{"id":2022,"date":"2026-03-30T11:48:17","date_gmt":"2026-03-30T09:48:17","guid":{"rendered":"https:\/\/seeketing.com\/retail\/"},"modified":"2026-04-08T19:07:04","modified_gmt":"2026-04-08T17:07:04","slug":"retail","status":"publish","type":"page","link":"https:\/\/seeketing.com\/en\/phygital-solutions\/retail\/","title":{"rendered":"Retail"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"2022\" class=\"elementor elementor-2022 elementor-1688\" data-elementor-post-type=\"page\">\n\t\t\t\t<div class=\"elementor-element elementor-element-fc36b0c e-flex e-con-boxed e-con e-parent\" data-id=\"fc36b0c\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-88dd068 elementor--h-position-center elementor--v-position-middle elementor-arrows-position-inside elementor-pagination-position-inside elementor-widget elementor-widget-slides\" data-id=\"88dd068\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;navigation&quot;:&quot;both&quot;,&quot;autoplay&quot;:&quot;yes&quot;,&quot;pause_on_hover&quot;:&quot;yes&quot;,&quot;pause_on_interaction&quot;:&quot;yes&quot;,&quot;autoplay_speed&quot;:5000,&quot;infinite&quot;:&quot;yes&quot;,&quot;transition&quot;:&quot;slide&quot;,&quot;transition_speed&quot;:500}\" data-widget_type=\"slides.default\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"elementor-slides-wrapper elementor-main-swiper swiper\" role=\"region\" aria-roledescription=\"carousel\" aria-label=\"Slides\" dir=\"ltr\" data-animation=\"fadeInUp\">\n\t\t\t\t<div class=\"swiper-wrapper elementor-slides\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"elementor-repeater-item-7400d5f swiper-slide\" role=\"group\" aria-roledescription=\"slide\"><div class=\"swiper-slide-bg\" role=\"img\" aria-label=\"retail\"><\/div><div class=\"elementor-background-overlay\"><\/div><div class=\"swiper-slide-inner\" ><div class=\"swiper-slide-contents\"><div class=\"elementor-slide-heading\">Advanced marketing metrics and tools to optimize the management of your points of sale\u200b<\/div><div class=\"elementor-slide-description\">Unlike camera-based solutions, which are limited to counting individuals, Seeketing can track real customer behavior over time. This enables data-driven validation of any operational decision that impacts each store\u2019s performance.<\/div><\/div><\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-2cc344c e-flex e-con-boxed elementor-invisible e-con e-parent\" data-id=\"2cc344c\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;animation&quot;:&quot;fadeIn&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-9a162a0 elementor-invisible elementor-widget elementor-widget-heading\" data-id=\"9a162a0\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;_animation&quot;:&quot;fadeIn&quot;}\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Phygital Analytics: The X-ray of your store<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-84f0285 elementor-widget elementor-widget-text-editor\" data-id=\"84f0285\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p data-pm-slice=\"1 1 []\">Under the <strong>IoP (Internet of People) <\/strong>paradigm, our technology enables the anonymous detection of customers in strict compliance with <strong>GDPR<\/strong> and LOPD regulations. This data intelligence makes it possible to audit visitor behavior in order to maximize the operational and commercial performance of the point of sale <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-d025092 e-flex e-con-boxed e-con e-parent\" data-id=\"d025092\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-793c3eb elementor-widget elementor-widget-heading\" data-id=\"793c3eb\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Use cases<\/h4>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-393d431 e-flex e-con-boxed e-con e-parent\" data-id=\"393d431\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5858147 elementor-widget elementor-widget-n-accordion\" data-id=\"5858147\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;default_state&quot;:&quot;all_collapsed&quot;,&quot;max_items_expended&quot;:&quot;one&quot;,&quot;n_accordion_animation_duration&quot;:{&quot;unit&quot;:&quot;ms&quot;,&quot;size&quot;:400,&quot;sizes&quot;:[]}}\" data-widget_type=\"nested-accordion.default\">\n\t\t\t\t\t\t\t<div class=\"e-n-accordion\" aria-label=\"Accordion. Open links with Enter or Space, close with Escape, and navigate with Arrow Keys\">\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-9260\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"1\" tabindex=\"0\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-9260\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> Metrics by zones and concepts <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-9260\" class=\"elementor-element elementor-element-4c07afd e-con-full e-flex e-con e-child\" data-id=\"4c07afd\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-9260\" class=\"elementor-element elementor-element-07312df e-flex e-con-boxed e-con e-child\" data-id=\"07312df\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-d611ac1 elementor-widget elementor-widget-text-editor\" data-id=\"d611ac1\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p data-pm-slice=\"1 1 [\"bullet_list\",{},\"list_item\",{}]\">Measure traffic and conversion for <em>store-within-a-store<\/em> formats, specific departments, floor layouts, and kiosks.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-9261\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"2\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-9261\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> Conversion rates by product category <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-9261\" class=\"elementor-element elementor-element-19d68bc e-con-full e-flex e-con e-child\" data-id=\"19d68bc\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-9261\" class=\"elementor-element elementor-element-9bc899c e-flex e-con-boxed e-con e-child\" data-id=\"9bc899c\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a6c3174 elementor-widget elementor-widget-text-editor\" data-id=\"a6c3174\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p data-pm-slice=\"1 1 [\"bullet_list\",{},\"list_item\",{}]\">Analyze how many people pass by a specific product and how many actual sales it generates (physical attribution).<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-9262\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"3\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-9262\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> Strategic promotion planning <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-9262\" class=\"elementor-element elementor-element-5a5a550 e-con-full e-flex e-con e-child\" data-id=\"5a5a550\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-9262\" class=\"elementor-element elementor-element-e88778d e-flex e-con-boxed e-con e-child\" data-id=\"e88778d\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7332ae8 elementor-widget elementor-widget-text-editor\" data-id=\"7332ae8\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p data-pm-slice=\"1 1 [\"bullet_list\",{},\"list_item\",{}]\">Determine the optimal timing for each type of in-store promotion based on real traffic flow behavior.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-9263\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"4\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-9263\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> Strategic product placement <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-9263\" class=\"elementor-element elementor-element-4d778b6 e-flex e-con-boxed e-con e-child\" data-id=\"4d778b6\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-9263\" class=\"elementor-element elementor-element-1b4f834 e-con-full e-flex e-con e-child\" data-id=\"1b4f834\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-eb86fef elementor-widget elementor-widget-text-editor\" data-id=\"eb86fef\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p data-pm-slice=\"1 1 [\"bullet_list\",{},\"list_item\",{}]\">Determine the exact placement for each item to achieve the maximum number of impressions in the shortest possible time.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-9264\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"5\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-9264\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> Monetization of physical space <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-9264\" class=\"elementor-element elementor-element-018f92c e-flex e-con-boxed e-con e-child\" data-id=\"018f92c\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-9264\" class=\"elementor-element elementor-element-c49d59b e-con-full e-flex e-con e-child\" data-id=\"c49d59b\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-639de18 elementor-widget elementor-widget-text-editor\" data-id=\"639de18\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p data-pm-slice=\"1 1 [\"bullet_list\",{},\"list_item\",{}]\">Identify the most profitable areas to lease space for product displays, digital signage, and other advertising assets.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-9265\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"6\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-9265\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> Traffic cycle analysis <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-9265\" class=\"elementor-element elementor-element-6591c67 e-flex e-con-boxed e-con e-child\" data-id=\"6591c67\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-9265\" class=\"elementor-element elementor-element-a32bef2 e-con-full e-flex e-con e-child\" data-id=\"a32bef2\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-cf61690 elementor-widget elementor-widget-text-editor\" data-id=\"cf61690\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p data-pm-slice=\"1 1 [\"bullet_list\",{},\"list_item\",{}]\">Identify footfall patterns and cycles by hour, day, week, or season.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-9266\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"7\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-9266\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> Staff optimization (Customer-to-Employee ratio) <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-9266\" class=\"elementor-element elementor-element-d82a9ad e-flex e-con-boxed e-con e-child\" data-id=\"d82a9ad\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-9266\" class=\"elementor-element elementor-element-551c6d8 e-con-full e-flex e-con e-child\" data-id=\"551c6d8\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-373b05e elementor-widget elementor-widget-text-editor\" data-id=\"373b05e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p data-pm-slice=\"1 1 [\"bullet_list\",{},\"list_item\",{}]\">Adjust staffing levels in real time to maximize sales and ensure an exceptional customer experience.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-d15fcea e-flex e-con-boxed elementor-invisible e-con e-parent\" data-id=\"d15fcea\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;animation&quot;:&quot;fadeIn&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5cfe3d3 elementor-widget elementor-widget-heading\" data-id=\"5cfe3d3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Visit generation, sales, and upselling: Phygital Intelligence<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a11340e elementor-widget elementor-widget-text-editor\" data-id=\"a11340e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p data-pm-slice=\"1 1 []\"><strong>Seeketing transforms every physical visit into an opportunity for conversion into a purchase.<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e52f86a elementor-widget elementor-widget-text-editor\" data-id=\"e52f86a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p data-pm-slice=\"1 1 []\">Our solution natively integrates physical behavioral data with the responsiveness of mobile communication (with and without an app). This architecture enables the execution of <strong>proximity marketing<\/strong> strategies aimed at increasing traffic, optimizing average ticket value through <strong>upselling<\/strong>, and fostering customer loyalty based on real visitation patterns. By replacing generic visit data with phygital behavioral data, the platform transforms digital communication into a direct driver of conversion and in-store sales.  <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-43c8ffd e-flex e-con-boxed e-con e-parent\" data-id=\"43c8ffd\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-aea04b4 elementor-widget elementor-widget-video\" data-id=\"aea04b4\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;video_type&quot;:&quot;hosted&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t\t\t\t<div class=\"e-hosted-video elementor-wrapper elementor-open-inline\">\n\t\t\t\t\t<video class=\"elementor-video\" src=\"https:\/\/seeketing.com\/wp-content\/uploads\/2026\/04\/Phygital-Retail-EN-1.mp4\" controls=\"\" preload=\"metadata\" controlsList=\"nodownload\"><\/video>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-726a129 e-flex e-con-boxed e-con e-parent\" data-id=\"726a129\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-cc70975 elementor-invisible elementor-widget elementor-widget-heading\" data-id=\"cc70975\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;_animation&quot;:&quot;fadeIn&quot;}\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Key features<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ec51634 elementor-widget elementor-widget-n-accordion\" data-id=\"ec51634\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;default_state&quot;:&quot;all_collapsed&quot;,&quot;max_items_expended&quot;:&quot;one&quot;,&quot;n_accordion_animation_duration&quot;:{&quot;unit&quot;:&quot;ms&quot;,&quot;size&quot;:400,&quot;sizes&quot;:[]}}\" data-widget_type=\"nested-accordion.default\">\n\t\t\t\t\t\t\t<div class=\"e-n-accordion\" aria-label=\"Accordion. Open links with Enter or Space, close with Escape, and navigate with Arrow Keys\">\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-2470\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"1\" tabindex=\"0\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-2470\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> PHYGITAL behavior analysis (physical + digital) <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-2470\" class=\"elementor-element elementor-element-b7a77ea e-con-full e-flex e-con e-child\" data-id=\"b7a77ea\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-2470\" class=\"elementor-element elementor-element-9db90eb e-flex e-con-boxed e-con e-child\" data-id=\"9db90eb\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-9a815d0 elementor-widget elementor-widget-text-editor\" data-id=\"9a815d0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p data-pm-slice=\"1 1 [\"bullet_list\",{},\"list_item\",{}]\">Understand how customers interact across both worlds in an integrated way.