Abstract
Consumer shopping decision-making style is a kind of mental orientation, which represents the way consumers make decisions and has cognitive and emotional characteristics. It determines the behaviors of consumers and is relatively stable for a long time, as a result of which can be used as the basis for market segmentation. The traditional way to determine shopping decision-making style is mostly through questionnaire, which is time-consuming and laborious. This paper uses e-commerce data, combines the different commodities purchased by consumers, and comprehensively measures the mental characteristics of consumers when they make purchasing decisions based on their behaviors. By clustering analysis, the shopping decision-making behaviors of consumers are divided into two categories. Considering that clustering is an unsupervised method, its results are often not perfect, which is especially reflected in the data at the junction of two categories. In view of this, we divide the data at the junction of two categories into unclassified data and then train naive Bayes with classified data to classify unclassified data. Ultimately, by synthesizing all shopping decision-making behaviors of each consumer, the decision-making styles of consumers are divided into three categories: direct style, cautious style and neutral style. The experimental results show that the model proposed in this paper makes the classification of consumer decision-making behaviors more intuitive and is obviously superior to the comparison model. This model can effectively determine the consumer decision-making style.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Xue, H.: Review and prospect of research on consumer shopping decision style scale. Consum. Econ. (05), 92–96 (2007)
Ching, C., You, C.: Classifying the segmentation of customer value via RFM model and RS theory. Expert Syst. Appl. 36(3), 4176–4184 (2009)
Xu, X., Wang, J., Tu, H., Mu, M.: Customer classification of E-commerce based on improved RFM model. J. Comput. Appl. 32(05), 1439–1442 (2012)
Andreas, M., Thomas, R.: An improved collaborative filtering approach for predicting cross-category purchases based on binary market basket data. J. Retail. Consum. Serv. 10(3), 123–133 (2003)
David, C.S., Donald, G.M., Richard, C.: Counting your customers: who are they and what will they do next? Manag. Sci. 33(1), 1–24 (1987)
Ma, S.: The Pareto/NBD model extensions. Syst. Eng. 26(8), 123–126 (2008)
Sun, J., Liu, J., Zhao, L.: Clustering algorithms research. J. Softw. 19(01), 48–61 (2008)
Zgou, T., Lu, H.: Clustering algorithm research advances on data mining. Comput. Eng. Appl. 48(12), 100–111 (2012)
Sproles, G.B., Kendall, E.L.: A methodology for profiling consumers’ decision-marking styles. J. Consum. Affairs 20(2), 267–279 (1986)
Zhang, J., Zou, Y.: The mediating role of adolescents’ consumption values between family financial education and consumer decision-making styles. J. Psychol. Sci. 35(02), 376–383 (2012)
Bao, Z., Zhao, Y., Zhao, Y., Hu, X., Gao, F.: Segmentation of Baidu takeaway customer based on RFA model and cluster analysis. Comput. Sci. 45(S2), 436–438 (2018)
Sun, L., Zhang, W.: Electronic recommendation mechanism based on RFM model and collaborative filtering. J. Jiangsu Univ. Sci. Technol. (Nat. Sci. Ed.) 24(03), 285–289 (2010)
Zhang, Z., Yan, J., Chen, F., Li, M.: Feature extraction of customer purchase behavior based on genetic algorithm. Pattern Recognit. Artif. Intell. 23(02), 256–266 (2010)
Yi, Z., Shawkat, A., Katsutoshi, Y.: Consumer purchasing behavior extraction using statistical learning theory. Procedia Comput. Sci. 35, 1464–1473 (2014)
Yada, K.: String analysis technique for shopping path in a supermarket. J. Intell. Inf. Syst. 36(3), 385–402 (2011)
Hui, S.K., Bradlow, E.T., Fader, P.S.: Testing behavioral hypotheses using an integrated model of grocery store shopping path and purchase behavior. J. Consum. Res. 36(3), 478–493 (2009)
Song, T., Wang, X.: Customer behavior prediction for card consumption based on two-step clustering and hidden Markov chain. J. Comput. Appl. 36(07), 1904–1908 (2016)
Zhang, P., Tang, S.: Privacy preserving Naive Bayes classification. Chin. J. Comput. (08), 1267–1276 (2007)
He, J., Meng, Z., Chen, X., Wang, Z., Fan, X.: Semi-supervised ensemble learning approach for cross-project defect prediction. J. Softw. 28(06), 1455–1473 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Liu, B., Guo, W., Lu, X., Xu, M., Cui, L. (2019). A Method of Analysis on Consumer Behavior Characteristics Based on Self-supervised Learning. In: Sun, Y., Lu, T., Yu, Z., Fan, H., Gao, L. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2019. Communications in Computer and Information Science, vol 1042. Springer, Singapore. https://doi.org/10.1007/978-981-15-1377-0_47
Download citation
DOI: https://doi.org/10.1007/978-981-15-1377-0_47
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1376-3
Online ISBN: 978-981-15-1377-0
eBook Packages: Computer ScienceComputer Science (R0)
