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","media",{"__typename":316,"id":317,"caption":218,"imageRight":238,"pgSharedTitle":318,"pgSharedCtaLink":218,"textMedia":319,"pgSharedText":338},"ParagraphPgTextMedia","36551","Beyond footfall analytics: the shop floor is retail’s biggest blind spot",{"id":320,"sharedMediaLegend":218,"mediaImage":321,"name":337},"173538",{"url":322,"width":221,"height":222,"alt":323,"title":218,"size":324,"mime":225,"variations":218,"textMediaVariations":325},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002Fretail_header.jpeg","Young Asian woman carrying a shopping basket while grocery shopping in supermarket, standing along the dairy aisle, reading the nutrition label on a packet of fresh organic cheese. Making healthier food choices",106531,[326,330,331,332,336],{"name":327,"url":328,"width":329,"height":252},"FIXED_WIDTH_SMALL","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffixed_width_small\u002Fpublic\u002Fimages\u002Fretail_header.jpeg.webp?itok=UnsBlQQ-",900,{"name":327,"url":328,"width":329,"height":252},{"name":327,"url":328,"width":329,"height":252},{"name":333,"url":334,"width":335,"height":243},"FIXED_WIDTH_MEDIUM","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffixed_width_medium\u002Fpublic\u002Fimages\u002Fretail_header.jpeg.webp?itok=gPQqi3Jn",1200,{"name":333,"url":334,"width":335,"height":243},"retail_header.jpeg",{"value":339,"processed":340,"format":341},"\u003Cp>Online retailers trace every step from click to purchase. Point-of-sale systems know what customers bought, and footfall counters know how many came in. But neither answers the questions that decide a sale:\u003C\u002Fp>\u003Cul>\u003Cli data-list-item-id=\"e5f9ffc6762704bb46ea994ea17147343\">How long do customers wait?\u003C\u002Fli>\u003Cli data-list-item-id=\"ed5f25fc37293e6b0f933d74bc833b3a0\">Where does attention go on the shelf?\u003C\u002Fli>\u003Cli data-list-item-id=\"e680b02f03aa9043dd1d24805bd360c17\">Which questions go unanswered?\u003C\u002Fli>\u003Cli data-list-item-id=\"eda1e863c50a74d119b45c2c88a6844e3\">Why do visitors leave without buying?\u003C\u002Fli>\u003C\u002Ful>","\u003Cp>Online retailers trace every step from click to purchase. Point-of-sale systems know what customers bought, and footfall counters know how many came in. But neither answers the questions that decide a sale:\u003C\u002Fp>\u003Cul>\u003Cli>How long do customers wait?\u003C\u002Fli>\u003Cli>Where does attention go on the shelf?\u003C\u002Fli>\u003Cli>Which questions go unanswered?\u003C\u002Fli>\u003Cli>Why do visitors leave without buying?\u003C\u002Fli>\u003C\u002Ful>","full_html",{"__typename":316,"id":343,"caption":218,"imageRight":7,"pgSharedTitle":218,"pgSharedCtaLink":344,"textMedia":347,"pgSharedText":362},"36596",{"title":345,"url":346,"internal":7},"Get the in-store analytics whitepaper","#section-36558",{"id":348,"sharedMediaLegend":218,"mediaImage":349,"name":361},"173890",{"url":350,"width":221,"height":222,"alt":351,"title":218,"size":352,"mime":225,"variations":218,"textMediaVariations":353},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002Fai-retail.jpeg","Customer photographing dress with smart phone in boutique",46152,[354,356,357,358,360],{"name":327,"url":355,"width":329,"height":252},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffixed_width_small\u002Fpublic\u002Fimages\u002Fai-retail.jpeg.webp?itok=6AyQIRbp",{"name":327,"url":355,"width":329,"height":252},{"name":327,"url":355,"width":329,"height":252},{"name":333,"url":359,"width":335,"height":243},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffixed_width_medium\u002Fpublic\u002Fimages\u002Fai-retail.jpeg.webp?itok=YYLGyc0C",{"name":333,"url":359,"width":335,"height":243},"ai-retail.jpeg",{"value":363,"processed":363,"format":341},"\u003Cp>AI-powered in-store analytics answers all four in two layers. \u003Cstrong>Analytical AI works in the background\u003C\u002Fstrong>, using computer vision to reveal movement, dwell and waiting times, while consent-based, anonymised conversation analysis shows what customers need and where service breaks down. \u003Cstrong>Interactive AI works in the foreground\u003C\u002Fstrong>, guiding customers to the right product or person.