{"id":93666,"date":"2026-04-21T11:48:17","date_gmt":"2026-04-21T10:48:17","guid":{"rendered":"https:\/\/flameanalytics.com\/?p=93666"},"modified":"2026-04-21T11:48:17","modified_gmt":"2026-04-21T10:48:17","slug":"pompeii-retail-analytics-case-study","status":"publish","type":"post","link":"https:\/\/flameanalytics.com\/en\/pompeii-retail-analytics-case-study\/","title":{"rendered":"Pompeii case study: how retail analytics powers in-store decisions"},"content":{"rendered":"<style>\n.fa-faq details { background: #ffffff; border: 1px solid #e5e7eb; border-radius: 12px; margin-bottom: 12px; transition: box-shadow 0.25s ease, border-color 0.25s ease; box-shadow: 0 1px 2px rgba(15, 23, 42, 0.03); overflow: hidden; }\n.fa-faq details[open] { box-shadow: 0 6px 20px rgba(12, 111, 213, 0.08); border-color: #c7e6fc; }\n.fa-faq summary { cursor: pointer; padding: 20px 24px; font-weight: 600; font-size: 15px; color: #15163a; list-style: none; display: flex; justify-content: space-between; align-items: center; gap: 16px; line-height: 1.45; }\n.fa-faq summary::-webkit-details-marker { display: none; }\n.fa-faq summary::marker { content: \"\"; }\n.fa-faq summary::after { content: \"+\"; flex: 0 0 auto; width: 28px; height: 28px; border-radius: 999px; background: #eff6ff; color: #0c6fd5; font-size: 18px; font-weight: 500; line-height: 28px; text-align: center; transition: transform 0.25s ease, background 0.25s ease; }\n.fa-faq details[open] summary::after { content: \"\u2212\"; background: #31b1f8; color: #ffffff; }\n.fa-faq details[open] summary { border-bottom: 1px solid #f1f5f9; }\n.fa-faq .fa-faq-answer { padding: 16px 24px 22px; font-size: 15px; line-height: 1.75; color: #374151; margin: 0; }\n.fa-faq details > p:not(.fa-faq-answer) { padding: 0 24px 20px; margin: 0; }\n<\/style>\n<div class=\"fa-lead\" style=\"border-left: 4px solid #31b1f8; padding: 4px 0 4px 20px; margin-bottom: 32px;\">\n<p style=\"font-size: 16px; line-height: 1.75; color: #374151;\">Pompeii, one of Spain&#8217;s leading footwear brands, decided to stop running its physical store network on gut feel and start running it on data. With the Flame Analytics retail platform, the brand now measures footfall, dwell time, customer journeys, conversion and profitability in every single store \u2014 and turns that intelligence into better product placement, better staffing, and a better in-store experience.<\/p>\n<\/div>\n<div class=\"fa-meta\" style=\"border-top: 1px solid #E5E7EB; border-bottom: 1px solid #E5E7EB; padding: 14px 0; margin-bottom: 36px; display: flex; gap: 24px; flex-wrap: wrap; font-size: 13px; color: #6b7280;\">\ud83d\udcc5 Updated 2026<br \/>\u23f1 5 min read<br \/>\ud83c\udfec Customer story \u00b7 Retail \u00b7 Fashion &amp; footwear<\/div>\n<div style=\"display: flex; gap: 16px; margin-bottom: 40px; flex-wrap: wrap;\">\n<div style=\"flex: 1; min-width: 180px; background: #eff6ff; border-radius: 10px; padding: 20px 24px; text-align: center;\">\n<div style=\"font-size: 22px; font-weight: bold; color: #0c6fd5; margin-bottom: 6px;\">Footfall<\/div>\n<div style=\"font-size: 13px; color: #374151;\">Accurate measurement of passers-by, visitors and store capture rate<\/div>\n<\/div>\n<div style=\"flex: 1; min-width: 180px; background: #eff6ff; border-radius: 10px; padding: 20px 24px; text-align: center;\">\n<div style=\"font-size: 22px; font-weight: bold; color: #0c6fd5; margin-bottom: 6px;\">Dwell time<\/div>\n<div style=\"font-size: 13px; color: #374151;\">Time spent in store and movement patterns across every zone<\/div>\n<\/div>\n<div style=\"flex: 1; min-width: 180px; background: #eff6ff; border-radius: 10px; padding: 20px 24px; text-align: center;\">\n<div style=\"font-size: 22px; font-weight: bold; color: #0c6fd5; margin-bottom: 6px;\">Loyalty<\/div>\n<div style=\"font-size: 13px; color: #374151;\">Real returning-customer rates and behaviour across visits<\/div>\n<\/div>\n<div style=\"flex: 1; min-width: 180px; background: #eff6ff; border-radius: 10px; padding: 20px 24px; text-align: center;\">\n<div style=\"font-size: 22px; font-weight: bold; color: #0c6fd5; margin-bottom: 6px;\">Conversion<\/div>\n<div style=\"font-size: 13px; color: #374151;\">Sales