What Are the Different Methods of Sales Forecasting?

Sales forecast refers to the forecast of future sales based on past sales and the use of built-in or user-defined sales forecast models in the system. Sales forecast can directly generate the same type of sales plan. One of the central tasks of a sales plan is sales forecasting. No matter the size of the enterprise or the number of sales personnel, sales forecasting affects all aspects of sales management including planning, budgeting, and sales determination.

Sales Forecast

Sales forecast refers to the forecast of future sales based on past sales and the use of built-in or user-defined sales forecast models in the system. Sales forecast can directly generate the same type of sales plan. One of the central tasks of a sales plan is sales forecasting. No matter the size of the enterprise or the number of sales personnel, sales forecasting affects all aspects of sales management including planning, budgeting, and sales determination.

Chinese name

Sales Forecast

Foreign name

sales forecasting

Sales forecast refers to the estimation of the sales quantity and sales amount of all products or specific products in a specific time in the future. The sales forecast is based on full consideration of various future influencing factors, combined with the company's sales performance, and puts forward a practical sales target through a certain analysis method.

1. Through the sales forecast, the enthusiasm of the sales staff can be mobilized, and the product can be sold as soon as possible to complete the change from use value to value.

2. Enterprises can set production based on sales and arrange production according to sales forecast data to avoid product backlog.

3. The product inventory can be managed reasonably and effectively. After the forecast, an inventory warning can be set up for the product, which has guiding significance for the production schedule.

4. After the sales forecast, you can provide reference data for the product replenishment arrangement.

along with

The data mining tools currently available for sales forecasting are mainly

In order to overcome the shortcomings of traditional forecasting systems, data mining technology is applied to sales forecasting. Our design of a data mining-based sales forecast support system consists of

When using data mining technology for sales forecasting, existing data mining tools can automatically complete many tasks, but every step in the mining process should be particularly careful, otherwise incorrect conclusions will be derived. Data mining does not necessarily follow a specific process, but the general steps include the following aspects

Before you start data mining, you must clearly understand the goals of data mining. Clearly identify the business goals of mining in advance and determine the evaluation method to achieve the goals, which will greatly reduce the difficulty and workload of mining.

Select data. This data can be data warehouses or data marts, or it can be data in various online transaction processing systems.

Data pre-processing. This process can improve the quality of sales data, which can help improve the accuracy and performance of subsequent mining processes. High-quality sales decisions must rely on high-quality data. Detecting data anomalies, adjusting data as soon as possible, and reducing the data to be analyzed will result in high returns in the decision-making process.

Before you start digging, you need to specify every detail, determine which ideas need to be verified, and which aspects require tools to draw hypotheses from the data.

Construct a data mining model. Generally, the data is first divided into two parts using a random number as a seed: a training set and a test set, which are used to construct and evaluate the model. Use data mining tools to test the quality of the data and compare the results of various tools to accurately construct the model.

Verify the conclusion. Determine whether the conclusion is correct and meet the business requirements. If the mining result is wrong, look for the cause of the error, re-mine the data, and reconstruct the model.

The rapid development of information processing technology, coupled with the tireless pursuit of sales forecasting results, has made the application of data mining technology in sales forecasting a very natural choice. As a new technology, data mining can analyze sales data in depth, extract useful information hidden in the data, discover and grasp new market opportunities, and provide a scientific basis for enterprise management decisions. The challenges brought by data mining technology to sales forecasting technology will undoubtedly promote the development of sales forecasting.