This article is part of a larger series on Retail Management.
Demand forecasting is a type of predictive analytics that helps you anticipate your upcoming consumer demand so that you can make better supply chain, management, and budgeting decisions. This can look like anything from setting aside a few units of new arrivals for your regular customers to performing an in-depth analysis to help inform your upcoming buying trip.
In this article, we will look at the different types of demand forecasting, how to predict demand for your business, and how you can use forecasting to make smarter business decisions.
Types of Demand Forecasting
There are many different strategies you can use to do demand forecasting. Whether you use all of the strategies or stick to just one or two, they all can provide unique insights about your business and will help you to run your store more efficiently.
Qualitative forecasting is a type of demand forecasting that uses descriptive data such as expert opinions, focus groups, competitive analysis, employee input, customer testimonials, and market research rather than numerical or quantitative sources to make demand predictions.
When to use qualitative forecasting: It’s always a good idea to get qualitative input when possible. Typically, you will use it to validate quantitative findings. If you just started a retail business or are opening a specific location or launching a brand-new product to your market, this is the easiest way to get a quick insight into what demand might be like.
In action: At my boutique, for example, on top of looking at historic numbers and economic trends, before buying trips we would also send out surveys via our Instagram page. Here we would get insights from our most loyal customers about color preferences and trends they liked and didn’t, as well as information about how they’re shopping and what they enjoy about our brand. These qualitative insights in conjunction with our numbers helped us to make better buying decisions and better understand our customers.
A trend analysis, or the trend projection method, uses historical data to identify demand trends. Through trend analysis, you can see cycles in demand, as well as seasonal or time-based trends that increase or decrease demand. You can then apply these findings to the future by predicting demand based on historical ebbs and flows.
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 annually.
In action: For example, at my store, based on our seasonal trend analysis, we knew that there was always a spike in traffic and sales around the holidays and in the midsummer months. We then knew to stock up on cold-weather goodies, gift options, and hot summer clothes.
Life Cycle Model
Life cycle modeling is a type of retail forecasting that looks at the typical “life cycle” of a product and anticipates demand based on how frequently customers repurchase. The life cycle of each product type will be different, and what gives them their end date varies.
For example, flowers and face cream both expire; however their timelines are quite different with flowers lasting days and creams lasting months. There is also the life cycle of something like fast fashion. While inexpensive, trendy clothing items do not technically expire, they do typically require regular replacement based on seasonal fashion trends.
When to use life cycle modeling: Life cycle modeling works for retailers that sell items that either expire, require regular maintenance or a subscription, go out of season, or need periodic replacement.
In action: For example, Small Engine Masters knows that people typically need to replace the mower blade once a year. With this knowledge, it can better anticipate how many blades to stock the store with each year.
Short-term forecasting looks at only a short period of time, between three and 12 months, in order to make demand predictions. This forecasting strategy allows you to focus on seasonal trends and short-term fluctuations so you can better understand and plan for them.
When to use short-term forecasting: Retailers that operate seasonal businesses with predictable fluctuations in demand can use short-term forecasting. The holiday selling season is another time when short-term forecasting comes into play. You can also use this strategy if you don’t have much data to pull from.
In action: Breeze Ski Rental knows that the majority of its traffic is seasonal and comes in September through April. Thus, it knows that demand for goods will be low from May through August, so it doesn’t make sense to use data from that period to forecast demand for the winter season. Instead, it uses short-term data to look at the winter season trends so it can understand the business’s seasonal cycles and trends.
Long-term forecasting looks at trends over a period of one to four years in order to predict demand. Rather than looking at things granularly, long-term forecasting looks at the big picture in order to make long-term strategic decisions.
When to use long-term forecasting: Long-term forecasting is helpful in larger business planning—for instance, if you’re thinking about expanding online or to a bigger or additional storefront. Like trend analysis, it’s also helpful for identifying cycles and patterns that repeat time and time again.
In Action: Returning to my own boutique experience, during the years I was there, we expanded our brand to another location and opened a “sister store.” This store sold different, slightly more refined, and higher-end pieces, and appealed to a demographic that used to shop at my boutique but had outgrown its style. We knew about this group, their buying power, and loyalty to our brand based on a long-term analysis of consumer trends and shopper profiles.
How to Forecast Demand in 3 Steps
With all the demand forecasting strategies in mind, it’s time to learn how to actually do demand forecasting for your business. Here we will look at the three steps to forecasting customer demand and examine how you can use your demand forecast to make the best decisions for your business.
1. Choose a Forecasting Strategy
First, you should choose one (or many) of the forecasting strategies we covered earlier to inform your demand forecast. Before you start crunching numbers and gathering research, you’ll want to take a step back and consider which approach will be best suited to accomplish your larger business goals.
For example, if you’re trying to plan for your 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 would be better suited to your needs.
Lay out the needs of your business and then set forecasting objectives to support those goals.
2. Collect Data
Once you know the forecasting strategy that you are going to use, you can begin collecting data. While you can keep track and file your data manually, I would recommend using an integrated POS system like Lightspeed.
Lightspeed integrates your POS system, online store, accounting software, employee scheduling software, and more into one place. These integrations make it easy to look back on old sales numbers, pull reports for seasonal trends, and get lots of other useful and accurate insights into your business. In turn, your data will allow you to make better-informed demand forecasting decisions that will lead your business to success.