conversion by store and zone, directly comparable across locations<\/div>\n<\/div>\n<\/div>\n<nav class=\"fa-toc\" style=\"background: #f9fafb; border: 1px solid #e5e7eb; border-radius: 8px; padding: 24px 28px; margin-bottom: 40px;\">\n<p style=\"font-weight: bold; font-size: 16px; color: #15163a; margin-bottom: 12px;\">Table of contents<\/p>\n<ol style=\"margin: 0; padding-left: 20px; font-size: 14px; line-height: 2; color: #374151;\">\n<li><a style=\"color: #0c6fd5; text-decoration: none;\" href=\"#challenge\">The challenge: making in-store decisions without reliable data<\/a><\/li>\n<li><a style=\"color: #0c6fd5; text-decoration: none;\" href=\"#solution\">The solution: retail analytics with Flame Analytics<\/a><\/li>\n<li><a style=\"color: #0c6fd5; text-decoration: none;\" href=\"#what-they-measure\">What Pompeii measures in every store<\/a><\/li>\n<li><a style=\"color: #0c6fd5; text-decoration: none;\" href=\"#testimonial\">The view from the Pompeii team<\/a><\/li>\n<li><a style=\"color: #0c6fd5; text-decoration: none;\" href=\"#application\">How they apply the data day to day<\/a><\/li>\n<li><a style=\"color: #0c6fd5; text-decoration: none;\" href=\"#results\">Outcomes and benefits for the brand<\/a><\/li>\n<li><a style=\"color: #0c6fd5; text-decoration: none;\" href=\"#faq\">Frequently asked questions<\/a><\/li>\n<\/ol>\n<\/nav>\n<h2 id=\"challenge\" style=\"font-size: 26px; font-weight: bold; color: #15163a; margin: 40px 0 18px;\">The challenge: making in-store decisions without reliable data<\/h2>\n<p style=\"font-size: 15px; line-height: 1.75; color: #374151; margin-bottom: 16px;\">In an increasingly competitive retail environment, understanding real customer behaviour inside the physical store has become a decisive factor for improving shopping experience and profitability. Pompeii, a brand built on its product and its direct relationship with the consumer, faced a challenge that is common across the sector: strong sales performance, but limited visibility into <strong>what is actually happening inside each store<\/strong>.<\/p>\n<p style=\"font-size: 15px; line-height: 1.75; color: #374151; margin-bottom: 16px;\">The brand needed to capture real data at its points of sale to make strategic decisions based on facts rather than assumptions. Specifically, it needed to measure:<\/p>\n<ul style=\"font-size: 15px; line-height: 1.8; color: #374151; margin-bottom: 16px; padding-left: 22px;\">\n<li>Footfall on the street and inside the store<\/li>\n<li>Dwell time and customer flow patterns<\/li>\n<li>High-traffic and low-traffic zones within each space<\/li>\n<li>Visit-to-sale conversion, by store and by zone<\/li>\n<li>The impact of traffic on each store&#8217;s profitability<\/li>\n<\/ul>\n<h2 id=\"solution\" style=\"font-size: 26px; font-weight: bold; color: #15163a; margin: 40px 0 18px;\">The solution: retail analytics with Flame Analytics<\/h2>\n<p style=\"font-size: 15px; line-height: 1.75; color: #374151; margin-bottom: 16px;\">Pompeii found the answer in Flame Analytics&#8217; <a style=\"color: #0c6fd5; text-decoration: none;\" href=\"\/en\/traffic-insights\/\">in-store traffic analytics platform<\/a>. Using dedicated sensors and computer vision, the brand now has a continuous view of how customers interact with each physical space.<\/p>\n<p style=\"font-size: 15px; line-height: 1.75; color: #374151; margin-bottom: 16px;\">From the number of passers-by to dwell time, from customer loyalty to store-level conversion rates, Pompeii works with accurate and comparable data across its network \u2014 intelligence that allows the team to <strong>identify patterns, optimise decisions and improve the overall performance of every point of sale<\/strong>.<\/p>\n<div class=\"fa-callout\" style=\"background: #eff6ff; border-left: 4px solid #0c6fd5; padding: 16px 20px; border-radius: 0 8px 8px 0; margin: 24px 0;\">\n<p style=\"font-size: 14px; color: #1e40af; margin: 0;\"><strong>Zero biometrics principle:<\/strong> Flame Analytics measures aggregate in-store behaviour with no facial recognition, no biometric data and no individual identification. GDPR compliant by design.