\u003C\u002Fp>\u003Cp>\u003Cstrong>The principle is simple: analyse patterns, not individuals. Focus on topics, advice quality and process times, not employee performance. \u003C\u002Fstrong>\u003C\u002Fp>",{"__typename":365,"id":366,"pgTitle":367,"text":218,"link":368,"coloredBackground":238,"compactMode":7,"actionVariant":26,"buttonText":218,"pgEmbededCode":370},"ParagraphPgCtaDefault","36624","Explore practical ways to put AI to work in your stores",{"title":369,"url":346,"internal":7},"Download the whitepaper",{"value":371,"processed":371,"format":372},"","code",{"__typename":229,"id":374,"columns":375,"listItems":376,"text":218,"title":411,"variant":412},"36556","2col",[377,387,395,403],{"id":378,"icon":379,"link":218,"logo":218,"media":218,"text":383,"title":386},"36552",{"id":380,"file":381},"171942",{"url":382},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002F2025-07\u002Fplus-triple-icon.svg",{"value":384,"processed":385,"format":266},"\u003Cp>\u003Cstrong>Shorter waits, proactive engagement from staff and interactive AI, and clearer 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upskilling",{"id":404,"icon":405,"link":218,"logo":218,"media":218,"text":407,"title":410},"36555",{"id":380,"file":406},{"url":382},{"value":408,"processed":409,"format":266},"\u003Cp>\u003Cstrong>Workforce scheduling built on actual footfall insights from analytical AI, rather than intuition alone\u003C\u002Fstrong>\u003C\u002Fp>\u003Cul>\u003Cli data-list-item-id=\"e62eda7c4af8ca432f4dd1fbc570c28b8\">Less under- and overstaffing and lower inefficiency costs.\u003C\u002Fli>\u003C\u002Ful>","\u003Cp>\u003Cstrong>Workforce scheduling built on actual footfall insights from analytical AI, rather than intuition alone\u003C\u002Fstrong>\u003C\u002Fp>\u003Cul>\u003Cli>Less under- and overstaffing and lower inefficiency costs.\u003C\u002Fli>\u003C\u002Ful>","Operations","From data to decision: what changes and what it produces  ","icon",{"__typename":316,"id":414,"caption":218,"imageRight":238,"pgSharedTitle":415,"pgSharedCtaLink":416,"textMedia":418,"pgSharedText":435},"36557","A store that 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mockup",619664,[427,429,430,431,433],{"name":327,"url":428,"width":422,"height":423},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffixed_width_small\u002Fpublic\u002Fimages\u002Fmock-up-ai-in-retial-en_1.png.webp?itok=5BSSrsud",{"name":327,"url":428,"width":422,"height":423},{"name":327,"url":428,"width":422,"height":423},{"name":333,"url":432,"width":422,"height":423},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffixed_width_medium\u002Fpublic\u002Fimages\u002Fmock-up-ai-in-retial-en_1.png.webp?itok=cAwsM6ZT",{"name":333,"url":432,"width":422,"height":423},"mock-up-ai-in-retial-en.png",{"value":436,"processed":437,"format":341},"\u003Cp>\u003Cstrong>AI-driven customer journey analytics is already making previously invisible in-store interactions measurable\u003C\u002Fstrong>. \u003Cspan>Zühlke has deployed computer vision and consent-based conversation analysis across three stores for a leading Swiss 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Where is your shop floor losing revenue today? Which use case can create measurable impact fastest? And what could a realistic architecture and pilot look like?\u003C\u002Fp>\u003Cp>You'll speak to the authors of the whitepaper: \u003C\u002Fp>\u003Cul>\u003Cli data-list-item-id=\"ed770c6989eb6fdcee5f0d07afe446dfc\">\u003Cstrong>Dr Fredrik Gundelsweiler\u003C\u002Fstrong>, Principal Business Consultant & Partner, Switzerland\u003C\u002Fli>\u003Cli data-list-item-id=\"e1c9129bf1ca22e402f91ccace5241b9d\">\u003Cstrong>Franz Georg Niemczik\u003C\u002Fstrong>, Head of Retail Innovation, Germany\u003C\u002Fli>\u003C\u002Ful>","\u003Cp>Together, we’ll identify your strongest levers. Where is your shop floor losing revenue today? Which use case can create measurable impact fastest? And what could a realistic architecture and pilot look like?\u003C\u002Fp>\u003Cp>You'll speak to the authors of the whitepaper: \u003C\u002Fp>\u003Cul>\u003Cli>\u003Cstrong>Dr Fredrik Gundelsweiler\u003C\u002Fstrong>, Principal Business Consultant & Partner, Switzerland\u003C\u002Fli>\u003Cli>\u003Cstrong>Franz Georg Niemczik\u003C\u002Fstrong>, Head of Retail Innovation, Germany\u003C\u002Fli>\u003C\u002Ful>",{"__typename":614,"id":615,"title":616,"lead":218,"tabs":617},"ParagraphTab","36615","Frequently asked questions about in-store analytics",[618,625,632,639,646,653],{"infoText":619,"pgSharedParagraphs":620},"What is in-store analytics?",[621],{"id":622,"ctaLink":218,"pgSharedText":623,"pgSharedTitle":619,"pgSharedCtaLink":218},"36611",{"value":624,"processed":624,"format":341},"\u003Cp>In-store analytics measures what happens on the shop floor: how customers move, how long they dwell and wait, and what they ask for. AI adds what point-of-sale data and footfall counters miss, so retailers can track the store KPIs that decide a sale.