{"id":161,"date":"2015-12-23T07:42:24","date_gmt":"2015-12-23T07:42:24","guid":{"rendered":"http:\/\/demo.firm.in\/inode\/index.php\/2016\/06\/01\/top-6-must-have-features-in-a-retail-analytics-tool\/"},"modified":"2026-05-18T12:40:38","modified_gmt":"2026-05-18T07:10:38","slug":"top-6-must-have-features-in-a-retail-analytics-tool","status":"publish","type":"post","link":"https:\/\/www.intelligencenode.com\/blog\/top-6-must-have-features-in-a-retail-analytics-tool\/","title":{"rendered":"Retail Data Analytics: A Modern Guide for Enterprise Retailers &amp; Brands"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">Enterprise retailers and&nbsp;brands today&nbsp;aren\u2019t&nbsp;facing a shortage of data;&nbsp;they\u2019re&nbsp;facing a visibility gap.&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Most teams have strong internal analytics: sales, margins, inventory, and campaign performance are all tracked in detail. But&nbsp;what\u2019s&nbsp;often missing is a real-time view of the external market: competitor price points, promotions, availability shifts, and digital shelf changes that directly influence performance.&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">That\u2019s&nbsp;where the gap begins.&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">According to&nbsp;<a href=\"https:\/\/www.statista.com\/outlook\/emo\/ecommerce\/worldwide?currency=USD\" target=\"_blank\" rel=\"noreferrer noopener\">Statista<\/a>, US retail eCommerce sales are projected to surpass&nbsp;<strong>$1.7 trillion&nbsp;<\/strong>by 2027. At that scale, even small competitive blind spots can translate into significant revenue impact.&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The question is:&nbsp;<em>Are you only seeing&nbsp;what\u2019s&nbsp;happening inside your business or&nbsp;what\u2019s&nbsp;shaping it outside as well?<\/em><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-what-is-retail-data-analytics-the-full-picture\">What is Retail Data Analytics? (The Full Picture)<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">At its core, retail data analytics is the process of using data to improve pricing, merchandising, operations, and overall performance.&nbsp;As retail becomes more real-time, analytics is no longer&nbsp;just about reporting&nbsp;performance.&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A recent&nbsp;<a href=\"https:\/\/www.mckinsey.com\/industries\/retail\/our-insights\/from-dashboards-to-decisions-empowering-merchants-with-agentic-ai\" target=\"_blank\" rel=\"noreferrer noopener\">McKinsey &amp; Company analysis<\/a>&nbsp;found that retailers and brands using AI-driven analytics and merchandising solutions are seeing up to&nbsp;<strong>5%&nbsp;<\/strong>sales growth and<strong>&nbsp;3%<\/strong>&nbsp;margin improvement, showing how faster, smarter decisions are becoming&nbsp;a real competitive&nbsp;advantage.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In 2026, it goes beyond internal dashboards and&nbsp;operates&nbsp;across two critical layers.&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Layer 1: Internal data (what you already track)&nbsp;<\/strong>&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Sales performance&nbsp;&nbsp;&nbsp;<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Inventory levels&nbsp;&nbsp;&nbsp;<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Shopper behavior&nbsp;&nbsp;&nbsp;<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Marketing ROI&nbsp;&nbsp;&nbsp;<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Store and eCommerce operations&nbsp;&nbsp;&nbsp;<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">This is what every retail analytics platform covers. It shows&nbsp;<em>what is&nbsp;happening inside your business<\/em>.&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Layer 2: External competitive data (what shapes outcomes)&nbsp;<\/strong>&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Market pricing&nbsp;<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Promotions&nbsp;&nbsp;&nbsp;<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Product availability&nbsp;&nbsp;&nbsp;<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Digital shelf position&nbsp;&nbsp;&nbsp;<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>New launches&nbsp;&nbsp;&nbsp;<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>MAP compliance&nbsp;&nbsp;&nbsp;<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">This is where retail data intelligence becomes critical, helping you understand&nbsp;what\u2019s&nbsp;happening in your market right now.&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The real value comes from combining both.&nbsp;Internal data tells you what changed, while external data explains why it changed and what to do next.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-internal-vs-external-retail-data-nbsp-what-s-nbsp-the-difference\">Internal vs. External Retail Data:&nbsp;What\u2019s&nbsp;the Difference?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Here\u2019s how internal and external data compare in practice.