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Accurately forecasting demand is a key issue for brands, as crucial as it is difficult to achieve. It’s especially critical in the fashion industry, where retailers face demand uncertainty, seasonal trends, and a lack of historical data. But even if fashion brands did have accurate historical data, they still need to make future decisions based on old data. In an industry where consumers are hard-to-predict and often impulsive, demand is sometimes sporadic, and volatility is high – the past cannot accurately predict demand.
Having real-time data on your customers behavior in-store and online can be a game changer. While this has happened for e-commerce, when attempting to forecast demand in brick-and-mortar stores, where 70% of the business is happening, you are still working blind. With no visibility, forecasting in physical stores is still largely founded on sales data, accuracy history and assumptions rather than real-time data on customer behavior and in-store sales funnel analytics.
Patented technology by Nexite changes all that with real-time in-store data and AI-powered analytics and insights.
What is Demand Forecasting
Demand forecasting is the process of creating an estimate of future customer demand in retail. In the fashion industry, retail demand forecasting aspires to achieve optimal inventory levels while giving customers what they want, where and when they want it. It relies on analytics that take into account various factors, from inventory levels to seasonality and market trends to optimize forecasting.
The Importance of Demand Forecasting
Fashion is a tough business: customers have high expectations, competition is fierce, and supply chains are challenging. In today’s fashion landscape, protecting market share, profit margins, and turnover rates is a never-ending battle. Getting inventory just right has a big part in winning it.
For their business to thrive, retailers need to find the right balance. They need the right inventory levels: no overstocking or understocking. Too much, and you get overstock and lose profit margins due to unnecessary markdowns. Too little, and you lose revenue or even worse, run out of stock and risk driving customers away. Overstocking and understocking can also impact supply chain planning and inventory costs, such as warehousing.
Brands also aspire to have the right assortment of inventory at every location in terms of style, size and fit. Customers who find what they want, buy more and help grow sales and profits.
Omnichannel Forecasting
Omnichannel retailing has multiple touchpoints that allow customers to move across online and physical environments to fulfill their needs, from shopping to post-purchase support and returns. Customers expect the transition from digital to physical across channels and methods to be seamless and frictionless, generating new definitions such as showrooming (visit brick-and-mortar to research, buy online) and webrooming (check information online, buy in-store).
Omnichannel has also created new fulfillment alternatives, such as BOPIS (buy online, pick up in-store) and BORIS (buy online, return in-store). All, by the way, made easy with the contactless Nexite technology.
New purchase, fulfillment and post-purchase choices make it even harder for brands to get the right inventory to the right location and in the right quantity for both online and in-store sales.
Optimized availability and pricing for both physical and digital sales help brands enhance customer experience and reduce lost sales. Retailers need more in-store data and sophisticated data analysis to generate better forecasting.
Demand Forecasting Accuracy
The more you predict future demand accurately and rely on it for inventory planning and pricing, the more likely you are to drive profitability. For retail demand forecasting to be more accurate, it needs to be richer and include in-store data that was never available before.
Brands big and small can utilize high-quality real-life data from physical stores available through new technology such as the Nexite patented, first-of-its-kind, battery-free tag. The tags are attached to merchandise to offer a new stream of data and analytics around customer behavior. With in-store customer journey data (knowing what your customers are looking at, trying on, perhaps abandoning), brands can improve their inventory efficiency by better forecasting assortment and allocation of fashion items based on size, color, and style.
This is not to say that we expect demand forecasting to be 100% accurate, but better accuracy drives valuable advantages.
What are the Benefits of Forecast Accuracy?
- Promote inventory efficiency
- Increase inventory turnover
- Optimize pricing
- Allocate inventory to localize stores and meet demand
- Cut inventory costs (safety stocks, warehousing costs)
- Avoid unnecessary markdowns
- Improve supply chain planning
- Drive profitability
- Enhance customer experience
- Reduce waste
No matter what demand forecasting method you choose, once you have reliable forecasts of future demand, you can make better decisions.
Levels of Demand Forecasting
Creating demand forecasts can be done on different levels and categories. Brands can attempt to forecast demand for next year, next month or even the next day, predict demand for a style across a country or zoom into more specific regions and locations. Short-term forecasting focuses on factors such as the latest trends, marketing promotions, and seasonality across geographical areas. Long-term forecasting involves more data analysis to make decisions.
