# Marketing Decision Support System Based on Data Mining Technology
## 4 Analysis and Discussion of Marketing Experiment Simulation Results  
(1) Prediction of data mining in decision support systems  
Sales prediction is to analyze sales volume and sales profit through data mining technology to predict future development trends. There are many data mining algorithms used for sales prediction. This article introduces an artificial neural network method for sales prediction, designs a data mining model for sales prediction, and validates it with data from a sales data warehouse to obtain a better data mining model for sales prediction. BP networks are currently the most widely used neural network with strong mapping ability, which can achieve arbitrary nonlinear mapping between input and output. To establish a sales forecast model using the BP network, first of all, the factors that affect the sales forecast and sales historical data should be used as input parameters. Then, the predicted sales data are used as output parameters to establish a network model. When making a prediction, you can input the prediction time and calculate the sales value for the predicted time based on the network model. In order to validate the BP sales prediction network model, part of the sales data was extracted from the database FoodMart 2000 provided by Microsoft and converted into the sales data warehouse we created as simulation data. Select the sales information of the enterprise's food products in 2020 after being summarized on a monthly basis, take the data of the first eight months as a training sample, and the data of the ninth month as a verification sample. The network inputs are the sales time of the product, the predicted monthly average price of the product, the average revenue of the customer who purchased the product, and the monthly sales volume of the product in different regions. The sales volume of goods in different regions in the next month is used as the output of the network. In order to determine the length of historical data used for network input, this article selects the historical data length by comparing experimental results. The historical data length takes the historical sales volume and its influencing factors in the previous 1, 2, and 3 months, and compares the prediction accuracy of the three groups. Select the best group and use the length of historical data used as the network input for this type of product. Using the artificial neural network toolbox of MATLAB, a three-layer BP network prediction model with a hidden layer is constructed. The number of input neurons in the network is 27, 54, 81, and the number of output neurons is 24. The number of neurons in the hidden layer is determined experimentally. The activation functions of the hidden layer and the output layer are the hyperbolic tangent function tansig and the linear function purelin, respectively. Momentum term coefficient mc = 0.95; learning rate lr = 0.01; error_goal = 0.00001; iteration number epoch = 10,000; premmx normalization processing is performed on the input data before network training. The training method combines the additional momentum method and the adaptive learning rate method. By training and verifying the third group of network models, the results show that when the number of hidden layer neurons is 10, the training effect is better, so this article selects the number of hidden layer neurons as 10. Through training three sets of models, the average absolute percentage error results of training times and validation samples under different models are shown in Table 1.  
Through the validation of three sets of models of the BP sales prediction network, the average absolute percentage error of the three sets of validation samples is less than 20%, which is a good prediction model, indicating that using a BP neural network for sales prediction is feasible. From the perspective of prediction accuracy, the accuracy of the first three months of historical data length is significantly higher than that of the other two groups.