In-store Analytics Solutions

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

The most powerful “aha moments” that our retail partners discover with shelf digitization don’t just tweak KPIs. They redefine how retailers operate. ⁉️⁉️ “66% of what we thought was out-of-stock… wasn’t.” A major grocer discovered that most “missing” items were misplaced in-store. That single insight dramatically improved e-commerce fulfillment and reduced substitutions. 🥬🥬 “Fulfillment speed increased by 50%.” With precise item-location data, stores introduced optimized picking paths—what one exec called “Google Maps for associates.” 🎽🎽 “Our oversight scaled from 4 stores a day to 40.” Merchandising leaders now review entire chains remotely—10Xing visibility and accelerating in-store strategy from weeks to hours. 😄😄 “Our staff is 90% happier.” With automation handling tedious checks, store teams regained 25–50 hours per week to focus on customers—improving morale, service, and sales.

The retail window display is the oldest form of marketing. And most brands are still treating it the same way they did in 1925. For 100 years, the strategy hasn't changed: put the best products in the window, make it look beautiful, and hope people walk in. But "hope" is no longer a strategy. The most advanced fashion retailers are turning their store windows and entrances into data-capture engines. Take BOGGI MILANO, the global menswear brand with 235 stores. They stopped relying on intuition and deployed AI-powered sensors to measure exactly what happens at the store threshold. They didn't just count foot traffic. They measured the "capture rate" and connected it to shopper behavior inside the store. Out of 1,000 people who walk past the store: — How many slow down? — Which specific display caught their eye? — How long did they dwell? — And most importantly: did they walk through the door? By analyzing this data, Boggi Milano realized that high traffic didn't always mean high conversion. The problem wasn't the products — it was how the store was staffed to handle the traffic the windows generated. By aligning their labor model with real-time traffic analytics, Boggi Milano achieved staggering results: — 40% increase in Shopper Yield — 5-point increase in Conversion Rate — 15% increase in Average Ticket Size Visual merchandising and store operations are no longer just an art. They are a measurable science. If a window display has a high dwell time but a low capture rate, it means the product is interesting — but the price or the call-to-action is wrong. If the capture rate is high but the in-store conversion is low, the window is writing checks the store experience can't cash. The brands winning in physical retail aren't just making their windows look better. They are making them smarter. Are your store windows capturing data, or just gathering dust? #RetailTech #VisualMerchandising #RetailInnovation #StoreOperations #FashionRetail #RetailAnalytics #PhysicalRetail

Using Computer Vision and Operational AI for Retail-Occupancy Analytics. Traditional retail analytics often fall short. Operational AI offers a smarter solution: 𝗚𝗮𝗶𝗻 𝗮𝗰𝗰𝘂𝗿𝗮𝘁𝗲 𝗮𝗻𝗱 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗰𝗼𝗻𝘀𝘂𝗺𝗲𝗿 𝘁𝗿𝗮𝗳𝗳𝗶𝗰 𝗱𝗮𝘁𝗮: - Understand foot traffic patterns and consumer behavior. - Identify peak traffic hours and make data-driven decisions. - Optimize store layouts and staffing levels. 𝗖𝗿𝗲𝗮𝘁𝗲 𝗮𝗻 𝗲𝗻𝗴𝗮𝗴𝗶𝗻𝗴 𝗮𝗻𝗱 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗲𝗱 𝘀𝗵𝗼𝗽𝗽𝗶𝗻𝗴 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲: - Tailor your offerings and promotions based on real-time data. - Improve queue management and reduce waiting times. - Provide a seamless and convenient shopping journey. 𝗜𝗻𝗰𝗿𝗲𝗮𝘀𝗲 𝗳𝗿𝗼𝗻𝘁𝗹𝗶𝗻𝗲 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 𝗮𝗻𝗱 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆: - Analyze footfall trends, queue lengths, and conversion rates. - Improve staffing allocation and scheduling. - Optimize workflows and reduce operational costs. Video: Teknoir #ComputerVision #AI #Industry40 #DigitalTransformation #Retail #RetailTech #ConsumerInsights #SmartRetail #CustomerExperience #AIinRetail #RetailInnovation #QueueManagement

