Forecasting in Omni-Channel Retailing

Ever wonder why some e-commerce brands always seem to have the right products in stock, while others struggle with overstock or empty shelves? It all comes down to demand forecasting—and in 2025, it’s getting an AI-powered upgrade. ● From guesswork to precision Traditional forecasting relies on historical sales data. AI-driven tools now go beyond that, integrating real-time factors like weather, local events, and even social media trends. The result? Forecasts with 90%+ accuracy instead of the usual 50%. ● GenAI: the next step Generative AI takes it further by analyzing unstructured data (customer reviews, trends, emerging demand signals) and answering questions in plain language. No more complex spreadsheets—just instant insights for better inventory planning. ● AI tools leading the way: ✔ Simporter – AI-powered forecasting that integrates multiple data sources to predict sales trends. ✔ Forts – uses AI for demand and supply planning, ensuring optimized inventory. ✔ ThirdEye Data – AI-driven forecasting that factors in seasonality and customer behavior. ✔ Swap – AI-based logistics platform that enhances inventory management. ✔ Nosto – AI-driven personalization that recommends the right products at the right time. ● Why this matters for #ecommerce? ✔️ Avoid stockouts that frustrate customers ✔️ Reduce excess inventory and free up cash ✔️ Adapt quickly to market shifts How are you managing demand forecasting in your store? #shopify

Forecasting solutions touting the use of AI/ML models are hard to avoid these days. But there is a hidden risk that most companies tend to ignore. The latest models are great but I worry these are being applied in a way that will only amplify “the bullwhip effect”. What do I mean? Bull whip effect is the distortion of the demand signal as it travels from the consumption end of the supply chain to the production end, while traversing multiple physical and informational nodes along the way. As a result, the demand signal at the production end could be orders of magnitude variable than actual consumption. This is nothing new and we have known about this effect for two decades plus. As we apply algorithms to forecasts, unless we account for the bullwhip effect, we are bound to amplify distortion despite best intentions. Now, this is not about outlier elimination which I believe algorithms do a pretty good job of eliminating. I am talking about misinterpreting noise as signal, over-interpreting variability and causing inventory gyrations that ultimately hurt customers. A classic example is using order/shipment data at Distribution Centers for forecasting or worse yet, factory shipments as a proxy for demand. Most S&OP plans only focus on order and shipment data without systematically factoring in channel inventory and demand. So what is the fix? In my opinion, if you are a consumer company (CPG, Hi-Tech, Retail, Pharma/Healthcare, and even manufacturing), build the capability to forecast a demand signal that is as close to the final consumption point. For example, a CPG brand could forecast retail/e-commerce sell-through demand, normalize it for channel inventory and then propagate that signal up into the supply chain. And the best part - those same AI/ML models will work much better for you. To be honest, B2B and industrial companies also benefit from a similar approach by getting closer to end customer demand. Better yet, this unlocks better demand intelligence which fuels better S&OP judgements, new product forecasting quality, lifecycle management, capacity planning and more. If you are looking for a 10x transformation, this is one of them. It’s bizarre to me when I see companies side-stepping this fundamental step and then complain about forecast accuracy, or data cleanliness or something else hurting their supply chain service levels and costs. Leaders who are pursuing unlocking growth from their supply chains while reducing cost-to-serve need to lead from the front in championing this capability. 

If you’re feeding raw sales data into your forecasts, you’re making a big mistake. Retail is full of noise: stockouts, listing suspensions, influencer campaigns, promotions — all of it distorts the true demand signal. The worst thing you can do is use that raw data to feed your forecasting algorithms — you'll just forecast a new stockout or end up overstocking again. As my friend Nicolas Vandeput often says (and I agree), the best forecasting models are the ones that can consume all the context data: sales units, revenue, price history, ad spend, influencer activity, inventory levels… With that context, models can actually make sense of the past and project the future. The problem? Most brands don’t have that data organized, accessible, or reliable. So what’s the next best thing? Algorithms that automatically detect and adjust for outliers — cleaning the past before predicting the future. That’s exactly what Flieber does. The moment you connect your data, we run it through anomaly detection and correction before it ever reaches the forecasting engine. And we feed our algorithms the adjusted sales, instead of the actual sales. That step alone improves forecast accuracy by up to 40%. For planners, that’s not just a nice-to-have — it’s life-changing.

The question isn’t what drove sales yesterday. It’s where will your next $100k generate the highest incremental return tomorrow. At eTail™ Connect West, I showed why forecasting matters: 👉 Many brands chase the channels that look strong today while missing the ones with incremental headroom still to unlock. 👉 Fospha’s data shows significant growth potential remaining across channels: - TikTok: 63% - Demand Gen: 69% - YouTube: 60% - Paid Search: 60% - PMAX: 62% The smartest brands don’t pour budget into oversaturated channels. They use incremental forecasting to scale channels with room to grow, often increasing efficiency before diminishing returns set in. The best brands we work with do it differently: 1. They use Beam’s incremental forecasting to reveal the true value of every extra dollar they spend, so they can confidently scale where growth is available and cut back where channels are saturated, unlocking new revenue while avoiding wasted spend. 2. They combine last year’s peak results with year-to-date performance to plan this year’s allocation. 3. They place the next dollar where it will drive incremental growth tomorrow, not just where yesterday’s ROAS looked highest. Measurement shouldn’t be retrospective. It should guide the next best dollar.

Fastest way to lose $1M? Being OOS - You can fix your ads in a day - You can fix your pages in a week - Inventory mistakes last as long as your pipeline takes (for some categories that is 18 months) Compounding the actual lost sales in that time, your organic rank will also suffer, which will pull down your traffic and authority. * depending how long you are OOS this takes some time to build back How to avoid it? Forecasting First you need to make sure that you have separated what you want to be true from what is true. This is a first principle approach. Market: - What do my customers want? - How many of them? - What is the TAM (Total addressable market)? - What is the SOM (Serviceable obtainable market)? - What penetration does my brand have? - Break out by variant (color, size, flavor etc) Your brand: - What is my current share of voice? - How much of my traffic is paid? - How much of my paid traffic overlaps with market demand? Eg you promote the blue shirt so it's your top seller but the traffic to that page is 60%+ paid and the market (organic traffic and demand) is begging you for brown pants in a 38” waist. There is a delta here in what you wish were true vs what is actually true. 🚨 If you are a sub $20M a year brand this is going to become a huge cost for you. 🚨 For brands of all sizes this is a very large inventory risk. (this is how you get overstocked in the shirt and are OOS on the pants) If it diverges more than 30% separate paid demand from real demand before moving on. Tools: - If you have an Amazon account you should be running comparisons between P70 and P90 once a week so you can see the patterns in demand - You can use Google trends (its free) to run product search diagnostics AND phrasing diagnostics (eg there is a large difference in demand between “maternity pants” and pregnancy pants” you need to use both) From here you can start to build your reality based model. If you would like to see this in depth please grab the post in the comments. I'd love to hear your forecasting stories below 🙂 #ecommerce #amazon #reporting #analytics #forecasting #data