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Decision Tree Algorithm for Precision Marketing Via Network Channel Comput Syst Sci & Eng (2020) 4: 293–298 International Journal of © 2020 CRL Publishing Ltd Computer Systems Science & Engineering Decision Tree Algorithm for Precision Marketing via Network Channel ∗ Yulan Zheng College of Economic and Management, Fuzhou University, Fuzhou, Fujian 350108, China With the development of e-commerce, more and more enterprises attach importance to precision marketing for network channels. This study adopted the decision tree algorithm in data mining to achieve precision marketing. Firstly, precision marketing and C4.5 decision tree algorithm were briefly introduced. Then e-commerce enterprise A was taken as an example. The data from January to June 2018 were collected. Four attributes including age, income, occupation and educational background were selected for calculation and decision tree was established to extract classification rules.The results showed that the consumers of the products of the company were high-income young and middle-aged people, middle-income young people, middle-income middle-aged and elderly people with a college degree or above, low-income middle-aged people with a college degree or above and low-income elderly people with a state-owned enterprise. After precision marketing to these customers, it was found that the monthly sales volume of the enterprise increased by 22.82% and the marketing cost decreased by 28.21%, which verified the effectiveness and application value of precision marketing and showed that the decision tree algorithm could provide enterprises with decision support in precision marketing. Keywords: data mining, decision tree, precision marketing, C4.5 1. INTRODUCTION [You, Si, Zhang et al. (2015)] selected attributes through the RFM model. Then important attribute values were identified The rapid development of Internet and e-commerce has by CHAID decision tree and Pareto value, and customer brought challenges to traditional marketing methods. groups were classified. Next targeted marketing strategies Only accurate marketing methods can help enterprises was put forward. Finally, it was proved by the case study not be eliminated in the fierce market price competition that the scheme could provide enterprises with good precision [Luo and Li (2014)]. Marketing activities are inseparable from marketing strategies. Wan et al. [Wang, Fang and Wang data [Elsalamony (2014)]. In a large amount of marketing (2015)] combined EM clustering analysis and neural network data, there is a lot of valuable customer information. algorithm. Firstly, the sales data are preprocessed by EM Through the statistics, collection and analysis of these data clustering, and then classified by neural network to predict the [Ozyirmidokuz, Uyar and Ozyirmidokuz (2015)], precision purchase behavior of customers, which effectively improved the marketing can be realized, which can reduce marketing costs accuracy of direct marketing of enterprises. Hongda [Hongda and improve marketing efficiency [Abbas (2015); Mitik, (2017)] applied the decision tree algorithm to build a classifier Korkmaz, Karagoz et al. (2017)]. In order to extract valuable model by collecting historical data of power users and to mine information from massive data, data mining technology has been customer characteristics and preferences. Through the research widely applied [Karakatsanis, Alkhader, Maccrory et al. (2017)], on the promotion of palm power APP, it is found that this way such as decision tree, association rule, neural network, of precision marketing was effective. Zou and Wang [Zou and etc. [Abessi, Yazdi, Abessi et al. (2014)]. You et al. Wang (2008)] introduced the merchant gene bank model to improve the competitiveness of e-commerce enterprises. A ∗ Email: [email protected] customer preference model was proposed based on optimal vol 35 no 4 July 2020 293 DECISION TREE ALGORITHM FOR PRECISION MARKETING VIA NETWORK CHANNEL neighbor and utility function. Then the genetic algorithm m ( , , , ··· , ) =− ρ (ρ ), was improved and the two models were matched to achieve I s1 j s2 j s3 j smj ij log2 ij (3) = precision marketing. In the era of consumer supremacy, it is i 1 of great practical significance to grasp customer information ρ = sij where ij |S | represents the probability that a sample in S j through data mining technology and conduct targeted marketing j belongs to C j . for improving marketing effect and promoting enterprise The information gain of attribute D is development. Therefore, it is of great value to study the application of data mining technology in precision marketing. Gain(D) = I (s1, s2, s3, ··· , sm ) − E(D) (4) At present, the application of decision tree in precision marketing is seldom, but the algorithm of decision number has The split information of