Sales forecast formula

Sales forecasting can be a complicated process, depending on the method of forecasting used. Here are a few simple steps for how to gather information and calculate a basic sales forecast formula for your business.

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Sales forecast formula

Is it possible to answer the question, “What is your projected average monthly sales revenue for the next 12 months?” The ability to predict and understand future sales performance can make or break the success of a business and is vital to revenue operations. Sales forecasting is a way to predict the future, but the methods used to forecast can differ in results — especially if the only information available is historical data and a salesperson’s intuition. In fact, 55% of sales leaders do not have high confidence in their forecasting accuracy, according to research from Gartner.

Let’s review the sales forecasting process and how successful businesses can use a sales forecast formula to make strong, reliable predictions for their business.

What is a sales forecast?

A sales forecast is a projection of sales based on data. Companies can use sales forecasts to make business decisions that allow them to reach their best goals. Sales forecasting can help answer questions such as “What is the projected monthly sales revenue?” or “What project line should a business focus on next?” In the past, sellers have relied on historical data to inform the future. Yet the past can’t predict the future, which is why modern sales teams are turning to smarter tech that uses a broader set of data, including current data, to inform forecasts — but more on that later. First, we’ll dig into why sales forecasting is important and detail a simple way to create a basic sales forecast.

Why is using a sales forecast formula important?

Calculating sales forecasts can help a business of any size gain a deeper, more detailed knowledge of its operations. A business can use sales forecasting to:

  • Create financial documents: Sales forecast numbers can help a business create other financial documents, such as a profit-and-loss statement, cash flow statement, and balance sheet. These records can be useful for understanding the success of a business and describing it to stakeholders or auditors.
  • Set realistic goals: Knowing how many sales a business is going to make helps it decide how many employees to hire, how much money to spend on marketing, or what products to sell.
  • Make a smart budget: Since a business’s profits are its revenue minus its expenses, a sales forecast estimating future revenue can help a business calculate its profits. It can then build a budget, taking care to ensure spending does not outweigh earnings.
  • Impress investors: Especially for a new business or startup, sales forecasts can help convince investors to fund startup costs, operation costs, and new equipment. Investors who see a positive sales forecast are likely to have more confidence in the business’s success.
  • Properly manage inventory: A business can look at a sales forecast to determine how much inventory it may need to meet demand in the coming season. Planning the proper amount of supply or staff can save money, time, and effort, ensure customer satisfaction, and minimize waste.
  • Grow operations: Sales forecasts can enable businesses to make decisions that may help them earn more profits. For example, if a business predicts a decrease in sales, it can cut back on expenses or change its product’s features to be more competitive and desirable to customers.

How to gather info and execute a sales forecast formula

Sales forecasting can be a complicated process, depending on the method of forecasting used. Here are a few simple steps for how to gather information and calculate a basic sales forecast formula for your business:

1. Track available data

To calculate sales forecasts, it’s important to track the business’s financial data, especially sales for each product by month. The number of sales that are returned or canceled can also be tracked and subtracted from actual sales. There are many online software tools that track data, such as CRM systems. These enable sales teams to calculate sales forecasts, understand other business information, and better interpret customers’ priorities and tastes.

2. Determine your sales cycle and categories

Pinpoint a sales cycle or time period to measure, whether a month, quarter, a year, or otherwise. Then choose what to forecast. Instead of forecasting an entire company, or just one product, consider making categories of products to make the process easier and more accurate.

3. Choose a forecasting method

Different methods for sales forecasting have varying purposes and processes. Choosing the right one should be based on what the business’s goals are and how many calculations it wants to conduct. Here are a few types of sales forecasting:

  • Opinion: This model uses qualitative data and events that take the past into consideration, but it also can help with interpreting current events and their impact on buyer behavior. Primarily, this is used when data is scarce — for example, when a product is first introduced into a market. This method uses expert human judgment and rating schemes to turn qualitative information into quantitative estimates.
  • Historical: This forecasting method analyzes historical data to detect patterns and pattern changes. The statistical techniques of historical forecasting are used when several years of data for a product or product line are available and when trends are clear.
  • Prescriptive: This type of business forecasting combines data, expert intuition, connected networks, and deep learning to build forecasts that not only are more accurate than before but also proactively help sales teams reach their full potential. The most modern prescriptive sales forecasting tools are driven by artificial intelligence (AI) that supercharges both opinion and historical forecasting.

