There are a lot of ways small businesses can stay competitive, and it can be a lot simpler than you think. For some, it just comes down to demand forecasting. In fact, Amazon earns 24% of its North America retail revenue because local stores can’t forecast accurately. And retail technology has made accurate demand forecasting accessible for even small local businesses.
However, you’re likely already doing small acts of demand forecasting right in your shop. For example, when I used to manage spas, I would pull aside skincare products for our regulars ahead of their appointments if I knew they were running low on something. If a regular customer hasn’t purchased their cleanser in four months, they might be due for a refill. If we take that same principle and apply it to your business on a larger scale, you can accurately stock the right products, and reach customers at the right time.
We’ll walk through benefits and applications of retail forecasting, types of forecasting, how to do it, and factors to consider when anticipating consumer demand. However, if you are looking for a software solution to automatically provide these insights for you, Lightspeed offers a powerful retail analytics tool with data visualizations and actionable insights to optimize your stock, staff, and sales. Visit Lightspeed for a free trial.
What is Demand Forecasting
Demand forecasting in retail is when you research and analyze data and insights to estimate how many products consumers will want to buy from your store (online or brick and mortar) during a specific period of time in the future.
Retail forecasting helps store owners with inventory management, because it gives insight into how much of each product you’ll need in order to have the perfect amount of stock to meet demand—in other words, you use demand forecasting to avoid stockouts and dead stock. Demand forecasting is a form of predictive analytics, which essentially uses data to form projections.
Are you tracking your current stock levels? If you don’t yet have a defined inventory management process, start by reading our guide on How to Organize Inventory. Once you are tracking current stock levels, it will be much easier to forecast future needs.
Uses for Demand Forecasting
Retail forecasting is helpful for small businesses, even if you don’t have someone dedicated to crunching the numbers. Modern POS systems like Lightspeed have predictive analytics that make it easy for even the most time-strapped store owner to forecast demand.
When you forecast demand, it makes it easier to plan everything else in your business, from marketing to staffing and purchasing to store expansion.
Some use cases for small business retailers include:
- Plan visual merchandising and in-store displays
- Create a profitable pricing strategy
- Develop marketing campaigns to prevent dead stock
- Optimize inventory levels through automated purchasing
- Budget for future business investments
- Prevent out-of-stocks to keep customers coming back
- Develop/purchase new products and features based on high demand
Benefits of Forecasting Customer Demand
There are many advantages for demand forecasting, as it helps you optimize inventory levels. Some 74% of “winning retailers” rate demand forecasting technology as a “very important” contributor to success—compared to 58% of non-winning retailers.
Provide a Better Customer Experience
If a shopper visits your store, and you’ve run out of the item they want, they’re likely to not only feel frustrated but also visit a competitor. Consider this: stockouts cost retailers $1 trillion in 2018 alone, and 15% of online shoppers will look for the product elsewhere.
Even if you’re not out of stock, lack of demand forecasting can lead to delays in getting the products in customers hands, especially for online orders and pre-orders. You need enough lead time on the back end, as well as consider the logistics of getting the products in your customers’ hands.
Plus, when you know how many customers will visit your store during a given period of time, you can ensure you’re adequately staffed to meet their needs. Busy periods need more people on the floor. You can also sync your visual merchandising and product displays with surges in demand.
Cut Costs With Streamlined Operations
Inventory optimization also means expense optimization. When you have just the right amount of stock, you won’t lose money to missed sales, holding costs, or unsellable dead stock—not to mention the lost time spent managing it. (Industrial Distribution estimates that businesses can pay up to 30% more than the inventory’s value in carrying costs alone.)
Plus, to have all that capital tied up in dead or slow-moving stock can take away from other areas of your business where you need to invest. According to Harve Light, managing director at Conway MacKenzie, a 10% increase in forecast accuracy could increase profitability by more than $10 million for large retailers. While the scale isn’t the same for mom-and-pop shops, you can still use retail forecasting to improve your bottom line.
Demand forecasting gives you a peek into the future. Though there’s no 100% accuracy guarantee, you can budget and plan other areas of the business more effectively. For example, you won’t waste budget on social media ads for an item that sells out on its own without promotion. Or you won’t overstaff your store when you know few customers are likely to be there.
Types of Demand Forecasting
Just like there are many factors that influence demand, there are different ways you can go about forecasting. The most effective retail forecasting uses a combination of methods.
While demand forecasting includes math, qualitative forecasting is when you account for non-measurable factors. This includes things like focus groups, competitive analysis, employee input, customer testimonials, panels, consultant and expert analysis, market research, and more.
When to use qualitative forecasting: It’s always a good idea to get this input and validate your qualitative forecast with hard numbers when possible. If you’re new to retail or a specific location, or you’re launching a brand-new product to your market, this is the easiest way to get quick insight into what demand might be like.
A trend analysis, or the trend projection method, uses historical data to identify demand trends. You’ll be able to see cycles in demand, as well as seasonal or time-based trends, that affect demand. Then apply these findings to the future so you can calculate anticipated demand.
When to use trend analysis: Trend analysis forecasting is ideal when you have at least two years of data. You can then see two full year cycles and identify where demand cycles, spikes, or trends repeat.
Life Cycle Modeling
Life cycle modeling is a type of retail forecasting that anticipates when a product will become obsolete, or no longer usable to the original consumer. You can see how long these products last and how frequently customers repurchase them, and build forecasts around that.
When to use life cycle modeling: Life cycle modeling works for retailers that sell items that either expire or go out of season and are regularly replaced. Some products, like fresh flowers or fast fashion, expire quickly. Others have a long life cycle, like a bicycle or piece of art.
Short-term forecasting is when you predict demand no more than 12 months out. It’s helpful for day-to-day planning and when you don’t have a ton of historical data to pull from.
