Retail Analytics Software

Killer graph. Out of the £130 billion online non-food purchases we make in the UK, £27 billion of them get sent back to retailers. Our research with ZigZag Global shines a spotlight on the significant challenge online returns cause in the industry, focusing on those consumers who consistently and intentionally over-order - the "serial returners". Key stats ➡️ Around 11% of online shoppers are serial returners (frequently over-ordering with the intention of returning many items) ➡️They account for 24% of all online returns ➡️Serial returners send back, on average, £1,400 worth of online orders per year, compared with an average of £650. ➡️ This amounts to £6.6 billion of returns. ➡️ Almost three-quarters of serial returners are under the age of 45, and they return more than 42% of all their orders. A 1/4 of serial returners admit to over-ordering just to reach a minimum order value (often to trigger free delivery) only to return goods they had no intention of keeping. The same proportion also said they had returned items after finding them cheaper elsewhere or on promotions. While 18% admitted to returning items having already used them for a short period. There is no silver bullet here that is going to fix this issue for retailers. A nuanced understanding of specific triggers and barriers is essential to effectively target returners through pricing and returns options. 💥 For many boardrooms debating whether they should charge for returns, my thoughts are: 💥 The returns equation transcends simple binary choices between free or paid. Retailers must architect differentiated returns propositions that align commercial realities with customer lifetime value. Smart retailers will segment their returns strategy by customer profitability metrics, leveraging AI to identify purchase patterns that predict long-term value. This enables dynamic returns pricing that protects margins while fostering relationships with truly valuable customers. The goal isn't to punish returns – it's to price them according to their true cost to serve, while rewarding profitable shopping behaviours. There's also a paradox at play where customer acquisition costs are optimised but customer profitability is compromised. Many retailers are essentially subsidising unsustainable shopping behaviours at the expense of margin, unknowingly targeting customers they could do without. The real opportunity lies in leveraging returns data as a predictive indicator of customer profitability. By applying advanced analytics to returns patterns, seasonal purchasing behaviours, and cross-category browsing and mining deep behaviour insights, retailers can enable proactive intervention before profitability erodes. This shifts the conversation from universal policies to personalised solutions that can turn returns from a pure cost centre into a strategic lever for customer engagement and loyalty. Full research is available to download here ⬇️ https://lnkd.in/e5paRNWC

A few months back, I interviewed a senior demand planner from a global skincare brand. I asked a simple question: "How do you improve your forecast when the system gives you a number that feels... off?" She replied, "We talk to the right people before we talk to the system." That line stayed with me. In Demand Planning, we often focus heavily on historical data, statistical models, and software outputs. But what truly differentiates an average forecast from a high-confidence, actionable one - is the process of Demand Enrichment. And no, it’s not just a buzzword. It’s a discipline - a method of adding intelligence beyond what the system predicts. In fact, according to a McKinsey study, companies that effectively integrate enriched demand signals (like promotions, competitor moves, distribution expansion, influencer campaigns, and even climate effects) can improve forecast accuracy by up to 25%. When I worked for a consumer brand in North India, we noticed our system forecast underestimated demand by 18% during Q4. Why? Because it didn’t factor in the impact of a regional festival that doubled store footfall across 3 key states. Our statistical model was flawless. But our insights were incomplete. That’s when we built a cross-functional "Demand Intelligence Loop" - gathering inputs from marketing, sales, trade partners, and retailers - and feeding it back into planning. The result? Forecast accuracy jumped. Inventory positioning improved. And stockouts during peak weeks were cut in half. If you're a planner reading this: Don't just accept the forecast. Enrich it. Challenge it. Elevate it. That’s how Demand Planning transforms from reactive to strategic.

