Predicting what customers will buy sounds simple enough. Look at what sold last year, make a few adjustments, and place your orders. Unfortunately… retail doesn’t really work like that. Customer demand can change quickly. Seasonal trends, promotions, pricing, new products, changing shopping habits, and even the weather can all affect what ends up flying off the shelves. That makes demand forecasting a bit of a balancing act. Order too much and you’re left with excess stock. Order too little and you could be dealing with stockouts, missed sales, and unhappy customers. The good news is, you don’t need a crystal ball. With the right data, processes, and technology, retailers can make their forecasts more accurate and respond more quickly when demand changes. In this guide, we’ll look at 7 practical tips for improving demand forecasting, from making better use of your data to working more closely with suppliers. Demand forecasting is the process of predicting how much of a product customers are likely to buy over a set period. Retailers use forecasts to make decisions about: Historical sales data is often used as a starting point, but it’s not the whole picture. Modern forecasting can also take factors such as seasonality, promotions, price changes, and current sales patterns into account. Poor forecasting can create problems at both ends of the stock spectrum. Forecast too high and you could end up with excess inventory taking up valuable warehouse space. Products may eventually need to be discounted or written off. Forecast too low and you could run into stockouts, missed sales, and frustrated customers. Better forecasting can help retailers: It can also help different parts of the business work from the same information. That matters because demand forecasting doesn't happen in isolation. Purchasing, merchandising, warehousing, fulfilment, and suppliers all rely on demand information to make decisions. So, how can you improve it? Your sales history is one of the most useful tools you have. But simply looking at last year's sales figures isn't enough. Historical data needs context. A product might have sold particularly well because it was heavily promoted, for example. Or sales might have been lower because the product was out of stock for several weeks. Using those figures without accounting for what actually happened can give you a misleading picture of future demand. Start by looking at: The closer your data is to actual customer demand, the more useful it becomes for forecasting. Modern retail forecasting systems can also adjust historical data for factors such as promotions and out-of-stock periods. Some products sell steadily throughout the year. Others, like Christmas decorations and summer clothing, have very obvious peaks and troughs. If you don’t account for these patterns, your forecast could be wildly optimistic in one month and completely miss the mark in another. Look at previous seasonal trends and compare them with what’s happening now. You can then adjust your forecast based on the expected timing and size of upcoming peaks. Seasonality is particularly important when planning inventory around major retail events and holidays. Forecasting systems can incorporate seasonal patterns alongside other demand drivers to create a more realistic picture of future sales. A promotion can completely change the normal sales pattern of a product. A 20% discount, buy-one-get-one-free offer, or prominent in-store display could send demand through the roof. If your forecast only looks at normal sales, it might underestimate how much stock you’ll need. On the other hand, simply assuming every promotion will create the same uplift isn’t much better. Look at previous promotional activity and ask: This helps you separate normal demand from promotional demand and gives your merchandising and supply chain teams a much clearer picture when planning the next campaign. Historical data tells you what happened, but real-time data can tell you what’s happening right now. That’s particularly useful when customer demand changes quickly. For example, if a product suddenly starts selling faster than expected, waiting for the next monthly report to spot the trend could mean you've already missed the opportunity to replenish stock. Real-time sales and inventory data can help retailers: This is where good retail data and analytics can make a real difference. The more current your information is, the easier it becomes to make decisions based on what's actually happening rather than what happened three months ago. Demand forecasting shouldn’t sit with one person or department. Your sales team might know about an upcoming campaign. Your merchandising team might know that a product is being given more space in stores. Your marketing team could be planning a major launch. Bringing these teams together creates a more complete picture and makes sure everyone is working from the same forecast. In other words, sometimes the best way to improve a forecast is simply to get more people around the same table. AI is making its way into just about every retail conversation at the moment, and demand forecasting is no exception. Modern forecasting tools can analyse large volumes of data and identify patterns that might be difficult to spot manually. They can consider factors such as: Machine learning can also continuously analyse new information and adapt forecasts as patterns change. But there's no need to throw every forecasting process at AI and hope for the best. The technology is only as useful as the data behind it. If your data is incomplete, inaccurate, or poorly structured, a sophisticated forecasting system won’t magically fix it. AI is another tool in the toolbox, not a replacement for good data and sound retail knowledge. A forecast isn't finished once you've made it. You need to look back and see how close your predictions were to what actually happened. Track metrics such as: Then look for patterns. Are certain products consistently being over-forecast? Are particular locations regularly running out of stock? Do promotional forecasts tend to miss the mark? These insights can help you refine your forecasting process over time. It also means your team isn't constantly reinventing the wheel. If you understand why a forecast was wrong, you have a much better chance of getting the next one right. Demand forecasting and inventory management go hand in hand. If you have a clearer idea of what customers are likely to buy, you can make more informed decisions about how much stock to hold and where to put it. That can help retailers avoid two particularly expensive problems: too much stock and not enough stock. Better forecasting can support: It can also make your warehousing and storage operations more efficient. After all, there's not much point forecasting demand accurately if your warehouse then has to play Tetris with thousands of units of stock. Good forecasting starts with good data. But turning that data into useful action requires the right processes, systems and people. At Dee Set, we help retailers make better decisions with actionable retail data, alongside the operational support needed to keep products moving. From retail data and analytics to warehousing and storage and order fulfilment, we help connect the dots across your retail operation. Because a good forecast is only useful if you can act on it. Want to improve visibility across your retail operation? Get in touch with the Dee Set team today.
What is demand forecasting?
Why is accurate demand forecasting important?
7 tips for improving demand forecasting
1. Make better use of your historical sales data
2. Factor in seasonality
3. Don’t forget about promotions
4. Use real-time data where possible
5. Bring different teams into the conversation
6. Use AI and machine learning where they add value
7. Measure your forecast accuracy to keep improving
How does demand forecasting improve inventory management?
How can Dee Set help with demand forecasting?