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-2471\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"2\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-2471\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> Profile-based segmentation <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-2471\" class=\"elementor-element elementor-element-a57e053 e-con-full e-flex e-con e-child\" data-id=\"a57e053\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-2471\" class=\"elementor-element elementor-element-032d3fe e-flex e-con-boxed e-con e-child\" data-id=\"032d3fe\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-d7861ef elementor-widget elementor-widget-text-editor\" data-id=\"d7861ef\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p data-pm-slice=\"1 1 [\"bullet_list\",{},\"list_item\",{}]\">Build audience profiles based on real behavior patterns, identifying visit frequency and traffic flows to stores.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-2472\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"3\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-2472\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> First-party database capture <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-2472\" class=\"elementor-element elementor-element-6537ef6 e-con-full e-flex e-con e-child\" data-id=\"6537ef6\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-2472\" class=\"elementor-element elementor-element-66dde63 e-flex e-con-boxed e-con e-child\" data-id=\"66dde63\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a2e54ac elementor-widget elementor-widget-text-editor\" data-id=\"a2e54ac\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p data-pm-slice=\"1 1 [\"bullet_list\",{},\"list_item\",{}]\">Generate high-quality databases with your customers\u2019 personal data (First-Party Data).<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-2473\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"4\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-2473\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> Personalized proximity communication <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-2473\" class=\"elementor-element elementor-element-3dd22d9 e-flex e-con-boxed e-con e-child\" data-id=\"3dd22d9\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-2473\" class=\"elementor-element elementor-element-e9727e5 e-con-full e-flex e-con e-child\" data-id=\"e9727e5\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-ba9a661 elementor-widget elementor-widget-text-editor\" data-id=\"ba9a661\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p data-pm-slice=\"1 1 [\"bullet_list\",{},\"list_item\",{}]\">Engage customers inside the store via SMS, WhatsApp, or email, with messages based on their profile (age, gender) and their previous behavior both online and in the physical world.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-5fe1482 e-flex e-con-boxed elementor-invisible e-con e-parent\" data-id=\"5fe1482\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;animation&quot;:&quot;fadeIn&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-66b189c elementor-widget elementor-widget-heading\" data-id=\"66b189c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">FAQ<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6adcad2 elementor-widget elementor-widget-n-accordion\" data-id=\"6adcad2\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;default_state&quot;:&quot;all_collapsed&quot;,&quot;max_items_expended&quot;:&quot;one&quot;,&quot;n_accordion_animation_duration&quot;:{&quot;unit&quot;:&quot;ms&quot;,&quot;size&quot;:400,&quot;sizes&quot;:[]}}\" data-widget_type=\"nested-accordion.default\">\n\t\t\t\t\t\t\t<div class=\"e-n-accordion\" aria-label=\"Accordion. Open links with Enter or Space, close with Escape, and navigate with Arrow Keys\">\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-1120\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"1\" tabindex=\"0\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-1120\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> What technical specifications do Seeketing nodes have? <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-check-circle\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M504 256c0 136.967-111.033 248-248 248S8 392.967 8 256 119.033 8 256 8s248 111.033 248 248zM227.314 387.314l184-184c6.248-6.248 6.248-16.379 0-22.627l-22.627-22.627c-6.248-6.249-16.379-6.249-22.628 0L216 308.118l-70.059-70.059c-6.248-6.248-16.379-6.248-22.628 0l-22.627 22.627c-6.248 6.248-6.248 16.379 0 22.627l104 104c6.249 6.249 16.379 6.249 22.628.001z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-far-question-circle\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M256 8C119.043 8 8 119.083 8 256c0 136.997 111.043 248 248 248s248-111.003 248-248C504 119.083 392.957 8 256 8zm0 448c-110.532 0-200-89.431-200-200 0-110.495 89.472-200 200-200 110.491 0 200 89.471 200 200 0 110.53-89.431 200-200 200zm107.244-255.2c0 67.052-72.421 68.084-72.421 92.863V300c0 6.627-5.373 12-12 12h-45.647c-6.627 0-12-5.373-12-12v-8.659c0-35.745 27.1-50.034 47.579-61.516 17.561-9.845 28.324-16.541 28.324-29.579 0-17.246-21.999-28.693-39.784-28.693-23.189 0-33.894 10.977-48.942 29.969-4.057 5.12-11.46 6.071-16.666 2.124l-27.824-21.098c-5.107-3.872-6.251-11.066-2.644-16.363C184.846 131.491 214.94 112 261.794 112c49.071 0 101.45 38.304 101.45 88.8zM298 368c0 23.159-18.841 42-42 42s-42-18.841-42-42 18.841-42 42-42 42 18.841 42 42z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1120\" class=\"elementor-element elementor-element-ccf7bfa e-con-full e-flex e-con e-child\" data-id=\"ccf7bfa\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1120\" class=\"elementor-element elementor-element-19b314e e-flex e-con-boxed e-con e-child\" data-id=\"19b314e\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-0e6c109 elementor-widget elementor-widget-text-editor\" data-id=\"0e6c109\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><strong data-start=\"4\" data-end=\"23\">Seeketing nodes<\/strong> are IoT devices designed to analyze people\u2019s behavior in physical spaces (streets, stores, events, cities, etc.), connecting that behavior with the digital world. Technically, their specifications are not presented as a \u201cclassic hardware spec sheet\u201d (CPU\/RAM type), but rather as technological and operational capabilities. <\/p><h5><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f4e1.svg\" alt=\"\ud83d\udce1\">  Communication technologies<\/h5><ul data-start=\"556\" data-end=\"839\"><li data-section-id=\"10tewge\" data-start=\"556\" data-end=\"689\">They combine multiple wireless technologies:<br><ul data-start=\"605\" data-end=\"689\"><li data-section-id=\"2rc6q0\" data-start=\"605\" data-end=\"618\"><strong data-start=\"607\" data-end=\"618\">Cellular<\/strong><\/li><li data-section-id=\"mrgxy1\" data-start=\"621\" data-end=\"631\"><strong data-start=\"623\" data-end=\"631\">WiFi<\/strong><\/li><li data-section-id=\"10c7xnn\" data-start=\"634\" data-end=\"689\"><strong data-start=\"636\" data-end=\"649\">Bluetooth<\/strong><\/li><\/ul><\/li><li data-section-id=\"1t8v9dk\" data-start=\"690\" data-end=\"839\">Signal detection across multiple bands:<br><ul data-start=\"733\" data-end=\"839\"><li data-section-id=\"zodyae\" data-start=\"733\" data-end=\"752\">125 kHz, 13 MHz<\/li><li data-section-id=\"1aztjpj\" data-start=\"755\" data-end=\"770\">840\u2013960 MHz<\/li><li data-section-id=\"u3w4yr\" data-start=\"773\" data-end=\"839\">2.4 GHz, 3.6 GHz, and 5 GHz<\/li><\/ul><\/li><\/ul><p data-start=\"841\" data-end=\"931\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f449.svg\" alt=\"\ud83d\udc49\">  This enables them to detect mobile phones even in scenarios where other technologies fail.<\/p><hr data-start=\"933\" data-end=\"936\"><h5><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f4ca.svg\" alt=\"\ud83d\udcca\">  Detection capability<\/h5><ul data-start=\"967\" data-end=\"1285\"><li data-section-id=\"125m266\" data-start=\"967\" data-end=\"1074\">They detect between <strong data-start=\"984\" data-end=\"1023\">85%\u201390% of mobile devices<\/strong> in their area<\/li><li data-section-id=\"ztov2c\" data-start=\"1075\" data-end=\"1152\">Visitor identification:<br><ul data-start=\"1109\" data-end=\"1152\"><li data-section-id=\"17fgcgn\" data-start=\"1109\" data-end=\"1120\"><strong data-start=\"1111\" data-end=\"1120\">Unique<\/strong><\/li><li data-section-id=\"q52lod\" data-start=\"1123\" data-end=\"1152\"><strong data-start=\"1125\" data-end=\"1150\">Anonymous (GDPR-compliant)<\/strong><\/li><\/ul><\/li><li data-section-id=\"ys4bpr\" data-start=\"1153\" data-end=\"1285\">They do not depend on:<br><ul data-start=\"1173\" data-end=\"1285\"><li data-section-id=\"1vlpbfk\" data-start=\"1173\" data-end=\"1190\">Installed apps<\/li><li data-section-id=\"1oscfk8\" data-start=\"1193\" data-end=\"1285\">The user\u2019s WiFi connection<br data-start=\"1220\" data-end=\"1223\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f449.svg\" alt=\"\ud83d\udc49\"> Key advantage over iBeacon or traditional WiFi tracking.<\/li><\/ul><\/li><\/ul><hr data-start=\"1287\" data-end=\"1290\"><h5><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f4cd.svg\" alt=\"\ud83d\udccd\">  Coverage<\/h5><ul data-start=\"1308\" data-end=\"1536\"><li data-section-id=\"zy3xof\" data-start=\"1308\" data-end=\"1536\">Configurable coverage depending on the technology:<br><ul data-start=\"1353\" data-end=\"1536\"><li data-section-id=\"1cfaya8\" data-start=\"1353\" data-end=\"1452\"><strong data-start=\"1355\" data-end=\"1412\">From ~3 m\u00b2 up to 15,000 m\u00b2 per node (WiFi\/Bluetooth)<\/strong><\/li><li data-section-id=\"wq2dan\" data-start=\"1455\" data-end=\"1536\"><strong data-start=\"1457\" data-end=\"1496\">Up to several km\u00b2 using the cellular network<\/strong><\/li><\/ul><\/li><\/ul><p data-start=\"1538\" data-end=\"1587\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f449.svg\" alt=\"\ud83d\udc49\">  They can be used both indoors and outdoors.<\/p><hr data-start=\"1589\" data-end=\"1592\"><h5><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f50c.svg\" alt=\"\ud83d\udd0c\">  Installation and power<\/h5><ul data-start=\"1627\" data-end=\"1878\"><li data-section-id=\"a6elz4\" data-start=\"1627\" data-end=\"1718\"><strong data-start=\"1642\" data-end=\"1657\">Plug &amp; play<\/strong> devices (quick installation)<\/li><li data-section-id=\"1sw1dnd\" data-start=\"1719\" data-end=\"1878\">Operation:<br><ul data-start=\"1739\" data-end=\"1878\"><li data-section-id=\"1y8tri2\" data-start=\"1739\" data-end=\"1778\">With mains power (125\/220V)<\/li><li data-section-id=\"1fbfdw2\" data-start=\"1781\" data-end=\"1878\">Some associated sensors can run on battery<\/li><\/ul><\/li><\/ul><hr data-start=\"1880\" data-end=\"1883\"><h5><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f9e0.svg\" alt=\"\ud83e\udde0\">  Processing and identification<\/h5><ul data-start=\"1922\" data-end=\"2267\"><li data-section-id=\"v9kvo2\" data-start=\"1922\" data-end=\"2034\">They generate a <strong data-start=\"1935\" data-end=\"1960\">unique visitor ID<\/strong> by combining offline and online data<\/li><li data-section-id=\"1gxbq0z\" data-start=\"2035\" data-end=\"2162\">They enable:<br><ul data-start=\"2049\" data-end=\"2162\"><li data-section-id=\"5u98ca\" data-start=\"2049\" data-end=\"2097\">Recurrence tracking (repeat visits)<\/li><li data-section-id=\"1opmcjb\" data-start=\"2100\" data-end=\"2162\">Behavior analysis (dwell time, routes, etc.)<\/li><\/ul><\/li><li data-section-id=\"pn3j61\" data-start=\"2163\" data-end=\"2267\">They avoid typical issues such as:<br><ul data-start=\"2198\" data-end=\"2267\"><li data-section-id=\"nwj4al\" data-start=\"2198\" data-end=\"2267\">WiFi <strong data-start=\"2200\" data-end=\"2218\">randomized MAC addresses<\/strong><\/li><\/ul><\/li><\/ul><hr data-start=\"2269\" data-end=\"2272\"><h5><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f4e1.svg\" alt=\"\ud83d\udce1\">  Communication features<\/h5><ul data-start=\"2312\" data-end=\"2453\"><li data-section-id=\"egaq78\" data-start=\"2312\" data-end=\"2453\">Proximity messaging:<br><ul data-start=\"2350\" data-end=\"2453\"><li data-section-id=\"wecwsy\" data-start=\"2350\" data-end=\"2366\">SMS \/ WhatsApp<\/li><li data-section-id=\"1717an8\" data-start=\"2369\" data-end=\"2376\">Email<\/li><li data-section-id=\"1xa3odp\" data-start=\"2379\" data-end=\"2453\">Push notifications (if there is an app)<\/li><\/ul><\/li><\/ul><hr data-start=\"2455\" data-end=\"2458\"><h5><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f4c8.svg\" alt=\"\ud83d\udcc8\">  Types of data they generate<\/h5><ul data-start=\"2493\" data-end=\"2664\"><li data-section-id=\"1ftyzqm\" data-start=\"2493\" data-end=\"2529\">People flow (origin-destination)<\/li><li data-section-id=\"16sg3ub\" data-start=\"2530\" data-end=\"2553\">Visitor volume<\/li><li data-section-id=\"w7b0gh\" data-start=\"2554\" data-end=\"2577\">New vs. returning<\/li><li data-section-id=\"19lpv\" data-start=\"2578\" data-end=\"2596\">Areas of interest<\/li><li data-section-id=\"nd52ep\" data-start=\"2597\" data-end=\"2664\">Dwell time<\/li><\/ul><hr data-start=\"2666\" data-end=\"2669\"><h5><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f517.svg\" alt=\"\ud83d\udd17\">  Integration and architecture<\/h5><ul data-start=\"2704\" data-end=\"2888\"><li data-section-id=\"1wnufm\" data-start=\"2704\" data-end=\"2849\">Integration with:<br><ul data-start=\"2725\" data-end=\"2849\"><li data-section-id=\"1o4v4o\" data-start=\"2725\" data-end=\"2730\">Web<\/li><li data-section-id=\"yyubui\" data-start=\"2733\" data-end=\"2773\">Mobile apps (iOS, Android, HTML5 SDK)<\/li><li data-section-id=\"10t6w62\" data-start=\"2776\" data-end=\"2849\">Analytics and BI platforms<\/li><\/ul><\/li><li data-section-id=\"1ltn23g\" data-start=\"2850\" data-end=\"2888\">Omnichannel system (online + offline)<\/li><\/ul><hr data-start=\"2890\" data-end=\"2893\"><h5><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f9e9.svg\" alt=\"\ud83e\udde9\">  Complementary sensors and devices<\/h5><p data-start=\"2941\" data-end=\"2975\">Nodes can work alongside:<\/p><ul data-start=\"2976\" data-end=\"3091\"><li data-section-id=\"7084oz\" data-start=\"2976\" data-end=\"3012\">Counting sensors (footfall type)<\/li><li data-section-id=\"1ypezvm\" data-start=\"3013\" data-end=\"3091\">Remotely managed iBeacons<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-1121\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"2\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-1121\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> Is Seeketing a good option for retail? <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-check-circle\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M504 256c0 136.967-111.033 248-248 248S8 392.967 8 256 119.033 8 256 8s248 111.033 248 248zM227.314 387.314l184-184c6.248-6.248 6.248-16.379 0-22.627l-22.627-22.627c-6.248-6.249-16.379-6.249-22.628 0L216 308.118l-70.059-70.059c-6.248-6.248-16.379-6.248-22.628 0l-22.627 22.627c-6.248 6.248-6.248 16.379 0 22.627l104 104c6.249 6.249 16.379 6.249 22.628.001z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-far-question-circle\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M256 8C119.043 8 8 119.083 8 256c0 136.997 111.043 248 248 248s248-111.003 248-248C504 119.083 392.957 8 256 8zm0 448c-110.532 0-200-89.431-200-200 0-110.495 89.472-200 200-200 