No matter the size of your business, a sales forecast is an absolute necessity. The ability to anticipate future revenue spikes and dips lets you capitalize on sales growth and make adjustments as necessary.
In this guide, we will discuss what sales forecasting is, the value that a sound sales forecast brings to your business, and the steps necessary to predict future sales.
At a glance: Here’s how you can forecast your business’ sales
- Define your market segment
- Choose your forecasting model
- Qualify your data
- Evaluate historical trends
- Take future changes into account
- Validate your sales forecast
Overview: What is sales forecasting?
Sales forecasting is the process of predicting achievable sales revenue for a specific time period. Sales periods can be weekly, monthly, biannually, or annually. Businesses use historical sales data from a variety of sources — such as the revenue funnel, sales pipeline, and CRM software — to forecast sales.
Estimates by the sales team, user surveys, and expert analysis also play a role in predicting future revenue.
It doesn’t matter whether you’re new to the game or have been at it for quite some time — sales forecasting is no easy feat.
It requires solid market research. Data must be accurate, a fact that cannot be stressed enough, as all the time and effort you spend putting together a sales forecast will be for nothing if your data is unreliable.
Worse, inaccurate sales forecasts lead to bad business decisions. Sales overestimation can result in a company spending money that’s not coming in. Underestimating, on the other hand, can result in your inability to meet product demand, or long customer wait times because you’re short on staff — in other words, lost sales opportunities and possibly even bad word of mouth because of unsatisfactory customer experiences.
Why do business owners forecast sales?
Companies use sales forecasting to plan for growth, manage their workforces, or budget for lean times when they may be ill-equipped to address unforeseen expenses.
1. Predict business performance
The earlier you know what your sales performance will look like in the coming weeks, months, and years, the better prepared you will be.
In the restaurant industry, for example, some days are better than others. Let’s say that, based on historical data, you expect this coming Thursday to be the busiest day of the week. Given this piece of information, your sales forecast can help you determine:
- How much food to buy
- How many more staff to bring in to serve the influx of guests
Conversely, if you expect a certain day to be slower than usual, you can reduce the amount of food you buy and the number of servers you have onboard that day.
To entice more customers to visit your restaurant during those times when sales are usually low, your marketing team can run sales promotions, like an all-you-can-eat promotion from 11 a.m. to 2 p.m on weekdays.
2. Manage inventory
Businesses dealing with actual physical products, such as manufacturers, retailers, and e-commerce merchants, spend a lot of money on inventory. If inventory doesn’t move as expected, overstocking happens. Overstocking has its own set of disadvantages, including costly storage fees and items becoming obsolete as the popularity of a trend wanes.
To get rid of excess inventory, many companies resort to selling merchandise at deep discounts.
Understocking is also a problem. When customers come to you for a certain product and you don’t have it, they’ll likely turn to another business that can provide the item they’re looking for.
3. Cultivate investor confidence
Investors use sales forecasts to determine the movement of a company’s share price and as a basis for additional funding.
For publicly traded companies, sales forecasts set stock market expectations. If a company consistently lands within the ranges they forecast, they will be perceived as reliable and stable — characteristics investors consider when looking for companies to put money into.
How sales forecasting works
Sales forecasts guide business decisions.
They’re not expected to be perfect, but they should differ only slightly from actual results. As such, for sales forecasting to be as accurate as possible, it’s imperative that you have the right set of data and that you draw the right conclusions from that data.
Here’s an overview of the steps to forecast future sales:
1. Define your market segment
When defining your market, ask yourself questions like:
- What industry do you operate in?
- Are you serving a niche market? What are the trends and regulations affecting it?
- Is the market poised for growth or a downward trajectory?
- Are your products priced competitively?
The more accurately you can define your market, and the deeper you understand its inherent characteristics, the better.
If you’re in retail, for example, what type of retail store do you run? A specialty store, drug store, or convenience store?
The answers to these questions are important because, ultimately, you want a sales forecast that mirrors the realities of your specific niche, instead of the much broader retail market as a whole.
