Retail Technology Tools

📦 BMW had over $700M invested in returnable containers. And no idea where most of them were, until it implemented a simple passive RFID solution. Here is how the cycle works: 🏭 Suppliers fill containers with parts 🚛 Containers ship to the assembly line 🔧 Parts are consumed on the line ↩️ Empty containers return to a warehouse for cleaning 🔁 Then it repeats The problem? ✅ 10-15% of containers disappeared every year ✅ Replacements cost 3x the original price ✅ Roughly $300M in annual spend just to keep the cycle running ✅ Up to 30% were excess, sitting idle and invisible One senior manager found his own containers stacked above the walls of a competitor's plant. Not stolen. Just lost in a system with no visibility. The fix? RFID readers at the empties warehouse only. When a container did not return, BMW knew who had it and could charge for it. The mere threat of being charged established near-perfect compliance across the entire supplier network. Results: ✅ 30% reduction in total container inventory ✅ 75% reduction in reconciliation costs ✅ 65% reduction in substitute container costs ✅ 20% improvement in container turnaround time We designed and deployed this solution nearly 20 years ago. Total implementation cost: under $1M. The technology works. The ROI is clear. And there surely are lots of great success stories like this by now. Visibility is about making the right decisions, not about seeing everything, everywhere. 💬 What are your biggest supply chain visibility wins? #SupplyChain #RFID #Logistics #Innovation #Truckl

When I started out in RFID, we worked intently on product-first strategies, how to ensure the right inventory could be in the right place at the right time. As time passed, I worked more on location analytics and then the product cloud concept, exploring how digital twins would live in a universe of interconnected product clouds that shared information. Everything would become smarter, and we would get ecommerce-style data unlock in physical retail. Now we are working on the biggest shift. At Kezzler we're calling it the Data Marketplace. It would have been impossible to deliver without all of the work described above, but is definitely a next chapter. We’re moving to a shared architecture, where suppliers and retailers are connected through a common event repository, using a single language, EPCIS 2.0. It’s not theoretical. It’s working today, at companies like Migros, where together we are delivering: Events, not estimates — decisions based on what actually happened, not batch-level assumptions One version of the truth — suppliers and retailers working from the same data, not reconciling mismatched spreadsheets Frictionless compliance — regulatory reporting that’s a byproduct of good architecture, not an afterthought System-level efficiency — reducing delays and disputes by aligning upstream and downstream processes in real time We're doing this not by the open heart surgery required to build a centralised platform to replace every system, but by connecting existing systems through standards and translation. This isn’t about future potential. It’s about results that compound. And it’s the reason I believe product digitisation matters more today than it ever has. I’d love to hear how others are thinking about this shift — are you seeing signs of a data marketplace in your world too? #DigitalID #DataMarketplace #SupplyChain #Traceability #EPCIS #ProductDigitisation #Kezzler

𝗛𝗼𝘄 UNIQLO 𝗧𝘂𝗿𝗻𝗲𝗱 𝗮 𝟰¢ 𝗖𝗵𝗶𝗽 𝗶𝗻𝘁𝗼 𝗮 𝗕𝗿𝗮𝗻𝗱 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲. This weekend, I went shopping at Uniqlo. Picked up 10 different outfits for family members. At checkout, I placed my basket in a sleek bin—and bam—every item was scanned and billed within seconds. No barcode scanning. No errors. No waiting. Just plain delight. The magic behind it? 𝗥𝗙𝗜𝗗 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆. But what makes this truly brilliant is how Uniqlo uses RFID beyond checkout to power its supply chain strategy. Every tag is trackable from the factory floor to the store shelf, enabling real-time inventory accuracy, faster replenishment, fewer stockouts, and smarter demand prediction. This is operational efficiency meeting customer delight. Better data → better availability → better experience. 𝗟𝗲𝘀𝘀𝗼𝗻 𝗳𝗼𝗿 𝗺𝗮𝗿𝗸𝗲𝘁𝗲𝗿𝘀? Innovation doesn’t always have to scream AI. Even a humble 4¢ chip, when applied strategically, can deliver a serious brand edge. Have you seen other “quiet innovations” that changed the game?