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Aspect<\/strong>&nbsp;&nbsp;<\/td><td><strong>Internal Data<\/strong>&nbsp;&nbsp;<\/td><td><strong>External Data<\/strong>&nbsp;&nbsp;<\/td><\/tr><tr><td><strong>Definition<\/strong>&nbsp;&nbsp;<\/td><td>Data generated within your business systems&nbsp;&nbsp;<\/td><td>Data captured from the external market and competitors&nbsp;&nbsp;<\/td><\/tr><tr><td><strong>Key Sources<\/strong>&nbsp;&nbsp;<\/td><td>POS systems, CRM, inventory tools, marketing platforms, eCommerce analytics&nbsp;&nbsp;<\/td><td>Competitor websites, marketplaces, retailer portals, review platforms&nbsp;&nbsp;<\/td><\/tr><tr><td><strong>What It Tells You<\/strong>&nbsp;&nbsp;<\/td><td>Sales trends, margins, shopper behavior, campaign performance&nbsp;&nbsp;<\/td><td>Competitor pricing, promotions, availability, digital shelf position&nbsp;&nbsp;<\/td><\/tr><tr><td><strong>Role in Decisions<\/strong>&nbsp;&nbsp;<\/td><td>Identifies&nbsp;<em>what<\/em>&nbsp;changed in your business<\/td><td>Explains&nbsp;<em>why<\/em>&nbsp;it changed and what to do next<\/td><\/tr><tr><td><strong>Technology Layer<\/strong>&nbsp;&nbsp;<\/td><td>Core to every retail analytics platform&nbsp;&nbsp;<\/td><td>Powered by competitive retail analytics and external intelligence tools<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-6-types-of-retail-data-analytics-see-what-each-one-tells-you\">6 Types of Retail Data Analytics (See What Each One Tells You)<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Not all retail data analytics&nbsp;is&nbsp;created equal. Each type answers a different&nbsp;question&nbsp;and together, they form a complete decision-making system.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>1. Descriptive analytics (What happened)<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This is your baseline. Sales reports, inventory snapshots, and basket analysis help you understand past performance. Every retail analytics platform offers this, but it&nbsp;stops at&nbsp;visibility.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>2. Diagnostic analytics (Why it happened)<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This layer goes deeper,&nbsp;identifying&nbsp;root causes behind changes in sales, margins, or conversions. It connects internal performance with operational factors to explain&nbsp;<em>why<\/em>&nbsp;something changed.&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>3. Predictive analytics (What will happen)<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Powered by AI retail analytics, this uses historical data to forecast demand,&nbsp;identify&nbsp;trends, and expect shifts in shopper behavior. It helps teams prepare rather than react.&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>4. Prescriptive analytics (What you should do)<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This is where insight turns into action. From pricing recommendations to replenishment triggers and promotion optimization, prescriptive analytics&nbsp;drives&nbsp;faster, more confident decisions.&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>5. Competitive&nbsp;Pricing&nbsp;intelligence analytics (What your rivals are doing right now)<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This functionality is the layer that many brands still lack. It tracks market pricing, promotions, product launches, and availability in real-time, forming the core of price intelligence analytics. Without it, pricing strategies&nbsp;operate&nbsp;in isolation.&nbsp;In a market where pricing changes can happen multiple times a day, continuous&nbsp;<a href=\"https:\/\/www.intelligencenode.com\/solutions\/price-monitoring-software-for-ecommerce\/\" target=\"_blank\" rel=\"noreferrer noopener\">price&nbsp;monitoring<\/a>&nbsp;is no longer&nbsp;optional;&nbsp;it\u2019s&nbsp;a core business advantage.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>6. Digital shelf analytics (How your brand appears to shoppers)<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This focuses on your online&nbsp;presence&nbsp;search rankings, product content, imagery, reviews, and availability. It directly changes&nbsp;conversion, yet&nbsp;often&nbsp;remains&nbsp;invisible without a dedicated analytics layer.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-how-ai-is-transforming-retail-data-analytics-in-2026\">How AI is Transforming Retail Data Analytics in 2026<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The shift in retail data analytics is no longer about access to data;&nbsp;it\u2019s about&nbsp;speed, scale, and actionability. Traditional systems&nbsp;count on&nbsp;periodic reports, while AI retail analytics&nbsp;operates&nbsp;continuously, turning data into real-time informed decisions.&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">According to&nbsp;<a href=\"https:\/\/www.mckinsey.com\/industries\/retail\/our-insights\/merchants-unleashed-how-agentic-ai-transforms-retail-merchandising\" target=\"_blank\" rel=\"noreferrer noopener\">McKinsey &amp; Company<\/a>, fewer than&nbsp;<strong>10%&nbsp;<\/strong>of retail teams apply AI across most merchandising decisions, despite its proven impact. At the same time, over&nbsp;<a href=\"https:\/\/www.itpro.com\/technology\/artificial-intelligence\/ai-in-retail-industry-growth-nvidia?\" target=\"_blank\" rel=\"noreferrer noopener\">90% of retailers<\/a>&nbsp;are investing in or growing AI initiatives, showing how quickly adoption is accelerating.