\u003C\u002Fp>",{"infoText":626,"pgSharedParagraphs":627},"How is AI-powered in-store analytics different from footfall analytics? ",[628],{"id":629,"ctaLink":218,"pgSharedText":630,"pgSharedTitle":626,"pgSharedCtaLink":218},"36613",{"value":631,"processed":631,"format":341},"\u003Cp>Footfall analytics tells you how many people came in. AI also shows how long they waited, where their attention went and which questions went unanswered, using computer vision and consent-based, anonymised speech analytics. \u003C\u002Fp>",{"infoText":633,"pgSharedParagraphs":634},"How do you protect customer privacy in in-store analytics?",[635],{"id":636,"ctaLink":218,"pgSharedText":637,"pgSharedTitle":633,"pgSharedCtaLink":218},"36616",{"value":638,"processed":638,"format":341},"\u003Cp>\u003Cspan lang=\"EN-US\">By applying privacy by design: analyse patterns, not individuals; use consent-based, anonymised conversation analysis; and focus on topics, advice quality and process times rather than employee performance. The whitepaper shows how to build this in from day one.\u003C\u002Fspan>\u003Cspan> \u003C\u002Fspan>\u003C\u002Fp>",{"infoText":640,"pgSharedParagraphs":641},"Does the EU AI Act apply to AI in retail stores?",[642],{"id":643,"ctaLink":218,"pgSharedText":644,"pgSharedTitle":640,"pgSharedCtaLink":218},"36618",{"value":645,"processed":645,"format":341},"\u003Cp>It depends on what the system does. The EU AI Act distinguishes prohibited, high-risk and lower-risk uses. The whitepaper includes a classification table to help you place your use case.\u003C\u002Fp>",{"infoText":647,"pgSharedParagraphs":648},"Should in-store AI run at the edge or in the cloud?",[649],{"id":650,"ctaLink":218,"pgSharedText":651,"pgSharedTitle":647,"pgSharedCtaLink":218},"36620",{"value":652,"processed":652,"format":341},"\u003Cp>Edge AI processes data in the store itself, which keeps sensitive data on-site and shortens response times. The cloud scales more easily and centralises analysis. The whitepaper compares both so you can choose for your first store.\u003C\u002Fp>",{"infoText":654,"pgSharedParagraphs":655},"How long does it take to go from pilot to a scaling decision?",[656],{"id":657,"ctaLink":218,"pgSharedText":658,"pgSharedTitle":654,"pgSharedCtaLink":218},"36622",{"value":659,"processed":659,"format":341},"\u003Cp>The whitepaper lays out a 90-day path: one focused pilot, measured against clear KPIs, ending in an evidence-based decision on whether and how to scale.\u003C\u002Fp>",[661],{"id":662,"name":663},"9","Commerce & retail",[665,668,671,674,677,680,683,686],{"id":666,"name":667},"110","AI",{"id":669,"name":670},"124","Cloud",{"id":672,"name":673},"19","Digital strategy",{"id":675,"name":676},"887","Business transformation",{"id":678,"name":679},"886","Tech innovation",{"id":681,"name":682},"129","Experience design",{"id":684,"name":685},"944","User \u002F customer experience",{"id":687,"name":688},"891","New technologies",{"__typename":690,"id":691,"headerTitleDefault":692,"intro":693,"media":695,"link":709},"ParagraphPgHeaderDefault","36545","In-store analytics: make customer journeys measurable with AI",{"value":694},"Online, every click is measured. On the shop floor, visibility ends at the door. AI for retailers changes that: it measures the journey and guides customers in the aisle, without compromising trust.",{"id":217,"sharedMediaLegend":218,"mediaImage":696,"name":226},{"url":220,"width":221,"height":222,"alt":223,"title":218,"size":224,"mime":225,"variations":218,"mediaVariations":697},[698,700,701,705,708],{"name":469,"url":699,"width":243,"height":258},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_3_2_small\u002Fpublic\u002Fimages\u002Fretail_ai_whitepaper.jpeg.webp?itok=YLreV5aU",{"name":469,"url":699,"width":243,"height":258},{"name":702,"url":703,"width":335,"height":704},"FOCAL_POINT169_MEDIUM","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_16_9_medium\u002Fpublic\u002Fimages\u002Fretail_ai_whitepaper.jpeg.webp?itok=B77LjHwJ",675,{"name":706,"url":707,"width":221,"height":464},"FOCAL_POINT169_LARGE","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_16_9_large\u002Fpublic\u002Fimages\u002Fretail_ai_whitepaper.jpeg.webp?itok=qXWSsZxB",{"name":706,"url":707,"width":221,"height":464},{"title":417,"url":346,"internal":7}]