When to use short-term forecasting: Retailers that operate seasonal businesses with predictable fluctuations in demand can use short-term forecasting. For example, demand for snowboards will be low in May through August, so it doesn’t make sense to use that data to forecast demand for the winter season. The holiday selling season is another time when short-term forecasting comes into play.
While short-term forecasting has you covered for up to a year, long-term forecasting takes a look even further out, anywhere from a year to four years. This is a more strategic form of demand forecasting and doesn’t get as granular as short-term forecasting.
When to use long-term forecasting: Long-term forecasting is helpful in larger business planning—if you’re thinking about expanding online or to a bigger or additional storefront, for instance. Like trend analysis, it’s also helpful for identifying cycles and patterns.
How to Forecast Demand in Your Retail Store
Demand forecasting can be a powerful tool for small retail businesses. In order to use it effectively, you’ll need to collect, examine, and interpret the analytics strategically. The hardest part about retail forecasting is not just making sure you have the data available, but knowing that it’s accurate. In fact, 34% of the top 50 retailers suffer from poor forecasting accuracy. Lightspeed Analytics integrates directly with the POS system for data accuracy—and it offers built-in demand forecasting features.
Here’s how to perform retail demand forecasting, regardless of the POS system you use:
1. Choose a Forecasting Strategy
Before you start crunching numbers and gathering research, you’ll want to take a step back and consider which approach can help you best meet your needs. For example, if you’re trying to plan the holiday season, you might consider a combination of qualitative and short-term forecasting. But if you’re trying to choose which city to open your second store, long-term forecasting might be better suited to your needs.
Essentially, you want to think of your business needs and then set forecasting objectives to support those needs.
2. Collect Data
Once you know the angle you’re coming from, you can collect your data. More than three-quarters of “winning retailers” are actively investing in and using data to improve their businesses, and you don’t need to be a Fortune 500 company to do the same. Your POS system is rich with data including your bestselling products, top-spending customers, high-frequency shoppers, and busiest days of the week.
Some 15% of inventory distortion issues happen because software can’t talk to each other. If you haven’t already, it’s important to integrate all of your tools such as your POS system, online store, accounting software, and employee scheduling software. Choose platforms with easy apps, add-ons, and third-party integrations so you don’t have to connect the dots—let the software do it for you. Better yet, choose a POS system that has all of that functionality built-in.
When collecting data for demand forecasting purposes, pay extra attention to:
- Average order value
- Total sales
- Data and time of sales
- Sales by employee
- SKUs sold
- Sales by channel
- Product category
- Returns rate
Everything we described above is internal data, but there are external factors at play too. So we need to consider external data when collecting as well. External data might include:
- Unemployment rate
- Industry size
- Consumer spending
- Consumer debt
- Market conditions
3. Analyze Results
Once you have everything collected, it’s time to put the pieces together to find out the bigger picture. Use different data sources to validate other inputs and your hypotheses. Many POS systems, such as Lightspeed, analyze data for you, putting it into reports with data visualizations and breakdowns, some even offering action items.
Use Results to Inform Business Practices
Your learnings are only valuable if you implement them. Once you’ve forecast demand, it’s important to consider how that affects different aspects of your business. Here are some areas to consider:
- Optimize staffing levels for periods of high demand (more associates to help more shoppers) and low demand (fewer employees to cut expenses)
- Adjust purchasing to lower holding costs, and mitigate stockouts and dead stock
- Increase marketing spend and create visual displays for aging items with low anticipated demand
- Launch adjacent products to items with high demand but long life cycles
Predicting Customer Demand for Accurate Forecasting
Customer demand is only predictable to a certain extent. There are both internal and external factors at play—some are predictable (like seasons) other factors (like the economy) are harder to predict.
Customer Demand Can Change With the Seasons
Seasonality is particularly relevant for businesses that change products and offerings depending on the time of year. Bathing suits are one obvious example: They’re in high demand for spring and summer, but you’re not likely to find much demand in fall and winter. Seasonality is mostly predictable.
Consider the following in regards to seasonality in your demand forecasting:
- Holidays: Different types of products are relevant when holidays pop up, and there’s also the busy Black Friday/Cyber Monday and holiday shopping period.
- Seasons: Changes in the weather for winter, spring, summer, and fall also lead to changes in demand.
- Weddings: Most weddings happen in September, June, and October.
- Occasions: In the US, election time is in November, with the Presidential Election happening every four years, as one example.
Here’s an example of what a demand chart might look like for a seasonal business:
The Types of Products You Sell Impacts Customer Demand
What you’re actually selling influences demand. You can sell two different products to the same customers from your same store and have two very different projections. When you think about staples with expiration dates—things like milk and eggs—the demand is more cyclical. But for items like surfboards, where a customer might buy one every few years, you’ll need to take a different approach to forecast demand.
Consider Your Competition
When you first open your store, you might be the first and only of your kind in your locale. But over time, competition might enter the market, both locally and online. This affects your demand because demand is then shared across multiple businesses instead of just your retail store.
The Economy Can Boom or Bust Demand
Economic trends and changes also affect demand. High unemployment rates and economic recessions and depressions lead to less discretionary spending. Demand for essentials might increase, while non-essential goods see a decrease. And as overall spending decreases, so does demand. On the flip side, a thriving economy means more demand.
Bottom Line: Demand Forecasting in Retail
Demand forecasting sounds advanced, but with today’s technology, it’s accessible to retailers of all sizes. Neglecting to forecast demand could cause you to miss out on sales or lose money to unsellable inventory. You’re likely already using a POS system that already has a lot of the features and analytics you need to calculate demand for your own store. If you’re not currently using a POS system, try Lightspeed. It offers one of the most powerful retail analytics tools at a price that’s accessible for small businesses.