&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">What\u2019s changing is simple but powerful:&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>1. From late reporting to real-time intelligence<\/strong>&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">AI continuously processes both internal and external data, enabling decisions based on&nbsp;what\u2019s&nbsp;happening&nbsp;<em>now<\/em>, not last week.&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>2. From manual monitoring to automated competitive visibility<\/strong>&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">AI systems crawl competitor websites, marketplaces, and retailer platforms at scale, powering real-time competitive retail analytics without manual effort.&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>3. From insights to action<\/strong>&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Modern systems go beyond dashboards, triggering pricing changes, promotion responses, and alerts automatically, bringing price intelligence analytics into execution.&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Overall, the real shift is from reacting to predicting AI analyzes pricing patterns, competitor behavior, and demand signals to&nbsp;anticipate&nbsp;moves before they happen. In a market where margins shift in hours, this switch from hindsight to foresight defines the leaders.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-retail-data-analytics-in-action-5-use-cases-for-enterprise-brands\">Retail Data Analytics in Action: 5 Use Cases for Enterprise Brands<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The true value of&nbsp;retail data analytics&nbsp;becomes clear when it directly solves everyday business challenges. Here\u2019s how enterprise brands are putting it to work:&nbsp;&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-use-case-1-real-time-competitor-price-monitoring\">Use case 1: Real-time competitor price monitoring<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Track competitor price&nbsp;changes across channels, markets, and SKUs in real time. Instead of discovering pricing gaps after performance drops, teams can respond instantly, protecting margins and being competitive.&nbsp;&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-use-case-2-map-violation-detection-and-brand-protection\">Use case 2: MAP violation detection and brand protection<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Unauthorized discounting by retail partners is able to erode both brand equity and profitability.&nbsp;Retail data analytics continuously&nbsp;monitors&nbsp;listings across partners, flagging violations as they happen so teams can take immediate action.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-use-case-3-digital-shelf-performance-and-content-compliance\">Use case 3: Digital shelf performance and content compliance<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Your product\u2019s visibility depends on reliable content, strong rankings, and consistent availability. Analytics helps&nbsp;monitor&nbsp;digital shelf position, alerting teams when products go out of stock; content falls out of compliance, or competitors gain visibility.&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Intelligence Node combines competitive price intelligence, digital shelf analytics, and MAP monitoring in a single enterprise platform, giving pricing and eCommerce teams one real-time view of the entire competitive landscape.&nbsp;&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">See it working on your category in a 15-minute personalized demo \u2192&nbsp;<a href=\"https:\/\/info.intelligencenode.com\/digital-shelf-software-demo?\" target=\"_blank\" rel=\"noreferrer noopener\">Click here to secure your spot<\/a><\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-use-case-4-assortment-gap-identification\">Use case 4: Assortment gap identification<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Identify&nbsp;products that competitors offer that you do not. Through analyzing assortments at the SKU level across markets, brands can uncover missed opportunities and expand strategically before losing market share.&nbsp;&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-use-case-5-promotion-intelligence-and-competitive-response\">Use case 5: Promotion intelligence and competitive response<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Monitor competitor promotions in real-time, understanding discount depth, timing, and frequency. This enables smarter decisions on whether to respond, hold pricing, or counter strategically using competitive retail analytics.&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Each of these use cases shows a shift from reactive decision-making to real-time, insight-led action in which every move is informed by both internal performance and external market forces.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-what-to-look-for-in-a-retail-data-analytics-platform-nbsp\">What to Look for in a Retail Data Analytics Platform&nbsp;<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Here are six criteria that matter in 2026 and beyond:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>1. Real-time data, not batch updates<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Speed is a competitive advantage. On marketplaces like Amazon, prices and promotions can change multiple times a day. Lagging data puts both visibility and margins at risk.