<\/p>\n<\/div>\n<h2 id=\"what-they-measure\" style=\"font-size: 26px; font-weight: bold; color: #15163a; margin: 40px 0 18px;\">What Pompeii measures in every store<\/h2>\n<p style=\"font-size: 15px; line-height: 1.75; color: #374151; margin-bottom: 16px;\">The platform consolidates into a single dashboard the key indicators the Pompeii team needs to operate and compare its network:<\/p>\n<div style=\"overflow-x: auto; margin: 24px 0;\">\n<table style=\"width: 100%; border-collapse: collapse; font-size: 14px;\">\n<thead>\n<tr style=\"background: #15163a; color: #fff;\">\n<th style=\"padding: 12px 16px; text-align: left;\">Metric<\/th>\n<th style=\"padding: 12px 16px; text-align: left;\">What it measures<\/th>\n<th style=\"padding: 12px 16px; text-align: left;\">How they use it<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr style=\"border-bottom: 1px solid #e5e7eb;\">\n<td style=\"padding: 12px 16px; color: #374151;\">Passers-by &amp; visitors<\/td>\n<td style=\"padding: 12px 16px; color: #374151;\">People walking past vs. people walking in<\/td>\n<td style=\"padding: 12px 16px; color: #374151;\">Window appeal and capture rate<\/td>\n<\/tr>\n<tr style=\"border-bottom: 1px solid #e5e7eb; background: #f9fafb;\">\n<td style=\"padding: 12px 16px; color: #374151;\">Dwell time<\/td>\n<td style=\"padding: 12px 16px; color: #374151;\">Time spent in store and movement patterns<\/td>\n<td style=\"padding: 12px 16px; color: #374151;\">Assess real interest and space distribution<\/td>\n<\/tr>\n<tr style=\"border-bottom: 1px solid #e5e7eb;\">\n<td style=\"padding: 12px 16px; color: #374151;\">Loyalty<\/td>\n<td style=\"padding: 12px 16px; color: #374151;\">Returning customers over time<\/td>\n<td style=\"padding: 12px 16px; color: #374151;\">Measure brand strength at each location<\/td>\n<\/tr>\n<tr style=\"border-bottom: 1px solid #e5e7eb; background: #f9fafb;\">\n<td style=\"padding: 12px 16px; color: #374151;\">Conversion<\/td>\n<td style=\"padding: 12px 16px; color: #374151;\">Sales-to-visitor ratio, by store and zone<\/td>\n<td style=\"padding: 12px 16px; color: #374151;\">Benchmark performance across points of sale<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<h2 id=\"testimonial\" style=\"font-size: 26px; font-weight: bold; color: #15163a; margin: 40px 0 18px;\">The view from the Pompeii team<\/h2>\n<blockquote style=\"border-left: 4px solid #31b1f8; background: #f9fafb; padding: 20px 24px; margin: 24px 0; border-radius: 0 8px 8px 0;\">\n<p style=\"font-size: 16px; line-height: 1.75; color: #15163a; margin: 0 0 12px;\">&#8220;Flame is a really comprehensive tool that helps us get daily data we can turn into action plans \u2014 working to improve and optimise our stores so they are as profitable as possible and so both staff and customers enjoy the point of sale.<\/p>\n<p style=\"font-size: 16px; line-height: 1.75; color: #15163a; margin: 0 0 12px;\">On top of that, technical and customer support are fast and always available when you need them. Our experience has been a very positive one.&#8221;<\/p>\n<p style=\"font-size: 14px; color: #6b7280; margin: 0;\"><strong style=\"color: #15163a;\">Jose Antonio Huertas<\/strong> \u00b7 Retail Manager at <a style=\"color: #0c6fd5; text-decoration: none;\" href=\"https:\/\/www.linkedin.com\/company\/pompeiibrand\/\" target=\"_blank\" rel=\"nofollow noopener\">Pompeii<\/a><\/p>\n<\/blockquote>\n<h2 id=\"application\" style=\"font-size: 26px; font-weight: bold; color: #15163a; margin: 40px 0 18px;\">How they apply the data day to day<\/h2>\n<p style=\"font-size: 15px; line-height: 1.75; color: #374151; margin-bottom: 16px;\">What makes the Pompeii case interesting is not that they collect data \u2014 it is that they use it. The team has embedded traffic analytics into its day-to-day operation, turning decisions that used to be intuition-driven into decisions grounded in evidence:<\/p>\n<ul style=\"font-size: 15px; line-height: 1.8; color: #374151; margin-bottom: 16px; padding-left: 22px;\">\n<li><strong>Product layout:<\/strong> they adjust placement and floor layout to create more intuitive paths that favour spontaneous purchase.<\/li>\n<li><strong>Zone-based promotions:<\/strong> they tailor offers and communication to the highest-traffic areas inside each store.