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

🎄 AI + Holiday Retail: Why NOW Is the Time to Level Up Your Customer Experience 🤖✨ (Insights pulled directly from Chapters 3, 5, and 7 of my book AI for Retail Success, co-authored with Sharon Gee!) As retailers head into the holiday rush, shopper expectations are sky-high — faster service, effortless navigation, accurate pricing, and a seamless blend of tech + human touch. And with tighter staffing and higher operational pressures, this is exactly the moment where AI becomes your competitive edge. Here are three AI tools highlighted in the book — and the companies we recommend — that can make an immediate holiday impact: 🧭 1. Heat Mapping & Dwell Time Analytics From Chapter 3: The Smart Store Floor Heat mapping shows where shoppers move, pause, engage, and convert. This helps retailers optimize holiday merchandising and ensure top sellers are placed where they’ll perform best. Recommended in the book: • VusionGroup – real-time heat maps + shelf sensors • RetailNext – in-store behavioral analytics • Walkbase – traffic + zone analytics Perfect for: creating “holiday hotspot” displays, gift zones, and impulse add-ons that actually convert. 📊 2. Real-Time Traffic Counting From Chapter 5: Data-Driven Staffing & Efficiency Holiday traffic surges are unpredictable. AI counting lets you deploy your team exactly when needed — not overstaffing or missing conversion opportunities. Recommended in the book: • Dor Technologies – accurate, budget-friendly counters • Density – enterprise-level occupancy and traffic tracking • Trax Group – shopper flow + camera-based insights Perfect for: maximizing conversion during micro-rushes, aligning labor with traffic, and protecting sales when teams are lean. 💬 3. AI-Enhanced Customer Service Tools From Chapter 7: The Future of the Customer Experience AI doesn’t replace people — it empowers them. Holiday associates can access instant product info, loyalty lookups, inventory visibility, and personalized recommendations faster than ever. Recommended in the book: • Salesfloor – AI-assisted associate tools • Tulip Interfaces Retail – clienteling + guided selling • @Kore.ai – AI-powered service support and chat Perfect for: reducing wait times, boosting loyalty enrollment, and freeing staff to create moments of joy on your sales floor. ✨ Holiday shoppers have endless choices. They’ll stay loyal to the brands that make their experience seamless, warm, and memorable. AI isn’t the future — it’s the differentiator this season. Are you using AI tools in your stores this holiday? Which platforms have made a difference for you? Get your copy on Amazon today in digital, paperback, and hard cover versions and start today. We would love your comments and feedback and if you need help with strategy or implementation, DM us today. https://a.co/d/dRWKKXa #ai #TopRetailExpert #RetailVoicesbyNRF #thoughtleader #rethinkretail #keynote #consultant Sharon Gee Storytelling and AI for Retail Brands

𝐎𝐧𝐞 𝐀𝐩𝐩, 𝐎𝐧𝐞 𝐒𝐭𝐚𝐜𝐤: 𝐔𝐧𝐢𝐟𝐲𝐢𝐧𝐠 𝐁𝐞𝐯𝐞𝐫𝐚𝐠𝐞 𝐑𝐞𝐭𝐚𝐢𝐥 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭 There is a recurring challenge for large beverage CPGs that operate across modern and traditional trade channels: how to ensure that what happens in-store is captured, connected, and acted on across every layer of the business, without forcing frontline teams into disconnected tools or offline spreadsheets. When store visits, orders, promotions, and shelf KPIs live in one mobile suite - and that suite connects directly into the Microsoft stack - you can do more than just report on execution. You can harmonize the field signal with unified data in Microsoft Dataverse, unlock AI summarization and recommendations via Copilot or Azure AI Foundry, surface instant dashboards in Power BI, and feed supply chain and customer engagement processes in Dynamics 365. For example, CT Mobile combined with CT Orders and CT Vision can capture offline store orders, advanced pricing, and shelf compliance in a single flow, push that data into PowerBI, or enrich Dynamics 365 Customer Insights so trade and media spend aligns with shelves that actually sell. The same dataset can be governed in Microsoft Dynamics, while field reps still work in an offline-first mobile app. This model is already validated in beverage manufacturing scenarios where route-to-market execution and promo compliance metrics must be tracked without adding new burdens to the field. Integration patterns are pragmatic: Microsoft Dynamics connects into Fabric, Power Platform extensions for local automations, and embedded AI in Copilot to brief managers or guide store calls in real time. It is less about creating yet another silo and more about wiring execution data directly into the enterprise nervous system - securely, at scale, with an eye on actionable insight. For beverage CPGs, that means higher order accuracy, faster issue resolution, and the ability to adapt promotions, product mix, and routes based on what is really happening at the shelf. If you are exploring how to bridge mobile retail execution and Microsoft’s consumer goods portfolio, this pairing offers a map that is already being used in the field. The outcome is a tighter loop between store-level reality and enterprise decision making - something every beverage brand competing for space and share can relate to. How quickly can you adapt promotions or product mix based on what’s actually happening in-store?

Most brands know what sold. Very few know what actually happened on the shelf. 🛒 That’s the blind spot Hetal Retail is solving. I first met David Roger while he was building his last company and was fortunate to write the first check into Hetal. The founding team previously worked together scaling Felix Gray into major retail channels like Target, CVS, and Best Buy, where they saw firsthand how little visibility brands have once products leave the warehouse and hit physical retail shelves. While over 80% of purchases still happen in retail, execution remains a massive problem: • Products out of stock • Displays set incorrectly • Pricing mismatches • Shelf placement issues • Lost sales due to poor merchandising Hetal is building what I think of as “Google Analytics for retail shelves.” 📊 Using a network of everyday shoppers combined with computer vision AI, brands can finally understand what’s happening in-store and connect shelf execution directly to sales outcomes. In just six months, the company surpassed 100 customers and now works with brands including Caraway, Cleancult, Freestyle, Lalo, Windmill, Grüns, Lemme, Oats Overnight, David Protein, Starface, and Everyday Dose. Physical retail is becoming more important, not less. The brands that win won’t just have better products — they’ll have better visibility into what happens after products hit the shelf. See what David Roger, Michael Rostowsky, Bobby Shomrony, and Mike W. have to say in this week’s Q&A 👇