doi:

DOI: 10.3724/SP.J.1249.2017.03300

Journal of Shenzhen University Science and Engineering (深圳大学学报理工版) 2017/34:3 PP.300-305

A keyword-based mining method for customer segmentation


Abstract:
We propose a novel customer segmentation method using keyword-based data mining approach. First, keywords about customer characteristics from original customer information are extracted by natural semantic processing. Then, keywords related to intrinsic characteristics are tagged. Based on the keywords, customers with the specific characteristics are identified. Finally, we use the identified customers as the training samples to obtain more keywords about the customer characteristics, and conduct a new round of customer segmentation. After the learning process, customer segmentation groups based on intrinsic characteristics are obtained. Compared with the benchmarking method of random selection of feature keywords for customer segmentation, the feasibility and validity of the proposed method are verified by a case study where a high level of accuracy rate and robustness is observed in the customer segmentation results.

Key words:artificial intelligence,natural language processing,knowledge engineering,customer segmentation,keyword mining,customer characteristics,data mining

ReleaseDate:2017-06-16 14:08:45



[1] 王扶东,马玉芳.基于数据挖掘的客户细分方法的研究[J].计算机工程与应用,2011,47(4): 215-218. Wang Fudong, Ma Yufang. Research of method for customer segment based on data mining[J]. Computer Engineering and Application, 2011, 47(4): 215-218.(in Chinese)

[2] Foedermayr E K, Diamantopoulos A. Market segmentation in practice: review of empirical studies, methodological assessment, and agenda for future research[J]. Journal of Strategic Marketing, 2008, 16(3): 223-265.

[3] Saliba S J, Turner R E. Marketing management: analysis, planning, implementation and control[M]. 8th ed. Philip K, Ronald E T. Scarborough, Canadian: Prentice-Hall, 1995.

[4] Tsiptsis K, Chorianopoulos A. Data mining techniques in CRM: inside customer segmentation[M]. Hoboken, USA: John Wiley & Sons, 2011.

[5] Liang Daolei, Chen Haibo. An online mall CRM model based on data mining[C]//Quantitative Logic and Soft Computing. Hangzhou, China: Springer International Publishing, 2017, 510: 599-606.

[6] Chan K Y, Kwong C K, Hu B Q. Market segmentation and ideal point identification for new product design using fuzzy data compression and fuzzy clustering methods[J]. Applied Soft Computing, 2012, 12(4): 1371-1378.

[7] Green P E, Carroll J D, Goldberg S M. A general approach to product design optimization via conjoint analysis[J]. The Journal of Marketing, 1981, 45(3): 17-37.

[8] Butler J C, Dyer J S, Jia Jianmin, et al. Enabling e-transactions with multi-attribute preference models[J]. European Journal of Operational Research, 2008, 186(2): 748-765.

[9] Rojanavasu P, Dam H H, Abbass H A, et al. A self-organized, distributed, and adaptive rule-based induction system[J]. IEEE Transactions on Neural Networks, 2009, 20(3): 446-459.

[10] Brusco M J, Cradit J D, Stahl S. A simulated annealing heuristic for a bicriterion partitioning problem in market segmentation[J]. Journal of Marketing Research, 2002, 39(1): 99-109.

[11] Ayoubi M. Customer segmentation based on CLV model and neural network[J]. International Journal of Computer Science Issues, 2016, 13(2): 31.

[12] Liu H-H, Ong C-S. Variable selection in clustering for marketing segmentation using genetic algorithms[J]. Expert Systems with Applications: an international Journal, 2008, 34(1): 502-510.

[13] Lee E-K, Cook D, Klinke S, et al. Projection pursuit for exploratory supervised classification[J]. Journal of Computational and Graphical Statistics, 2012, 14(4): 831-846.

[14] Duda R O, Hart P E. Pattern classification and scene analysis[M]. New York, USA: Wiley, 1973.

[15] Swain P H, Hauska H. The decision tree classifier: design and potential[J]. IEEE Transactions on Geoscience Electronics, 1977, 15(3): 142-147.

[16] Zou K H, Warfield S K, Bharatha A, et al. Statistical validation of image segmentation quality based on a spatial overlap index 1[J]. Academic radiology, 2004, 11(2): 178-189.

[17] Budayan C, Dikmen I, Birgonul M T. Comparing the performance of traditional cluster analysis, self-organizing maps and fuzzy c-means method for strategic grouping[J]. Expert Systems with Applications, 2009, 36(9): 11772-11781.

[18] 袁 兵,黄 静, 曾一帆.网络评论语言的抽象性对消费者品牌态度与购买意愿的影响——一项基于语言类别模型 (LCM) 的实证研究[J].营销科学学报,2013(3):17-30. Yuan Bing, Huang Jing, Zeng Yifan. The effect of language abstraction in online reviews on consumer's brand attitude and buying intention: an empirical research based on the linguistic category model (LCM)[J]. Journal of Marketing Science, 2013, 9(3): 17-30.(in Chinese)

[19] 陈星宇,黄俊文,周 展,等. 基于本体论的大数据下用户需求表征[J]. 深圳大学学报理工版,2017,34(2):173-180. Chen Xingyu, Huang Junwen, Zhou Zhan, et al. Ontology-based user requirements representation in the context of big data[J]. Journal of Shenzhen University Science and Engineering, 2017,34(2): 173-180.(in Chinese)

[20] 张 润,王永滨.机器学习及其算法和发展研究[J].中国传媒大学学报自然科学版,2016,23(2):10-18. Zhang Run, Wang Yongbin. Research on machine learning with algorithms and development[J]. Journal of Communication University of China Science and Technology, 2016, 23(2): 10-18.(in Chinese)

[21] Davies J, Sure Y, Grobelnik M, et al. Automated knowledge discovery in advanced knowledge management[J]. Journal of Knowledge Management, 2005, 9(5):132-149.

[22] Lüthje C. Characteristics of innovating users in a consumer goods field: an empirical study of sport-related product consumers[J]. Technovation, 2004, 24(9): 683-695.