<\/li>\n<li><strong>Staff planning:<\/strong> shifts are aligned with actual footfall, improving service levels while controlling staffing cost.<\/li>\n<li><strong>In-store experience:<\/strong> they identify friction and dead zones early, before they hurt conversion.<\/li>\n<li><strong>Store-level profitability:<\/strong> portfolio decisions are made on directly comparable data, store by store.<\/li>\n<\/ul>\n<h2 id=\"results\" style=\"font-size: 26px; font-weight: bold; color: #15163a; margin: 40px 0 18px;\">Outcomes and benefits for the brand<\/h2>\n<p style=\"font-size: 15px; line-height: 1.75; color: #374151; margin-bottom: 16px;\">With Flame Analytics, Pompeii moved from running its stores with partial information to having a <strong>continuous business view<\/strong> across the entire network. That visibility translates into measurable improvements on four fronts:<\/p>\n<ol style=\"font-size: 15px; line-height: 1.8; color: #374151; margin-bottom: 16px; padding-left: 22px;\">\n<li>Higher conversion and profitability per store by understanding what works \u2014 and what does not \u2014 inside each space.<\/li>\n<li>A stronger customer experience, with stores that are more comfortable, better laid out and better staffed.<\/li>\n<li>Better-informed expansion and portfolio decisions, grounded in real benchmarks across locations.<\/li>\n<li>The ability to react quickly to trend shifts, campaigns and seasonality.<\/li>\n<\/ol>\n<h2 id=\"faq\" style=\"font-size: 26px; font-weight: bold; color: #15163a; margin: 40px 0 18px;\">Frequently asked questions<\/h2>\n<div class=\"fa-faq\">\n<details>\n<summary>What exactly does Pompeii measure with Flame Analytics?<\/summary>\n<p class=\"fa-faq-answer\">Street and in-store footfall, dwell time, movement patterns, high-traffic and low-traffic zones, customer loyalty, and conversion rates by store and zone. All of it is consolidated in a single dashboard, comparable across points of sale.<\/p>\n<\/details>\n<details>\n<summary>Is the solution GDPR compliant?<\/summary>\n<p class=\"fa-faq-answer\">Yes. Flame Analytics works with aggregate, anonymous data \u2014 no facial recognition and no biometric processing. No individual is identified, so the system is designed to operate within the GDPR framework.<\/p>\n<\/details>\n<details>\n<summary>Do you need to replace the cameras or install new hardware?<\/summary>\n<p class=\"fa-faq-answer\">In most cases, no. Flame Analytics can be deployed on top of existing CCTV infrastructure or with dedicated low-power sensors, reducing cost and time to value.<\/p>\n<\/details>\n<details>\n<summary>Is this type of analytics only relevant for fashion and footwear?<\/summary>\n<p class=\"fa-faq-answer\">Not at all. Although Pompeii is a fashion case, in-store traffic analytics applies to any retail format with a physical network: footwear, sports, grocery, electronics, home, pharmacy or shopping centres. The value increases the more points of sale you run.<\/p>\n<\/details>\n<details>\n<summary>How can we see the platform applied to our business?<\/summary>\n<p class=\"fa-faq-answer\">We can show you in a personalised demo how Flame&#8217;s retail analytics would fit your stores, metrics and objectives. Book a session from the contact form.<\/p>\n<p><a style=\"display: inline-block; background: #31b1f8; color: #15163a; font-weight: bold; font-size: 14px; text-transform: uppercase; letter-spacing: 1px; padding: 14px 32px; border-radius: 6px; text-decoration: none; margin-top: 8px;\" href=\"#contact\">Request a demo<\/a><\/p>\n<\/details>\n<\/div>\n<p><script type=\"application\/ld+json\">{\"@context\":\"https:\/\/schema.org\",\"@type\":\"FAQPage\",\"mainEntity\":[{\"@type\":\"Question\",\"name\":\"What exactly does Pompeii measure with Flame Analytics?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Street and in-store footfall, dwell time, movement patterns, high-traffic and low-traffic zones, customer loyalty, and conversion rates by store and zone.\"}},{\"@type\":\"Question\",\"name\":\"Is the solution GDPR compliant?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Yes. 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