Business Finance

Forecasting Methods to Predict Business Performance

Oct 24, 2022 • 10 min read
financial forecasting for small business
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      Forecasting involves making educated projections about a company’s future performance. It’s an essential aspect of financial planning for small business owners that’s often used to inform business decisions and a key request from many lenders during the loan application process.

      However, there are many different forecasting methods, and their effectiveness depends significantly on your circumstances, such as your business model, industry, and growth stage.

      Here’s what you should know about the most popular forecasting techniques to decide which ones are most suitable for your current situation and incorporate them into your financial planning.

      5 Types of Forecasting Methods

      MethodTypeRequirements
      Straight-LineQuantitativeHistorical company data
      Weighted Moving AverageQuantitativeHistorical company data
      Linear RegressionQuantitativeHistorical data on independent and dependent variables
      DelphiQualitativePanel of experts and a coordinator
      Market ResearchQualitativeTarget market data

      Quantitative Methods

      Quantitative forecasting methods generally use historical data and mathematical formulas to make predictions. As a result, they produce clearly defined projections and are usually the preferred option when available.

      Let’s explore some of the most popular quantitative forecast techniques, how they work, and when they’re a good idea.

      Straight-Line Method

      The straight-line method of forecasting is the simplest way to make financial forecasts. It assumes that a company’s historical growth rate will remain consistent and uses it to estimate future results.

      It’s often most useful when performing revenue forecasting on mature companies that have experienced steady sales growth for years and expect it to continue.

      Conversely, it’s much less practical for companies in the early stages of development. They often experience significant volatility, and their future performance won’t correlate much with their previous results.

      The method’s lack of complexity makes it easy to make quick, rough estimates. However, it’s rarely the most accurate option since businesses never grow at the same rate indefinitely.

      However, you can use different historical data ranges to calculate your growth rate and improve the method’s accuracy. For example, say you have the following sales data for your small business and want to project your sales in 2022.

      • 2017: $37,000
      • 2018: $68,000
      • 2019: $112,000
      • 2020: $118,000
      • 2021: $125,000

      You know your revenue grew significantly in the first few years as you gained traction. However, your growth stabilized in 2019, and you expect it to progress at a similar rate in the future.

      Therefore, you use your average growth rate from 2019 to 2021 to project your sales for 2022. Your formulas would be:

      ([(118,000 – 112,000)/118,000] + [(125,000 – 118,000)/125000]) ÷ 2 = 5.3% average growth rate

      $125,000 x 105.3% = $131,625 in sales in 2022

      Weighted Moving Average Method

      The weighted moving average forecasting method is similar to the straight-line approach, using historical data to estimate the future. It involves calculating a weighted average of previous data points to predict the next entry in the sequence.

      The technique has a smoothing effect that can help account for trends and seasonals, making it most effective for repeated, short-term projections. Businesses often use it to estimate the next month’s revenue, cash flow, or expenses.

      Once again, you can manipulate the formula to emphasize the impact of certain data points if you think it’ll improve the accuracy of your projections. For example, say you have the following net cash flow over the last 5 months:

      • January: $2,600
      • February: $2,750
      • March: $2,700
      • April: $2,900
      • May: $3,075

      You use the weighted average method to perform your cash flow forecasting for June. You believe it’s likely to be most similar to more recent months, so your formula looks like the following:

      ($2,600 x 10%) + ($2,750 x 15%) + ($2,700 x 20%) + ($2,900 x 25%) + ($3,075 x 30%) = $2,860

      To estimate your cash flow for July, you’d repeat the same formula using the months of February through June. You could continue the process for future months, but your forecasts would become increasingly inaccurate the more they rely on projected data.

      Linear Regression Method

      Linear regression can be a more sophisticated way of creating quantitative forecasts. It relies on the relationship between one or more independent variables and a dependent variable to predict the latter.

      As a result, it’s generally most accurate when there is a strong correlation between multiple activities you control and the financial account you want to predict.

      However, multiple linear regression is slightly beyond the scope of this article, so here’s an example with a single independent variable.

      Say you sell lawn mowing services and exclusively use cold calling to generate leads. You believe it’s the primary driver of your monthly revenue, so you use a simple linear regression model on last year’s data to project your next month’s earnings.