I'm excited to share my latest data analytics project: a comprehensive Retail Performance Analysis Dashboard. Problem: The retail company struggled with a lack of clear insights, making it difficult to track overall performance, understand customer behavior, and manage inventory efficiently. Solution: I developed and deployed an interactive, end-to-end Power BI dashboard. By connecting directly to SQL databases, the solution provides a real-time, holistic view of the business, analyzing key KPIs like sales, profit margins, customer segmentation, supplier performance, and stock health. 📊 Tools Used: Power BI | SQL | Excel | DAX | Data Modeling 💡 Key Insights & Highlights: • Total Sales: ₹5.34M • Profit Margin: 28.77% • YoY Sales Growth: 23.48% • Top Performers: The North Region (₹1.52M) and the supplier "Boat" (₹1.1M) were the primary drivers of sales. • Operational Health: Maintained a 65% delivery rate against a 9.17% return rate. • Actionable Inventory: Identified 3 critical products as "Low Stock" (Stock = Reorder Level), flagging them for immediate re-purchasing. Dashboard Link: https://lnkd.in/gHTPaTce #PowerBI #SQL #DataAnalytics #BusinessIntelligence #Dashboard #DataVisualization #RetailAnalytics #DataInsights

In food supply chains, every extra day in inventory is a cost… and sometimes a loss. Food manufacturers are operating under extreme pressure today. Demand swings are sharper, shelf life is shorter, and visibility is still limited. Industry estimates show that up to 30–40% of food is wasted globally, and a significant portion of that loss is linked to poor inventory planning and lack of real-time tracking. At the same time, out-of-stocks in grocery categories can reach 8–10%, directly impacting revenue and customer trust. The core pain point is simple but critical you can’t manage what you can’t see. Most organizations still rely on delayed data, batch updates, and historical trends. By the time a demand shift is visible, the damage is already done: excess stock expires in one region while another location faces shortages. This imbalance increases write-offs, logistics costs, and working capital pressure. This is where technologies like RFID and digital twins are starting to change the game. Companies adopting RFID are seeing inventory accuracy improve from ~70% to over 95%, enabling real-time tracking of products across the supply chain. Digital twins take it a step further by allowing businesses to simulate demand and supply scenarios before execution, helping reduce waste, optimize replenishment, and improve service levels. The shift is clear food supply chains are moving from reactive planning to predictive and simulation-driven decision-making. The question now is not whether these technologies are valuable, but how quickly organizations can adopt them to stay competitive. Because in this industry, every percentage of accuracy gained is revenue saved… and waste reduced. #SupplyChain #FoodIndustry #RFID #DigitalTwin #InventoryManagement #Planning LinkedIn News LinkedIn News India

I've been feeding Claude Code user behavior data (via Posthog MCP) + transcripts (customer interviews, moderated user tests) + company context - and I'm loving it. I wrote a Substack article (link in comment) unpacking exactly how and what this unlocks. Most major product analytics tools (Mixpanel, Amplitude, Heap, Pendo, etc.) offer MCPs. Posthog's MCP lets Claude directly query your analytics via SQL in natural language - already a huge upgrade from hopelessly staring at funnel reports. But the real magic is combining quant with qual: ❓️ Revenue investigation: "Why did revenue drop?" Claude checks if it's significant (considering seasonality), drills into payment methods, cross-references deployment logs. ❓️ Onboarding friction: "Where are users dropping off, and why?" Combine new user test transcripts + behavior data to identify which friction point to fix first. ❓️ Behavioral segmentation: "Who are my power users?" Claude proposes segment definitions based on your product/ICP, then tests them against real data. ❓️ Finding Aha: "What makes users stick?" Test hypotheses ("users who accept 5+ AI suggestions retain better"), mine interview transcripts for when the product "clicked," then validate patterns in behavioral data. ❓️ Validating opportunities: "Should we build mobile-first coding?" Claude searches transcripts (zero mentions) + checks usage data (0.3% mobile sessions). 10-minute analysis vs. a week of manual work.

