4 Types of Forecasting Models with Examples

By Indeed Editorial Team

Updated June 22, 2022 | Published February 25, 2020

Updated June 22, 2022

Published February 25, 2020

New and existing companies tend to function better when they have a visual reference that provides an overview of expected outcomes and trends. Successful companies often incorporate forecasting models when planning for the future.

In this article, we will discuss how the most common types of models are used and get an overview of how to create basic models.

What is a forecasting model?

Forecasting models are one of the many tools businesses use to predict outcomes regarding sales, supply and demand, consumer behavior and more. These models are especially beneficial in the field of sales and marketing. There are several forecasting methods businesses use that provide varying degrees of information. From the simple to the complex, the appeal of using forecasting models comes from having a visual reference of expected outcomes.

Related: Marketing's Promotional Mix: Definition and How to Use It

Four common types of forecasting models

While there are numerous ways to forecast business outcomes, there are four main types of models or methods that companies use to predict actions in the future. You'll have a better understanding of how companies use these methods to enhance their business practices and improve the customer experience with the following examples of common forecasting models:

  • Time series model

  • Econometric model

  • Judgmental forecasting model

  • The Delphi method

Time series model

This type of model uses historical data as the key to reliable forecasting. You'll be able to visualize patterns of data better when you know how the variables interact in terms of hours, weeks, months or years.

While there are several methods of completing a time series model, you can follow these general steps in a spreadsheet to estimate outcomes using information gleaned from recent analytical data:

  1. Have your time-based data available for use (time series and values series).

  2. Input the compiled data involving time or duration in the first column.

  3. Insert remaining values you want to forecast in the next column.

  4. Select relevant data

  5. Click the Data tab, then select Forecast Group, then choose Forecast Sheet.

  6. Access the sheet, then select the line or bar graph option you want to use.

  7. In the Forecast End box, determine your end date and hit Create.

Once you've set up your forecasting model, you will then move onto interpreting it to formulate your best estimation of the future.

Econometric model

Those employed in the field of economics often use an econometric model to forecast changes in supply and demand, as well as prices. These models incorporate complex data and knowledge throughout the process of creation. Like the name infers, this type of statistical model proves valuable when predicting future developments in the economy.

Here is the basic structure behind this type of model:

  1. Decide what your independent and dependent variables are. Which economic relation do you want to test? For example, you may ask "Does X have an effect on Y?"

  2. Formulate a hypothesis to test this relationship. Consider other factors that may have an effect on "Y" and label them "Z," also known as the control variables.

  3. Gather the data set encompassing "Y," "Z" and "X."

  4. Plot this data to find any anomalies or outliers.

  5. Determine whether the relationship between "Y" and "X" is linear, quadratic or something else.

  6. Calculate the transformations using a mathematical method you understand.

  7. Interpret the effect that "Y" has on "X." What is the significance of "X" about your hypothesis?

  8. Add the "W" variables to this regression to further analyze your findings.

Judgmental forecasting model

Various forecasting models of the judgmental kind utilize subjective and intuitive information to make predictions. For instance, there are times when there is no data available for reference. Launching a new product or facing unpredictable market conditions also creates situations in which judgmental forecasting models prove beneficial.

Here are some characteristics of judgmental models:

  • Takes a subjective, opinionated approach

  • Assumes specific variables

  • Comes with limitations

  • Accuracy improves with the addition of new information

This type of forecasting model is especially helpful in the field of research and development. Focus groups and expert panels can provide insight that no computerized model would have. For instance, when surveying a group of people about what they look for in a product, companies can better assess their direction when developing specific product features.

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The Delphi method

This method is commonly used to forecast trends based on the information given by a panel of experts. This series of steps is based on the Delphi method, which is about the Oracle of Delphi. It assumes that a group's answers are more useful and unbiased than answers provided by one individual. The total number of rounds involved may differ depending on the goal of the company or group's researchers.