2. Choose your forecasting model
There are several forecasting techniques you can use to predict sales. These are generally classified as quantitative and qualitative.
Quantitative sales forecasting
This style of forecasting is dependent on past data. Some of the most popular methods are:
- Straight-line method. This technique relies on sales trends and historical figures to forecast future revenue. The growth rate is determined, then used to calculate future sales.
- Regression analysis. This forecasting approach explores the relationship between variables. Variables that can affect future sales include current industry conditions, inflation rate, seasonal demands, and your organization’s marketing initiatives.
Again, these methods require historical data, and there are a lot more quantitative techniques you can evaluate. Each has its advantages and disadvantages, so do your research to find what works best for you.
Qualitative sales forecasting
In contrast to the strictly mathematical nature of quantitative forecasting, qualitative forecasting methods are subjective. They rely on customer surveys and the expert opinion of market leaders to predict demand. These forecasting methodologies are useful in the absence of hard data to guide your projections.
Commonly used techniques include:
- Market survey. Examples are focus groups, online surveys, or interviews with individual customers over the phone or face to face to understand their motivation for purchasing a product or service.
- Expert panel. Also known as the Delphi method, this technique uses answers to several rounds of questionnaires distributed to a panel of experts. The goal is for experts to express their opinions without restriction until a consensus is reached.
Note that qualitative and quantitative forecasting methods complement each other and are best used together.
3. Qualify your data
The data you use for forecasting sales should be as clean as possible, meaning there has to be a consistent and accurate way of collecting data in the first place. Remember, incorrect input equals faulty output.
Once you’ve gathered your data, it’s time for a "sanity check."
Take out data that doesn’t make sense. See what jumps out and determine if a sudden spike (or dip) is the result of seasonality, a pricing change, a new customer acquisition (or loss), or a coding error.
When using market surveys or expert recommendations for your forecasts, make sure they’re trustworthy. If possible, compare them against other sources. Even sales estimates by your own sales reps can sometimes be overly optimistic, so watch out for those as well.
4. Evaluate historical trends
Using various statistical sales forecasting methods, past data can be extrapolated to project future sales. For example, your sales data for the past two years can be plotted on a graph for analysis, allowing you to visually spot seasonal trends that affect your sales year by year.
Keep in mind that data extrapolation is only reliable in a sales environment that’s stable and not prone to significant market fluctuations.
If you’re just starting out and have no past sales data to work with, this step is not applicable. Instead, look at data that’s already available, such as competitor reports or market research, to build your projections.
5. Take future changes into account
Sales forecasting is best approached with a certain degree of flexibility. Unforeseen circumstances can happen. Be ready to refine your forecast and incorporate product or market changes that can affect future revenue. These can include:
- New product introductions
- Additional sales reps
- Pricing changes
- Marketing promotions
- New branches or locations
- New customers as a result of new territories
- Regulatory changes
- Customer churn
- Suppliers not meeting requirements
6. Validate your sales forecast
Now that your forecast is ready, the next step is to validate it. Check if projected sales increases (or decreases) are reasonable. If not, you may need to go back and make changes to your forecast.
Let’s say you’ve won over a new client, a big-box retailer with several branches in the area. Your forecast projects a huge number of product orders in the coming months to the tune of a 1,000% increase in sales. Before you start jumping for joy, step back and see if this is a logical expectation. Go through previous records and check for a similar event, such as another big customer acquisition.
If you don’t have that information, look for an applicable competitor or industry reports.
Accurate sales forecasts equal good business decisions
Sales forecasting is a skill you don’t develop overnight. But once you get the hang of it, you’re better able to manage the different aspects of your business and adjust to changing conditions. When developing your forecasting strategy, be mindful of the data you use and make sure your data collection process is reliable.
Above all, remember that actual sales deviating from forecasts is normal. Sales forecasting is not pure guesswork, nor should you expect it to be 100% accurate. Sales forecasts are dynamic in nature and must continuously be updated to reflect changes.