110.491 0 200 89.471 200 200 0 110.53-89.431 200-200 200zm107.244-255.2c0 67.052-72.421 68.084-72.421 92.863V300c0 6.627-5.373 12-12 12h-45.647c-6.627 0-12-5.373-12-12v-8.659c0-35.745 27.1-50.034 47.579-61.516 17.561-9.845 28.324-16.541 28.324-29.579 0-17.246-21.999-28.693-39.784-28.693-23.189 0-33.894 10.977-48.942 29.969-4.057 5.12-11.46 6.071-16.666 2.124l-27.824-21.098c-5.107-3.872-6.251-11.066-2.644-16.363C184.846 131.491 214.94 112 261.794 112c49.071 0 101.45 38.304 101.45 88.8zM298 368c0 23.159-18.841 42-42 42s-42-18.841-42-42 18.841-42 42-42 42 18.841 42 42z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1121\" class=\"elementor-element elementor-element-c4382af e-con-full e-flex e-con e-child\" data-id=\"c4382af\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1121\" class=\"elementor-element elementor-element-34cc33d e-flex e-con-boxed e-con e-child\" data-id=\"34cc33d\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c3a1e33 elementor-widget elementor-widget-text-editor\" data-id=\"c3a1e33\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p data-start=\"295\" data-end=\"381\"><span class=\"whitespace-normal\">Seeketing<\/span> systems are especially powerful for:<\/p><ul data-start=\"382\" data-end=\"546\"><li data-section-id=\"1d0o2za\" data-start=\"382\" data-end=\"423\">Analyzing <strong data-start=\"393\" data-end=\"423\">customer behavior<\/strong><\/li><li data-section-id=\"lw170m\" data-start=\"424\" data-end=\"470\">Measuring <strong data-start=\"432\" data-end=\"470\">recurrence (customers who return)<\/strong><\/li><li data-section-id=\"1w39x82\" data-start=\"471\" data-end=\"508\">Understanding <strong data-start=\"482\" data-end=\"508\">dwell times<\/strong><\/li><li data-section-id=\"ylid2m\" data-start=\"509\" data-end=\"546\">Activating <strong data-start=\"519\" data-end=\"546\">proximity marketing<\/strong><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-1122\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"3\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-1122\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> What does each technology measure? <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-check-circle\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M504 256c0 136.967-111.033 248-248 248S8 392.967 8 256 119.033 8 256 8s248 111.033 248 248zM227.314 387.314l184-184c6.248-6.248 6.248-16.379 0-22.627l-22.627-22.627c-6.248-6.249-16.379-6.249-22.628 0L216 308.118l-70.059-70.059c-6.248-6.248-16.379-6.248-22.628 0l-22.627 22.627c-6.248 6.248-6.248 16.379 0 22.627l104 104c6.249 6.249 16.379 6.249 22.628.001z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-far-question-circle\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M256 8C119.043 8 8 119.083 8 256c0 136.997 111.043 248 248 248s248-111.003 248-248C504 119.083 392.957 8 256 8zm0 448c-110.532 0-200-89.431-200-200 0-110.495 89.472-200 200-200 110.491 0 200 89.471 200 200 0 110.53-89.431 200-200 200zm107.244-255.2c0 67.052-72.421 68.084-72.421 92.863V300c0 6.627-5.373 12-12 12h-45.647c-6.627 0-12-5.373-12-12v-8.659c0-35.745 27.1-50.034 47.579-61.516 17.561-9.845 28.324-16.541 28.324-29.579 0-17.246-21.999-28.693-39.784-28.693-23.189 0-33.894 10.977-48.942 29.969-4.057 5.12-11.46 6.071-16.666 2.124l-27.824-21.098c-5.107-3.872-6.251-11.066-2.644-16.363C184.846 131.491 214.94 112 261.794 112c49.071 0 101.45 38.304 101.45 88.8zM298 368c0 23.159-18.841 42-42 42s-42-18.841-42-42 18.841-42 42-42 42 18.841 42 42z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1122\" class=\"elementor-element elementor-element-a54549d e-con-full e-flex e-con e-child\" data-id=\"a54549d\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1122\" class=\"elementor-element elementor-element-49f6310 e-flex e-con-boxed e-con e-child\" data-id=\"49f6310\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1482159 elementor-widget elementor-widget-text-editor\" data-id=\"1482159\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p data-start=\"246\" data-end=\"315\">Computer vision cameras are primarily designed to:<\/p><ul data-start=\"317\" data-end=\"422\"><li data-section-id=\"15upj5w\" data-start=\"317\" data-end=\"364\">Count <strong data-start=\"326\" data-end=\"345\">pass-by events<\/strong> (entries\/exits)<\/li><li data-section-id=\"wvnr6t\" data-start=\"365\" data-end=\"382\">Measure <strong data-start=\"373\" data-end=\"382\">flow<\/strong><\/li><li data-section-id=\"stg4az\" data-start=\"383\" data-end=\"422\">Calculate <strong data-start=\"394\" data-end=\"422\"><strong data-start=\"394\" data-end=\"422\">real-time occupancy<\/strong><\/strong><\/li><li data-section-id=\"stg4az\" data-start=\"383\" data-end=\"422\"><strong data-start=\"1951\" data-end=\"1981\">Cameras do not count unique people<\/strong>; they count <strong data-start=\"1991\" data-end=\"2010\">visits\/events<\/strong>.<br><p data-start=\"2014\" data-end=\"2024\">And that is why:<\/p><ul data-start=\"2026\" data-end=\"2115\"><li data-section-id=\"1tm6hg7\" data-start=\"2026\" data-end=\"2064\">Cameras \u2192 <strong data-start=\"2038\" data-end=\"2064\">how many times someone enters<\/strong><\/li><li data-section-id=\"zytdh0\" data-start=\"2065\" data-end=\"2115\">Seeketing \u2192 <strong data-start=\"2079\" data-end=\"2115\">who (approximately) enters and whether they return<\/strong><\/li><li data-section-id=\"zytdh0\" data-start=\"2065\" data-end=\"2115\"><img decoding=\"async\" class=\"emoji\" style=\"color: inherit; font-size: 1.25rem;\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f4f7.svg\" alt=\"\ud83d\udcf7\"><span style=\"background-color: transparent; color: inherit; font-size: 1.25rem;\"> <b>Cameras<\/b><\/span><\/li><li data-section-id=\"zytdh0\" data-start=\"2065\" data-end=\"2115\"><ul data-start=\"860\" data-end=\"963\"><li data-section-id=\"wpr83b\" data-start=\"860\" data-end=\"888\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2714.svg\" alt=\"\u2714\ufe0f\">  Total traffic (visits)<\/li><li data-section-id=\"1nk6h96\" data-start=\"889\" data-end=\"930\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2714.svg\" alt=\"\u2714\ufe0f\">  Conversion (if you cross-reference with sales)<\/li><li data-section-id=\"7qwis3\" data-start=\"931\" data-end=\"963\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/274c.svg\" alt=\"\u274c\">  Unique people (in general)<\/li><\/ul><hr data-start=\"965\" data-end=\"968\"><h5><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f4e1.svg\" alt=\"\ud83d\udce1\"> <span class=\"whitespace-normal\"><b>Seeketing<\/b><\/span><\/h5><ul data-start=\"1014\" data-end=\"1166\"><li data-section-id=\"b51hru\" data-start=\"1014\" data-end=\"1062\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2714.svg\" alt=\"\u2714\ufe0f\">  Unique people (estimated by device)<\/li><li data-section-id=\"h8llam\" data-start=\"1063\" data-end=\"1103\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2714.svg\" alt=\"\u2714\ufe0f\">  Recurrence (who returns another day)<\/li><li data-section-id=\"7srndx\" data-start=\"1104\" data-end=\"1129\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2714.svg\" alt=\"\u2714\ufe0f\">  Visit frequency<\/li><li data-section-id=\"13iqqai\" data-start=\"1130\" data-end=\"1166\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/274c.svg\" alt=\"\u274c\">  Exact physical counting of entries<\/li><\/ul><\/li><\/ul><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-1123\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"4\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-1123\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> Which technologies offer both unique visitor counting and opportunity counting? <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-check-circle\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M504 256c0 136.967-111.033 248-248 248S8 392.967 8 256 119.033 8 256 8s248 111.033 248 248zM227.314 387.314l184-184c6.248-6.248 6.248-16.379 0-22.627l-22.627-22.627c-6.248-6.249-16.379-6.249-22.628 0L216 308.118l-70.059-70.059c-6.248-6.248-16.379-6.248-22.628 0l-22.627 22.627c-6.248 6.248-6.248 16.379 0 22.627l104 104c6.249 6.249 16.379 6.249 22.628.001z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-far-question-circle\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M256 8C119.043 8 8 119.083 8 256c0 136.997 111.043 248 248 248s248-111.003 248-248C504 119.083 392.957 8 256 8zm0 448c-110.532 0-200-89.431-200-200 0-110.495 89.472-200 200-200 110.491 0 200 89.471 200 200 0 110.53-89.431 200-200 200zm107.244-255.2c0 67.052-72.421 68.084-72.421 92.863V300c0 6.627-5.373 12-12 12h-45.647c-6.627 0-12-5.373-12-12v-8.659c0-35.745 27.1-50.034 47.579-61.516 17.561-9.845 28.324-16.541 28.324-29.579 0-17.246-21.999-28.693-39.784-28.693-23.189 0-33.894 10.977-48.942 29.969-4.057 5.12-11.46 6.071-16.666 2.124l-27.824-21.098c-5.107-3.872-6.251-11.066-2.644-16.363C184.846 131.491 214.94 112 261.794 112c49.071 0 101.45 38.304 101.45 88.8zM298 368c0 23.159-18.841 42-42 42s-42-18.841-42-42 18.841-42 42-42 42 18.841 42 42z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1123\" class=\"elementor-element elementor-element-f913580 e-flex e-con-boxed e-con e-child\" data-id=\"f913580\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1123\" class=\"elementor-element elementor-element-9cca865 e-con-full e-flex e-con e-child\" data-id=\"9cca865\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-8b5d8c7 elementor-widget elementor-widget-text-editor\" data-id=\"8b5d8c7\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h5><b>1. Computer vision + Re-identification (Re-ID) <\/b><\/h5><h5><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f4f7.svg\" alt=\"\ud83d\udcf7\"> <b>What it is<\/b><\/h5><p data-start=\"426\" data-end=\"511\">AI-powered cameras that apply <strong data-start=\"462\" data-end=\"503\"><span class=\"whitespace-normal\">Re-identification<\/span><\/strong> (Re-ID)<\/p><h5><b><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2705.svg\" alt=\"\u2705\">  What it measures<\/b><\/h5><ul data-start=\"527\" data-end=\"683\"><li data-section-id=\"1rpnu5r\" data-start=\"527\" data-end=\"564\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2714.svg\" alt=\"\u2714\ufe0f\">  Total entries (opportunities)<\/li><li data-section-id=\"1d61zb4\" data-start=\"565\" data-end=\"603\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2714.svg\" alt=\"\u2714\ufe0f\">  Unique visitors (deduplicated)<\/li><li data-section-id=\"1ngng40\" data-start=\"604\" data-end=\"632\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2714.svg\" alt=\"\u2714\ufe0f\">  Recurrence (if they return)<\/li><li data-section-id=\"1tptomh\" data-start=\"633\" data-end=\"656\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2714.svg\" alt=\"\u2714\ufe0f\">  Routes and zones<\/li><li data-section-id=\"ta6tlp\" data-start=\"657\" data-end=\"683\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2714.svg\" alt=\"\u2714\ufe0f\">  Dwell time<\/li><\/ul><h5><b><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2699.svg\" alt=\"\u2699\ufe0f\">  How it works<\/b><\/h5><ul data-start=\"705\" data-end=\"894\"><li data-section-id=\"1kwuzf9\" data-start=\"705\" data-end=\"747\">Detects people using computer vision<\/li><li data-section-id=\"1ysujb7\" data-start=\"748\" data-end=\"802\">Extracts features (clothing, silhouette, movement)<\/li><li data-section-id=\"1nop9j1\" data-start=\"803\" data-end=\"827\">Generates an anonymous ID<\/li><li data-section-id=\"xbvks9\" data-start=\"828\" data-end=\"894\">Re-identifies the same person at different times\/cameras<\/li><\/ul><p data-start=\"896\" data-end=\"983\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f449.svg\" alt=\"\ud83d\udc49\">  Result:<br data-start=\"909\" data-end=\"912\"><strong data-start=\"912\" data-end=\"983\">if they enter at 10:00 and at 12:00 \u2192 it counts 1 unique person and 2 visits<\/strong><\/p><p data-start=\"985\" data-end=\"1052\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f4ca.svg\" alt=\"\ud83d\udcca\">  It is designed specifically to solve the problem you mentioned.<\/p><p data-start=\"1054\" data-end=\"1164\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f4cc.svg\" alt=\"\ud83d\udccc\">  In fact, it enables \u201cdeduplicated unique counting,\u201d avoiding double counting.<\/p><hr data-start=\"1166\" data-end=\"1169\"><h5><b><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2696.svg\" alt=\"\u2696\ufe0f\">  Pros \/ Cons<\/b><\/h5><p data-start=\"1192\" data-end=\"1335\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2714.svg\" alt=\"\u2714\ufe0f\">  Very accurate (almost census-level)<br data-start=\"1220\" data-end=\"1223\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2714.svg\" alt=\"\u2714\ufe0f\"> Does not depend on a mobile phone<br data-start=\"1245\" data-end=\"1248\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2714.svg\" alt=\"\u2714\ufe0f\"> GDPR-compliant (no biometrics)<br data-start=\"1278\" data-end=\"1281\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/274c.svg\" alt=\"\u274c\"> More expensive<br data-start=\"1291\" data-end=\"1294\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/274c.svg\" alt=\"\u274c\"> Requires good camera installation<\/p><hr data-start=\"1337\" data-end=\"1340\"><h5><b><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f4e1.svg\" alt=\"\ud83d\udce1\">  2. WiFi \/ Bluetooth tracking \u2192 partial hybrid<\/b><\/h5><h5><b><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f4f6.svg\" alt=\"\ud83d\udcf6\"> What it is<\/b><\/h5><p data-start=\"1408\" data-end=\"1458\">Tracking of mobile devices (MAC, signals)<\/p><h5><b><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2705.svg\" alt=\"\u2705\">  What it measures<\/b><\/h5><ul data-start=\"1474\" data-end=\"1560\"><li data-section-id=\"1uqsdj2\" data-start=\"1474\" data-end=\"1514\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2714.svg\" alt=\"\u2714\ufe0f\">  Unique visitors (by device)<\/li><li data-section-id=\"11mgw6q\" data-start=\"1515\" data-end=\"1531\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2714.svg\" alt=\"\u2714\ufe0f\">  Recurrence<\/li><li data-section-id=\"1vdwvh9\" data-start=\"1532\" data-end=\"1560\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2714.svg\" alt=\"\u2714\ufe0f\">  Dwell time<\/li><\/ul><h5><b><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/26a0.svg\" alt=\"\u26a0\ufe0f\">  Limitation<\/b><\/h5><ul data-start=\"1579\" data-end=\"1662\"><li data-section-id=\"1syka6v\" data-start=\"1579\" data-end=\"1623\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/274c.svg\" alt=\"\u274c\">  Does not measure the true total number of people well<\/li><li data-section-id=\"13tohet\" data-start=\"1624\" data-end=\"1662\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/274c.svg\" alt=\"\u274c\">  Depends on having an active mobile phone<\/li><\/ul><p data-start=\"1664\" data-end=\"1675\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f449.svg\" alt=\"\ud83d\udc49\">  That is why:<\/p><ul data-start=\"1676\" data-end=\"1739\"><li data-section-id=\"18j3wct\" data-start=\"1676\" data-end=\"1702\">It is good for <strong data-start=\"1692\" data-end=\"1702\">unique visitors<\/strong><\/li><li data-section-id=\"1wcq0wq\" data-start=\"1703\" data-end=\"1739\">Poor for <strong data-start=\"1715\" data-end=\"1739\">real opportunities<\/strong><\/li><\/ul><p data-start=\"1741\" data-end=\"1844\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f4cc.svg\" alt=\"\ud83d\udccc\">  In addition, it loses accuracy today due to privacy (randomized MAC addresses).