Profit is not a report. It is a constraint. Most businesses treat profit as an outcome to analyse. - A number on a dashboard. - A line on a P&L. - A summary at month end. But profit is not something you discover. It is something you design for. A pilot does not check fuel after landing to decide if the route worked. Fuel calculations shape the flight path before take-off. Profit should do the same for spend. When margin is only reviewed after campaigns run, stock is ordered, discounts are applied, and budgets are spent, the control point has already passed. By the time finance highlights an issue, the commercial decisions that caused it are weeks old. That is not a reporting problem. It is a decision architecture problem. High-performing teams do something different. They treat profit as a constraint that shapes upstream decisions: • Which products deserve budget • Which channels can absorb spend at target margin • When to protect contribution instead of chasing volume • How discounting impacts blended margin, not just conversion rate • Whether customer acquisition cost aligns with lifetime value Profit becomes part of the operating model, not just the review meeting. In retail and ecommerce especially, this matters. Revenue is visible. ROAS is seductive. Volume feels like momentum. But if margin is not embedded into bidding logic, forecasting, and promotional planning, growth becomes fragile. Discovering margin erosion at month end means control was already lost earlier in the chain. Sustainable growth does not come from chasing revenue spikes. It comes from building systems where every major decision is made inside a profit guardrail. Profit is not the final slide in the board deck. It is the rule that shapes every slide before it. #digitalmarketing #ecommerce #retailstrategy #profitability #growthstrategy #performancemarketing #decisionmaking #businessstrategy

In retail, speed is no longer a competitive advantage—it’s the price of admission. The difference between leaders and laggards comes down to one thing: real-time data. You either see the moment as it unfolds, or you react after the market has already moved on.   When I sit down with retail leaders, I often talk about what I call the low-hanging fruits—not because they’re easy, but because they deliver disproportionate impact, fast.   - First, ERP integration. When buyers and suppliers operate on the same live version of truth, friction disappears. Decisions get sharper. Trust goes up. - Second, intelligent agents. Not dashboards that explain yesterday, but systems that think in the moment—forecasting demand, monitoring inventory, and optimizing logistics as conditions change. - Third, next-generation VMI. Inventory that manages itself—cutting stockouts without tying up capital in excess stock.   These aren’t moonshots. They’re practical, achievable today, and they build momentum quickly.   Recently, we partnered with a leading luxury retailer to bring this vision to life. Their reality was familiar: no real-time visibility, an overwhelming flood of OMS events, legacy infrastructure that couldn’t scale, and legitimate concerns about protecting sensitive data. We re-architected the foundation. A serverless AWS platform capable of processing millions of OMS events in real time. A secure, centralized data lake. AI and ML models embedded into the flow of operations. And live dashboards that put insight directly into the hands of business leaders.   The outcomes spoke for themselves: - Real-time and historical visibility across the enterprise - A scalable, cost-efficient technology backbone - A future-ready platform for advanced analytics and faster decision-making   This isn’t about operational efficiency alone. This is about competitive advantage.   The next wave of retail disruption is already here. The winners will be the ones who master real-time analytics and AI—not as experiments, but as core capabilities embedded into how they run the business. #AIinRetail

Inflation can erode consumer purchasing power, forcing businesses to rethink their pricing and product strategies. #BigBazaar, one of India’s leading retail chains, turned to real-time sales data to make smarter, faster decisions—and here’s how they did it. 🔍 𝐓𝐡𝐞 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞: With rising inflation, BigBazaar noticed: ✔️ A decline in premium product sales ✔️ More customers opting for smaller pack sizes ✔️ A shift toward private-label and economy brands Without clear data insights, adjusting to these changes would have been a guessing game. 📈 𝐓𝐡𝐞 𝐃𝐚𝐭𝐚-𝐃𝐫𝐢𝐯𝐞𝐧 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧: Instead of reacting late, BigBazaar leveraged real-time analytics to track purchasing patterns at the SKU level. This enabled them to: ✅ Identify a growing preference for budget-friendly alternatives ✅ Adjust procurement and stocking strategies to align with demand ✅ Optimize promotions by offering targeted discounts on trending products rather than blanket price cuts 💡 The Result: ✔️ A 12% increase in sales for private-label products (Tasty Treat, Golden Harvest) ✔️ A 9% improvement in customer retention among price-sensitive shoppers ✔️ Reduced excess inventory of slow-moving premium items 🎯 Key Takeaway: In uncertain times, data beats intuition. Businesses that track real-time trends can pivot quickly—ensuring they meet customer needs while protecting profitability. 𝑯𝒐𝒘 𝒊𝒔 𝒚𝒐𝒖𝒓 𝒃𝒖𝒔𝒊𝒏𝒆𝒔𝒔 𝒖𝒔𝒊𝒏𝒈 𝒅𝒂𝒕𝒂 𝒕𝒐 𝒏𝒂𝒗𝒊𝒈𝒂𝒕𝒆 𝒊𝒏𝒇𝒍𝒂𝒕𝒊𝒐𝒏? #DataDrivenDecisionMaking #DataAnalytics #

