Abstract
The current intense market competition environment force many enterprises take more and more attention to customer demands, and adopt effective methods to evaluate the importance of customer satisfaction. In order to analysis the customer’s actual need, enterprises need to use the effective data analysis method to analyze customer satisfaction. The economic development of e-commerce era has made the original offline entity transactions convert into online transactions. The way of traditional survey is no longer suitable for the analysis of customer satisfaction. For the lafite wine which sells on the tmall market, the author collected the data of many shops, adopted the method of SVM (support vector machine), analyzed the main factors that affect customer satisfaction, and find their own shortcomings at the end. This method improved the precision of the analysis of customer satisfaction, and can help policymakers understand the demand of customers.
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References
Xiao, Y., Liu, B., Luo, D., et al.: Multi-agent system for customer relationship management with SVMs tool. Int. J. Intell. Inf. Database Syst. 4(2), 121–136 (2010)
Al-nsour, S., Alryalat, H., Alhawari, S.: Integration between Cloud Computing Benefits and Customer Relationship Management (CRM) Processes to Improve Organization’s Performance. Int. J. Cloud Appl. Comput. 4(2), 73–86 (2014)
Mohammadhossein, N., Ahmad, M.N., Zakaria, N.H., et al.: A study towards the relation of customer relationship management customer benefits and customer satisfaction. Int. J. Enterp. Inf. Syst. (IJEIS) 10(1), 11–31 (2014)
Turker, M., Koc-San, D.: Building extraction from high-resolution optical spaceborne images using the integration of support vector machine (SVM) classification, Hough transformation and perceptual grouping. Int. J. Appl. Earth Obs. Geoinf. 34, 58–69 (2015)
Yu, L., Wang, S., Lai, K.K.: Developing an SVM-based ensemble learning system for customer risk identification collaborating with customer relationship management. Front. Comput. Sci. China 4(2), 196–203 (2010)
Acknowledgment
This work was supported by National NSF of China (61170038), NSF of Shandong, China (ZR2011FM001), Technology development projects of Shandong, China (2012G0020314), Soft scienceresearch project of Shandong, China (2013RZB01019), Jinan City independent innovation plan project in Colleges and Universities, China (201401202), Ministry of education of Humanities and social science research projects, China (12YJA630152), Social Science Fund Project of Shandong, China (11CGLJ22), outstanding youth scientist foundation of Shandong, China (BS2013DX037), young star of science and technology plan project, Jinan (20120108), science and technology development project, Jinan (201211003), science and technology development project, Jinan (201305004).
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Jiang, Z., Zang, W., Liu, X. (2016). Customer Satisfaction Analysis Based on SVM. In: Zu, Q., Hu, B. (eds) Human Centered Computing. HCC 2016. Lecture Notes in Computer Science(), vol 9567. Springer, Cham. https://doi.org/10.1007/978-3-319-31854-7_63
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DOI: https://doi.org/10.1007/978-3-319-31854-7_63
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