<\/p><hr data-start=\"1846\" data-end=\"1849\"><h5><b><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f500.svg\" alt=\"\ud83d\udd00\">  3. Hybrid systems (vision + WiFi) \u2192 <img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f9e9.svg\" alt=\"\ud83e\udde9\"> the most used in large retail<\/b><\/h5><p data-start=\"1931\" data-end=\"1988\">Typical example: <span class=\"whitespace-normal\">FootfallCam<\/span><\/p><h5><b><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2699.svg\" alt=\"\u2699\ufe0f\">  How they work<\/b><\/h5><p data-start=\"2011\" data-end=\"2020\">They combine:<\/p><ul data-start=\"2021\" data-end=\"2116\"><li data-section-id=\"1eogzvl\" data-start=\"2021\" data-end=\"2064\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f3a5.svg\" alt=\"\ud83c\udfa5\">  Cameras \u2192 total counting (opportunities)<\/li><li data-section-id=\"yvijtl\" data-start=\"2065\" data-end=\"2116\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f4e1.svg\" alt=\"\ud83d\udce1\">  WiFi \u2192 identification of unique devices<\/li><\/ul><p data-start=\"2118\" data-end=\"2134\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f4cc.svg\" alt=\"\ud83d\udccc\">  Literally:<\/p><ul data-start=\"2135\" data-end=\"2239\"><li data-section-id=\"pfyegc\" data-start=\"2135\" data-end=\"2166\">video = \u201cfootfall count\u201d<\/li><li data-section-id=\"4vi2dp\" data-start=\"2167\" data-end=\"2239\">WiFi = \u201creturning customers\u201d<\/li><\/ul><hr data-start=\"2241\" data-end=\"2244\"><h5><b><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2705.svg\" alt=\"\u2705\">  What they achieve<\/b><\/h5><ul data-start=\"2265\" data-end=\"2367\"><li data-section-id=\"3xpux0\" data-start=\"2265\" data-end=\"2297\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2714.svg\" alt=\"\u2714\ufe0f\">  Total traffic (very accurate)<\/li><li data-section-id=\"10ierw3\" data-start=\"2298\" data-end=\"2332\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2714.svg\" alt=\"\u2714\ufe0f\">  Unique visitors (estimated)<\/li><li data-section-id=\"11mgw6q\" data-start=\"2333\" data-end=\"2349\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2714.svg\" alt=\"\u2714\ufe0f\">  Recurrence<\/li><li data-section-id=\"19mjia8\" data-start=\"2350\" data-end=\"2367\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2714.svg\" alt=\"\u2714\ufe0f\">  Dwell time<\/li><\/ul><p data-start=\"2369\" data-end=\"2424\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f449.svg\" alt=\"\ud83d\udc49\">  It is the standard for many mid-sized\/large retailers.<\/p><hr data-start=\"2426\" data-end=\"2429\"><p><b>  Clear comparison<\/b><\/p><div class=\"TyagGW_tableContainer\"><div class=\"group TyagGW_tableWrapper flex flex-col-reverse w-fit\" tabindex=\"-1\"><table class=\"w-fit min-w-(--thread-content-width)\" data-start=\"2455\" data-end=\"2837\"><thead data-start=\"2455\" data-end=\"2532\"><tr data-start=\"2455\" data-end=\"2532\"><th class=\"\" data-start=\"2455\" data-end=\"2468\" data-col-size=\"sm\">Technology<\/th><th class=\"\" data-start=\"2468\" data-end=\"2494\" data-col-size=\"sm\">Opportunities (visits)<\/th><th class=\"\" data-start=\"2494\" data-end=\"2512\" data-col-size=\"sm\">Unique people<\/th><th class=\"\" data-start=\"2512\" data-end=\"2532\" data-col-size=\"sm\">Overall accuracy<\/th><\/tr><\/thead><tbody data-start=\"2606\" data-end=\"2837\"><tr data-start=\"2606\" data-end=\"2655\"><td data-start=\"2606\" data-end=\"2624\" data-col-size=\"sm\">Basic cameras<\/td><td data-start=\"2624\" data-end=\"2628\" data-col-size=\"sm\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2705.svg\" alt=\"\u2705\"><\/td><td data-start=\"2628\" data-end=\"2632\" data-col-size=\"sm\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/274c.svg\" alt=\"\u274c\"><\/td><td data-start=\"2632\" data-end=\"2655\" data-col-size=\"sm\">High (traffic only)<\/td><\/tr><tr data-start=\"2656\" data-end=\"2692\"><td data-start=\"2656\" data-end=\"2675\" data-col-size=\"sm\">WiFi \/ Bluetooth<\/td><td data-start=\"2675\" data-end=\"2679\" data-col-size=\"sm\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/274c.svg\" alt=\"\u274c\"><\/td><td data-start=\"2679\" data-end=\"2683\" data-col-size=\"sm\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2705.svg\" alt=\"\u2705\"><\/td><td data-start=\"2683\" data-end=\"2692\" data-col-size=\"sm\">Medium<\/td><\/tr><tr data-start=\"2693\" data-end=\"2741\"><td data-start=\"2693\" data-end=\"2705\" data-col-size=\"sm\">Seeketing<\/td><td data-start=\"2705\" data-end=\"2719\" data-col-size=\"sm\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/26a0.svg\" alt=\"\u26a0\ufe0f\"> estimated<\/td><td data-start=\"2719\" data-end=\"2723\" data-col-size=\"sm\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2705.svg\" alt=\"\u2705\"><\/td><td data-start=\"2723\" data-end=\"2741\" data-col-size=\"sm\">High for unique visitors<\/td><\/tr><tr data-start=\"2742\" data-end=\"2791\"><td data-start=\"2742\" data-end=\"2768\" data-col-size=\"sm\">Re-ID (advanced vision)<\/td><td data-start=\"2768\" data-end=\"2772\" data-col-size=\"sm\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2705.svg\" alt=\"\u2705\"><\/td><td data-start=\"2772\" data-end=\"2776\" data-col-size=\"sm\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2705.svg\" alt=\"\u2705\"><\/td><td data-start=\"2776\" data-end=\"2791\" data-col-size=\"sm\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f525.svg\" alt=\"\ud83d\udd25\"> Very high<\/td><\/tr><tr data-start=\"2792\" data-end=\"2837\"><td data-start=\"2792\" data-end=\"2818\" data-col-size=\"sm\">Hybrid (vision + WiFi)<\/td><td data-start=\"2818\" data-end=\"2822\" data-col-size=\"sm\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2705.svg\" alt=\"\u2705\"><\/td><td data-start=\"2822\" data-end=\"2826\" data-col-size=\"sm\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2705.svg\" alt=\"\u2705\"><\/td><td data-start=\"2826\" data-end=\"2837\" data-col-size=\"sm\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f525.svg\" alt=\"\ud83d\udd25\"> High<\/td><\/tr><\/tbody><\/table><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-1124\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"5\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-1124\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> What does Seeketing offer? <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-check-circle\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M504 256c0 136.967-111.033 248-248 248S8 392.967 8 256 119.033 8 256 8s248 111.033 248 248zM227.314 387.314l184-184c6.248-6.248 6.248-16.379 0-22.627l-22.627-22.627c-6.248-6.249-16.379-6.249-22.628 0L216 308.118l-70.059-70.059c-6.248-6.248-16.379-6.248-22.628 0l-22.627 22.627c-6.248 6.248-6.248 16.379 0 22.627l104 104c6.249 6.249 16.379 6.249 22.628.001z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-far-question-circle\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M256 8C119.043 8 8 119.083 8 256c0 136.997 111.043 248 248 248s248-111.003 248-248C504 119.083 392.957 8 256 8zm0 448c-110.532 0-200-89.431-200-200 0-110.495 89.472-200 200-200 110.491 0 200 89.471 200 200 0 110.53-89.431 200-200 200zm107.244-255.2c0 67.052-72.421 68.084-72.421 92.863V300c0 6.627-5.373 12-12 12h-45.647c-6.627 0-12-5.373-12-12v-8.659c0-35.745 27.1-50.034 47.579-61.516 17.561-9.845 28.324-16.541 28.324-29.579 0-17.246-21.999-28.693-39.784-28.693-23.189 0-33.894 10.977-48.942 29.969-4.057 5.12-11.46 6.071-16.666 2.124l-27.824-21.098c-5.107-3.872-6.251-11.066-2.644-16.363C184.846 131.491 214.94 112 261.794 112c49.071 0 101.45 38.304 101.45 88.8zM298 368c0 23.159-18.841 42-42 42s-42-18.841-42-42 18.841-42 42-42 42 18.841 42 42z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1124\" class=\"elementor-element elementor-element-2809bc2 e-flex e-con-boxed e-con e-child\" data-id=\"2809bc2\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1124\" class=\"elementor-element elementor-element-162f64f e-con-full e-flex e-con e-child\" data-id=\"162f64f\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-df7242d elementor-widget elementor-widget-text-editor\" data-id=\"df7242d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<ul data-start=\"179\" data-end=\"539\"><li data-section-id=\"1oy8py8\" data-start=\"179\" data-end=\"280\">Detects and identifies visitors with a <strong data-start=\"220\" data-end=\"240\">unique anonymous ID<\/strong><\/li><li data-section-id=\"1w2xyaa\" data-start=\"281\" data-end=\"391\">It can determine:<ul data-start=\"298\" data-end=\"391\"><li data-section-id=\"ry6u3x\" data-start=\"298\" data-end=\"330\">Who is new vs. returning<\/li><li data-section-id=\"bop26j\" data-start=\"333\" data-end=\"391\">Whether a person returns the same day or on different days<\/li><\/ul><\/li><li data-section-id=\"dew6jt\" data-start=\"392\" data-end=\"539\">It even positions itself as:<br>\ud83d\udc49 \u201cthe only technology that lets you know if someone has entered before\u201d<\/li><\/ul><p data-start=\"541\" data-end=\"593\">\u2714\ufe0f This makes it very powerful compared to cameras or WiFi.<\/p><ul><li data-section-id=\"1px5p7n\" data-start=\"1739\" data-end=\"1773\">\u2714\ufe0f Unique people \u2192 Seeketing<\/li><li data-section-id=\"t5lh9g\" data-start=\"1774\" data-end=\"1827\">\u2714\ufe0f Real opportunities \u2192 sensors (camera\/laser)<\/li><li data-section-id=\"oxj4b3\" data-start=\"1828\" data-end=\"1875\">\u2714\ufe0f Recurrence + behavior \u2192 Seeketing<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-1125\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"6\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-1125\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> How many people without a mobile phone can enter a store today in any Western country? <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-check-circle\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M504 256c0 136.967-111.033 248-248 248S8 392.967 8 256 119.033 8 256 8s248 111.033 248 248zM227.314 387.314l184-184c6.248-6.248 6.248-16.379 0-22.627l-22.627-22.627c-6.248-6.249-16.379-6.249-22.628 0L216 308.118l-70.059-70.059c-6.248-6.248-16.379-6.248-22.628 0l-22.627 22.627c-6.248 6.248-6.248 16.379 0 22.627l104 104c6.249 6.249 16.379 6.249 22.628.001z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-far-question-circle\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M256 8C119.043 8 8 119.083 8 256c0 136.997 111.043 248 248 248s248-111.003 248-248C504 119.083 392.957 8 256 8zm0 448c-110.532 0-200-89.431-200-200 0-110.495 89.472-200 200-200 110.491 0 200 89.471 200 200 0 110.53-89.431 200-200 200zm107.244-255.2c0 67.052-72.421 68.084-72.421 92.863V300c0 6.627-5.373 12-12 12h-45.647c-6.627 0-12-5.373-12-12v-8.659c0-35.745 27.1-50.034 47.579-61.516 17.561-9.845 28.324-16.541 28.324-29.579 0-17.246-21.999-28.693-39.784-28.693-23.189 0-33.894 10.977-48.942 29.969-4.057 5.12-11.46 6.071-16.666 2.124l-27.824-21.098c-5.107-3.872-6.251-11.066-2.644-16.363C184.846 131.491 214.94 112 261.794 112c49.071 0 101.45 38.304 101.45 88.8zM298 368c0 23.159-18.841 42-42 42s-42-18.841-42-42 18.841-42 42-42 42 18.841 42 42z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1125\" class=\"elementor-element elementor-element-b05b428 e-flex e-con-boxed e-con e-child\" data-id=\"b05b428\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1125\" class=\"elementor-element elementor-element-5c57fb7 e-con-full e-flex e-con e-child\" data-id=\"5c57fb7\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a8be338 elementor-widget elementor-widget-text-editor\" data-id=\"a8be338\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h5><b>Europe \/ Western countries<\/b><\/h5><ul data-start=\"252\" data-end=\"520\"><li data-section-id=\"8pyvzp\" data-start=\"252\" data-end=\"334\">~<strong data-start=\"255\" data-end=\"294\">85\u201391% of adults have a smartphone<\/strong><\/li><li data-section-id=\"9q7ujy\" data-start=\"335\" data-end=\"439\">~<strong data-start=\"338\" data-end=\"372\">98% have some type of mobile phone<\/strong> (including non-smartphones)<\/li><li data-section-id=\"apus0r\" data-start=\"440\" data-end=\"520\">Only ~<strong data-start=\"448\" data-end=\"480\">3\u201310% do not have any mobile phone<\/strong><\/li><\/ul><p data-start=\"522\" data-end=\"544\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f449.svg\" alt=\"\ud83d\udc49\">  Direct translation:<\/p><ul data-start=\"545\" data-end=\"641\"><li data-section-id=\"1yclh52\" data-start=\"545\" data-end=\"590\">People without a mobile phone \u2192 <strong data-start=\"568\" data-end=\"590\">very few (\u22483\u201310%)<\/strong><\/li><li data-section-id=\"2d4r0\" data-start=\"591\" data-end=\"641\">People without a smartphone \u2192 <strong data-start=\"619\" data-end=\"641\">somewhat more (\u224810\u201315%)<\/strong><\/li><\/ul><hr data-start=\"643\" data-end=\"646\"><h5><b>In cities (typical retail)<\/b><\/h5><ul data-start=\"679\" data-end=\"762\"><li data-section-id=\"81t54d\" data-start=\"679\" data-end=\"762\">Up to <strong data-start=\"687\" data-end=\"722\">89% actively use a smartphone<\/strong><\/li><\/ul><p data-start=\"764\" data-end=\"793\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f449.svg\" alt=\"\ud83d\udc49\">  In realistic urban retail:<\/p><ul data-start=\"794\" data-end=\"873\"><li style=\"list-style-type: none;\"><ul data-start=\"794\" data-end=\"873\"><li data-section-id=\"znanw6\" data-start=\"794\" data-end=\"822\"><strong data-start=\"796\" data-end=\"822\">90%+ carry a smartphone<\/strong><\/li><li data-section-id=\"1734nxm\" data-start=\"823\" data-end=\"873\">But that does NOT mean everyone is detectable<\/li><li data-section-id=\"1734nxm\" data-start=\"823\" data-end=\"873\"><b style=\"color: inherit; font-size: 1.25rem; background-color: transparent;\">Why \u201chaving a phone\u201d \u2260 \u201cbeing measured\u201d<\/b><\/li><\/ul><\/li><\/ul><p data-start=\"1128\" data-end=\"1159\">Even with 90% smartphones:<\/p><h5><b><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f534.svg\" alt=\"\ud83d\udd34\">  Non-detectable cases<\/b><\/h5><ul data-start=\"794\" data-end=\"873\"><li style=\"list-style-type: none;\"><ul data-start=\"794\" data-end=\"873\"><li style=\"list-style-type: none;\"><ul data-start=\"1188\" data-end=\"1410\"><li data-section-id=\"8zoyaa\" data-start=\"1188\" data-end=\"1224\">Phone with WiFi\/Bluetooth turned off<\/li><li data-section-id=\"3qxxe\" data-start=\"1225\" data-end=\"1239\">Airplane mode<\/li><li data-section-id=\"ol86v8\" data-start=\"1240\" data-end=\"1273\">Randomized MAC (very common today)<\/li><li data-section-id=\"cn3lc1\" data-start=\"1274\" data-end=\"1306\">Weak signal \/ interference<\/li><li data-section-id=\"2r3xm8\" data-start=\"1307\" data-end=\"1342\">User with multiple devices<\/li><li data-section-id=\"obgeka\" data-start=\"1343\" data-end=\"1410\">People who are not carrying their phone (less frequent, but it happens)<\/li><\/ul><\/li><\/ul><\/li><\/ul><p