Most “sales dashboards” are just prettier spreadsheets. This one by Gandes Goldestan is a control panel for decisions. 🔍 Highlighting this Merchandise Sales Overview built in Tableau. Here’s what stands out: 1️⃣ Category tiles that tell a story in 3 seconds Across the top-left you get Clothing, Ornaments, and Other with:  • Revenue for the current scope  • % vs. last December  • A mini 12-month trend You don’t have to dig— you instantly see which category is sliding and which is stable. 2️⃣ Location + product view that actually plays nice On the right, a map shows where revenue is concentrated while the “Top Products by Revenue” bar list shows what is driving that revenue. Perfect combo for questions like: “What are people buying in this region, and which SKUs should we feature more?” 3️⃣ Row-level context without clutter The transaction history table gives:  • Order ID, type, date, revenue  • A clear satisfaction indicator for each order You can jump from “sales are down” to “which orders and experiences are causing it?” without leaving the page. 4️⃣ Customer voice front and center The customer rating widget (3.8 ⭐ with distribution by star level) anchors the whole thing in reality: revenue means less if satisfaction is tanking. This makes it way easier for a manager to say, “𝘞𝘦 𝘥𝘰𝘯’𝘵 𝘫𝘶𝘴𝘵 𝘯𝘦𝘦𝘥 𝘮𝘰𝘳𝘦 𝘴𝘢𝘭𝘦𝘴, 𝘸𝘦 𝘯𝘦𝘦𝘥 𝘣𝘦𝘵𝘵𝘦𝘳 𝘦𝘹𝘱𝘦𝘳𝘪𝘦𝘯𝘤𝘦𝘴.” 5️⃣ Smart demographic breakdown “Revenue by Gender & Age Group” shows who is actually buying, so marketing and merchandising can align on which segments to push and which to grow. Dashboards like this do what every retail team needs:  • Tell you what’s happening now  • Show you who and where it’s happening  • Hint at what to do next Awesome work, Gandes Goldestan—clean design, clear hierarchy, and built for action, not just aesthetics. #Tableau #DataVisualization #RetailAnalytics #MerchandisePlanning #AnalyticsDesign

Building Better Retail Analytics: What We’re Improving at TASS Vision “Great products don’t just happen—they evolve. At TASS Vision, we’re always listening to retailers and improving our platform to make data more actionable, accurate, and easy to use. Here’s what’s new: ✅ FaceID Recognition – Personalizing customer experiences while ensuring security with white and black list in real-time. ✅ More Accurate People Counting in high traffic zones – AI update improves precision by 15%. ✅ Smarter Dashboards – Custom reports, real-time insights, and deeper conversion analytics. ✅ Shelf Analytics – Tracks product engagement and stock availability to prevent lost sales. ✅ Edge Computing – Faster processing directly on devices, reducing cloud dependency. ✅ Multi-Store Comparison – Easily benchmark performance across locations to spot trends. Every update brings us closer to helping retailers make smarter decisions—to drive more sales by 20%. What’s one feature you wish your retail analytics platform had? Let’s talk!” #AI #ComputerVision #RetailAnalytics #instoreAnalytics #EMEA

