\n\n<p><span style=\"font-family: archivo, sans-serif; color: #666666;\">FootfallCam\u2019s insight? You can\u2019t plan staffing effectively without understanding unmet demand. And unmet demand comes from one core issue: retailers are forecasting staff needs based on the wrong signals\u2014footfall and past sales\u2014instead of real purchase intent.<\/span><\/p>\n<p>\u00a0<\/p>\n\n\n\n<p><span style=\"font-family: archivo, sans-serif;\"><strong>What the Webinar Covered<\/strong><\/span><\/p>\n<p>\u00a0<\/p>\n\n\n\n<p><span style=\"font-family: archivo, sans-serif; color: #666666;\">Our session introduced a suite of apps designed to help retailers operationalise the deep behavioural data already captured by FootfallCam sensors. The centrepiece was the Staff Planning App\u2014a tool that closes the gap between data abundance and business action.<\/span><\/p>\n<p>\u00a0<\/p>\n\n\n\n<p><span style=\"font-family: archivo, sans-serif; color: #666666;\">We walked through:<\/span><\/p>\n<ul>\n<li><span style=\"font-family: archivo, sans-serif; color: #666666;\">How the app identifies intended buying groups (not just visitors)<\/span><\/li>\n<li><span style=\"font-family: archivo, sans-serif; color: #666666;\">How to measure unmet demand\u2014the people who wanted to buy but walked out<\/span><\/li>\n<li><span style=\"font-family: archivo, sans-serif; color: #666666;\">A complete staff planning lifecycle: Predict \u2192 Plan \u2192 Execute \u2192 Review<\/span><\/li>\n<li><span style=\"font-family: archivo, sans-serif; color: #666666;\">How to align staffing to actual demand, store by store, hour by hour<\/span><\/li>\n<\/ul>\n<p>\u00a0<\/p>\n\n\n\n<p><span style=\"font-family: archivo, sans-serif; color: #666666;\">The Staff Planning App introduces a third, more accurate metric: intent-based modelling. It uses AI to predict staffing needs based on behaviour patterns\u2014identifying who is likely to convert, and when.<\/span><\/p>\n<p>\u00a0<\/p>\n\n\n\n<p><span style=\"font-family: archivo, sans-serif; color: #666666;\">This lets planners make smarter decisions, supported by:<\/span><\/p>\n<ul>\n<li><span style=\"font-family: archivo, sans-serif; color: #666666;\">Real-time supply-demand curves<\/span><\/li>\n<li><span style=\"font-family: archivo, sans-serif; color: #666666;\">Predictive staffing rosters<\/span><\/li>\n<li><span style=\"font-family: archivo, sans-serif; color: #666666;\">Automated feedback loops that measure how well the plan worked<\/span><\/li>\n<\/ul>\n<p>\u00a0<\/p>\n\n\n\n<p><span style=\"font-family: archivo, sans-serif;\"><strong>Closing the Loop with Real Metrics<\/strong><\/span><\/p>\n<p>\u00a0<\/p>\n\n\n\n<p><span style=\"font-family: archivo, sans-serif; color: #666666;\">Most planning models break down in two places:<\/span><\/p>\n<ul>\n<li><span style=\"font-family: archivo, sans-serif; color: #666666;\">Prediction: Without measuring intent, forecasts are built on shaky assumptions.<\/span><\/li>\n<li><span style=\"font-family: archivo, sans-serif; color: #666666;\">Review: Without tracking unmet demand, there\u2019s no feedback to improve.<\/span><\/li>\n<\/ul>\n<p>\u00a0<\/p>\n\n\n\n<p><span style=\"font-family: archivo, sans-serif; color: #666666;\">Our app tackles both. After execution, it compares:<\/span><\/p>\n<ul>\n<li><span style=\"font-family: archivo, sans-serif; color: #666666;\">What you planned<\/span><\/li>\n<li><span style=\"font-family: archivo, sans-serif; color: #666666;\">Who actually turned up to work<\/span><\/li>\n<li><span style=\"font-family: archivo, sans-serif; color: #666666;\">What should have been the right staff count, based on observed demand<\/span><\/li>\n<\/ul>\n<p>\u00a0<\/p>\n\n\n\n<p><span style=\"font-family: archivo, sans-serif; color: #666666;\">The result? A retrospective view of your staffing accuracy and the sales opportunity lost due to over- or understaffing. This closes the loop and allows the AI model to learn and adapt each week.