4. Use a formula to calculate

One of the simplest sales forecasting formulas is calculating a historical annual sales forecast, which can help project sales for the rest of the year based on sales that have already happened at the start of the year. This method assumes sales are relatively stable, so the math is easier and quicker than that of other strategies. First, determine the average monthly sales rate based on the sales revenue thus far. Use that to forecast the sales for the rest of the year. Add the sales from months so far to the estimated sales for the rest of the year to get the annual forecast. Here are the formulas to follow:

  • Total sales revenue so far / number of months so far = average monthly sales rate
  • Average monthly sales rate x number of months left in the year = Possible sales revenue for the rest of the year
  • Total sales revenue so far + possible sales revenue for the rest of the year = annual sales forecast

It’s also possible to multiply the business’s total sales from last year by the rate of inflation and add it to last year’s sales to guess the sales for the next year. The formula looks like this: Total sales last year + (total sales last year x rate of inflation) = annual sales forecast.

5. Keep in mind factors that may impact sales

Sales forecasting is a powerful tool for predicting revenue, but the actual sales may change based on various external factors. Remember to round down when calculating sales forecasts and keep your numbers conservative so you don’t overestimate. Businesses can also refine their sales forecast over time to update them. Some factors that may impact sales include economic forecasts and conditions, price changes in raw materials, employee contract renegotiations, competition, new sales contracts, market shifts, and new marketing strategies.

Sales forecasting examples

A simple forecast can be calculated on a piece of paper or a computer, depending on what is being measured. Here is a sales forecasting example based on a simplified historical sales forecasting method. Let’s look at an engineering company that sells engines and generators to other businesses. It would like to compare the annual sales forecast of its engines and generators to see which product is most likely to do better for the upcoming year. Last year, it earned $2 million in engine sales and $3.5 million in generator sales, and the inflation rate is 0.5%. Here are the calculations:

  • Engines: $2,000,000 + ($2,000,000 x 0.005) = $2,010,000 annual sales forecast
  • Generators: $3,500,000 + ($3,500,000 x 0.005) = $3,517,500 annual sales forecast

Here is another example of a simplified historical sales forecast. A SaaS company was established about two years ago. The founders would like to calculate a sales forecast to show potential grant funders the estimated success of their business. From January through May of 2021, the company has made a total of $100,000 in revenue. Here are the calculations:

  • $100,000 / 5 = $20,000 average monthly sales rate
  • $20,000 x 7 = $140,000 possible sales revenue for the rest of the year
  • $100,000 + $140,000 = $240,000 annual sales forecast

How to forecast sales for a new product with no history?

Knowing how to forecast sales for a new product is difficult because there are elements of the unknown that are unavoidable. The two most common methods of sales forecasting for new products are opinion and historical forecasting. Using these traditional forecasting methods for new products requires slightly different types of data than sellers would use to forecast a well-established revenue stream. Market research, past demand for similar products, and a sales team’s instinct can lead to an approximate estimation of demand.

Limitations of these sales forecasting methods

Let’s be honest — traditional sales forecasting formulas have limitations. Historical data is an important piece of the overall puzzle, but it is only one input of a strong forecast. A robust, reliable forecast must be paired with qualitative data too, such as how sales people feel about specific accounts or what their gut instincts say. It’s hard to crunch feelings when they’re not in the form of numbers. This is why modern, prescriptive forecasting tools that combine historical and opinion forecasting have the greatest impact.

Additionally, data has to be accurate if a business is to get a high-quality forecast. Many businesses rely on their sales teams to input notes and other information into a CRM, which they usually do after a long day of sales prospecting and calls. As a result, these inputs may not always be reliable because a tedious administrative task is the last thing a salesperson wants to do at the end of a long day. Unfortunately, incomplete data leads to inaccurate predictions. Collective[i]’s Intelligent WriteBackTM closes the data management loop by automatically updating sellers’ CRM data and keeping it up to date as deals progress.

Finally, the forecasting numbers that come from the formulas above may deliver a good baseline estimate, but to create consistent, accurate goals for a sales team, sales forecasts should be adjusted regularly to reflect changing market conditions and buyer behavior. Developed by a team of leading data scientists, Intelligent ForecastTM is the first automated, daily, and adaptive forecast on the market. It uses AI to remove bias, time, and human error from the forecasting process. We augment traditional signals such as stages, activity levels, and seller confidence with IntelligenceTM’s vast network of dynamic market data. Receive updates, recommendations, and risk alerts — along with our forecast — that allow sellers to act swiftly and change outcomes for the better. The result is a forecasting process that doesn’t bog down sellers in the minutia of data but empowers them to trust their own instincts and make better, more informed decisions.

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