We've all been there - you see a great deal advertised, rush to the store, and find...an empty shelf with a sale tag mocking you. The other day I was shopping at my favorite grocery retailer and I had this exact situation. This retailer arguably has some of the best POS data available. And the OOS product was from arguably one of the most sophisticated CPGs. It is 2025 - why are promotional out-of-stocks STILL a problem? It's easy to blame out-of-stocks on a bad promotional plan or a faulty demand plan. But what if the plans were solid...and the failure came somewhere else? Maybe the product is sitting in the backroom on a pallet. Maybe it never made it to the store floor. Maybe the data that informed the plan was flawed to begin with - the ghost of a past out-of-stock dragging down the forecast. And here's the kicker: this single OOS could represent hundreds of lost units and disappointed consumers who may switch brands. Studies show promotional out-of-stocks can be 2-3x more damaging than regular stockouts because consumer expectations are heightened. Now this out-of-stock will further depress the sales signal, distorting the next cycle's plan. The ghost grows stronger. We chase optimization. We layer on AI. But how often do we stop and ask: - Was this a planning miss or an execution breakdown? - Is our demand signal clean, or clouded by past failures? - Are we fixing the forecast, when we should be fixing the shelf? This is where connected planning is supposed to shine. But unless we connect the full chain - not just Finance and Sales, but Brand, Demand Planning, and Retail Execution - we're just automating dysfunction. What's the most frustrating promotional out-of-stock you've encountered? Was it a planning problem or execution failure? Thanks to Daniel Vanden Brink for his input and insights. #TradePromotionManagement #SupplyChain #ConnectedPlanning

What forces are redefining the future of grocery? Progressive Grocer's Gina Acosta sat down with our Managing Director Dolinda Meeker to talk about the role of consumer validation in a crowded marketplace — and how Product of the Year helps brands and retailers turn innovation into lasting growth. 🎥 Watch the full interview here: https://lnkd.in/e-ZtD59F

How #KrogerGroceryDataScraping Transforms Inventory & Pricing Strategies In today's fast-moving grocery market, having up-to-date data from leaders like Kroger isn't a nice-to-have — it’s essential. When retailers #scrape product, price, promotion, and stock data from Kroger, they unlock strategic insights that directly improve efficiency, profitability, and competitiveness. Here’s what this kind of intelligence enables: #DynamicPricingOptimisation : Monitor Kroger’s real-time pricing, discounts, and promotions to adjust your prices proactively. This helps you stay competitive while protecting margins. #InventoryManagement & #StockVisibility : Track stock levels and restocking trends across Kroger’s network to anticipate shortages or overstock situations. Enables smarter ordering, reduced waste, and improved product availability. Competitive Benchmarking: See how your assortments and pricing stack up against Kroger’s, especially for key SKUs. Identify categories where Kroger is gaining, or where there are opportunities for differentiation. Promotion & Assortment Insights: Understand which promotions work well, which products are moving fastest, and how packaging, layout, or bundle offers from Kroger affect demand. Enhanced Forecasting & Demand Prediction: Using historical Kroger data on sales, promotions, and price shifts helps in modelling demand better — reducing missed sales and minimizing spoilage. Retailers using such data aren’t just reacting to trends — they’re anticipating them. Through Kroger data scraping, you get ahead on pricing changes, optimize your inventory, and craft strategies grounded in what the market is actually doing. Learn more: https://lnkd.in/dV7Zi73d #KrogerData #GroceryAnalytics #InventoryStrategy #PricingOptimization #CompetitiveIntelligence #RetailTech #DataDrivenDecisions #DynamicPricing #DemandForecasting #FMCGInsights #WebScraping #SmartRetail #AssortmentPlanning #RetailGrowth

🔎 Mind the gap! Empty slots in this coffee shelf (Swedish hypermarket). A clear case of distribution gap. These gaps represent much more than just missing products: ☕ Lost sales (of course…) ☕ Dissapointed shoppers who may switch brands (loss of loyalty) ☕ Irritated shoppers who may leave the shelf without buying anything, or even switch store (loss of category volume) But please, do not ONLY measure distribution – we must also understand rotation (sales per SKU per day or week). 👉🏼 High distribution with poor rotation = risk of missed sales due to poorly optimized assortment or shelf space (planogram) 👉🏼 High rotation with poor distribution = lost growth potential. How to do it? 🤝 Collaborate with retailers and stores 📊 Use POS-data on store level (Retail Direct Data) 😎 Democratize insight so that sales reps can act fast and efficiently At CatMan Solution and Redslim we strongly believe our tools create positive impact for shoppers, stores, retailers and suppliers 🤗

