Using Data Mining To Improve Company Strategies 应用数据挖掘改进公司策略 Nan Jiang | njiang6@gmu.edu The Volgenau School of Engineering  | George Mason University ABSTRACT Recent years, data science is getting more and more popular. Data analytics, as a tool discipline, is being used in marketing. Data mining is a branch of data analytics focusing on finding more statistics in a database, or somewhere data is produced and analysis their relationship. The marketing industry has used data to help them make good predictions and right decisions in the coming season for two to three decades. Meanwhile, many companies have gained benefits indeed by practicing data mining in operation. Due to many advantages of data mining, people devote to study data science, and more data correlation methods were discovered to explore valuable information in marketing. This literature review is going to investigate how data mining could be used in marketing to make decisions and find good selling strategies helping companies make better survivals. RESULTS Much useful information is behind the hidden patterns and not showing on the surface, which needs to be excavated. In huge databases, the knowledge such as data mining is the key to support kinds of organizational decisions [3].  Data mining helps business analysts produce hypotheses, but it does not validate the hypotheses. Data mining findings can lead to improvement in the understanding and use of the data to make decisions [4]. Data mining is utilized to build six types of statistical models with the intent to solve business problems: classification, regression, time series, clustering, association analysis, and sequence discovery. Classification and regression are used to make predictions. Analysts usually use association and sequence discovery to describe behavior. Besides, clustering can be used for either forecasting or description [4]. As [5] pointed out, the application of data mining comes in two ways, directed and undirected. Directed data mining attempts to explain or categorize some specific elements such as income or response. Undirected data mining tries to find patterns or similarities among groups of records without the use of a specific field or collection of classes designed in advance. In [6], market basket analysis is also called product affinity analysis. It is already widely-known in modern retailing as a powerful and common practice. It aims to find pairs or sets of products that are jointly observed in large samples of baskets, based on the assumption that purchase of one or more of the products within a set would lead to purchase of the remaining ones, thereby providing leads for cross-selling, bundling, product positioning, etc. Based on [1], market basket analysis is a method to explore customer shopping behavior or pattern. They thought people extract relations or collecting data that happen at the same time. In [7], they indicate customer shopping behaviors are the derived conducts based on people’s demands and needs. According to [8], they classify customer shopping behavior as two types. One is influenced by lifestyle, income levels or age; another one is formed from personal preferences and satisfaction. In many cases, Big Data programs will lead to increased visibility and accountability for decisions that staff will make based on this data. Companies could use digital advertisement to drive their marketing strategies and improve efficiency and effectiveness. [9] When facing large and small basket shoppers, stores want to compete more for large-basket shoppers. Using data analytics program can overcome the problem that stores without information on shopping basket composition end up leaving money on the table. [10] Figure 3 Caption DISCUSSION Through investigating market basket analysis with the question that how data mining could be used, I find that data mining is mostly used as a tool to find the relationship between customers. In another word, I found studying customer shopping behaviors is the way previous research have shown. Therefore, the new information I got form reviewing the literatures is offering different service to different kinds of customers is important. The companies may get benefits by classify customers. Figure 1 Caption FUTURE RESEARCH Future research may explore more about organizing the huge data in the database and generate a complete system to manage data in order to utilize the sufficient data. Data analytics, as a new popular tool discipline, can help industries solve problems. The connection of data mining and marketing needs to be developed. INTRODUCTION Previously research on marketing has met some challenges. One of the challenges for companies is to focus on customer data collection in order to know how to extract important information from the huge customer databases, thereby gain rival superiority. Currently business is facing the challenge of a continue updating market where customer demands are changing all the time. In another word, in [1] and [2], both of them have claimed that for the present, the application of data analysis has not been universal; there are still many companies make mistakes without data analysis. The companies need to utilize a system to deal with data. In this way, the situation they are facing will be easier. Therefore, the purpose of this literature review is to investigate how data mining could be used in marketing to make decisions and find good selling strategies helping companies make better survivals. REFERENCES: [1] Y. Chen, K. Tang, R. Shen and Y. Hu, "Market basket analysis in a multiple store environment", Decision Support Systems, Vol. 40, No. 2, pp. 339-354, 2005. [2] M. Chen, A. Chiu and H. Chang, "Mining changes in customer behavior in retail marketing", Expert Systems with Applications, Vol. 28, No. 4, pp. 773-781, 2005. [3] M. Shaw, C. Subramaniam, G. Tan and M. Welge, "Knowledge management and data mining for marketing", Decision Support Systems, Vol. 31, No.1, pp.127-137,2001. [4] C. Rygielski, J. Wang and D. Yen, "Data mining techniques for customer relationship management", Technology in Society, Vol. 24, No. 4, pp. 483-502, 2002. [5] B. Radhakrishnan, G. Shineraj and K. Anver Muhammed, “Application of Data Mining In Marketing”, IJCSN International Journal of Computer Science and Network, Vol. 2, No. 5, pp. 41-46, 2013. [6] W. Kamakura, "Sequential market basket analysis", Marketing Letters, Vol. 23, No. 3, pp. 505-516, 2012. [7] M. Vahidi Roodpishi and R. Aghajan Nashtaei, "Market basket analysis in insurance industry", Management Science Letters, Vol. 5, No. 4, pp. 393-400, 2015. [8] V. Miguéis, A. Camanho and J. Falcão e Cunha, "Customer data mining for lifestyle segmentation", Expert Systems with Applications, Vol. 39, No. 10, pp. 9359-9366, 2012. [9] C. Jobs, D. Gilfoil and S. Aukers, "How marketing organizations can benefit from big data advertising analytics", Academy of Marketing Studies Journal, Vol. 20, No. 1, pp. 18-35, 2016. [10] N. Kumar and R. Rao, "Research Note—Using Basket Composition Data for Intelligent Supermarket Pricing", Marketing Science, Vol. 25, No. 2, pp. 188-199, 2006. RESEARCH QUESTION What is the relationship between data mining and decision making? What is the current application of data mining in marketing? What is market basket analysis? How to classify customer shopping behavior? How can this data help companies improve selling strategies? Figure 2 Caption