Business Forecasting: How it Works & Real-Life Examples

A rapidly evolving modern business climate has proven how fast things can change, with businesses evolving beside it to succeed. In fact, today’s world requires agile strategy and management.

This is where business forecasting can help, enabling businesses to plan for unexpected events. In this article, you’ll learn the basic principles of business forecasting and how to implement forecasting techniques in your business planning.

What is business forecasting?

Business forecasting involves forecasting tools and techniques to help businesses predict certain developments, such as revenue, sales, and growth. Through analytics, data, insights, and experience, business forecasting provides organizations with the information they can use to improve their decision-making. Whether you have a large or small company or offer products or services, accurate forecasts can help your business prepare for future events and future trends.

For example, let’s say a new company started the year with few sales. During Q3, their sales began to skyrocket because of a new marketing technique spreading brand awareness. Applying a business forecasting technique, the team can better gauge Q4 sales—preparing inventory, expanding their team, and taking the necessary steps to have a successful quarter.

Common types of business forecasting

When put into action, business forecasting can help organizations create powerful strategies to better their companies. In fact, there are several business forecasting types, which can fit into one of two forecasting models: qualitative or quantitative.

Qualitative forecasting

For making short-term predictions, many companies use qualitative models. Qualitative forecasting is most practical when data is ambiguous because it enables forecasters to use non-numerical data and instead allows them to follow judgment, instinct, and experience.

Relying on opinion over measurable data and predictive analytics, this method has a much more limited scope. However, qualitative methods are still successful, especially for companies that don’t have access to much data.

Popular qualitative forecasting methods include:‍

Delphi

This business forecasting process polls a panel of experts for their opinions on specific business topics. Forecasters collect opinions anonymously to make their predictions. This method is effective since participants are questioned individually and don’t face the risk of groupthink. By questioning participants individually, companies reduce the possibility of peer pressure and other biases.‍

Sales force polling

This qualitative forecasting technique relies on salespeople providing insights about the future of the market. This technique is based on the premise that if salespeople work closely with customers, they should be able to provide essential information as to what customers want. For this reason, salespeople are polled, and the polls are averaged to create the forecast.‍

Consumer surveys

This technique uses surveys to gain insight into the market. To gain this information, companies use interviews, questionnaires, and telephone calls. From there, they can perform extensive statistical analysis to test hypotheses surrounding consumer behavior.

Quantitative forecasting

On the other hand, quantitative models heavily rely on long-range data. Unlike qualitative models, this type of approach solely relies on data, ignoring opinions and commentary from insiders or industry experts. Instead, this method pulls patterns from past data sets to predict likely outcomes.

Below are approaches to quantitative forecasting:

Economic modeling technique

This forecasting process uses a mathematical model, predicting significant economic shifts and how they might impact the company. By comparing multiple economic variables and internal sales data, this technique can forecast future economic developments. The economic modeling technique is a single model that utilizes multiple-regression equations, testing the relationship between data points and the consistency or fluctuations of those points over time.

Time series forecasting

This method in business forecasting pulls raw data points from historical data. Then, forecasters make assumptions on how seasonality, cycles, and trends will repeat moving forward. Time series forecasting often uses line charts to present the data and is used across multiple business fields, including finance, operations, and sales.‍

Indicator approach

The indicator approach in business forecasting depends on the relationship between indicators to forecast an economic event. Economic indicators might include the rate of unemployment, gross domestic product (GDP), and productivity. This approach studies the relationships between these indicators and then estimates future performance.

Unlike the economic modeling technique, this approach is far less mathematically rigorous. Instead, forecasters use leading economic indicators to gauge the performance of lagging indicators.

3 examples of business forecasting in action

Now that you understand the basics of business forecasting, it’s time to see how it works in practice. Read the following examples to better understand the different approaches to business forecasting.

1. A company forecasting its sales through the end of the year

Let’s suppose a small greeting card company wants to forecast its sales through the end of the year. The company has just a year and a half of experience and limited data to use for predictions. Though the first few quarters were slow to start, they have gained a great reputation in the last three quarters. For this reason, sales are on the rise.

Since the business has limited historical data, they might consider a qualitative model for predicting future sales. By polling their customers, the greeting card company can gauge the willingness of their audience to buy new cards and pricing for the remaining quarters of the year. Market surveys are a type of qualitative forecasting, which utilizes questionnaires to estimate future customer behavior.

2. A company forecasting sales for the next quarter

In this example, let’s suppose a well-established shoe brand is forecasting profits for the next quarter. Normally, this company would use the time series forecasting technique to estimate profits for the next quarter. However, economic conditions have shifted, and the unemployment rate is higher than normal. As a result, the company chooses the indicator approach to predict the actual performance of its product.

In this scenario, the company might compare two variables: employment rate and spending rates. With this business forecasting approach, the company predicts it will have a decrease in profits for the upcoming quarter. Following this prediction, it chooses to produce fewer items in response to economic changes and adjust budgets accordingly.

3. A company forecasting returns on a new product

In this next example, let’s suppose a loungewear company plans on rolling out a new product: slippers. Since this product is new to the company, there are no official metrics for pricing and popularity. For this reason, the company needs to gauge the interest level of its target audience.

In this case, demand forecasting would be a great approach to gauge how much customers are willing to spend and how much the company will need to invest in terms of materials. By using this forecasting process, the loungewear company can decide if the product will perform well and what kind of demand exists. Ultimately, this will help the team make informed business decisions for production as well as sales.

Build a better team, forecast a brighter future

Business forecasting allows companies to make informed decisions when deciding on things that could impact the future of their organizations. Let’s say a tech company is anticipating growth after their stock grew significantly, but they don’t know how long this shift will last. To combat this, they employ the help of an independent financial forecaster.

Luckily, finding an expert financial planner isn’t difficult. Upwork provides businesses with access to well-qualified professionals who can help meet new demands. In this scenario, the tech company can rely on financial experts to advise them and provide a business forecast to make the wisest business decisions possible.