attribute D is low computational complexity and good effect on processing m large data. In this study, the decision tree algorithm in data s j + s j +···+smj SplitInf o(D) =− 1 2 mining technology was used. C4.5 algorithm was used for S classifying the collected customer historical data, constructing j=1 s + s +···+s decision tree and extracting classification rules, so as to 1 j 2 j mj . log2 (5) obtain customer categories with high purchase probability and S conduct precision marketing on them. Moreover an example Then, the information gain rate of the attribute D is analysis was carried to verify the reliability of the decision tree algorithm. This work provides some theoretical supports for Gain(D) GainRatio(D) = . (6) the further application of decision tree algorithm in precision SplitInfo(D) marketing and is beneficial to the better and faster development of precision marketing in the field of e-commerce. The attribute with the largest information gain rate is selected. The data set is divided into different subsets, and the decision tree is established. 2. DECISION TREE ALGORITHM Data classification is a kind of data mining. Through 3. PRECISION MARKETING BASED ON data classification, precise marketing can be realized by DECISION TREE ALGORITHM classifying customer information data. The methods of data classification include genetic algorithm, decision tree 3.1 Data Acquisition and neural network. Decision tree with simple structure and high efficiency has a very good performance in data E-commerce enterprise A is taken as the research subject. The prediction [Wang, Li, Ran et al., 2018] and data classification transaction pattern of e-commerce enterprise A is mainly on [Berhane, Lane, Wu et al. (2018)]. In this study, C4.5 algorithm the line, which helps enterprise A to achieve communication [Li, Jiang, Yao et al. (2017)] in the decision tree algorithm is with customers through the network platform. Its marketing adopted to achieve precision marketing. mode is mainly in the form of Internet advertising, which attracts Training sample set is expressed as S. The sample number of customers to buy by placing advertisements in search engines, the i-th result in category C (i = 1, 2, ··· , m) is expressed as i WeChat, weibo and other online channels. In order to achieve s . The probability that any sample belongs to is expressed as i precision marketing, this paper classifies customers through the ρ . Then it was found that: i decision tree to determine which customers may buy the products S of the enterprise, and is worth putting in the marketing plan. ρ = i , (1) i S The browsing and purchasing information of enterprise products from January to June 2018 was collected, and SQL Server was and the expected information for the result is: used to establish a database. m ( , , ··· , ) =− ρ (ρ ). I S1 S2 Sm i log2 i (2) i=1 3.2 Data Processing Suppose there are u different values of D, D = {d1, d2, ··· , du}. Attribute D is used to divide training sample Since there may be a large number of vacant values and set S,and{s1, s2, s3, ··· , su} is obtained. Suppose that S j incomplete data in the collected data, it is necessary to clean contains training sample set S and there is a sample with value up these data and screen the data to select the information d j in attribute D.IfD is the best splitting property, then these which is valuable to precision marketing, in order to improve samples are branches growing out of the nodes in training sample the data quality and improve the accuracy of data classification. set S. Sij is set as the number of samples belonging to Ci in In the perspective of consumer privacy, private information subset S j , and the expected information of the subset divided such as family status and marital status were not selected. In according to attribute D is: this study, attributes including age, income, occupation and education background were selected to establish the decision u s + s + s +···+S E(D) = 1 j 2 j 3 j mj I (s , s , s ··· , s ) tree. Y was used to represent the final purchase, and N was used S 1 j 2 j 3 j mj j=1 to represent no purchase. The classification is shown in Table 1. 294 computer systems science & engineering Y. Z H E N G Table 1 Client attribute classification. Customer number Age Income Occupation Education Classification background 1 34 5600 Private enterprise College degree Y 2 36 6700 State-owned enterprises College degree Y 3 21 1800 Private enterprise High school N diploma 4 34 5800 State-owned enterprises College degree N 5 56 4500 Individual High school N diploma 6 27 3600 Private enterprise College degree Y …… …… …… …… …… …… n Xn Xn Xn Xn Yn Table 2 Training sample set. Customer Age Income Occupation Education Classification number background 1 Middle-aged Medium income Non-state-owned Below university N
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