The Fast Feedback Loop for Replenishment We don’t wait for perfect data — we move when the signals rhyme. Every week, we pulse three simple signals to guide replenishment decisions: 1. Sell-thru by SKU + store — is it moving as expected? 2. Store leader observations — what are shoppers actually doing? 3. Supply flags — in-transit, delayed, or pending receipts If 2 out of 3 point red, we adjust now: shift allocation, bring forward receipts, push substitutes, throttle promos, or trigger transfers. No fancy dashboards. Just an aligned weekly rhythm that keeps us close to reality. Why it works: it’s simple, fast, and accountable. Everyone knows the pulse, the trigger, and the next best move. Question: What’s in your weekly pulse? #Logistics #Replenishment #DecisionFramework #RetailOps #SupplyChain #Agility

What is effective category management for retailers? It’s not just about pricing. Successful managers focus on consumer behavior, product relationships and the shopping journey to boost sales and margins. In our latest blog, our experts outline how to move beyond price and build a strategy that truly drives impact. Discover now: skp.link/gz7u #RetailRevenueManagement #Profitability #PricingStrategy #ConsumerBehavior #CategoryManagement #Consumer

Midwest grocery trends are shifting fast—are you ready? In the past 6 months, fresh produce sales in the Midwest outpaced national averages by 14%. Independent retailers are prioritizing multicultural assortments and rapid promo execution—yet 60% say they struggle to track ROI at store level. Curious about what’s driving these shifts? Zinco’s on-the-ground insights reveal which categories are winning, where promo dollars work hardest, and how transparent reporting builds retailer trust. Connect with us to see the full Midwest trend report and discover how your brand can grow smarter in 2025.

𝗪𝗵𝗲𝗻 𝘁𝗼 𝗨𝘀𝗲 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝘃𝘀 𝗜𝗻𝗰𝗿𝗲𝗺𝗲𝗻𝘁𝗮𝗹 𝗦𝗮𝗹𝗲𝘀 𝗖𝗼𝗻𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹𝘀 𝗶𝗻 𝗖𝗣𝗚 Ever been in a meeting where the deck looks great, but someone inevitably asks: “𝗬𝗲𝘀, 𝗯𝘂𝘁 𝗰𝗮𝗻 𝘆𝗼𝘂 𝗽𝗿𝗼𝘃𝗲 𝗶𝘁?” or “𝗜 𝘄𝗶𝘀𝗵 𝘄𝗲 𝗵𝗮𝗱 𝗳𝗼𝗿𝗺𝘂𝗹𝗮 𝘁𝗼 𝘀𝗵𝗼𝘄 𝘁𝗵𝗶𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝗸𝘀”. I’ve seen teams with a great narrative, but no model behind it to validate their incredible story. I'm sharing this because I hope it helps you - If you’re trying to explain total sales lift across multiple levers: media, retail pricing, promotion, and velocity, then 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 is the right tool. It quantifies relationships, accounts for seasonality and competitive noise, and helps guide strategy. It’s great for annual planning alignment. But if you need to isolate the real impact of a specific campaign, channel, or tactic, then the 𝗜𝗻𝗰𝗿𝗲𝗺𝗲𝗻𝘁𝗮𝗹 𝗦𝗮𝗹𝗲𝘀 𝗖𝗼𝗻𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 model is the way to go. It shows what actually moved the needle (or will move the needle), not just what correlated with movement. Meaning, it doesn’t just show that sales went up when spend or distribution went up (that’s correlation). It proves cause and effect. For example: this campaign drove 12% lift, this promotion generated 70,000 incremental units, and this display justified $300,000 trade dollars. I recently used the 𝗜𝗻𝗰𝗿𝗲𝗺𝗲𝗻𝘁𝗮𝗹 𝗦𝗮𝗹𝗲𝘀 𝗖𝗼𝗻𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 (ISC) formula to prove that our brand would outsell the incumbent at Walmart if given the shelf space. That wasn’t a correlation story; it was proof that incremental demand justified the switch. These kinds of models translate analytics into real retail decisions, where shelf space, promo calendars, and trade dollars have real impact. I think it's important to use both: • Regression gives you the map. • Incrementality tells you which roads actually got you somewhere. #CPG #Incrementality #Retail #ConsumerProducts #ConsultingTools #DataDrivenDecisions

🎄 Why Causal Clarity Matters This Season 🎄 Most retail and CPG leaders are planning blind for Q4. Forecasts look great… until reality hits. That’s why the smartest teams are combining predictive waterfalls (what could happen) with Causal ML (what actually caused outcomes). Example: A retailer forecasted a 30% lift from a deep discount. Causal ML showed half the buyers would have purchased anyway → pure margin loss. Takeaway: Predictions set direction. Causal evidence gives confidence. 👉 Question: What’s the riskiest assumption you wish you could test before Q4? #causalml #decisionintelligence #holidayretail #smartgrowth