&nbsp;<a href=\"https:\/\/www.intelligencenode.com\/solutions\/price-monitoring-software-for-ecommerce\/\" target=\"_blank\" rel=\"noreferrer noopener\">AI-powered solutions<\/a>&nbsp;that&nbsp;offer near real-time refresh rates as fast as every&nbsp;<strong>10<\/strong>&nbsp;seconds, ensuring decisions are based on current market conditions, not yesterday\u2019s data.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>2. High accuracy at scale<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">At enterprise scale, even small inaccuracies compound quickly. At Intelligence Node, we deliver&nbsp;<strong>99%<\/strong>&nbsp;product matching accuracy, allowing reliable price intelligence analytics across millions of SKUs.&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>3. Deep competitive visibility<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A strong platform should go beyond internal metrics to deliver real-time information on competitor price points, promotions, assortment, and availability, powered by competitive retail analytics. This is what enables true market response.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>4. Global coverage with enterprise-scale data<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Enterprise brands need visibility across markets, languages, and categories. Our solutions track&nbsp;<strong>1B+<\/strong>&nbsp;products across thousands of retailers globally, assuring no blind spots in your competitive landscape.&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>5. AI-powered product matching &amp; data normalization<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Matching products across competitors is one of the hardest problems in retail. Advanced AI engines automate this at scale, unlocking&nbsp;accurate&nbsp;benchmarking across global catalogs.&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>6. Actionability through APIs and automation<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Insights matter only when they drive action. Choose APIs, alerts, and recommendations that plug into your workflows so teams can act faster with less effort.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-conclusion\">Conclusion<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">In a market where prices and digital shelf positions shift constantly, delayed data creates a real competitive gap. The brands pulling ahead&nbsp;aren\u2019t&nbsp;just analyzing performance;&nbsp;they\u2019re&nbsp;responding in real time with full market visibility.&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Leaders like Nestl\u00e9, Kroger, and Prada&nbsp;aren\u2019t&nbsp;ahead because they have more data but because their teams act on intelligence that is continuously updated and directly tied to decisions.&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">What\u2019s&nbsp;been missing for many enterprise teams&nbsp;isn\u2019t&nbsp;more data;&nbsp;it\u2019s&nbsp;a connected, real-time view of the market. Intelligence Node brings this together, combining pricing, digital shelf, assortment, and promotions into one unified layer so teams can move with the market, not behind it.&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><em>To see how it works in your category \u2192&nbsp;<\/em><a href=\"https:\/\/info.intelligencenode.com\/digital-shelf-software-demo\" target=\"_blank\" rel=\"noreferrer noopener\"><em>Book a DEMO now<\/em><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Enterprise retailers and&nbsp;brands today&nbsp;aren\u2019t&nbsp;facing a shortage of data;&nbsp;they\u2019re&nbsp;facing a visibility gap.&nbsp;&nbsp; Most teams have strong internal analytics: sales, margins, inventory, and campaign performance are all tracked in detail. But&nbsp;what\u2019s&nbsp;often missing&#8230;<\/p>\n","protected":false},"author":2,"featured_media":14323,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"content-type":"","_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[45],"tags":[117,193,569,609,738],"class_list":["post-161","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-retail-analytics","tag-big-data","tag-competitor-benchmarking","tag-pricing-strategy","tag-retail-analytics","tag-trend-prediction"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.5 (Yoast SEO v27.8) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Mastering Retail Data Analytics: A Guide for Enterprise Brands<\/title>\n<meta name=\"description\" content=\"Scale your brand with modern retail data analytics. Read our comprehensive enterprise guide to mastering data-driven retail operations.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.intelligencenode.com\/blog\/top-6-must-have-features-in-a-retail-analytics-tool\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Retail Data Analytics: A Modern Guide for Enterprise Retailers &amp; Brands\" \/>\n<meta property=\"og:description\" content=\"Scale your brand with modern retail data analytics. 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