As a CFO, can you report profitability in ways that actually make the Board lean in? Let me share one metric that never fails to spark conversation — Customer or Channel Profitability. Imagine I’m buying a pair of glasses from Lenskart through two different channels: Channel 1: Online Purchase I visit the Lenskart website or app, browse through frames using filters, optionally try them on virtually, select the one I like, upload my prescription, choose a lens package (blue-cut, photochromic, etc.), and proceed to checkout. Payment is made digitally, and within a few days, the glasses are delivered to my doorstep. Channel 2: In-store Purchase I walk into a nearby Lenskart store, get my eyes tested by an optometrist, try on multiple frames with the help of staff, finalize my lens and prescription details, and make the payment. The glasses are then custom-made and either delivered to my home or collected from the store. While the product is the same, the cost of operations — store infrastructure, staff time, equipment — is significantly higher than the online channel. So, if I see from the cost standpoint: The product is the same. Even the material and production costs are the same. But from a profitability standpoint, the two experiences are radically different. Why? Because of operational costs. In-store transactions include: [1] Retail infrastructure costs [2] Salaries of store executives [3] Equipment & maintenance [4] Utility overheads [5] Inventory handling costs In contrast, online sales operate with leaner overheads — primarily driven by technology infrastructure and a centralized development team managing the backend. Same revenue. Very different profitability. And here's the catch — you can’t charge the customer differently just because they chose a different channel. That’s why channel-level profitability analytics is a critical tool in the CFO’s reporting arsenal. It helps uncover insights like: [1] Which channels drive true profitability [2] Where operational inefficiencies lie [3] Which customer segments are sustainable to serve Boards don’t just want toplines and bottomlines anymore — they want clarity/focus on where the real value is being created. This is Sankalp, signing off for Week 28/52 of Value Accounting – Part 6.1: Customer and Channel/Market Profitability Analytics (PA). Next week, we’ll dive deeper into the underlying system structures required to build a robust profitability analytics framework. #linkedin #finance #accountingandaccountants #startups

The cost to retailers and brands of failing to align inventory and marketing teams is exponential. While outdated C-suites remain fixated on traditional metrics such as lowering Customer Acquisition Cost (CAC) or driving higher Return on Ad Spend (ROAS), the most effective, forward-thinking teams are focusing on how to leverage inventory insights alongside marketing strategies to enhance overall profitability. To achieve this, teams need to take a more integrated approach by: 1. Understanding which products have depth to market Inventory depth refers to the quantity and availability of a product across sales channels. Knowing which products have strong stock levels enables marketing teams to prioritise campaigns that avoid stockouts and capitalise on sustained demand. For example, a product with healthy inventory can be promoted continuously, creating consistent revenue streams without risking customer dissatisfaction due to unavailability. 2. Identifying products suitable as headline sale offers Headline offers are the star attractions in promotional campaigns — products that draw customers in. These typically have a strong appeal or brand recognition, combined with sufficient inventory to meet increased demand. By aligning marketing efforts with inventory data, brands can ensure that headline products are always available in quantities that support campaign goals, maximising footfall or online traffic without disappointing buyers. 3. Determining which products require deeper discounts to accelerate cash conversion cycles Some products may have slower turnover or be approaching end-of-season, requiring more aggressive pricing to convert inventory into cash swiftly. Marketing and inventory teams must collaborate to identify these items early and design targeted promotions with deeper discounts to reduce holding costs, free up warehouse space, and improve liquidity. This approach not only drives cash flow but also reduces the risk of markdown erosion across the entire product range. By fostering close collaboration between inventory management and marketing functions, retailers and brands can create more intelligent, data-driven promotional strategies. This alignment ensures that marketing spend is optimally directed to products that can deliver maximum impact — whether that means maintaining steady sales on well-stocked items, driving customer acquisition through attractive headline deals, or clearing excess inventory via tactical discounting. Ultimately, this integrated approach transforms profitability from a simple function of volume or acquisition metrics into a sustainable balance of supply and demand, cash flow, and customer satisfaction.

