Forecasting Methods | 10 mins read

Forecasting Methods for Any Industry- Everything You Need to Know

forecasting methods for any industry everything you need to know
Jin Hyun

By Jin Hyun

Generally speaking, the important markers of enterprise success are profitability and continuous growth. Both are reliant on how well a company can respond to changes in market conditions, product-market fit, and consumer demand.

All of these tasks, however, are easier said than done. For one, the average small and medium-sized enterprise in the US only has a five-year survival rate of 51.2%.

Unfortunately, there's no way to predict the exact future of a company, but what businesses can do is reduce risks by eliminating guesswork. This is where business forecasting comes in.

What is Business Forecasting?

This is the systematic process of predicting future developments, such as revenue, profits, expenditures, and even economic conditionsall of which influence the financial performance of a company.

Enterprise leaders use different forecasting methods to create both short-term and long-term business plans and strategies.

For example, you might use past sales information over the last five years to identify patterns in seasonal demand, with peaks occurring in November and December due to the holiday season and drops between March and May due to it being an off-peak period for tourism. Forecasting allows you to plan what resources you may need, whether it's manpower or inventory, to meet the conditions of your forecast.

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Types of Forecasting Methods

As mentioned earlier, businesses use a wide variety of forecasting methods for all kinds of situations and goals. However, all of them fall under two categories- quantitative and qualitative forecasting.

Quantitative Forecasting

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This involves using historical data to make objective predictions and spot consistent patterns in sales data. Quantitative methods include-

  • Moving averages - This technique calculates the overall trend or momentum in a data set. In the context of operations management, the data set can be, for example, the sales volume from historical sales data. Calculating moving averages requires statistical work and is highly useful for predicting short-term trends.
  • Regression analysis - This method covers a group of techniques that model and analyze the underlying factors that may influence the variables being forecasted. To put it simply, regression analysis involves comparing a dependent variable (GDP) with one or more independent variables (unemployment rate and inflation).
  • Exponential smoothing - This is a relatively simple method that uses weight averages of past data sets to forecast new values. Exponential smoothing is particularly useful for forecasting seasonal demand as it combines error, trend, and seasonal patterns into an algorithm that crunches past data.
  • Adaptive smoothing - This refers to a collection of methods that allow businesses to determine the probability of events or results happening based on statistical data and variable analysis. For example, a business may use adaptive smoothing to determine how varying revenue numbers will affect its ability to maintain liquidity or scale in the future.
  • Graphical methods - As the name suggests, this broad category covers techniques that use graphs to plot past sales data and predict future sales. A line is drawn on plotted points to establish meeting points. The distance between the line and the point represents the minimum forecasted sales.

Qualitative Forecasting

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On the other hand, qualitative forecasting is a subjective interpretation of data based on the opinions of market leaders and consumers. Qualitative data is often used in situations when there is insufficient historical data and is best suited for smaller or newer businesses.

  • Delphi method - This forecasting method, developed by the think tank RAND, involves asking a small group of experts multiple rounds of questions. The group members answer the questions anonymously and individually, after which their answers are statistically interpreted to arrive at a number that represents the group response. This process repeats itself until the responses align into some semblance of a group consensus.
  • Expert opinions - With this method, you will have to collect and weigh the opinions of experts in the field or area you wish to make a forecast in to arrive at meaningful predictions. For example, a consensus among hiring managers of the year's most in-demand skills can help you plan your hiring strategy over the next 12 months.
  • Market research - Market research is a broad term that includes everything from interviewing your customers personally or through the phone to observing shoppers as they interact with your store displays. Your choice (or choices) depends on the kind of data you need from your market.
  • Focus groups - This technique involves engaging a small group of people who represent your target audience. A moderator facilitates an open discussion on topics such as your brand, your products and services, your messaging, and other business concepts.
  • Historical analogy - This method observes the sales history of a product that's closely related to a currently-stocked product of a company. The idea is to make inferences based on this relationship and past sales performance to forecast future sales.

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Criteria for an Efficient Forecasting Model

With so many forecasting techniques and strategies out there, choosing a particular method can be confusing. You can use the following criteria to narrow down your choices.

  • Accuracy - Forecasts should be based on real data to arrive at an actionable conclusion. For example, a forecast that states, there will be a demand in goods in the near future doesn't say much. In contrast, a forecast that states, there will be an increase in demand of X amount of goods by 25% over the next 12 months is more reliable.
  • Durability - The chosen method should be dependable over long periods as the forecast process can involve a lot of time, resources, and effort.
  • Flexibility - Forecasts should be adaptable to change, particularly changes in the variables being investigated. They should also consider future business risks, such as losses in sales.
  • Acceptability - The forecast should be executed using a combination of simple methods as well as dependable statistical models. This will ultimately depend on what your business finds most practical.
  • Availability - A forecasting method should have access to readily available and up-to-date data to be useful to the enterprise.
  • Reasonability - The method of forecasting should be plausible and easily understood by the people using it.
  • Economy - A forecasting method must have clear economic benefits. The cost and effort required by the forecast should be worth the economic gains.