💡 𝗧𝗿𝗮𝗱𝗲 𝗮𝗿𝗲𝗮𝘀 𝘄𝗲𝗿𝗲 𝗻𝗲𝘃𝗲𝗿 𝗮𝗯𝗼𝘂𝘁 𝗰𝗼𝗻𝘀𝘂𝗺𝗲𝗿𝘀 💡 They were a workaround for not knowing who consumers actually were. Draw a circle. Pull demographics. Estimate drive time. Assume the people inside the boundary represent the people who'll shop the store. We did this for 50 years because it was the best we had. It is no longer the best we have. 𝗧𝗵𝗲 𝗙𝗿𝗮𝗺𝗲 𝗧𝗵𝗮𝘁 𝗛𝗶𝗱 𝘁𝗵𝗲 𝗥𝗲𝗮𝗹 𝗦𝘁𝗼𝗿𝘆: A 3-mile radius treats every household inside it as equally likely to convert. It treats them as the same household. It assumes distance is destiny. It ignores who they are, what they prefer, when they shop, how they move, and what else competes for the trip. The boundary was never the customer. The boundary was just easier to draw than the customer. 𝗪𝗵𝗮𝘁 𝗖𝗵𝗮𝗻𝗴𝗲𝗱: Location data alone was never the answer. The shift is bigger than that. We can now see who is actually in the trade area. Not a census tract average. Specific household composition, life stage, brand affinities, category propensities, daily and seasonal habits. We can see how they move through the entire path to purchase. What triggers awareness. What drives consideration. Where they transact. What brings them back. 𝘈𝘐 𝘴𝘵𝘪𝘵𝘤𝘩𝘦𝘴 𝘢𝘭𝘭 𝘰𝘧 𝘪𝘵 𝘪𝘯𝘵𝘰 𝘢 𝘴𝘪𝘯𝘨𝘭𝘦 𝘱𝘪𝘤𝘵𝘶𝘳𝘦. 𝘎𝘦𝘰𝘨𝘳𝘢𝘱𝘩𝘺. 𝘐𝘥𝘦𝘯𝘵𝘪𝘵𝘺. 𝘉𝘦𝘩𝘢𝘷𝘪𝘰𝘳. 𝘐𝘯𝘵𝘦𝘯𝘵. The accuracy gap is not subtle. It is the difference between deciding on packaging and deciding on signal. 𝗪𝗵𝗮𝘁 𝗧𝗵𝗶𝘀 𝗠𝗲𝗮𝗻𝘀 𝗳𝗼𝗿 𝗖𝗥𝗘: Portfolios were underwritten using the workaround. Tenant mix decisions were made using the workaround. Lease values were set using the workaround. That math is now visible to anyone with the new tools. The gap between what was assumed and what consumers actually do is sitting inside every retail asset right now. The firms still drawing circles aren't conservative. They're exposed. --- I'll be unpacking this at 𝗜𝗖𝗦𝗖 𝗟𝗮𝘀 𝗩𝗲𝗴𝗮𝘀: how AI and location intelligence didn't improve trade area analysis. They replaced the reason it ever existed. What are you seeing at #TheCornerOfMainAndMain? #CRE #RetailRealEstate #LocationIntelligence #AI #ICSC

The cheapest revenue in your store is post-purchase. Think about it. The 60 seconds after a customer pays is the highest-intent moment in your entire funnel. CAC is already paid. Price sensitivity is at its floor. Purchase anxiety is gone. And most stores send them to a bare confirmation screen and call the funnel finished. Every accepted post-purchase offer is near-pure contribution margin. No new traffic. No additional CAC. Here's the system we build for brands: → Post-purchase page: one-click upsell (complementary product, 10-15% off, single CTA, no re-entering payment info). Below that, a referral prompt or loyalty enrollment. → Thank-you page: social proof reinforcement, a how-to guide or video, a cross-sell (different from your upsell), and a subscription/replenishment offer if it's consumable. → Post-purchase email flow: order confirmation with cross-sell, shipping updates with content, delivery confirmation with review request + referral, 14-day check-in with replenishment. The framework is simple: 1. Map the 60-second post-checkout window 2. Build the one-click upsell first (highest immediate ROI) 3. Layer in thank-you page elements 4. Build the email sequence Stop buying more traffic before you've built the system that extracts full value from the traffic you already have.