Retail Merchandising Analytics

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

💡 𝐁𝐞𝐚𝐮𝐭𝐢𝐟𝐮𝐥 𝐝𝐢𝐬𝐩𝐥𝐚𝐲𝐬 𝐝𝐨𝐧’𝐭 𝐬𝐞𝐥𝐥 — 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝐝𝐨𝐞𝐬. Visual merchandising is not just about creating a beautiful store image — i𝐭’𝐬 𝐚 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲. When done correctly, it’s one of the most profitable tools you can implement to drive sales. Yet, I still see many visual merchandisers setting up stores without looking at the data: -What’s selling and what’s not -Which products have the highest margins -Which items are overstocked -What competitors are offering -How trends are shifting Too often, the focus is only on mannequins and displays. Don’t get me wrong — mannequins can help. But you could remove every mannequin from your store and still grow sales… if you place the right product, in the right spot, at the right time. That’s where data comes in. 📊 By analyzing numbers and making strategic decisions, visual merchandising becomes far more than decoration — it becomes a 𝐬𝐚𝐥𝐞𝐬 𝐝𝐫𝐢𝐯𝐞𝐫. This is why our freelance visual merchandisers start with data before every store visit. We focus our time on strategy and efficiency, ensuring that after a VM visit, you see not only a stronger store image but also a measurable impact on sales. 🔖 #VisualMerchandising #RetailStrategy #WholesaleBusiness #RetailTrends #DataDriven #StoreDesign

How does a 400-store menswear retailer experience a 25% drop in sales almost overnight? This is what we had to work out when we got a call from a stressed-out CEO a few years ago. And the most puzzling thing? The decline was consistent across all product categories. From our experience at the time, the problem would come down to seasonal styles that didn’t land with customers, or core products within trend-driven ranges that had either been under-bought or under-delivered. But, ultimately, none of it added up. The core lines had been appropriately planned, purchased, and delivered to stores. We headed to one of the flagship locations with the CEO to investigate. The store presentation was excellent — visually compelling, well coordinated, and inspirational. It was understandable why senior leadership was confused; the store looked strong, yet sales were sharply down. What had changed? The answer was staring us in the face, in rows of neatly colour-coordinated garments. A newly appointed visual merchandising team had gotten creative and reorganised the store to present all products by colour story. Ah! Despite creating a cohesive, exciting, and inspirational environment, this approach fundamentally disrupted the customer journey. A shopper searching for a core product — for example, a basic T-shirt in white, blue, red, or grey — now had to navigate multiple colour stories across the store to locate each option. Core commodities, which were underpinned by strong value messaging and significant volume planning, had effectively been fragmented. In prioritising aesthetic storytelling, they had compromised commercial navigation. After reviewing the data and debating the issue at length, we concluded that this change in visual merchandising strategy was the primary driver of the sudden sales decline. Within 48 hours, sections of the store were re-merchandised back into clear core commodity groupings. Sales rebounded almost immediately. The experience served as a powerful reminder: while customers appreciate inspiration, they also need to navigate core commercial product easily. Accessibility and clarity drive conversion. And here’s the thing. When I visited one of the UK’s largest retailers last week, they too had adopted a full colour-story merchandising approach. It meant that after spotting a jacket on display, I had to visit four separate parts of the store just to locate the different colour options. It’s interesting to see retailers making the same mistakes. As in-store retail becomes more challenging than ever, merchandisers need to remember that seasonal trend planning is underpinned by clearly presented core volume items and executed through a commercially grounded visual merchandising strategy. This will consistently deliver both seasonal impact and sustainable performance. Commercial clarity does not diminish creativity – it enables profitability.

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

Many retailers think Visual Merchandising is about making stores look pretty. The numbers tell a different story. The pattern is clear: brands bring VM teams in after collections ship. Beautiful stores and single item sales? Here's what actually moves the needle: Window displays drive 23% more foot traffic. Not 5%. Nearly a quarter more customers through your doors. Mannequins boost conversion by 66%. Two thirds more sales from showing customers how to wear the products. Many stores still hang everything on rails. Strategic color placement increases sales 35%. Warm colors near promotions create urgency. Cool tones where you want browsing time. Cross merchandising lifts basket size 20%. Start organizing by customer need. That blouse, trouser, blazer, accessory display? Margin architecture. The compound effect is massive. More traffic times higher conversion times bigger baskets equals exponential growth. Luxury brand Coach understands this. Their Visual Experience team sits at product development from day one. Not six months later. They build the visual story into collection DNA. The brands winning tomorrow are making VM a core commercial function with clear KPIs: • Sales per square foot • Dwell time by zone • Stock turn by display type • Conversion by mannequin placement Your VM team knows customer shopping behavior better than anyone. They see what stops traffic. What drives trial. What triggers purchase. Are you leveraging that intelligence at product planning? Or asking them to make magic with whatever lands in the stockroom? Which metric surprises you most? The 23% traffic lift, 66% conversion boost or 20% basket increase?