These experts answer a series of questions in continuous rounds that ultimately lead to the "correct answer" a company is looking for. The quality of information improves with each round as the experts revise their previous assumptions following additional insight from other members in the panel. The method ends upon completion of a predetermined metric.

Here is a list of steps you can take to make your own judgmental forecasting model:

1. Select a facilitator

Before choosing a facilitator who will manage the discussion, consider the neutrality of the individual and the person's experiences conducting research. The head of research and development may choose this role, for example.

2. Choose your experts

When businesses research a product that is not yet on the market, they rely on a panel of anonymous experts who can weigh in on the matter. Experts can be anyone with substantial experience in a given topic. For example, in the instance of developing a new swim product, a company may reach out to instructors or safety experts in the field. They may even approach professional athletes or loyal customers who use similar products.

3. Define the problem

Companies looking to solve a problem must first provide the details surrounding the problem, as well as the significant details that can help them make an informed decision. This ensures that everyone understands what is being asked of them. Businesses may want to create a new monofin with features that none of their competitors have tried before.

4. Round one questions

This first round of questions introduces the topic and starts the conversation. The experts will read the information, provide anonymous feedback and submit their information back to the facilitator.

5. Round two questions

After the facilitator has reviewed the answers provided by the panel, edited content, filtered out irrelevant data and scanned through the content to find common themes, the facilitator then submits new information to the panel. Members of the panel have the chance to review the previous responses anonymously and based on the new information, can resubmit a response to another's statement. They again send their responses back to the facilitator.

6. Round three questions

For possibly the last time, the facilitator will review the new responses and again sort through the information presented before sending out the surveys to the panel. However, the process may continue until a general consensus is achieved, which can end in three or four iterations.

7. Take action

Once the researchers have received sufficient information, they can move ahead with any plans to implement their findings. This may be the start of new product development or the start of production on an item they were unsure about.

Artificial intelligence methods

Companies in the field of technology use methods of artificial intelligence (AI) to forecast a specific area of growth. This forecasting method provides extremely accurate results using mathematical algorithms. The science behind artificial intelligence predicts numerous user outcomes and helps generate those "you may also like" suggestions that appear on certain sites.

Here are some examples of popular forecasting methods using artificial intelligence:

Recommendations for products and content

Large online companies use AI to predict customer behavior on their sites, including the likelihood of a purchase in the future. Also, site users receive recommended products through a practice called "collaborative filtering." Offering relevant results to shoppers takes place by clustering and interpreting consumers' data in conjunction with profile info and demographics. More data produces higher quality results.

Example: You're looking at a board game called "Fender Bender" on a popular online shopping site. You scroll down to the bottom of the web page that there are similar games suggested, based on those who like Fender Bender.

Search engine accuracy

Methods of artificial intelligence drive the accuracy of results you see appear on the search engine optimization page (SERP). Google uses an algorithm based on machine learning to provide searchers with quality results, and now, other companies in the e-commerce sector use similar techniques of artificial intelligence to improve their search engines as well.

Example: You're searching for "boots for women" using a popular search engine. You click the search icon and see a page of results that shows boots for women. Many of them provide winter boots, dressy boots, rain boots, and other suggestions so you decide to narrow your search even further and type in "winter boots for women," then click the search button again to see a more curated list of results.

Related: Tips on How to Get Better Search Results on Indeed.com

Predictive analytics

Companies use artificial intelligence to enhance the customer service experience by looking at information for data sets and predicting future trends. Call center managers can make decisions about the number of employees needed to staff a particular day or week utilizing the information provided through AI technology.

Example: A manager of a call center checks his computer software to see a forecast of how many calls the company may have that day. He decides to have four people on staff and let the rest take the day off.

Jobs similar to forecast modeling

If you're interested in using forecasting models in your role, you might consider one of these related positions:

1. Demand planner

2. Data scientist

3. Data analyst

4. Financial planner

5. Forecasting analyst

6. Forecasting scientist

7. Financial planning analyst

8. Business analyst

9. Forecasting manager

10. Risk modeling analyst

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