# Marketing Decision Support System Based on Data Mining Technology
###### Abstract  
With the continuous development of business intelligence technology, the application research of decision support systems (DSSs) is deepening. In China, the work in this area started relatively late, and there are few DSS research cases to assist in marketing decision-making. Currently, marketing decision support systems have shortcomings in data integration, historical data, query functions, and data analysis. This article analyzes the characteristics of marketing decision-making, discusses the application of data warehouse, OLAP, and data mining technology in marketing decision support systems, and designs a marketing decision support system based on data mining technology. The system uses a BP neural network to conduct data mining marketing forecasting. A three-layer network model for marketing prediction is established, with sales time, product price, and customer purchasing power as network inputs and output as the sales volume of a certain type of product in different locations. The test results show that the average absolute percentage error of this method is 15.13%, and the prediction accuracy is high. Research shows that with the continuous development of data mining technology, the system cannot only help users conduct scientific and reasonable marketing decision-making analyses, making the marketing decision-making process more scientific and reasonable, but also can bring new ideas to enterprise decision-makers, promoting the continuous improvement and progress of the system. data mining (DM); marketing decisions; decisions support system (DSS) +
Footnote †: journal: Applied  
The development of DM technology provides strong support for enterprises to improve their decision-making ability. Experts, data warehouses, data mining, online analytical processing, and other technologies can not only help users make structured quantitative decisions, they can also help users to make semi-structured and unstructured qualitative decisions, which can greatly improve the effectiveness of decision-making by senior managers [5; 6]. DM can automatically process a large amount of raw data in the database through preset algorithms. It uses various methods and means to extract necessary and meaningful patterns from a large amount of data, excavate the specific relationship between objects, find out people's answers to the required questions, and serve decision-making [7]. DM is a specific step of knowledge discovery. The so-called knowledge discovery refers to the whole process of discovering useful knowledge from data, while DM is the process of extracting patterns from data with special algorithms [8; 9]. The main methods and techniques used to obtain these results include statistics, cluster analysis and pattern recognition, classification of decision numbers, artificial neural network and genetic algorithm, rule induction, and visualization techniques [10]. DM can help decision-makers make more scientific decisions by converting original data into comprehensive data and providing analysis tools for multi-dimensional analysis of data [11]. DM is based on the use of new information about consumers and the market and their mastery [12; 13]. At present, the marketing decision support system still has deficiencies in data integration, historical data, query function, and data analysis. There are few domestic research cases of decision support systems (DSSs) for auxiliary marketing decision-making, so this paper constructs a marketing decision support system based on data mining technology. The system collects, processes, and stores all kinds of information related to marketing decisions according to users' habits and requirements for solving problems, and makes use of mathematical models, expert experience, and knowledge discovered through data mining to conduct scientific and reasonable marketing decision analysis. The results of this study are conducive to a more scientific and reasonable marketing decision-making process, thus improving the efficiency of market management.