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

🚨 𝐀𝐫𝐞 𝐲𝐨𝐮 𝐦𝐚𝐤𝐢𝐧𝐠 𝐢𝐧𝐯𝐞𝐧𝐭𝐨𝐫𝐲 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬 𝐛𝐚𝐬𝐞𝐝 𝐨𝐧 𝐠𝐮𝐭 𝐟𝐞𝐞𝐥𝐢𝐧𝐠? It is time to level up with 𝐀𝐁𝐂 𝐗𝐘𝐙 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 — the ultimate duo for smarter stock management. 🧠📦 Let us break it down so you can optimize inventory, reduce waste, and keep customers happy. 🔍 What is ABC XYZ Analysis? It is a combined inventory classification method used in supply chain and inventory management. It merges two powerful frameworks: 𝐀𝐁𝐂 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Categorizes inventory based on 𝐯𝐚𝐥𝐮𝐞 𝐜𝐨𝐧𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧 (e.g. revenue or cost). ↳ A-items: High value, low quantity ↳ B-items: Moderate value and quantity ↳ C-items: Low value, high quantity 𝐗𝐘𝐙 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Classifies items based on 𝐝𝐞𝐦𝐚𝐧𝐝 𝐯𝐚𝐫𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲. ↳ X: Predictable demand ↳ Y: Moderate demand variability ↳ Z: Highly irregular demand By combining them, you get a 3x3 matrix (like AX, BY, CZ...) to identify what really matters in your inventory. ⚙️ How does it work? 1️⃣ Run ABC classification by analyzing cumulative consumption value (Pareto principle). 2️⃣ Run XYZ classification by calculating demand variability (coefficient of variation). 3️⃣ Cross-tabulate the results to assign inventory strategies: ↳ 🔺 AX: High-value & stable → tight control, frequent review ↳ 🔻 CZ: Low-value & erratic → minimal investment, possibly phase out 📦 Real-Life Example Imagine a retailer with 1,000 SKUs: ↳ iPhones: High value, stable sales → AX ↳ Phone cases: Low value, steady demand → CX ↳ Christmas lights: Low value, unpredictable sales → CZ Now the retailer can: ✅ Prioritize planning and forecasting for iPhones ✅ Bulk order phone cases less frequently ✅ Avoid overstocking seasonal items 🎯 Benefits ↳ Improved forecasting and procurement ↳ Reduced holding and obsolete inventory costs ↳ More focused inventory strategy ⚠️ Challenges ↳ Requires accurate data and analysis ↳ Demand patterns may shift (e.g. due to market trends or seasonality) ↳ Risk of oversimplifying complex SKUs ✅ Conclusion ABC XYZ Analysis is not just a tool — it is a strategy. By classifying items based on value and predictability, you can drive efficiency, cut costs, and boost customer satisfaction. 📈 ✨ Whether you are in retail, manufacturing, or logistics — this technique can transform how you manage stock. 𝐒𝐭𝐚𝐫𝐭 𝐚𝐧𝐚𝐥𝐲𝐳𝐢𝐧𝐠, 𝐬𝐭𝐚𝐫𝐭 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐢𝐧𝐠.

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