Retail Growth in FMCG isn’t about luck… it’s about tracking the right numbers. Daily. Religiously. Most sales executives run behind sales targets… But the best ones? They chase KPIs that drive retail fundamentals — every single day. After leading GT sales across zones, I’ve realized one hard truth: “Retail growth is the outcome. Daily KPIs are the inputs. Miss them, and you’re just shooting in the dark.” 🧭 1. Productivity Metrics (Quantity of Work) • 🧍♂️ Outlets Covered vs. Targeted – Did you beat plan get executed 100%? • 📞 Calls Made vs. Productive Calls – Calling 40 outlets means nothing if only 12 give orders. • 🎯 Strike Rate (%) = Productive Calls / Total Calls 70%+ is excellent. Below 50%? Time to revisit call list quality. • 📦 Line Productivity – Avg SKUs sold per call. Aim for 4–5 lines minimum. • 💸 Order Value per Productive Call – ₹500 vs. ₹2000 makes a huge difference in your growth path. 🧊 2. Execution Metrics (Quality of Work) • 🧊 Must-Stock SKU Availability – Is your hero SKU actually present in the shelf? • 🧩 Planogram Compliance – For key outlets or MT, is your product placed as per visibility norms? • 🛍️ Promotional Scheme Execution – Are posters/schemes visible and communicated? • 🌱 NPD Push – Did you pitch and bill the new launch or just ignored it? “Execution builds pull. No execution = you’re just pushing boxes.” 🧮 3. Retailing Metrics (Business Health) • 💰 Retailer Billing Value per Beat – Compare vs. historical average. Decline = early warning. • 🗓️ Outlet Coverage Frequency – When was the last time you visited X outlet? • 📸 Visibility Deployment Score – How many outlets got branding today? • 📦 Distributor Fill Rate – Ordered 10, got 4? That’s a red flag for retailer confidence. • ⚠️ Stock Age Feedback – Are retailers sitting on old inventory? 💳 4. Financial Hygiene & Claims • 💸 Credit Exposure Per Outlet – Especially for semi-urban/rural beats. • 🧾 Discount & Scheme Accuracy – Any off-book deal kills pricing hygiene. • 🔁 Returns & Claims – Track expiry/damage immediately. Avoid disputes later. • 🔍 Scheme Communication – Was scheme explained to retailer clearly? 📍 5. Beat Hygiene & Discipline • 🛣️ Beat Adherence – Did you actually follow the mapped route or skip tougher outlets? • 📲 App/Tracking Compliance – Was check-in/check-out done properly? • 📝 No Order Reason Capture – “Didn’t order” is not an answer. Find out why. • ♻️ Outlet Stock Rotation Check – Is last month’s stock still lying untouched? 📌 Final Thought: “Your KRAs may be monthly. But your career grows daily. Track the right KPIs, and retail will reward you with repeat orders, retailer trust, and boss’s respect.” If you’re a first-line manager, share this with your team. If you’re a sales executive, start tracking these today. And if you’re in marketing or supply chain… now you know what sales really battles daily.

💪 I just spent 10 hours writing a new article for the Simmer blog, and it was worth every minute. Path analysis in #GA4 is powerful in theory… but almost unusable in practice. So I rebuilt the user journeys of Google’s Merchandise Store using #R and #BigQuery. In the article, I walk through the full process step by step, and introduce a new approach that combines path analysis + funnel analysis to surface insights GA4 can’t show you. Most importantly, I focus on the business impact, not just pretty charts. Here are the questions we answer: 1. Where do users drop off most frequently? 2. What are the most common entry points? 3. Which landing pages behave like “dead ends”? 4. How far do users typically progress through the purchase funnel? 5. How do promotion views affect conversions? 6. What happens after users sign in? If you work in digital analytics, UX, ecommerce, or CRO, this is for you, and the full R code is included. Link to the article in the comments. #Rstats #DigitalAnalytics #DataScience #Ecommerce #UX #MarketingAnalytics

Why the Best Retail Insights Happen When Data Meets Execution Retail teams are drowning in data. Sell-through rates. Compliance %. In-stock reports. Traffic counts. Basket size. The dashboards look sharp. But data alone doesn’t create insight. Insight happens when numbers meet physical reality. Because here’s the truth: A spreadsheet can tell you a SKU is underperforming. It can’t tell you that: • It’s positioned outside natural shopper flow • It’s sitting below eye level • The adjacent category is stealing attention • The secondary display is technically compliant — but invisible • The visual sequence creates friction Data shows you what is happening. Execution explains why. I’ve seen stores with strong compliance scores still miss sales targets. Not because the data was wrong. But because no one connected it to: • Sightlines • Fixture height • Traffic direction • Visual hierarchy • Shopper movement patterns Retail performance doesn’t live in Excel. It lives in physical space. The best retail insights happen when analytics and in-store execution talk to each other. When numbers confirm what your eyes see. And your eyes validate what the numbers suggest. That’s where real optimization begins. If you're a brand or retailer trying to connect performance data with real in-store execution, I’m always open to exchanging perspectives. #RetailExecution #RetailStrategy #Merchandising #CategoryManagement #RetailAnalytics #ShopperMarketing #CPG #InStoreExecution