The successful operation of companies in the field of sales requires implementing such a managerial function as forecasting. This is a scientifically based process of predicting sales volumes for the future period, which is probabilistic in nature and is carried out based on a number of factors that affect its result.
If you are thinking about how to forecast sales effectively, note that its primary goal is to determine the sales volume that will be most profitable for the business in the future. In accordance with this, companies allocate their resources in such a way as to achieve the appropriate indicators.
The validity of sales forecasting depends to some extent on the principles used. It is known that sales forecasting is a responsible and difficult task. The application of appropriate principles can ensure the accuracy of the results obtained in the forecasting process.
The main principles of sales forecasting include the following:
- Objectivity. It consists in ensuring the correspondence between the state of the environment on which the forecast is based and its results;
- Minimization of the number of market factors affecting forecasting results. This is because a significant number of factors complicate the forecasting process. The forecaster is faced with the problem of determining their real impact on the forecast value;
- The use of different estimates of forecasting results (optimistic, most probable, and pessimistic). This is due to the development possibilities of various market situations (favorable conditions, the worst situations, intermediate predictions between these two extremes);
- Compliance of the forecast with the purpose of using its results. Such a purpose is usually the substantiation of planned tasks for the sale of goods.
There are different forecasting methods, each providing a corresponding sequence of actions. Historical forecasting is one of the most common and important today. Learn more about it below.
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What Is Historical Data and What Role Does It Play in Sales Forecasting?
Historical data is data on sales over a certain period, such as a year or several months. It plays a significant role in sales forecasting, as it is one of the most important informational tools for planning the activities of both the company as a whole and each of its divisions:
- The finance department uses historical data to plan cash flows, make investment decisions, and draw up operating budgets;
- The production department analyzes historical data to determine volumes, draw up production schedules, and manage inventory;
- The human resources department analyzes historical data for planning the need of employees and as a source of information when concluding collective agreements;
- The purchasing department uses historical data to plan the company’s aggregate need for materials and schedule their supply;
- The marketing department considers historical data to plan marketing programs.
The company can assess the market potential by analyzing the economic environment and collecting all the necessary information (number of consumers, their preferences, etc.). Then, by identifying its own strengths and weaknesses, as well as the advantages of the product, the company can assess sales potential.
After that, it is necessary to take into account all other market constraints, make an initial sales forecast and compare it with the company’s goals. If the initial sales forecast matches these targets, then the forecast can be approved.
Three Key Methods of Analyzing Historical Data in Sales Forecasting
In the simplest case, forecasting sales using historical data can be reduced, for example, to the fact that sales in the next year will equal sales in the current year. This forecast may be quite accurate for a mature industry with little market growth. However, it is often necessary to use more complex methods for analyzing historical data: moving average, exponential smoothing, and decomposition.
Moving Average
The moving average is one of the oldest and most common technical analysis tools. But it is too simple to create an accurate sales forecast. Using this method, the forecast of any period is nothing more than taking the average of several time series observations. For example, if you choose a three-month moving average, the forecast for May will be the average of February, March, and April.
Calculations using moving averages are often much more efficient than methods based on long-term observations. For example, you forecast the sales volume of any product. The average volume for the last few years is 1000 units. Therefore, if a company is planning a significant reduction in the staff of sales agents, it is logical to assume that the average monthly sales volume will also decrease.
Exponential Smoothing
Regression-based methods, such as the Trend and Growth functions, apply the same formula to all forecast points. This makes it much more difficult to respond quickly to shifts in baseline levels. Exponential smoothing circumvents this problem. This is a way to ensure that your forecast responds quickly to all events that occur during the baseline length period.
The basic idea of exponential smoothing is that each new sales forecast is obtained by moving the previous one in the direction that would give better results.
Decomposition
A sales forecasting method called decomposition is commonly used to analyze monthly or quarterly data when there are seasonal sales fluctuations and the manager wants to get sales forecasts not only for the entire specific year but for each of its periods. Historical data makes it possible to determine what proportion of changes in sales volumes are due to fundamental reasons and what is explained by the seasonality of demand.
Conclusion
Forecasting, based on the analysis of historical data, presents possible scenarios for the development of the situation depending on market restrictions. The results of the forecast are the starting point for the development of a sales plan. The resulting forecast is compared with the company’s goals, which allows managers to make decisions about changing the marketing strategy or the goals set, taking into account real indicators.
Data-driven sales forecasts are necessary for investors, as they help to make decisions about investments in new risky businesses. Sales managers use them to formulate pay schemes, set sales quotas, and evaluate the sales staff’s performance. In addition, forecasting sales volumes is one of the important stages of assortment management.