data-start=\"1412\" data-end=\"1430\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f449.svg\" alt=\"\ud83d\udc49\">  Real outcome:<\/p><ul data-start=\"794\" data-end=\"873\"><li style=\"list-style-type: none;\"><ul data-start=\"794\" data-end=\"873\"><li style=\"list-style-type: none;\"><ul data-start=\"1431\" data-end=\"1555\"><li data-section-id=\"7tjdu6\" data-start=\"1431\" data-end=\"1487\">WiFi \/ Seeketing-type systems <strong data-start=\"1464\" data-end=\"1487\">do not detect 100%<\/strong><\/li><li data-section-id=\"1ofbnsv\" data-start=\"1488\" data-end=\"1555\">They typically remain at:<br><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f449.svg\" alt=\"\ud83d\udc49\"> <strong data-start=\"1521\" data-end=\"1555\">70\u201390% coverage (estimated)<\/strong><\/li><\/ul><\/li><\/ul><\/li><\/ul><hr data-start=\"1557\" data-end=\"1560\"><h5><b><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f4ca.svg\" alt=\"\ud83d\udcca\">  So, in a real store:<\/b><\/h5><h5>Typical scenario (Europe, urban retail)<\/h5><p data-start=\"1642\" data-end=\"1669\">Out of 100 people who enter:<\/p><ul data-start=\"794\" data-end=\"873\"><li style=\"list-style-type: none;\"><ul data-start=\"1671\" data-end=\"1776\"><li data-section-id=\"1dp1g93\" data-start=\"1671\" data-end=\"1696\">~90 have a smartphone<\/li><li data-section-id=\"1dhc6ff\" data-start=\"1697\" data-end=\"1737\">~70\u201385 are correctly detectable<\/li><li data-section-id=\"7zwtes\" data-start=\"1738\" data-end=\"1776\">~15\u201330 are not detected or are modeled<\/li><\/ul><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-1126\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"7\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-1126\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> Can I use Seeketing to get data from 90% of the people who enter a store? <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-check-circle\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M504 256c0 136.967-111.033 248-248 248S8 392.967 8 256 119.033 8 256 8s248 111.033 248 248zM227.314 387.314l184-184c6.248-6.248 6.248-16.379 0-22.627l-22.627-22.627c-6.248-6.249-16.379-6.249-22.628 0L216 308.118l-70.059-70.059c-6.248-6.248-16.379-6.248-22.628 0l-22.627 22.627c-6.248 6.248-6.248 16.379 0 22.627l104 104c6.249 6.249 16.379 6.249 22.628.001z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-far-question-circle\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M256 8C119.043 8 8 119.083 8 256c0 136.997 111.043 248 248 248s248-111.003 248-248C504 119.083 392.957 8 256 8zm0 448c-110.532 0-200-89.431-200-200 0-110.495 89.472-200 200-200 110.491 0 200 89.471 200 200 0 110.53-89.431 200-200 200zm107.244-255.2c0 67.052-72.421 68.084-72.421 92.863V300c0 6.627-5.373 12-12 12h-45.647c-6.627 0-12-5.373-12-12v-8.659c0-35.745 27.1-50.034 47.579-61.516 17.561-9.845 28.324-16.541 28.324-29.579 0-17.246-21.999-28.693-39.784-28.693-23.189 0-33.894 10.977-48.942 29.969-4.057 5.12-11.46 6.071-16.666 2.124l-27.824-21.098c-5.107-3.872-6.251-11.066-2.644-16.363C184.846 131.491 214.94 112 261.794 112c49.071 0 101.45 38.304 101.45 88.8zM298 368c0 23.159-18.841 42-42 42s-42-18.841-42-42 18.841-42 42-42 42 18.841 42 42z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1126\" class=\"elementor-element elementor-element-f0f4b60 e-flex e-con-boxed e-con e-child\" data-id=\"f0f4b60\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1126\" class=\"elementor-element elementor-element-93faecc e-con-full e-flex e-con e-child\" data-id=\"93faecc\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-992e4c2 elementor-widget elementor-widget-text-editor\" data-id=\"992e4c2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p data-start=\"156\" data-end=\"321\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f449.svg\" alt=\"\ud83d\udc49\">  With <span class=\"whitespace-normal\">Seeketing<\/span>, you can have <strong data-start=\"214\" data-end=\"271\">data for approximately 70%\u201390% of visitors<\/strong>,<br data-start=\"272\" data-end=\"275\"><strong data-start=\"275\" data-end=\"320\">but it is not a guaranteed or uniform 90%<\/strong>.<\/p><hr data-start=\"323\" data-end=\"326\"><h5><b><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f9e0.svg\" alt=\"\ud83e\udde0\">  Why it is NOT always 90%<\/b><\/h5><p data-start=\"360\" data-end=\"441\">Even if many people have a phone, <strong data-start=\"395\" data-end=\"440\">actual detection depends on several factors<\/strong>:<\/p><h5><b><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f4c9.svg\" alt=\"\ud83d\udcc9\">  Factors that reduce coverage<\/b><\/h5><ul data-start=\"483\" data-end=\"688\"><li data-section-id=\"1neyyzc\" data-start=\"483\" data-end=\"512\">WiFi and Bluetooth turned off<\/li><li data-section-id=\"erm4xr\" data-start=\"513\" data-end=\"555\">Privacy systems (randomized MAC)<\/li><li data-section-id=\"1vedrup\" data-start=\"556\" data-end=\"592\">Weak signals or interference<\/li><li data-section-id=\"tvlg4w\" data-start=\"593\" data-end=\"649\">People with multiple devices (distorts data)<\/li><li data-section-id=\"1jruamm\" data-start=\"650\" data-end=\"688\">People who are not carrying their phone<\/li><\/ul><p data-start=\"690\" data-end=\"740\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f449.svg\" alt=\"\ud83d\udc49\">  This means that:<br><strong data-start=\"708\" data-end=\"740\">having a phone \u2260 being detectable<\/strong><\/p><hr data-start=\"742\" data-end=\"745\"><h5><b><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f4ca.svg\" alt=\"\ud83d\udcca\">  What happens in practice (real retail)<\/b><\/h5><p data-start=\"791\" data-end=\"822\">In a typical store in Europe:<\/p><ul data-start=\"824\" data-end=\"952\"><li data-section-id=\"162lzss\" data-start=\"824\" data-end=\"847\">100 people enter<\/li><li data-section-id=\"dxga92\" data-start=\"848\" data-end=\"873\">~90 carry a smartphone<\/li><li data-section-id=\"1s4hu9q\" data-start=\"874\" data-end=\"915\">~70\u201385 are reliably detected<\/li><li data-section-id=\"cwhdlg\" data-start=\"916\" data-end=\"952\">~15\u201330 are excluded or modeled<\/li><\/ul><p data-start=\"954\" data-end=\"1030\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f449.svg\" alt=\"\ud83d\udc49\">  Result:<br><strong data-start=\"968\" data-end=\"1030\">Seeketing works with a large sample, but not a complete one<\/strong><\/p><hr data-start=\"1032\" data-end=\"1035\"><h5><b><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2696.svg\" alt=\"\u2696\ufe0f\">  How to interpret that 70\u201390%<\/b><\/h5><p data-start=\"1071\" data-end=\"1085\">This is key:<\/p><p data-start=\"1087\" data-end=\"1171\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f449.svg\" alt=\"\ud83d\udc49\">  Seeketing is NOT a census counter<br data-start=\"1124\" data-end=\"1127\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f449.svg\" alt=\"\ud83d\udc49\"> It is a <strong data-start=\"1144\" data-end=\"1171\">highly robust statistical<\/strong> system<\/p><p data-start=\"1173\" data-end=\"1187\">That means:<\/p><ul data-start=\"1189\" data-end=\"1409\"><li data-section-id=\"92rhmy\" data-start=\"1189\" data-end=\"1315\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2714.svg\" alt=\"\u2714\ufe0f\">  Very good for:<br><ul data-start=\"1212\" data-end=\"1315\"><li data-section-id=\"1ojtgtc\" data-start=\"1212\" data-end=\"1226\">Trends<\/li><li data-section-id=\"1dcpkbj\" data-start=\"1229\" data-end=\"1276\">Comparisons (day vs. day, store vs. store)<\/li><li data-section-id=\"1ewnv49\" data-start=\"1279\" data-end=\"1294\">Recurrence<\/li><li data-section-id=\"di7cxx\" data-start=\"1297\" data-end=\"1315\">behavior<\/li><\/ul><\/li><li data-section-id=\"10rj68p\" data-start=\"1317\" data-end=\"1409\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/26a0.svg\" alt=\"\u26a0\ufe0f\">  Less reliable for:<br><ul data-start=\"1343\" data-end=\"1409\"><li data-section-id=\"15ijeuj\" data-start=\"1343\" data-end=\"1372\">Exact entry counting<\/li><li data-section-id=\"e3qfma\" data-start=\"1375\" data-end=\"1409\">Absolute KPIs without calibration<\/li><\/ul><\/li><\/ul><hr data-start=\"1411\" data-end=\"1414\"><h5><b><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f50d.svg\" alt=\"\ud83d\udd0d\">  Practical example<\/b><\/h5><p data-start=\"1439\" data-end=\"1447\">Imagine:<\/p><ul data-start=\"1449\" data-end=\"1528\"><li data-section-id=\"1t24ihs\" data-start=\"1449\" data-end=\"1492\">Seeketing detects 800 \u201cunique visitors\u201d<\/li><li data-section-id=\"19352ky\" data-start=\"1493\" data-end=\"1528\">In reality, 1,000 people entered<\/li><\/ul><p data-start=\"1530\" data-end=\"1579\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f449.svg\" alt=\"\ud83d\udc49\">  You do not know the exact number\u2026<br data-start=\"1559\" data-end=\"1562\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f449.svg\" alt=\"\ud83d\udc49\"> But you do know:<\/p><ul data-start=\"1580\" data-end=\"1654\"><li data-section-id=\"1at9y4o\" data-start=\"1580\" data-end=\"1599\">How many return<\/li><li data-section-id=\"1nkq13h\" data-start=\"1600\" data-end=\"1623\">How long they stay<\/li><li data-section-id=\"1ocsv3s\" data-start=\"1624\" data-end=\"1654\">How traffic evolves<\/li><\/ul><hr data-start=\"1656\" data-end=\"1659\"><h5><b><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f9e9.svg\" alt=\"\ud83e\udde9\">  How companies solve it <\/b><\/h5><p data-start=\"1716\" data-end=\"1755\">Serious implementations do this:<\/p><p data-start=\"1757\" data-end=\"1810\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f449.svg\" alt=\"\ud83d\udc49\">  combine Seeketing + physical counting (cameras\/laser)<\/p><p data-start=\"1812\" data-end=\"1825\">This way you get:<\/p><ul data-start=\"1826\" data-end=\"1909\"><li data-section-id=\"m9qnd9\" data-start=\"1826\" data-end=\"1861\">Cameras \u2192 1,000 real entries<\/li><li data-section-id=\"1tvyrz2\" data-start=\"1862\" data-end=\"1909\">Seeketing \u2192 behavior of ~800 people<\/li><\/ul><p data-start=\"1911\" data-end=\"1949\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f449.svg\" alt=\"\ud83d\udc49\">  And you can scale\/correct the data<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-1127\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"8\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-1127\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> Which is more accurate at counting people? <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-check-circle\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M504 256c0 136.967-111.033 248-248 248S8 392.967 8 256 119.033 8 256 8s248 111.033 248 248zM227.314 387.314l184-184c6.248-6.248 6.248-16.379 0-22.627l-22.627-22.627c-6.248-6.249-16.379-6.249-22.628 0L216 308.118l-70.059-70.059c-6.248-6.248-16.379-6.248-22.628 0l-22.627 22.627c-6.248 6.248-6.248 16.379 0 22.627l104 104c6.249 6.249 16.379 6.249 22.628.001z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-far-question-circle\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M256 8C119.043 8 8 119.083 8 256c0 136.997 111.043 248 248 248s248-111.003 248-248C504 119.083 392.957 8 256 8zm0 448c-110.532 0-200-89.431-200-200 0-110.495 89.472-200 200-200 110.491 0 200 89.471 200 200 0 110.53-89.431 200-200 200zm107.244-255.2c0 67.052-72.421 68.084-72.421 92.863V300c0 6.627-5.373 12-12 12h-45.647c-6.627 0-12-5.373-12-12v-8.659c0-35.745 27.1-50.034 47.579-61.516 17.561-9.845 28.324-16.541 28.324-29.579 0-17.246-21.999-28.693-39.784-28.693-23.189 0-33.894 10.977-48.942 29.969-4.057 5.12-11.46 6.071-16.666 2.124l-27.824-21.098c-5.107-3.872-6.251-11.066-2.644-16.363C184.846 131.491 214.94 112 261.794 112c49.071 0 101.45 38.304 101.45 88.8zM298 368c0 23.159-18.841 42-42 42s-42-18.841-42-42 18.841-42 42-42 42 18.841 42 42z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1127\" class=\"elementor-element elementor-element-d4f14ec e-flex e-con-boxed e-con e-child\" data-id=\"d4f14ec\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1127\" class=\"elementor-element elementor-element-048fa47 e-con-full e-flex e-con e-child\" data-id=\"048fa47\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-4315038 elementor-widget elementor-widget-text-editor\" data-id=\"4315038\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p data-start=\"189\" data-end=\"274\">In a real retail scenario (employees + customers who enter multiple times per day):<\/p><ul data-start=\"276\" data-end=\"1093\"><li data-section-id=\"1n3lw91\" data-start=\"276\" data-end=\"617\"><strong data-start=\"278\" data-end=\"329\">Traditional cameras (entry counting only)<\/strong><br><ul data-start=\"334\" data-end=\"617\"><li data-section-id=\"17lstt9\" data-start=\"334\" data-end=\"422\">Each entry is counted as \u201cone person,\u201d even if it is the same person multiple times.<\/li><li data-section-id=\"10d6y3n\" data-start=\"425\" data-end=\"492\">Result: <strong data-start=\"438\" data-end=\"457\">overestimation<\/strong>, because it does not distinguish duplicates.<\/li><li data-section-id=\"xuduoy\" data-start=\"495\" data-end=\"561\">Example: 1 employee enters 5 times \u2192 5 \u201cpeople\u201d in the count.<\/li><li data-section-id=\"gdtoks\" data-start=\"564\" data-end=\"617\">Conclusion: <strong data-start=\"578\" data-end=\"616\">it is not accurate for unique people<\/strong>.<\/li><\/ul><\/li><li data-section-id=\"7nbf0x\" data-start=\"619\" data-end=\"1093\"><strong data-start=\"621\" data-end=\"669\">Seeketing (unique device detection)<\/strong><br><ul data-start=\"674\" data-end=\"1093\"><li data-section-id=\"qrc1zs\" data-start=\"674\" data-end=\"735\">Each detected mobile device is associated with a unique ID.<\/li><li data-section-id=\"7zjgm7\" data-start=\"738\" data-end=\"900\">Result: it <strong data-start=\"751\" data-end=\"812\">approximates the real number of different people much better<\/strong>, because duplicates (the same person entering multiple times) are counted only once.<\/li><li data-section-id=\"1ipeyag\" data-start=\"903\" data-end=\"1029\">Limitation: it <strong data-start=\"917\" data-end=\"983\">does not detect people without a phone or phones that are not detectable<\/strong>, so it may slightly underestimate.<\/li><li data-section-id=\"gtt4kl\" data-start=\"1032\" data-end=\"1093\">Typical coverage: ~70\u201390% of all people who enter.<\/li><\/ul><\/li><\/ul><hr data-start=\"1095\" data-end=\"1098\"><h5><b>Conclusion <\/b><\/h5><ul data-start=\"1122\" data-end=\"1487\"><li data-section-id=\"1j5uzba\" data-start=\"1122\" data-end=\"1200\"><strong data-start=\"1124\" data-end=\"1198\">To count unique people, Seeketing is more accurate than cameras.<\/strong><\/li><li data-section-id=\"tj5cs0\" data-start=\"1201\" data-end=\"1338\">Cameras <strong data-start=\"1215\" data-end=\"1237\">only measure visits<\/strong>, and therefore <strong data-start=\"1248\" data-end=\"1285\">overestimate the number of people<\/strong> when the same person enters multiple times.<\/li><li data-section-id=\"4yfess\" data-start=\"1339\" data-end=\"1487\">Seeketing <strong data-start=\"1351\" data-end=\"1376\">slightly underestimates<\/strong> due to people without a phone, but it still provides <strong data-start=\"1421\" data-end=\"1486\">a better approximation of the real number of distinct individuals<\/strong>.