Types of Forecasting: Uses and Benefits of Different Approaches
by DJ Team, on September 14, 2020
Forecasting is an important component in business success. While you can build a business without this type of analysis in your decision making, you won't be able to develop a clear strategy with excellent ROI. There are different types of forecasting that can be useful in all areas of business.
In general, forecasting techniques start with data. With some types of forecasting, you'll use historical data that's internal to the company, target audience, sales, and growth. Other types of forecasting will be informed by external data, such as competitor analysis and overall industry trajectory. The purposes of forecasting can be varied. Overall, you use this information to help make informed business decisions about where to invest funds, which products should be increased or upgraded, and which areas of your marketing should be increased or decreased.
Good forecasting techniques can help prepare you for upcoming fluctuations in the industry, the overall market, and for your specific business. While there is no crystal ball that can guarantee you a future of certainty, the information garnered through forecasting can help keep your business decisions on track for success. Businesses that spend time analyzing their own progress and the overall market are in a better position to anticipate changes and pivot when the unexpected happens.
While some businesses can grow organically without investing in this type of informed analysis, the massive ROI you can see when solid predictive methods are used highlights the importance of forecasting. Oftentimes the first step to forecasting future sales is to make sure you have a firm grasp of what efforts are driving revenue. This is often easier said than done, but a comprehensive marketing attribution solution can make that first step much easier. We are getting ahead of ourselves, let’s take a step back and look at the types of sales forecasting and the benefits of each.
Types of Forecasting in Decision Making
Virtually every business uses some types of forecasting in decision making, overall or by department. Your sales department, for instance, almost certainly reports a growth rate over the past year in order to predict future sales and benchmark. There are a lot of different models that you can use in decision making and the calculation methods can vary. For instance, statistical forecasting uses data or statistics over a set period of past performance in order to predict upcoming events. One example of this might be in investing.
People who invest in the stock market pay close attention to the past performance of the set stock and the market as a whole to determine when to buy and sell and which stocks will do well. A savvy investor will also pay attention to other current events and facts in the world. For instance, investors who thought a few steps ahead during the pandemic may have invested in companies like Zoom and IoT technologies because there would be such an increase in use with everyone working from home.
There are specific formulas to calculate forecasting, depending on which model you use. Forecasting sales can be fairly straightforward with simple formulas and many of these can be done within an excel workbook or other type of accounting software. There are also more complicated formulas that can reap different results.
Demand Forecasting: Types of Forecasting Techniques
Demand forecasting models attempt to predict customer demand. This is useful in a few different areas. It helps businesses estimate production in order to meet customer demand. Often you'll use historical data of past purchases and logistics to help inform the number of products you'll need to meet the demands of the market.
Demand forecasting can use short term or long term historical data. For a company that has incremental growth or a pretty steady rate of growth, historical data can be enough to give a good overall view of what to expect in the market. Other factors may come into play for a company with quick growth or if there are unusual changes impacting your industry. If you've added new marketing initiatives to increase sales, short term demand forecasting techniques might need to be applied in order to stay on track to meet new customer demands.
Quantitative vs Qualitative Forecasting Methods
In terms of predicting your customer demand and business growth, you'll often talk about qualitative forecasting examples or quantitative forecasting examples. These are overall terms for the type of data sets that you're using to arrive at your insights.
Qualitative forecasting examples use industry opinions and business insights that are garnered from experience. They do use the current data, as well, but they don't rely solely on data. Instead, it uses interpretations based on past experience and deep knowledge of the subject matter, industry, and historical growth. Simply put, seasoned professionals develop a better grasp of what might be coming simply because they've seen it before in the industry.
With quantitative forecasting, you're solely using data sets and numbers to help inform your predictions. There is no opinion or hunches involved in quantitative forecasting, though you might use the quantitative forecasting results to help inform your qualitative forecasting predictions, or to check them with hard data.
Types of Sales Forecasting
There are many sales forecasting methods that you can choose from and you might use any number of them together or separately. Sales forecasting is an important component of benchmarking your company's success in performance, customer service, product development, and marketing. After all, every department in your business works toward the goal of increasing sales and profits.
Forecasting models are an important component in preparing your business for the immediate and long term future. The results can help you make hiring decisions, balance your revenue, prepare your inventory, and otherwise shore up production in order to meet the demands of the market to exceed customer service needs.
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