What is business forecasting (with definition and methods)?
By Indeed Editorial Team
Published 12 April 2022
The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.
Business forecasting helps organisations predict future performance. Forecasting requires specialised skills, such as data analytics and statistical analysis, in addition to knowledge of forecasting methods. Forecasting the financial returns of a project and estimating the effect of decisions can take months or hours, depending on your tools and methods. In this article, we define ‘What is business forecasting?' and talk about its tools and its past and current methods.
What is business forecasting?
To answer ‘What is business forecasting?', it's first key to understand its background. Business forecasting remains the process of predicting future outcomes and this process continues to develop over time. Early methods chiefly used intuition and personal knowledge gained from the analyst's experience. These methods still exist, but sophisticated forecasting practices that use technology now supplement them.
Business forecasting refers to the process of predicting sales, revenue, profits or other financial outcomes in a particular industry or business sector. A company's forecast is a tool for decision-making. It helps managers to plan their strategies, identify any obstacles that might arise in the future and see how decisions of management can affect outcomes.
Business analytics vs business forecasting
Business analytics and business forecasting are both parts of modern business practices. Both help companies to make better decisions with the data they have. Business forecasting is largely financial and, as such, is part of the overall predictive analytics process. Business analytics is wider in scope and can deal with everything from manufacturing to customer satisfaction.
How does a business forecast?
The process begins by looking at the current situation of a company, industry and market. The next step is to forecast future performance based on certain factors such as economy, competition and industry trends. After that, businesses follow different steps to analyse data, both past and present and to make decisions like business development plans or marketing strategies for the future.
Gathering data in preparation for forecasting in business
Business forecasting uses data gathering to prepare a forecast in the following manner:
Choosing data points
This means identifying key factors in the business that have a significant impact on sales, expenses, revenue and profits. Data points used in forecasting often include market trends and demographic and financial data. They use digital information, including social media follows, web page visits, course registration and attendance, completed surveys and successful and abandoned cart information.
Choosing variables and a data set
To forecast, you would choose which variables to use from the data points and how many. For example, if you're analysing website shopping carts, the data points could be the numbers of abandoned carts versus successful purchases. The variables would be the demographics of the users and the items added to the carts that were successful or abandoned.
Then you can decide what is an ideal data set from which to collect this information. In this case, the data set would come from the page that contains the cart and a spreadsheet might store the information. Many website platforms have built-in analytics programmes that can record the numbers of successful and failed purchases, user demographics and the store items that interest consumers.
Business forecast methodology
Forecasting methodologies use data to help a business make predictions about future events so it can plan strategically. Below are the steps used in business forecasts:
Forming a hypothesis
After selecting the data points and variables, you would form a hypothesis about the information you've collected. To do this, indicate what you think the data is saying about the future. To use the shopping cart example, you have roughly the same type of T-shirts that are successfully purchased and abandoned in the same numbers. So you hypothesise that the issue about the final purchase is a demographic factor.
Choosing forecast models
It's key that the variables used in the model are relevant to your hypothesis. For you to explain your hypothesis to stakeholders using variables, visualisations can help. There are a variety of statistical models used in this process, including the regression model, which analyses data and estimates the relationships between variables. For example, to model your shopping cart, you can decide on visualisations that show the numbers of a few demographics that successfully purchase T-shirts of a particular kind versus the same demographics that abandon their carts.
Once you have chosen your data, formed a hypothesis and chosen the modelling methods to use, you can analyse the data. You can do this by choosing the best parameters for the models, calculating errors and incorporating uncertainty. You can identify any problems that present and tweak variables according to your hypothesis.
During your shopping cart analysis, you conclude that one particular demographic purchases more T-shirts because of the lower shipping costs for their area. The other demographics are abandoning the cart because of higher shipping costs. Your forecasts predict this might continue to be the case, so you market more heavily to the group that is purchasing and lower the shipping charges for the other groups to sell more to them.
What is quantitative business forecasting?
Quantitative forecasting is an approach that forecasts using a range of data based on statistical models. Quantitative forecasting uses data mining to discover patterns in historical data. When you find patterns in past information, it's easier to predict future outcomes with a high probability rate.
Indicator approach: The indicator approach helps managers make sense of a wide range of inputs to make better decisions. This includes economic indicators, market data and company trends.
Econometric modelling: This is a statistical method that estimates the relationship between economic variables.
Time series methods: Time series forecasting is one of the most common quantitative techniques and uses historical data to describe behaviour and predict future values by using mathematical models and statistical algorithms.
What is qualitative business forecasting?
Qualitative business forecasting is the study of the economic and social activities of a company. Qualitative forecasting doesn't just consider financial data but also examines factors like customer reviews that are relevant to understanding the ultimate success or failure of a particular product. The Delphi method aids qualitative forecasting:
Qualitative forecasting and the Delphi method
The Delphi method is a forecasting technique that involves asking a panel of experts to estimate the probability of a particular outcome and then analysing their responses. You would gather opinions through different platforms like online surveys and polls. You might categorise the results differently, based on the types of questions asked, as multiple-choice responses might yield slightly different conclusions than written answers.
Business forecasting with financial data analysis
Financial data analysis is a process that uses different statistical tools to explore business data from different perspectives. The main objective of a financial analyst is to analyse the complex relationship between variables in different contexts, improve business performance through insight and provide recommendations for making changes in current or future plans, budgets, forecasts and policies. Business forecasting helps financial analysts evaluate performance and plan for future goals.
Using spreadsheets for business forecasting
Business forecasting can be a long and tedious process of collecting information and making calculations about your business and that of your competitors. Spreadsheets can aid business forecasting because they allow different views of the data and editing is easy. They can also help keep your data organised by saving all of your information on one sheet so that it's readily available, making predictions easier.
There are many benefits to using spreadsheets as part of your forecasting process. Spreadsheets can also help with:
adding future variables that can affect the business in the next year
calculating how many units of a product a company might sell in a quarter
tracking the performance of departments
Business forecasting tools
Business forecasting software is the use of data mining techniques to provide forecasts for businesses with real-time data and machine learning algorithms. Today, most forecasting techniques use complex mathematical models like ARIMA modelling, a form of statistical analysis used to forecast future business performance. But recent advances in computer technology have enabled auto-ARIMA in business forecasting to save time.
Advances in artificial intelligence mean you can provide results in a relatively short amount of time, as opposed to the months it normally takes to examine historical data. AI can also analyse data in real time, so that the results of your forecast are not outdated. They analyse social media trends and predict future popular products. These tools also issue reports on their findings, which can include:
Cash flow statements: These determine the income of the business versus how much money it spends.
Industry reports: If you collect data about your competitors, this information can help you see where you stand in relation to them.
Internal assessments: These evaluate production and marketing.
Production charts: These tell you how much of a product is being manufactured as compared to how many units customers buy.
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