<\/li><li data-section-id=\"4yfess\" data-start=\"1339\" data-end=\"1487\"><h5><b>Example scenario<\/b><\/h5><p data-start=\"193\" data-end=\"225\">Suppose a store in one day:<\/p><ul data-start=\"227\" data-end=\"392\"><li data-section-id=\"rqqbou\" data-start=\"227\" data-end=\"255\"><strong data-start=\"229\" data-end=\"248\">Unique customers<\/strong>: 100<\/li><li data-section-id=\"1z0lfuj\" data-start=\"256\" data-end=\"277\"><strong data-start=\"258\" data-end=\"271\">Employees<\/strong>: 10<\/li><li data-section-id=\"uxug4n\" data-start=\"278\" data-end=\"392\"><strong data-start=\"280\" data-end=\"305\">Entry frequency<\/strong>:<br><ul data-start=\"309\" data-end=\"392\"><li data-section-id=\"9xxexz\" data-start=\"309\" data-end=\"337\">Each customer enters once<\/li><li data-section-id=\"1vfnke5\" data-start=\"340\" data-end=\"392\">Each employee enters 5 times (exits and re-entries)<\/li><\/ul><\/li><\/ul><hr data-start=\"394\" data-end=\"397\"><h5><b><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/31-20e3.svg\" alt=\"1\ufe0f\u20e3\">  Counting with cameras (entries only)<\/b><\/h5><ul data-start=\"441\" data-end=\"482\"><li data-section-id=\"1yzg0gj\" data-start=\"441\" data-end=\"482\">Cameras count <strong data-start=\"459\" data-end=\"481\">all entries<\/strong>:<\/li><\/ul><div class=\"TyagGW_tableContainer\"><div class=\"group TyagGW_tableWrapper flex flex-col-reverse w-fit\" tabindex=\"-1\"><table class=\"w-fit min-w-(--thread-content-width)\" data-start=\"484\" data-end=\"739\"><thead data-start=\"484\" data-end=\"525\"><tr data-start=\"484\" data-end=\"525\"><th class=\"\" data-start=\"484\" data-end=\"491\" data-col-size=\"sm\">Type<\/th><th class=\"\" data-start=\"491\" data-end=\"511\" data-col-size=\"sm\">People\/entries<\/th><th class=\"\" data-start=\"511\" data-end=\"525\" data-col-size=\"sm\">Comment<\/th><\/tr><\/thead><tbody data-start=\"566\" data-end=\"739\"><tr data-start=\"566\" data-end=\"611\"><td data-start=\"566\" data-end=\"577\" data-col-size=\"sm\">Customers<\/td><td data-start=\"577\" data-end=\"583\" data-col-size=\"sm\">100<\/td><td data-start=\"583\" data-end=\"611\" data-col-size=\"sm\">Each customer enters once<\/td><\/tr><tr data-start=\"612\" data-end=\"669\"><td data-start=\"612\" data-end=\"624\" data-col-size=\"sm\">Employees<\/td><td data-start=\"624\" data-end=\"638\" data-col-size=\"sm\">10 x 5 = 50<\/td><td data-start=\"638\" data-end=\"669\" data-col-size=\"sm\">Each employee enters 5 times<\/td><\/tr><tr data-start=\"670\" data-end=\"739\"><td data-start=\"670\" data-end=\"702\" data-col-size=\"sm\"><strong data-start=\"672\" data-end=\"701\">Total counted by cameras<\/strong><\/td><td data-start=\"702\" data-end=\"708\" data-col-size=\"sm\">150<\/td><td data-start=\"708\" data-end=\"739\" data-col-size=\"sm\">Overestimates unique people<\/td><\/tr><\/tbody><\/table><\/div><\/div><p data-start=\"741\" data-end=\"878\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2705.svg\" alt=\"\u2705\">  Note: Cameras <strong data-start=\"768\" data-end=\"796\">do not distinguish duplicates<\/strong>, so the real number of unique people is 110, but cameras report 150.<\/p><hr data-start=\"880\" data-end=\"883\"><h5><b><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/32-20e3.svg\" alt=\"2\ufe0f\u20e3\">  Counting with Seeketing (detectable unique people)<\/b><\/h5><ul data-start=\"943\" data-end=\"1004\"><li data-section-id=\"wmqwnz\" data-start=\"943\" data-end=\"1004\">Assuming <strong data-start=\"956\" data-end=\"1003\">80% detection of people with a phone<\/strong>:<\/li><\/ul><div class=\"TyagGW_tableContainer\"><div class=\"group TyagGW_tableWrapper flex flex-col-reverse w-fit\" tabindex=\"-1\"><table class=\"w-fit min-w-(--thread-content-width)\" data-start=\"1006\" data-end=\"1300\"><thead data-start=\"1006\" data-end=\"1065\"><tr data-start=\"1006\" data-end=\"1065\"><th class=\"\" data-start=\"1006\" data-end=\"1013\" data-col-size=\"sm\">Type<\/th><th class=\"\" data-start=\"1013\" data-end=\"1031\" data-col-size=\"sm\">Real people<\/th><th class=\"\" data-start=\"1031\" data-end=\"1051\" data-col-size=\"sm\">Detected (~80%)<\/th><th class=\"\" data-start=\"1051\" data-end=\"1065\" data-col-size=\"sm\">Comment<\/th><\/tr><\/thead><tbody data-start=\"1123\" data-end=\"1300\"><tr data-start=\"1123\" data-end=\"1175\"><td data-start=\"1123\" data-end=\"1134\" data-col-size=\"sm\">Customers<\/td><td data-start=\"1134\" data-end=\"1140\" data-col-size=\"sm\">100<\/td><td data-start=\"1140\" data-end=\"1145\" data-col-size=\"sm\">80<\/td><td data-start=\"1145\" data-end=\"1175\" data-col-size=\"sm\">Detected with their phones<\/td><\/tr><tr data-start=\"1176\" data-end=\"1223\"><td data-start=\"1176\" data-end=\"1188\" data-col-size=\"sm\">Employees<\/td><td data-start=\"1188\" data-end=\"1193\" data-col-size=\"sm\">10<\/td><td data-start=\"1193\" data-end=\"1197\" data-col-size=\"sm\">8<\/td><td data-start=\"1197\" data-end=\"1223\" data-col-size=\"sm\">Detected with phones<\/td><\/tr><tr data-start=\"1224\" data-end=\"1300\"><td data-start=\"1224\" data-end=\"1260\" data-col-size=\"sm\"><strong data-start=\"1226\" data-end=\"1259\">Total detected by Seeketing<\/strong><\/td><td data-start=\"1260\" data-end=\"1265\" data-col-size=\"sm\">88<\/td><td data-start=\"1265\" data-end=\"1300\" data-col-size=\"sm\">Approximately unique people<\/td><td data-col-size=\"sm\"> <\/td> <\/tr><\/tbody><\/table><\/div><\/div><p data-start=\"1302\" data-end=\"1469\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2705.svg\" alt=\"\u2705\">  Note: Seeketing <strong data-start=\"1327\" data-end=\"1352\">slightly underestimates<\/strong> (people without a phone or not detectable), but it removes duplicates, so it reflects <strong data-start=\"1449\" data-end=\"1468\">unique people<\/strong> much better.<\/p><hr data-start=\"1471\" data-end=\"1474\"><h5><b>Direct comparison<\/b><\/h5><div class=\"TyagGW_tableContainer\"><div class=\"group TyagGW_tableWrapper flex flex-col-reverse w-fit\" tabindex=\"-1\"><table class=\"w-fit min-w-(--thread-content-width)\" data-start=\"1502\" data-end=\"1760\"><thead data-start=\"1502\" data-end=\"1568\"><tr data-start=\"1502\" data-end=\"1568\"><th class=\"\" data-start=\"1502\" data-end=\"1512\" data-col-size=\"sm\">Metric<\/th><th class=\"\" data-start=\"1512\" data-end=\"1522\" data-col-size=\"sm\">Cameras<\/th><th class=\"\" data-start=\"1522\" data-end=\"1534\" data-col-size=\"sm\">Seeketing<\/th><th class=\"\" data-start=\"1534\" data-end=\"1568\" data-col-size=\"sm\">Real number of unique people<\/th><\/tr><\/thead><tbody data-start=\"1635\" data-end=\"1760\"><tr data-start=\"1635\" data-end=\"1671\"><td data-start=\"1635\" data-end=\"1653\" data-col-size=\"sm\">Unique people<\/td><td data-start=\"1653\" data-end=\"1659\" data-col-size=\"sm\">150<\/td><td data-start=\"1659\" data-end=\"1664\" data-col-size=\"sm\">88<\/td><td data-start=\"1664\" data-end=\"1671\" data-col-size=\"sm\">110<\/td><\/tr><tr data-start=\"1672\" data-end=\"1760\"><td data-start=\"1672\" data-end=\"1714\" data-col-size=\"sm\">Error vs. real unique people<\/td><td data-start=\"1714\" data-end=\"1735\" data-col-size=\"sm\">+36% (overestimates)<\/td><td data-start=\"1735\" data-end=\"1754\" data-col-size=\"sm\">-20% (underestimates)<\/td><td data-start=\"1754\" data-end=\"1760\" data-col-size=\"sm\">0%<\/td><\/tr><\/tbody><\/table><\/div><\/div><hr data-start=\"1762\" data-end=\"1765\"><h5> <b>Interpretation<\/b><\/h5><ul data-start=\"1788\" data-end=\"2167\"><li data-section-id=\"bwxx0o\" data-start=\"1788\" data-end=\"1905\"><strong data-start=\"1790\" data-end=\"1802\">Cameras:<\/strong> overestimate unique people when there are multiple entries (employees, customers who go out and come back in).<\/li><li data-section-id=\"xi2ytq\" data-start=\"1906\" data-end=\"2028\"><strong data-start=\"1908\" data-end=\"1922\">Seeketing:<\/strong> slightly underestimates due to non-total coverage, but <strong data-start=\"1974\" data-end=\"2025\">it is much more accurate for measuring unique people<\/strong>.<\/li><li data-section-id=\"1hamutz\" data-start=\"2029\" data-end=\"2167\">In scenarios with many multiple entries, <strong data-start=\"2076\" data-end=\"2150\">Seeketing will always be closer to the real number of distinct people<\/strong> than cameras.<\/li><\/ul><hr data-start=\"2169\" data-end=\"2172\"><h5><b>Rule of thumb for retail<\/b><\/h5><ul data-start=\"2207\" data-end=\"2414\"><li data-section-id=\"1fg52bq\" data-start=\"2207\" data-end=\"2260\"><strong data-start=\"2209\" data-end=\"2258\">Goal: measure unique visitors \u2192 Seeketing<\/strong><\/li><li data-section-id=\"1yi31r9\" data-start=\"2261\" data-end=\"2317\"><strong data-start=\"2263\" data-end=\"2315\">Goal: measure sales opportunities \u2192 Cameras<\/strong><\/li><li data-section-id=\"1rdkjtl\" data-start=\"2318\" data-end=\"2414\">Combine both systems \u2192 <strong data-start=\"2346\" data-end=\"2379\">the best possible approximation<\/strong>: unique people + real visits.<\/li><\/ul><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-1128\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"9\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-1128\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> How many different people pass by, and how many times does each person enter? <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-check-circle\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M504 256c0 136.967-111.033 248-248 248S8 392.967 8 256 119.033 8 256 8s248 111.033 248 248zM227.314 387.314l184-184c6.248-6.248 6.248-16.379 0-22.627l-22.627-22.627c-6.248-6.249-16.379-6.249-22.628 0L216 308.118l-70.059-70.059c-6.248-6.248-16.379-6.248-22.628 0l-22.627 22.627c-6.248 6.248-6.248 16.379 0 22.627l104 104c6.249 6.249 16.379 6.249 22.628.001z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-far-question-circle\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M256 8C119.043 8 8 119.083 8 256c0 136.997 111.043 248 248 248s248-111.003 248-248C504 119.083 392.957 8 256 8zm0 448c-110.532 0-200-89.431-200-200 0-110.495 89.472-200 200-200 110.491 0 200 89.471 200 200 0 110.53-89.431 200-200 200zm107.244-255.2c0 67.052-72.421 68.084-72.421 92.863V300c0 6.627-5.373 12-12 12h-45.647c-6.627 0-12-5.373-12-12v-8.659c0-35.745 27.1-50.034 47.579-61.516 17.561-9.845 28.324-16.541 28.324-29.579 0-17.246-21.999-28.693-39.784-28.693-23.189 0-33.894 10.977-48.942 29.969-4.057 5.12-11.46 6.071-16.666 2.124l-27.824-21.098c-5.107-3.872-6.251-11.066-2.644-16.363C184.846 131.491 214.94 112 261.794 112c49.071 0 101.45 38.304 101.45 88.8zM298 368c0 23.159-18.841 42-42 42s-42-18.841-42-42 18.841-42 42-42 42 18.841 42 42z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1128\" class=\"elementor-element elementor-element-2958039 e-flex e-con-boxed e-con e-child\" data-id=\"2958039\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1128\" class=\"elementor-element elementor-element-34d1755 e-con-full e-flex e-con e-child\" data-id=\"34d1755\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-e19ff32 elementor-widget elementor-widget-text-editor\" data-id=\"e19ff32\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h5><b>Key metrics that truly matter<\/b><\/h5><ol data-start=\"216\" data-end=\"698\"><li data-section-id=\"6wid0y\" data-start=\"216\" data-end=\"448\"><strong data-start=\"219\" data-end=\"238\">Unique people<\/strong><br><ul data-start=\"244\" data-end=\"448\"><li data-section-id=\"qy1vxr\" data-start=\"244\" data-end=\"315\">How many distinct individuals have entered the store over a period.<\/li><li data-section-id=\"7r324k\" data-start=\"319\" data-end=\"381\">This includes <strong data-start=\"334\" data-end=\"358\">customers + employees<\/strong>, but without duplicates.<\/li><li data-section-id=\"1qf1axz\" data-start=\"385\" data-end=\"448\">It enables measurement of <strong data-start=\"401\" data-end=\"417\">real reach<\/strong> and <strong data-start=\"420\" data-end=\"447\">customer penetration<\/strong>.<\/li><\/ul><\/li><li data-section-id=\"kudlvy\" data-start=\"450\" data-end=\"698\"><strong data-start=\"453\" data-end=\"489\">Visits per person (frequency)<\/strong><br><ul data-start=\"495\" data-end=\"698\"><li data-section-id=\"1nbxo7p\" data-start=\"495\" data-end=\"567\">How many times each person enters during the day, week, or month.<\/li><li data-section-id=\"1qhip4u\" data-start=\"571\" data-end=\"698\">This makes it possible to identify <strong data-start=\"598\" data-end=\"613\">recurrence<\/strong>, <strong data-start=\"615\" data-end=\"659\">employees who generate multiple entries<\/strong>, and real customer behavior.<\/li><\/ul><\/li><\/ol><hr data-start=\"700\" data-end=\"703\"><h5><b><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/26a0.svg\" alt=\"\u26a0\ufe0f\">  Why traditional systems fail<\/b><\/h5><ul data-start=\"753\" data-end=\"1111\"><li data-section-id=\"9n44sg\" data-start=\"753\" data-end=\"949\"><strong data-start=\"755\" data-end=\"774\">Simple cameras<\/strong> \u2192 only count footfall\/entries<br><ul data-start=\"809\" data-end=\"949\"><li data-section-id=\"17pippw\" data-start=\"809\" data-end=\"878\">Problem: 1 person entering 5 times = 5 people \u2192 <strong data-start=\"863\" data-end=\"878\">overestimation<\/strong><\/li><li data-section-id=\"uzy6tr\" data-start=\"881\" data-end=\"949\">Children playing, employees going in and out \u2192 distorts data<\/li><\/ul><\/li><li data-section-id=\"kr11ew\" data-start=\"951\" data-end=\"1111\"><strong data-start=\"953\" data-end=\"977\">Simple WiFi tracking<\/strong> \u2192 only detects devices<br><ul data-start=\"1010\" data-end=\"1111\"><li data-section-id=\"1emf43o\" data-start=\"1010\" data-end=\"1072\">Problem: phones off, randomized MACs \u2192 <strong data-start=\"1057\" data-end=\"1070\">underestimation<\/strong><\/li><li data-section-id=\"1tfz4qb\" data-start=\"1075\" data-end=\"1111\">It only captures part of the audience<\/li><\/ul><\/li><\/ul><hr data-start=\"1113\" data-end=\"1116\"><h5><b><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2705.svg\" alt=\"\u2705\">  The real solution in retail<\/b><\/h5><p data-start=\"1150\" data-end=\"1253\">To answer <strong data-start=\"1167\" data-end=\"1242\">\u201chow many distinct people pass by\u201d and \u201chow many times each person enters\u201d<\/strong>, you need:<\/p><ol data-start=\"1255\" data-end=\"1621\"><li data-section-id=\"1rjjou4\" data-start=\"1255\" data-end=\"1450\"><strong data-start=\"1258\" data-end=\"1306\">A unique-person identification system<\/strong><br><ul data-start=\"1312\" data-end=\"1450\"><li data-section-id=\"4umtrg\" data-start=\"1312\" data-end=\"1364\">Example: <strong data-start=\"1323\" data-end=\"1336\">Seeketing<\/strong> (unique device ID)<\/li><li data-section-id=\"xpe2ok\" data-start=\"1368\" data-end=\"1450\">Advantage: removes duplicates, measures recurrence, differentiates customers from employees.<\/li><\/ul><\/li><li data-section-id=\"xf8c5r\" data-start=\"1452\" data-end=\"1621\"><strong data-start=\"1455\" data-end=\"1514\">Complementary physical counting (cameras, laser, sensors)<\/strong><br><ul data-start=\"1520\" data-end=\"1621\"><li data-section-id=\"1auj63l\" data-start=\"1520\" data-end=\"1570\">Ensures full coverage of real entries.