What are some of the factors that affect demand forecasting?
- Market trends
- Weather, seasonality, and special days
- Inventory levels
- Competitors
- Marketing and promotions
- Localization
Demand Forecasting in Fashion Stores
On every level, the fashion industry faces unique challenges to forecasting. Customer demand is volatile due to factors such as media, influencers, trends, and the weather. Volatility affects predictability, especially on an item level. It’s different to estimate a fashion chain will sell 15,000 shirts in December, for example, than accurately predicting the styles and sizes of these shirts.
Demand forecasting is further complicated by increased variety in stores and customer expectations for choices. In addition to the number of alternatives, customers expect the rapid introduction of new items. Today, fashion brands may have many collections coming out every year. Where it used to be no more than a collection per season, now the industry has shorter lifecycles.
Further complicating matters is manufacturing in distant locations. Far-away manufacturing facilities that offer lower costs have also resulted in longer lead times and supply chain issues.
Technology and Prediction
In the past, predictions relied on fashion experts attending the big runway shows and brainstorming on trends. Buyers and merchandise managers created “forecasting” based predominantly on their intuition and opinion about trends. To incorporate data, they used spreadsheets and manual processes.
Retailers today use demand forecasting methods with tools that leverage technology and data to predict the future. The forecasting method retailers use depends on multiple factors, including the information available to them. E-commerce data, for example, has helped decision-making for online sales. Now, in-store intelligence can do the same for physical stores, driving true omnichannel planning and execution.
E-com and In-store Data
The availability of information online, from e-commerce customer journeys to social media trends, provides fashion brands with insights into wishes, needs, and numbers. Yet this is not the case in physical retail, where data is still scarce or inaccurate.
Demand forecasting that promotes optimized inventory starts with high-quality data. With inventory data, fashion has real-time online inventory but only snapshot data on the stock in physical stores. Does the store have the full size range? Is any color missing? Is the item still in the back room or out on a shelf?
To make sure they are not missing sales, retailers today add safety stock in their demand forecasting to ensure in-store availability and fulfillment of online orders. Overstocking leads to waste and lost margins as items are left in backrooms and get sent back to distribution centers.
Fully automated inventory management drives better demand forecasting in fashion stores and improves production, allocation and replenishment decisions.
Online channels also have online customer journey data available that brick-and-mortar doesn’t. What are the customers seeing? What items are they engaging with? What do they abandon in the fitting rooms? How does it compare to the category? Seeing real-time customer behavior in physical retail can refine demand forecasting, and observing real-time demand can ultimately help increase store sales and empower exceptional customer experiences.
So how can fashion brands forecast in-store demand better? Instead of leaving the merchandise to just hang there, new technology brings them to life. They bring new connectivity through a stream of real-time data flowing from every item with the Nexite NanoBT tag and aggregated on the cloud.
The Nexite Platform is where you see your merchandise performance and optimization insights. When you look at the data, you can predict trends early on, like popular designs, colors, fabrics, cuts and more. You can also detect shifts in demand in real-time and quickly adapt. Gaining visibility can help you pinpoint the features that drive sales. You can also see what apparel customers are engaging with on an item level so you can plan and replenish based on real demand, rather than demand based on sales data that tends to be less accurate.
Now you can stock up on popular items, so you’re ready to satisfy demand while better handling items that don’t sell well. Once you understand why some goods fail, you can decide how to best react – change item location in the store, transfer specific items to a different store or offer markdowns, you decide.
Nexite the Future
Delivering accurate demand forecasting is both critical and challenging. To succeed, retailers need to leverage technology to create the Nexite next generation store with patented technology.
The Nexite patented nanoBT tag and AI-powered cloud give retailers never-before-available data. With an automatic flow of real-life data on every item’s location, availability, and performance, the Nexite Solution delivers real-time in-store sales funnel analytics aligned to customer journey behavior.
In-store customer journey is the missing link, the critical set of data that was invisible before and can now offer insight into the store and beyond, and support successful demand forecasting. Automatically flowing information and actionable insights on the Nexite Platform give brands a competitive edge, empower true phygital retail and drive profits across all channels. Today and into the future.
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