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. There are four key external factors that you will want to consider to help predict your customer demand:
For the most part, seasonal trends are predictable. A clothing store, for example, rotates its offerings with the seasons and can expect more traffic around the holidays. Or, an outdoor store changes its supply based on the weather and sees more customers in the summer months.
Seasonality is particularly relevant for businesses that change products and offerings depending on the time of year. It is also important for businesses that have strong season cycles, like ski shops or stadium gift shops.
You can find seasonal trends in your own business’s cycle using either long-term or trend analyses. This will help you anticipate when you need surplus inventory and when you can expect things to be slow.
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 periods when things tend to get busy.
- Weather patterns: Changes in the weather for winter, spring, summer, and fall also lead to changes in demand. For example, if you live in a cold climate, build in some snow days to your calendar.
- Weddings: Most weddings happen in September, June, and October, and this spurs shopping in apparel, home, entertaining, and many other industries.
- Occasions: Look out for any occasions that might impact shopping patterns. Elections, big sports games, etc., can all change the way people shop.
- Seasonal merchandise: Consider if there are certain times of the year when your merchandise is relevant and not. For example, few people need wool blankets in the winter, but they are popular when things get cold.
The type of merchandise that you sell will impact what your demand looks like and the cycles of buying behavior that you can expect. For example, staple products with expiration dates—things like milk and eggs—have a frequent and predictable demand cycle. A surfboard, on the other hand, is supposed to last a long time, be a large investment, and need replacement only every few years.
Do your research. Look at how your unique industry operates—the buying behaviors that you can expect and the growth your industry typically sees. You can use all these indicators to better anticipate your customers’ demand.
Another thing that you can examine to help better predict your customer demand is your competition. If there is already a neighboring store with similar options, know that some of your target market will go there. Conversely, if your business is unique to the area, you should anticipate that your entire market will look to you for your particular goods.
For example, my boutique was on a busy street with six women’s clothing stores, two of which had a similar price point and aesthetic. With information about our competition, we knew we couldn’t anticipate that all the women from our neighborhood would shop with us, but we also knew which segment of the population was our buyer and how we could breed their loyalty.
An additional factor that you should consider when predicting your customer demand is the economy. Economic dips and recessions lead to less discretionary spending and a decrease in demand, especially when coupled with high unemployment. Conversely, a strong economy incentivizes more spending and increases demand.
For example, when COVID-19 first struck and the recession began, my store still had inventory levels for a normal season. We had to run sale after sale, cutting into our margins, just to get through our stock. Then, for the following buying trips, our buyers got around 60% as much quantity as normal to account for the decrease in demand that came with the pandemic recession.
3. Analyze Results
Once you have your data, it’s time to analyze it in order to find trends, buying patterns, and cycles that will help you to better predict your upcoming demand. One of the biggest things you will want to examine is your customer and overall shopper trends at your business.
- How much do they typically spend in one visit?
- What factors—weather, sales, promotions—get them spending?
- What factors—seasons, competition, economy—deter them from shopping?
- How many units do they typically purchase?
- What are your most typical shopper profiles?
The next thing that you will want to do is validate other inputs and hypotheses with the numbers. You can use the hypotheses and questions that you consider throughout the demand forecasting process to inform how you analyze your data.
For example, say you thought that you sold around 50% more from November to January due to the holiday season. When analyzing your data, you would want to confirm that hypothesis with the numbers.
Using Demand Forecast to Improve Your Business
Demand forecasting is only useful if you use it to make smart decisions to run your business more efficiently. Take forecasting data into account when strategizing how to improve your customer experience, managing your inventory, and budgeting your cash flow and purchasing budgets.
Provide Better Customer Experience
Good demand forecasting will ensure that you have enough products in stock and adequate staff on the floor to provide a great experience for your customers. For example, say you didn’t do demand forecasting, so you didn’t see that you have a seasonal uptick in traffic during the summer months.
This leads you to not order enough merchandise or staff enough people during peak shopping season. Now, when shoppers visit and are ready to spend, many of the things they want are sold out and there are not enough people to help on the floor and run the registers. This not only creates a singular bad experience, but for the modern shopper, it might erode their loyalty completely and make them choose a competitor over your business in the future.
Improve Inventory Management
Good demand forecasting allows you to anticipate what and how much your customers are going to buy. This, in turn, will improve your inventory management.
Inventory Management: The process of having the right products, in ideal quantities, at the right time to sell to customers.
The benefits of good inventory management in retail are vast. Not only will you save on storage and management costs, but you will also have happier customers because you will always have what they want in stock. Additionally, good inventory management also makes working in your shop easier, creating happier and more efficient staff. You also won’t have to run sales just to get through old stock, so you’ll improve your margins.
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 your inventory or by downloading our free Inventory Management Workbook. Once you are tracking current stock levels, it will be much easier to forecast future demand.
Set Budgets to Maintain Profitability
Good demand forecasting will also help you to set and stick to a budget. Demand forecasting will give you a good idea of how much revenue you can expect in the coming period. In other words, it can help you predict your cash flow. Knowing what your revenue is going to look like will help you determine how much you are able to spend and the sales goals that you need to meet in order to cover your costs and yield a profit.
Good demand forecasting is based on strategy, data, and analysis, and can help you accomplish your business’s needs and goals. By understanding the types of demand forecasting strategies that are out there and utilizing them well, you can help your business cut costs, improve customer satisfaction, and improve your inventory management. Use the guide above to set you on the right track to do demand forecasting for your business.