<\/li><li data-section-id=\"12i94xb\" data-start=\"1574\" data-end=\"1621\">Corrects undercounting of people without a phone.<\/li><\/ul><\/li><\/ol><hr data-start=\"1623\" data-end=\"1626\"><p> <\/p><h4><b>How are they combined?<\/b><\/h4><div class=\"TyagGW_tableContainer\"><div class=\"group TyagGW_tableWrapper flex flex-col-reverse w-fit\" tabindex=\"-1\"><table class=\"w-fit min-w-(--thread-content-width)\" data-start=\"1651\" data-end=\"1929\"><thead data-start=\"1651\" data-end=\"1681\"><tr data-start=\"1651\" data-end=\"1681\"><th class=\"\" data-start=\"1651\" data-end=\"1661\" data-col-size=\"sm\">Metric<\/th><th class=\"\" data-start=\"1661\" data-end=\"1681\" data-col-size=\"sm\">Ideal technology<\/th><\/tr><\/thead><tbody data-start=\"1711\" data-end=\"1929\"><tr data-start=\"1711\" data-end=\"1768\"><td data-start=\"1711\" data-end=\"1729\" data-col-size=\"sm\">Unique people<\/td><td data-start=\"1729\" data-end=\"1768\" data-col-size=\"sm\">Seeketing (detected devices)<\/td><\/tr><tr data-start=\"1769\" data-end=\"1807\"><td data-start=\"1769\" data-end=\"1794\" data-col-size=\"sm\">Frequency\/recurrence<\/td><td data-start=\"1794\" data-end=\"1807\" data-col-size=\"sm\">Seeketing<\/td><\/tr><tr data-start=\"1808\" data-end=\"1851\"><td data-start=\"1808\" data-end=\"1834\" data-col-size=\"sm\">Real entries\/visits<\/td><td data-start=\"1834\" data-end=\"1851\" data-col-size=\"sm\">Cameras\/laser<\/td><\/tr><tr data-start=\"1852\" data-end=\"1929\"><td data-start=\"1852\" data-end=\"1888\" data-col-size=\"sm\">Final KPI: conversion per person<\/td><td data-start=\"1888\" data-end=\"1929\" data-col-size=\"sm\"><p> <\/p><\/td><\/tr><\/tbody><\/table><\/div><\/div><h5><b><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/1f4a1.svg\" alt=\"\ud83d\udca1\">  Insight<\/b><\/h5><p data-start=\"1950\" data-end=\"2011\">The <strong data-start=\"1953\" data-end=\"1977\">real value in retail<\/strong> is not in counting footfall, but in:<\/p><ul data-start=\"2013\" data-end=\"2105\"><li data-section-id=\"zypnb5\" data-start=\"2013\" data-end=\"2062\">Knowing <strong data-start=\"2021\" data-end=\"2053\">how many distinct individuals<\/strong> enter<\/li><li data-section-id=\"1fa8ch5\" data-start=\"2063\" data-end=\"2105\">Knowing <strong data-start=\"2071\" data-end=\"2103\">how many times each one enters<\/strong><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-1129\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"10\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-1129\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> Is WiFi tracking different from Seeketing, which uses a trained knowledge database with radio-frequency signal patterns and fingerprinting techniques? <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-check-circle\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M504 256c0 136.967-111.033 248-248 248S8 392.967 8 256 119.033 8 256 8s248 111.033 248 248zM227.314 387.314l184-184c6.248-6.248 6.248-16.379 0-22.627l-22.627-22.627c-6.248-6.249-16.379-6.249-22.628 0L216 308.118l-70.059-70.059c-6.248-6.248-16.379-6.248-22.628 0l-22.627 22.627c-6.248 6.248-6.248 16.379 0 22.627l104 104c6.249 6.249 16.379 6.249 22.628.001z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-far-question-circle\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M256 8C119.043 8 8 119.083 8 256c0 136.997 111.043 248 248 248s248-111.003 248-248C504 119.083 392.957 8 256 8zm0 448c-110.532 0-200-89.431-200-200 0-110.495 89.472-200 200-200 110.491 0 200 89.471 200 200 0 110.53-89.431 200-200 200zm107.244-255.2c0 67.052-72.421 68.084-72.421 92.863V300c0 6.627-5.373 12-12 12h-45.647c-6.627 0-12-5.373-12-12v-8.659c0-35.745 27.1-50.034 47.579-61.516 17.561-9.845 28.324-16.541 28.324-29.579 0-17.246-21.999-28.693-39.784-28.693-23.189 0-33.894 10.977-48.942 29.969-4.057 5.12-11.46 6.071-16.666 2.124l-27.824-21.098c-5.107-3.872-6.251-11.066-2.644-16.363C184.846 131.491 214.94 112 261.794 112c49.071 0 101.45 38.304 101.45 88.8zM298 368c0 23.159-18.841 42-42 42s-42-18.841-42-42 18.841-42 42-42 42 18.841 42 42z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1129\" class=\"elementor-element elementor-element-702c6c8 e-flex e-con-boxed e-con e-child\" data-id=\"702c6c8\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1129\" class=\"elementor-element elementor-element-9137f1c e-con-full e-flex e-con e-child\" data-id=\"9137f1c\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-2751455 elementor-widget elementor-widget-text-editor\" data-id=\"2751455\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h5 data-section-id=\"12cublp\" data-start=\"210\" data-end=\"237\"><b><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/31-20e3.svg\" alt=\"1\ufe0f\u20e3\">  Classic WiFi Tracking<\/b><\/h5><h5 data-section-id=\"1kjtrsp\" data-start=\"239\" data-end=\"257\">How it works:<\/h5><ul data-start=\"258\" data-end=\"459\"><li data-section-id=\"1i9rdpl\" data-start=\"258\" data-end=\"331\">Detects <strong data-start=\"268\" data-end=\"303\">WiFi signals from nearby smartphones<\/strong> (probe requests)<\/li><li data-section-id=\"1mlr87x\" data-start=\"332\" data-end=\"409\">Each device has a <strong data-start=\"361\" data-end=\"376\">MAC address<\/strong>, which is used as an identifier<\/li><li data-section-id=\"isb9mi\" data-start=\"410\" data-end=\"459\">People count = each MAC \u2192 one \u201cperson\u201d<\/li><\/ul><h5>Limitations:<\/h5><ul data-start=\"479\" data-end=\"767\"><li data-section-id=\"1wyuokw\" data-start=\"479\" data-end=\"572\">Many smartphones use <strong data-start=\"505\" data-end=\"522\">randomized MAC<\/strong>, which makes persistent identification difficult<\/li><li data-section-id=\"1quhkr1\" data-start=\"573\" data-end=\"620\">It only detects devices with WiFi enabled<\/li><li data-section-id=\"1ia18ao\" data-start=\"621\" data-end=\"704\">It does not reliably distinguish <strong data-start=\"641\" data-end=\"656\">recurrence<\/strong>, frequency, or real behavior patterns<\/li><li data-section-id=\"zeyrfl\" data-start=\"705\" data-end=\"767\">It does not use prior learning or behavior models<\/li><\/ul><p data-start=\"769\" data-end=\"780\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2705.svg\" alt=\"\u2705\">  The good:<\/p><ul data-start=\"781\" data-end=\"862\"><li data-section-id=\"1os35dy\" data-start=\"781\" data-end=\"813\">Low cost, easy to install<\/li><li data-section-id=\"o7eme3\" data-start=\"814\" data-end=\"862\">Provides traffic trends (footfall)<\/li><\/ul><p data-start=\"864\" data-end=\"874\"><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/274c.svg\" alt=\"\u274c\">  The bad:<\/p><ul data-start=\"875\" data-end=\"973\"><li data-section-id=\"185trt6\" data-start=\"875\" data-end=\"912\">Low accuracy for unique people<\/li><li data-section-id=\"xcg5jx\" data-start=\"913\" data-end=\"973\">Privacy issues (MAC tracking without anonymization)<\/li><\/ul><hr data-start=\"975\" data-end=\"978\"><h5> <b><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/32-20e3.svg\" alt=\"2\ufe0f\u20e3\"> Seeketing<br><\/b>How it works:<\/h5><ul data-start=\"1016\" data-end=\"1460\"><li data-section-id=\"40klv\" data-start=\"1016\" data-end=\"1087\">Detects radio-frequency signals (WiFi, Bluetooth, cellular, etc.)<\/li><li data-section-id=\"iffc8x\" data-start=\"1088\" data-end=\"1185\"><strong data-start=\"1090\" data-end=\"1132\">Uses fingerprinting and signal patterns<\/strong> to identify devices persistently<\/li><li data-section-id=\"bx19qm\" data-start=\"1186\" data-end=\"1319\">It relies on <strong data-start=\"1199\" data-end=\"1262\">a knowledge base trained on millions of patterns<\/strong> \u2192 it can better differentiate between different devices<\/li><li data-section-id=\"fr63d2\" data-start=\"1320\" data-end=\"1374\">Generates a <strong data-start=\"1332\" data-end=\"1352\">unique anonymous ID<\/strong> for each visitor<\/li><li data-section-id=\"1o3qd7o\" data-start=\"1375\" data-end=\"1460\">Able to detect <strong data-start=\"1395\" data-end=\"1410\">recurrence<\/strong>, entry frequency, dwell zones, etc.<\/li><\/ul><h5>Advantages over classic WiFi tracking:<\/h5><ul data-start=\"1504\" data-end=\"1768\"><li data-section-id=\"1j5evbk\" data-start=\"1504\" data-end=\"1574\">It does not depend on WiFi being enabled (it can use other signals)<\/li><li data-section-id=\"5aqlrd\" data-start=\"1575\" data-end=\"1650\">It recognizes devices better even if they change MAC or there is signal noise<\/li><li data-section-id=\"748lou\" data-start=\"1651\" data-end=\"1705\">High accuracy for unique people and behavior<\/li><li data-section-id=\"njo3yx\" data-start=\"1706\" data-end=\"1768\">GDPR-compliant because the IDs are anonymous and persistent<\/li><\/ul><hr data-start=\"1770\" data-end=\"1773\"><p><b><img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/33-20e3.svg\" alt=\"3\ufe0f\u20e3\">  Key differences summary<\/b><\/p><div class=\"TyagGW_tableContainer\"><div class=\"group TyagGW_tableWrapper flex flex-col-reverse w-fit\" tabindex=\"-1\"><table class=\"w-fit min-w-(--thread-content-width)\" data-start=\"1810\" data-end=\"2372\"><thead data-start=\"1810\" data-end=\"1864\"><tr data-start=\"1810\" data-end=\"1864\"><th class=\"\" data-start=\"1810\" data-end=\"1827\" data-col-size=\"sm\">Feature<\/th><th class=\"\" data-start=\"1827\" data-end=\"1851\" data-col-size=\"sm\">Classic WiFi tracking<\/th><th class=\"\" data-start=\"1851\" data-end=\"1864\" data-col-size=\"md\">Seeketing<\/th><\/tr><\/thead><tbody data-start=\"1918\" data-end=\"2372\"><tr data-start=\"1918\" data-end=\"1998\"><td data-start=\"1918\" data-end=\"1935\" data-col-size=\"sm\">Signals used<\/td><td data-start=\"1935\" data-end=\"1947\" data-col-size=\"sm\">WiFi only<\/td><td data-start=\"1947\" data-end=\"1998\" data-col-size=\"md\">WiFi, Bluetooth, cellular, multiple frequencies<\/td><\/tr><tr data-start=\"1999\" data-end=\"2099\"><td data-start=\"1999\" data-end=\"2028\" data-col-size=\"sm\">Persistent identification<\/td><td data-start=\"2028\" data-end=\"2055\" data-col-size=\"sm\">Limited (randomized MAC)<\/td><td data-start=\"2055\" data-end=\"2099\" data-col-size=\"md\">High (fingerprinting + pattern database)<\/td><\/tr><tr data-start=\"2100\" data-end=\"2153\"><td data-start=\"2100\" data-end=\"2118\" data-col-size=\"sm\">Unique people<\/td><td data-start=\"2118\" data-end=\"2135\" data-col-size=\"sm\">Low accuracy<\/td><td data-start=\"2135\" data-end=\"2153\" data-col-size=\"md\">High accuracy<\/td><\/tr><tr data-start=\"2154\" data-end=\"2198\"><td data-start=\"2154\" data-end=\"2181\" data-col-size=\"sm\">Recurrence \/ frequency<\/td><td data-start=\"2181\" data-end=\"2192\" data-col-size=\"sm\">Limited<\/td><td data-start=\"2192\" data-end=\"2198\" data-col-size=\"md\">Yes<\/td><\/tr><tr data-start=\"2199\" data-end=\"2243\"><td data-start=\"2199\" data-end=\"2219\" data-col-size=\"sm\">Overall accuracy<\/td><td data-start=\"2219\" data-end=\"2235\" data-col-size=\"sm\">Low to moderate<\/td><td data-start=\"2235\" data-end=\"2243\" data-col-size=\"md\">High<\/td><\/tr><tr data-start=\"2244\" data-end=\"2297\"><td data-start=\"2244\" data-end=\"2257\" data-col-size=\"sm\">Privacy<\/td><td data-start=\"2257\" data-end=\"2272\" data-col-size=\"sm\">Problematic<\/td><td data-start=\"2272\" data-end=\"2297\" data-col-size=\"md\">GDPR-compliant (anonymous)<\/td><\/tr><tr data-start=\"2298\" data-end=\"2372\"><td data-start=\"2298\" data-end=\"2316\" data-col-size=\"sm\">Infrastructure<\/td><td data-start=\"2316\" data-end=\"2325\" data-col-size=\"sm\">Simple<\/td><td data-start=\"2325\" data-end=\"2372\" data-col-size=\"md\">More complex; requires a knowledge base<\/td><\/tr><\/tbody><\/table><\/div><\/div><hr data-start=\"2374\" data-end=\"2377\"><h5>  Summary<\/h5><ul data-start=\"2393\" data-end=\"2681\"><li data-section-id=\"1bb72xc\" data-start=\"2393\" data-end=\"2477\"><strong data-start=\"2395\" data-end=\"2436\">Classic WiFi tracking = a simple sensor<\/strong> \u2192 it only detects \u201cvisible devices\u201d<\/li><li data-section-id=\"1hx2poh\" data-start=\"2478\" data-end=\"2681\"><strong data-start=\"2480\" data-end=\"2518\">Seeketing = an intelligent platform<\/strong> \u2192 it combines <strong data-start=\"2529\" data-end=\"2574\">sensors + AI + a pattern database<\/strong> to better identify visitors, measure recurrence and behavior, and generate reliable unique people<\/li><\/ul><p data-start=\"2683\" data-end=\"2701\">In other words:<\/p><blockquote data-start=\"2703\" data-end=\"2931\"><p data-start=\"2705\" data-end=\"2931\">Seeketing is <strong data-start=\"2718\" data-end=\"2752\">an evolution of WiFi tracking<\/strong>, using <strong data-start=\"2770\" data-end=\"2835\">fingerprinting techniques, pattern learning, and multi-signal analysis<\/strong>, focused on <strong data-start=\"2849\" data-end=\"2896\">people analytics and real behavior<\/strong>, not just device counting.<\/p><h1 data-start=\"2705\" data-end=\"2931\"><img fetchpriority=\"high\" decoding=\"async\" class=\" wp-image-1824 aligncenter\" src=\"https:\/\/seeketing.com\/wp-content\/uploads\/2026\/03\/ChatGPT-Image-29-mar-2026-10_19_05-a.m-200x300.png\" alt=\"\" width=\"211\" height=\"317\" srcset=\"https:\/\/seeketing.com\/wp-content\/uploads\/2026\/03\/ChatGPT-Image-29-mar-2026-10_19_05-a.m-200x300.png 200w, https:\/\/seeketing.com\/wp-content\/uploads\/2026\/03\/ChatGPT-Image-29-mar-2026-10_19_05-a.m-683x1024.png 683w, https:\/\/seeketing.com\/wp-content\/uploads\/2026\/03\/ChatGPT-Image-29-mar-2026-10_19_05-a.m-768x1152.png 768w, https:\/\/seeketing.com\/wp-content\/uploads\/2026\/03\/ChatGPT-Image-29-mar-2026-10_19_05-a.m.png 1024w\" sizes=\"(max-width: 211px) 100vw, 211px\" \/><\/h1><\/blockquote>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t<script type=\"application\/ld+json\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@type\":\"FAQPage\",\"mainEntity\":[{\"@type\":\"Question\",\"name\":\"What technical specifications do Seeketing nodes have?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Seeketing nodes are IoT devices designed to analyze people\\u2019s behavior in physical spaces (streets, stores, events, cities, etc.), connecting that behavior with the digital world. Technically, their specifications are not presented as a \\u201cclassic hardware spec sheet\\u201d (CPU\\\/RAM type), but rather as technological and operational capabilities.   Communication technologiesThey combine multiple wireless technologies:CellularWiFiBluetoothSignal detection across multiple bands:125 kHz, 13 MHz840\\u2013960 MHz2.4 GHz, 3.6 GHz, and 5 GHz  This enables them to detect mobile phones even in scenarios where other technologies fail.  Detection capabilityThey detect between 85%\\u201390% of mobile devices in their areaVisitor identification:UniqueAnonymous (GDPR-compliant)They do not depend on:Installed appsThe user\\u2019s WiFi connection Key advantage over iBeacon or traditional WiFi tracking.  CoverageConfigurable coverage depending on the technology:From ~3 m\\u00b2 up to 15,000 m\\u00b2 per node (WiFi\\\/Bluetooth)Up to several km\\u00b2 using the cellular network  They can be used both indoors and outdoors.  