A decision-making framework for precision marketing
Introduction
Due to the accelerated pace of economic globalization and increasing market competition, economic pressures and competition have led enterprise managers to face the problem of choosing the right strategic decision-making policies for selling the right products to the right customers at the right time, such that the companies can increase their profits. Recently, it has been recognized that precision marketing has become a key means of generating profit and is becoming increasingly important as customers become better informed about the products and their rights as consumers. The availability of customer data and transaction records provides better understanding of customers’ consumption behaviors and preferences. In the increasingly competitive environment, enterprises have to create a decision-making model for precision marketing that provides appropriate strategies to manage the market positioning system for fulfilling their customers’ needs. The research motivation of this paper is stemmed from a real-world project. This project considers a marketing problem where the supplier or manufacturer provides different products for retail customers, of which some may sell well in some customer segments and some may not. Products that are not sold will be returned back to the supplier. Therefore, the supplier needs to find a good marketing strategy that minimizes goods in stock and satisfies the supplier, retailers and consumers. In recent years, the decision-making problems have received much attention due to a wide range of real-world applications. Many decision-making techniques have been proposed in literature. Chen and Wang (2009) presented multi-criteria optimization and compromise solutions. Saen (2010) developed a technique for order performance via similarity to ideal solution. Hsu, Chiang, and Shu (2010) developed a nonlinear programming for decision-making, and Lin, Chen, and Ting (2011) presented a Linear programming for decision making.
When discussing the decision making in this context, it is important to pay attention to the role of artificial intelligence (AI). The decision support framework of AI is considered to be a major tool for obtaining information related to historical data collections using some artificial Intelligence technique such as genetic algorithm (GA), artificial colony optimization (Ghasab, Khamis, Mohammad, and Fariman, 2015), and other data-mining techniques (Chai, Liu, & Ngai, 2013). Nowadays, data-mining, which can extract useful customer information and discover the hidden customer’s behaviors from big data, has a great influence on guiding decision making and forecasting the effects of decisions. Guo, Yuan, and Tian (2009) used a hierarchical potential support vector machine (HPSVM) for supplier selection and improved the accuracy. Chang and Hung (2010) presented a rough set theory (RST) to analyze the rules of supplier selection and guide the decision making. Chai et al. (2013) conducted a systematic review of literature about the application of decision-making techniques in supplier selection. They classified these techniques into three categories: Multi-criteria decision making techniques (MCDM), mathematical programming techniques (MP), and artificial intelligence techniques (AI). MCDM is a methodological framework that aims to provide decision makers a knowledgeable recommendation amid a finite set of alternatives (Chai et al., 2013). Wang, Wu, Wang, Zhang, and Chen (2014) developed two kinds of prioritized aggregation operators of IVHFLNs for MCDM.
However, in practice, it is not an easy task to choose a good data mining tool since, each data-mining tool has its own advantages and disadvantages. For example, artificial neural networks (ANNs) involve too many hidden neurons and training parameters (Zhang, Jiang, & Li, 2005). Its disadvantages include the “black box” nature, greater computational burden and proneness to over-fitting. However, an advantage of ANN is that ANN has the ability to implicitly detect complex nonlinear relationships between dependent and independent variables. Decision trees are simple to use and easy to understand and they offer many advantages compared with other decision-making tools. However, the disadvantages of decision trees include their instability and relatively low performance. Hence, there is no single best model for all the cases (Akın, 2015). Recently, researchers attempt to combine different models together by considering their respective advantages. Guo, Wong, and Li (2013) presented a multivariate intelligent decision-making mode which combined three different model to complete every phase of the decision making process. Tadić, Zečević, and Krstić (2014) developed a novel hybrid MCDM model that combines fuzzy decision making trial and evaluation laboratory model to provide support to decision makers. Furthermore, Yan and Ma (2015) proposed a novel two-stage group decision-making approach to uncertain quality function deployment.
To the best of our knowledge, the marketing problem considered in this paper is still a new problem. The objective of this paper is to propose a decision-making framework for combining various data-mining algorithms to achieve precision marketing of real products. Real-world data that include historical monthly supply (quantity) and information of every customer were collected from a company in China. The goal is to find a model that can classify targeted customers and predict supply quantity and then provide a strategy for precision marketing, i.e., deciding the quantity of products that every store needs. Depending on the different characteristics and requirements of each phase of the marketing model, the decision-making framework uses four data-mining models/algorithms, which are K-means algorithm, decision tree, Pareto ratio method, and RFM model, for decision-making. Overall, the purpose of this study is to generate, using data-mining techniques, a decision-making model for products’ precision marketing. The proposed decision-making framework is more accurate due to its integrated precision strategies which combine prediction models, clustering, and classifying model. Moreover, the proposed framework incorporates RFM model and Pareto ratio into the process of customer segmentation so that the generated strategies are more convincing.
The rest of this paper is organized as follows. Section 2 introduces related works on common data-mining models and algorithms. Section 3 describes the methodology in our proposed framework briefly. Section 4 presents a case study and results of analyses. Finally, Section 5 concludes the paper.
Section snippets
Related works
Since a single data-mining model may only be suitable for a specific problem, such as prediction or clustering or classification, in the proposed framework, we have combined four data-mining models or algorithms to derive a precision marketing strategy for enterprises. The literature on these four models or algorithms was reviewed below.
Decision-making framework
This section details the method employed in this study. The decision-making framework that decides the weekly supply quantity of product among customers includes the following four components (Fig. 1): (1) Data preparation: prepare and analyze the quality of customers to determine their objectives analytically; (2) Supply quantity forecasting: predict the monthly supply quantity by using the appropriate model; (3) Customer classification: analyze every dimension of the customers’ information
Experimental results
Real-world data used in this section were collected from a company in China. The data include six products: Smith, Howard, David, Edward, Yun, and Edward1. The production information such as monthly supply quantity, ordered quantity, ordered times, D-value, Lever of customers, commercial activities, scale of operations, commercial environment, and marketing type are known. The details are given in Table 1. The data are processed to ensure confidentiality of the company’s business information.
Conclusions
This paper proposes a general decision-making framework which combines a trend prediction model, K-means algorithm, and CHAID algorithm. A trend prediction model is presented to forecast supply quantity, computational results show that the proposed model outperforms the well-known prediction models such as SARIMA and SVM from commercial software. RFM is used to select attributes for k-means algorithm. The result of clustering is used for the decision attribute of CHAID algorithm. Pareto ratio
Acknowledgments
The work was partially supported by the National Nature Science Foundation of China (61272003) and the Major Program of the National Social Science Foundation of China (Grant no. 13&ZD148) and University of Macau (under grant MYRG041(Y1-L1)-FST13-SYW and RG074/09-10S/SYW/FST).
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