The History of Business Forecasting

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The process of predicting future developments by combining historical data and present market conditions goes back to the dawn of the 20th century. If business forecasting had an inventor, that title is widely considered to go to American entrepreneur and business theorist Roger Babson.

In 1904, he founded the Babson Statistical Organization, which started out by selling information on stock and bond offerings. However, the Panic of 1907 would reveal an opportunity to broker a different type of information- the implication of business statistics for the future.

Two decades later, the ensuing economic upheaval brought about by the Great Depression would go on to highlight the importance of business forecasting once more. Governments and companies saw the need to understand and fix the global economic disaster, forcing statistical compilations and analysis techniques to take an evolutionary leap forward.

Today, business forecasting includes a wide range of variables such as sales, product seasonality, gross domestic product (GDP), housing prices, and mortgage rates. More importantly, accurate forecasting is proven to generate results.

According to research from the Aberdeen Group, companies with trustworthy sales forecasts are 10% more likely to increase their revenue year-over-year and 7.3% more likely to achieve quota.

Forecasting Steps Explained

While different organizations may rely on varying forecasting methods and processes, on a conceptual level, most forecasting strategies follow these steps.

1. Determine the Basis of the Forecast

The first step of business forecasting is to determine the basis of the forecast, that is, the subject of systematic investigation of things such as financial situations, industry position, and products/goods.

The results of this investigation will then become the foundation of any future estimates of sales and/or general business performance.

2. Predict Future Business and Industry Conditions

Next, businesses will need to evaluate current conditions and future events within a specific industry. Based on the information gathered over the course of the investigation, they will then have an idea of potential business conditions in the future. For example, an online retailer's forecast may predict an increase of cross-border e-commerce sales over the next 12 months driven by a new trade deal between multiple countries. In this case, the business owner can then plan to-


  • Improve the supply chain
  • Find a cross-border logistics partner
  • Market to international buyers and sellers

3. Compare the Forecast with Actual Results


The third step is comparing the forecast with actual business results to identify any deviations. At this point, businesses may be disappointed to learn that the forecast may be slightly off the mark. This is completely normal as forecasts are a constant work in progress. In fact, according to research by CSO Insights, the average percentage of deals that closed as originally predicted is only 48.2%.

Focus on determining the reasons for the deviation and take corrective measures in the future.

4. Review and Improve the Forecasting Process

After identifying the gaps between the forecasts and the actual results, businesses can then refine the overall process. To begin, ask questions such as-

  • Was a previously ignored variable excluded in the forecast?
  • Is there a need for more data to improve the accuracy of the forecast?
  • Apart from the quantity of data, is there high-quality information?

Primary vs. Secondary Sources of Data

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The success of any statistical investigation for business forecasting ultimately depends on data, whether it be quantitative or qualitative. This data can be gathered from primary or secondary sources.

Primary Data Sources

As the name suggests, data from primary sources are first-hand information that the researcher personally collects from-

  • Observations
  • Surveys
  • Interviews
  • Focus groups
Collecting data from primary sources can be time-consuming, not to mention resource-intensive. However, the advantage of this method is the higher level of control the researcher has over the data collection process, which, in turn, leads to better accuracy. The enterprise also owns any data collected which is crucial if it concerns trade secrets.

Secondary Data Sources

Secondary data sources, on the other hand, refer to data that's already published or collected and shared by other organizations. These sources include-

  • Government reports
  • Case studies commissioned by private companies
  • Existing business forecasts
  • Surveys
  • Journals, newspapers, and magazines
With secondary sources, the challenge is sorting through detailed reports to find information that's relevant, accurate, and timely.

Leverage Technology for More Accuracy

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The complexity of business forecasting and the time, money, and effort that goes into it can make this appear to be a herculean task. The good news, however, is that we've come a long way from the manual forecasting methods of the past.

Today, automation through software removes the need for people to work on tedious tasks such as crunching numbers and manually entering historical data sets. This, in turn, frees up time for managers and owners to focus on more strategic, high-level work.

Apart from efficiency, the use of forecasting software also leads to more accurate results. Companies like Warburtons, a British baking firm, have managed to increase the accuracy of their forecasts by 7% after transitioning to demand forecasting software.

Using the proper approach, data and tools will go a long way towards reaping the rewards of business forecasting. At the same time, it's important to remember that forecasting is a long-term commitmentone that requires regular monitoring and revisions to create the most accurate and up-to-date predictions of future business results.

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