      MonthCold CallsRevenue
      January1,205$6,074
      February869$4,345
      March709$3,722
      April924$4,813
      May1,402$7,048
      June1,035$5,167
      July1,335$6,489
      August1,042$5,739
      September765$3,524
      October1,253$5,966
      November859$4,121
      December722$3,652
      Total12,120$60,660
      Average1,010$5,055

      The average correlation between the two variables indicates that cold calling 1,010 times per month should generate roughly $5,055 in monthly revenue. You can use that knowledge to project your earnings for future months.

      For instance, you plan to make 950 cold calls in the following January and estimate that you’ll earn roughly $4,755 using the following formulas:

      1,010 ÷ $5,055 = $0.1998

      950 ÷ $0.1998 = $4,755 in sales

      Qualitative Methods

      Because quantitative forecasts involve the manipulation of historical data, they’re generally the most objective method of making forecasts. However, that data isn’t always accurate or available, especially for new businesses.

      In these scenarios, qualitative forecasting methods become more valuable. Here are some of the most popular approaches.

      Delphi Method

      The Delphi method is one of the most effective types of qualitative forecasting, but it’s also one of the most challenging to execute. It requires gathering a panel of experts to analyze your business and the market to predict your company’s performance.

      You’ll also need a facilitator to manage the process. They’ll provide questionnaires to the experts, who remain anonymous, then share summaries of everyone’s aggregated responses with the group.

      They’ll repeat the process multiple times, allowing the experts to change their answers freely in subsequent rounds until they reach a consensus. Ideally, the arrangement eliminates the bias and conflict that such groups often experience.

      Market Research Method

      The market research method is one of the most straightforward and flexible forms of qualitative forecasting. There are many ways to conduct the technique, which essentially involves gathering information about your target market to inform your projections.

      For example, that might include sending out surveys to consumers about their purchasing habits, analyzing your competitors’ marketing tactics, or studying the overall economic conditions that might affect demand for your product.

      While market research is essential for new businesses that don’t have much historical data to rely on, small business owners may also use it to formulate assumptions and supplement their quantitative forecasts.

      How to Choose Forecasting Methods

      Every forecasting method’s effectiveness depends on the circumstances, and choosing the right ones requires critical analysis. There’s rarely a perfect choice—just those you deem likely to produce the most accurate results.

      Some of the most important factors to consider when selecting your forecasting methods include:

      • Availability and accuracy of historical data
      • Time and capital you have to devote to forecasting
      • Level of seasonality that your business experiences
      • Your current growth stage and growth rate
      • Time horizon over which you’re looking to forecast

      For example, the owner of an 8-month-old startup has begun to see some traction and wants to project his sales over the next 3 months.

      He rules out the straight-line and weighted-average methods because he lacks data and the company is growing in leaps and bounds. He wants to supplement his quantitative forecasts with qualitative techniques, but the Delphi method is too costly. 

      As a result, he ultimately decides to use a combination of market research and linear regression to make projections for his gross revenue over the next quarter, which he believes currently correlates strongly with his Google ads investment.

      Don’t be afraid to experiment with multiple forecasting methods as your business grows. It takes practice to determine which techniques work best for you and to develop the skills necessary to execute them effectively.

      Use Forecasting Software

      Business analysts have traditionally used spreadsheets to build their financial models and complete forecasting techniques. While spreadsheets can be an effective tool, using them is time-consuming and leaves you highly vulnerable to human error.

      Forecasting software is much more efficient and minimizes the risk of mistakes. Fortunately, Lendio offers a forecasting tool that integrates seamlessly with our free bookkeeping tools, making it perfect for small businesses. Sign up for an account today!

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      The views and opinions expressed in this blog are those of the authors and do not necessarily reflect the official policy or position of Lendio. Any content provided by our bloggers or authors are of their opinion and are not intended to malign any religion, ethnic group, club, organization, company, individual or anyone or anything. The information provided in this post is not intended to constitute business, legal, tax, or accounting advice and is provided for general informational purposes only. Readers should contact their attorney, business advisor, or tax advisor to obtain advice on any particular matter.
      About the author
      Nick Gallo

      Nick Gallo is a Certified Public Accountant and content marketer for the financial industry. He has been an auditor of international companies and a tax strategist for real estate investors. He now writes articles on personal and corporate finance, accounting and tax matters, and entrepreneurship.

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