Installation and powerPlug &amp; play devices (quick installation)Operation:With mains power (125\\\/220V)Some associated sensors can run on battery  Processing and identificationThey generate a unique visitor ID by combining offline and online dataThey enable:Recurrence tracking (repeat visits)Behavior analysis (dwell time, routes, etc.)They avoid typical issues such as:WiFi randomized MAC addresses  Communication featuresProximity messaging:SMS \\\/ WhatsAppEmailPush notifications (if there is an app)  Types of data they generatePeople flow (origin-destination)Visitor volumeNew vs. returningAreas of interestDwell time  Integration and architectureIntegration with:WebMobile apps (iOS, Android, HTML5 SDK)Analytics and BI platformsOmnichannel system (online + offline)  Complementary sensors and devicesNodes can work alongside:Counting sensors (footfall type)Remotely managed iBeacons\"}},{\"@type\":\"Question\",\"name\":\"Is Seeketing a good option for retail?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Seeketing systems are especially powerful for:Analyzing customer behaviorMeasuring recurrence (customers who return)Understanding dwell timesActivating proximity marketing\"}},{\"@type\":\"Question\",\"name\":\"What does each technology measure?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Computer vision cameras are primarily designed to:Count pass-by events (entries\\\/exits)Measure flowCalculate real-time occupancyCameras do not count unique people; they count visits\\\/events.And that is why:Cameras \\u2192 how many times someone entersSeeketing \\u2192 who (approximately) enters and whether they return Cameras  Total traffic (visits)  Conversion (if you cross-reference with sales)  Unique people (in general) Seeketing  Unique people (estimated by device)  Recurrence (who returns another day)  Visit frequency  Exact physical counting of entries\"}},{\"@type\":\"Question\",\"name\":\"Which technologies offer both unique visitor counting and opportunity counting?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"1. Computer vision + Re-identification (Re-ID)  What it isAI-powered cameras that apply Re-identification (Re-ID)  What it measures  Total entries (opportunities)  Unique visitors (deduplicated)  Recurrence (if they return)  Routes and zones  Dwell time  How it worksDetects people using computer visionExtracts features (clothing, silhouette, movement)Generates an anonymous IDRe-identifies the same person at different times\\\/cameras  Result:if they enter at 10:00 and at 12:00 \\u2192 it counts 1 unique person and 2 visits  It is designed specifically to solve the problem you mentioned.  In fact, it enables \\u201cdeduplicated unique counting,\\u201d avoiding double counting.  Pros \\\/ Cons  Very accurate (almost census-level) Does not depend on a mobile phone GDPR-compliant (no biometrics) More expensive Requires good camera installation  2. WiFi \\\/ Bluetooth tracking \\u2192 partial hybrid What it isTracking of mobile devices (MAC, signals)  What it measures  Unique visitors (by device)  Recurrence  Dwell time  Limitation  Does not measure the true total number of people well  Depends on having an active mobile phone  That is why:It is good for unique visitorsPoor for real opportunities  In addition, it loses accuracy today due to privacy (randomized MAC addresses).  3. Hybrid systems (vision + WiFi) \\u2192  the most used in large retailTypical example: FootfallCam  How they workThey combine:  Cameras \\u2192 total counting (opportunities)  WiFi \\u2192 identification of unique devices  Literally:video = \\u201cfootfall count\\u201dWiFi = \\u201creturning customers\\u201d  What they achieve  Total traffic (very accurate)  Unique visitors (estimated)  Recurrence  Dwell time  It is the standard for many mid-sized\\\/large retailers.  Clear comparisonTechnologyOpportunities (visits)Unique peopleOverall accuracyBasic camerasHigh (traffic only)WiFi \\\/ BluetoothMediumSeeketing estimatedHigh for unique visitorsRe-ID (advanced vision) Very highHybrid (vision + WiFi) High\"}},{\"@type\":\"Question\",\"name\":\"What does Seeketing offer?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Detects and identifies visitors with a unique anonymous IDIt can determine:Who is new vs. returningWhether a person returns the same day or on different daysIt even positions itself as:\\ud83d\\udc49 \\u201cthe only technology that lets you know if someone has entered before\\u201d\\u2714\\ufe0f This makes it very powerful compared to cameras or WiFi.\\u2714\\ufe0f Unique people \\u2192 Seeketing\\u2714\\ufe0f Real opportunities \\u2192 sensors (camera\\\/laser)\\u2714\\ufe0f Recurrence + behavior \\u2192 Seeketing\"}},{\"@type\":\"Question\",\"name\":\"How many people without a mobile phone can enter a store today in any Western country?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Europe \\\/ Western countries~85\\u201391% of adults have a smartphone~98% have some type of mobile phone (including non-smartphones)Only ~3\\u201310% do not have any mobile phone  Direct translation:People without a mobile phone \\u2192 very few (\\u22483\\u201310%)People without a smartphone \\u2192 somewhat more (\\u224810\\u201315%)In cities (typical retail)Up to 89% actively use a smartphone  In realistic urban retail:90%+ carry a smartphoneBut that does NOT mean everyone is detectableWhy \\u201chaving a phone\\u201d \\u2260 \\u201cbeing measured\\u201dEven with 90% smartphones:  Non-detectable casesPhone with WiFi\\\/Bluetooth turned offAirplane modeRandomized MAC (very common today)Weak signal \\\/ interferenceUser with multiple devicesPeople who are not carrying their phone (less frequent, but it happens)  Real outcome:WiFi \\\/ Seeketing-type systems do not detect 100%They typically remain at: 70\\u201390% coverage (estimated)  So, in a real store:Typical scenario (Europe, urban retail)Out of 100 people who enter:~90 have a smartphone~70\\u201385 are correctly detectable~15\\u201330 are not detected or are modeled\"}},{\"@type\":\"Question\",\"name\":\"Can I use Seeketing to get data from 90% of the people who enter a store?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"With Seeketing, you can have data for approximately 70%\\u201390% of visitors,but it is not a guaranteed or uniform 90%.  Why it is NOT always 90%Even if many people have a phone, actual detection depends on several factors:  Factors that reduce coverageWiFi and Bluetooth turned offPrivacy systems (randomized MAC)Weak signals or interferencePeople with multiple devices (distorts data)People who are not carrying their phone  This means that:having a phone \\u2260 being detectable  What happens in practice (real retail)In a typical store in Europe:100 people enter~90 carry a smartphone~70\\u201385 are reliably detected~15\\u201330 are excluded or modeled  Result:Seeketing works with a large sample, but not a complete one  How to interpret that 70\\u201390%This is key:  Seeketing is NOT a census counter It is a highly robust statistical systemThat means:  Very good for:TrendsComparisons (day vs. day, store vs. store)Recurrencebehavior  Less reliable for:Exact entry countingAbsolute KPIs without calibration  Practical exampleImagine:Seeketing detects 800 \\u201cunique visitors\\u201dIn reality, 1,000 people entered  You do not know the exact number\\u2026 But you do know:How many returnHow long they stayHow traffic evolves  How companies solve it Serious implementations do this:  combine Seeketing + physical counting (cameras\\\/laser)This way you get:Cameras \\u2192 1,000 real entriesSeeketing \\u2192 behavior of ~800 people  And you can scale\\\/correct the data\"}},{\"@type\":\"Question\",\"name\":\"Which is more accurate at counting people?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"In a real retail scenario (employees + customers who enter multiple times per day):Traditional cameras (entry counting only)Each entry is counted as \\u201cone person,\\u201d even if it is the same person multiple times.Result: overestimation, because it does not distinguish duplicates.Example: 1 employee enters 5 times \\u2192 5 \\u201cpeople\\u201d in the count.Conclusion: it is not accurate for unique people.Seeketing (unique device detection)Each detected mobile device is associated with a unique ID.Result: it approximates the real number of different people much better, because duplicates (the same person entering multiple times) are counted only once.Limitation: it does not detect people without a phone or phones that are not detectable, so it may slightly underestimate.Typical coverage: ~70\\u201390% of all people who enter.Conclusion To count unique people, Seeketing is more accurate than cameras.Cameras only measure visits, and therefore overestimate the number of people when the same person enters multiple times.Seeketing slightly underestimates due to people without a phone, but it still provides a better approximation of the real number of distinct individuals.Example scenarioSuppose a store in one day:Unique customers: 100Employees: 10Entry frequency:Each customer enters onceEach employee enters 5 times (exits and re-entries)  Counting with cameras (entries only)Cameras count all entries:TypePeople\\\/entriesCommentCustomers100Each customer enters onceEmployees10 x 5 = 50Each employee enters 5 timesTotal counted by cameras150Overestimates unique people  Note: Cameras do not distinguish duplicates, so the real number of unique people is 110, but cameras report 150.  Counting with Seeketing (detectable unique people)Assuming 80% detection of people with a phone:TypeReal peopleDetected (~80%)CommentCustomers10080Detected with their phonesEmployees108Detected with phonesTotal detected by Seeketing88Approximately unique people    Note: Seeketing slightly underestimates (people without a phone or not detectable), but it removes duplicates, so it reflects unique people much better.Direct comparisonMetricCamerasSeeketingReal number of unique peopleUnique people15088110Error vs. real unique people+36% (overestimates)-20% (underestimates)0% InterpretationCameras: overestimate unique people when there are multiple entries (employees, customers who go out and come back in).Seeketing: slightly underestimates due to non-total coverage, but it is much more accurate for measuring unique people.In scenarios with many multiple entries, Seeketing will always be closer to the real number of distinct people than cameras.Rule of thumb for retailGoal: measure unique visitors \\u2192 SeeketingGoal: measure sales opportunities \\u2192 CamerasCombine both systems \\u2192 the best possible approximation: unique people + real visits.\"}},{\"@type\":\"Question\",\"name\":\"How many different people pass by, and how many times does each person enter?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Key metrics that truly matterUnique peopleHow many distinct individuals have entered the store over a period.This includes customers + employees, but without duplicates.It enables measurement of real reach and customer penetration.Visits per person (frequency)How many times each person enters during the day, week, or month.This makes it possible to identify recurrence, employees who generate multiple entries, and real customer behavior.  Why traditional systems failSimple cameras \\u2192 only count footfall\\\/entriesProblem: 1 person entering 5 times = 5 people \\u2192 overestimationChildren playing, employees going in and out \\u2192 distorts dataSimple WiFi tracking \\u2192 only detects devicesProblem: phones off, randomized MACs \\u2192 underestimationIt only captures part of the audience  The real solution in retailTo answer \\u201chow many distinct people pass by\\u201d and \\u201chow many times each person enters\\u201d, you need:A unique-person identification systemExample: Seeketing (unique device ID)Advantage: removes duplicates, measures recurrence, differentiates customers from employees.Complementary physical counting (cameras, laser, sensors)Ensures full coverage of real entries.Corrects undercounting of people without a phone. How are they combined?MetricIdeal technologyUnique peopleSeeketing (detected devices)Frequency\\\/recurrenceSeeketingReal entries\\\/visitsCameras\\\/laserFinal KPI: conversion per person   InsightThe real value in retail is not in counting footfall, but in:Knowing how many distinct individuals enterKnowing how many times each one enters\"}},{\"@type\":\"Question\",\"name\":\"Is WiFi tracking different from Seeketing, which uses a trained knowledge database with radio-frequency signal patterns and fingerprinting techniques?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Classic WiFi TrackingHow it works:Detects WiFi signals from nearby smartphones (probe requests)Each device has a MAC address, which is used as an identifierPeople count = each MAC \\u2192 one \\u201cperson\\u201dLimitations:Many smartphones use randomized MAC, which makes persistent identification difficultIt only detects devices with WiFi enabledIt does not reliably distinguish recurrence, frequency, or real behavior patternsIt does not use prior learning or behavior models  The good:Low cost, easy to installProvides traffic trends (footfall)  The bad:Low accuracy for unique peoplePrivacy issues (MAC tracking without anonymization)  SeeketingHow it works:Detects radio-frequency signals (WiFi, Bluetooth, cellular, etc.)Uses fingerprinting and signal patterns to identify devices persistentlyIt relies on a knowledge base trained on millions of patterns \\u2192 it can better differentiate between different devicesGenerates a unique anonymous ID for each visitorAble to detect recurrence, entry frequency, dwell zones, etc.Advantages over classic WiFi tracking:It does not depend on WiFi being enabled (it can use other signals)It recognizes devices better even if they change MAC or there is signal noiseHigh accuracy for unique people and behaviorGDPR-compliant because the IDs are anonymous and persistent  Key differences summaryFeatureClassic WiFi trackingSeeketingSignals usedWiFi onlyWiFi, Bluetooth, cellular, multiple frequenciesPersistent identificationLimited (randomized MAC)High (fingerprinting + pattern database)Unique peopleLow accuracyHigh accuracyRecurrence \\\/ frequencyLimitedYesOverall accuracyLow to moderateHighPrivacyProblematicGDPR-compliant (anonymous)InfrastructureSimpleMore complex; requires a knowledge base  SummaryClassic WiFi tracking = a simple sensor \\u2192 it only detects \\u201cvisible devices\\u201dSeeketing = an intelligent platform \\u2192 it combines sensors + AI + a pattern database to better identify visitors, measure recurrence and behavior, and generate reliable unique peopleIn other words:Seeketing is an evolution of WiFi tracking, using fingerprinting techniques, pattern learning, and multi-signal analysis, focused on people analytics and real behavior, not just device counting.\"}}]}<\/script>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-36d79b2 e-flex e-con-boxed e-con e-parent\" data-id=\"36d79b2\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-995b2f1 elementor-widget elementor-widget-spacer\" data-id=\"995b2f1\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Advanced marketing metrics and tools to optimize the management of your points of sale\u200bUnlike camera-based solutions, which are limited to counting individuals, Seeketing can track real customer behavior over time. 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