<\/span><\/p>\n<p>\u00a0<\/p>\n\n\n\n<p><span style=\"font-family: archivo, sans-serif;\"><strong>Why This Wasn\u2019t Possible Before<\/strong><\/span><\/p>\n<p>\u00a0<\/p>\n\n\n\n<p><span style=\"font-family: archivo, sans-serif; color: #666666;\">Why is this a breakthrough now? Because until recently, it was technically and economically impossible to measure:<\/span><\/p>\n<ul>\n<li><span style=\"font-family: archivo, sans-serif; color: #666666;\">Who walked into the store with real buying intent<\/span><\/li>\n<li><span style=\"font-family: archivo, sans-serif; color: #666666;\">Which of them got served\u2014and which didn\u2019t<\/span><\/li>\n<\/ul>\n<p>\u00a0<\/p>\n\n\n\n<p><span style=\"font-family: archivo, sans-serif; color: #666666;\">Now, with the latest generation of FootfallCam sensors (Pro2, Pro1, Centroid), we can track:<\/span><\/p>\n<ul>\n<li><span style=\"font-family: archivo, sans-serif; color: #666666;\">Customer group size and demographics<\/span><\/li>\n<li><span style=\"font-family: archivo, sans-serif; color: #666666;\">Staff availability in real time<\/span><\/li>\n<li><span style=\"font-family: archivo, sans-serif; color: #666666;\">Queue build-up, dwell zones, and unmet demand<\/span><\/li>\n<\/ul>\n<p>\u00a0<\/p>\n\n\n\n<p><span style=\"font-family: archivo, sans-serif; color: #666666;\">We\u2019ve turned these raw signals into powerful metrics that inform every stage of the staffing process.<\/span><\/p>\n<p>\u00a0<\/p>\n\n\n\n<p><span style=\"font-family: archivo, sans-serif;\"><strong>What\u2019s Next: A\/B Testing It In Your Stores<\/strong><\/span><\/p>\n<p>\u00a0<\/p>\n\n\n\n<p><span style=\"font-family: archivo, sans-serif; color: #666666;\">If you&#8217;re a FootfallCam client with sensors already installed, you\u2019re just a step away from running a real-world trial. We recommend a structured A\/B test:<\/span><\/p>\n<ul>\n<li><span style=\"font-family: archivo, sans-serif; color: #666666;\">Run 5 stores using the new staff planning model<\/span><\/li>\n<li><span style=\"font-family: archivo, sans-serif; color: #666666;\">Run 5 stores the old way<\/span><\/li>\n<li><span style=\"font-family: archivo, sans-serif; color: #666666;\">Compare sales uplift, unmet demand, and staffing efficiency<\/span><\/li>\n<\/ul>\n<p><span style=\"font-family: archivo, sans-serif; color: #666666;\">Then swap the groups, and compare again.<\/span><\/p>\n<p>\u00a0<\/p>\n\n\n\n<p><span style=\"font-family: archivo, sans-serif; color: #666666;\">We\u2019ll support you with onboarding, training, system integration, and review dashboards\u2014so you see the results for yourself.<\/span><\/p>\n<p>\u00a0<\/p>\n\n\n\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>&nbsp;<\/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=\"#datainsights\" target=\"_blank\" rel=\"noopener\">#datainsights<\/a> <a href=\"#peoplecounting\" target=\"_blank\" rel=\"noopener\">#peoplecounting<\/a> <a href=\"#retailtech\" target=\"_blank\" rel=\"noopener\">#retailtech<\/a> <a href=\"#operationsmanagement\" target=\"_blank\" rel=\"noopener\">#operationsmanagement<\/a> <a href=\"#businessefficiency\" target=\"_blank\" rel=\"noopener\">#businessefficiency<\/a> <a href=\"#staffplanning\" target=\"_blank\" rel=\"noopener\">#staffplanning<\/a> <a href=\"#smartanalytics\" target=\"_blank\" rel=\"noopener\">#smartanalytics<\/a> <a href=\"#webinar\" target=\"_blank\" rel=\"noopener\">#webinar<\/a><\/span><\/p>\n<p>&nbsp;<\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>FootfallCam\u2019s Staff Planning App empowers retailers to optimise labour allocation using real-time data and AI-driven demand forecasting. Moving beyond outdated staffing methods based on sales or raw footfall, the app &#8230;<\/p>\n","protected":false},"author":1,"featured_media":6164,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[719,188,722,860],"tags":[741,577,28,740,15,628,742,788,571],"_links":{"self":[{"href":"https:\/\/www.footfallcam.com\/blog\/wp-json\/wp\/v2\/posts\/6161"}],"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=6161"}],"version-history":[{"count":10,"href":"https:\/\/www.footfallcam.com\/blog\/wp-json\/wp\/v2\/posts\/6161\/revisions"}],"predecessor-version":[{"id":6819,"href":"https:\/\/www.footfallcam.com\/blog\/wp-json\/wp\/v2\/posts\/6161\/revisions\/6819"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.footfallcam.com\/blog\/wp-json\/wp\/v2\/media\/6164"}],"wp:attachment":[{"href":"https:\/\/www.footfallcam.com\/blog\/wp-json\/wp\/v2\/media?parent=6161"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.footfallcam.com\/blog\/wp-json\/wp\/v2\/categories?post=6161"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.footfallcam.com\/blog\/wp-json\/wp\/v2\/tags?post=6161"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}