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Development of Effective Electronic Customer Relationship Management (ECRM) Model by the Applications of Web Intelligence Analytics

Development of Effective Electronic Customer Relationship Management (ECRM) Model by the Applications of Web Intelligence Analytics

Salma Abdulaziz Alquhtani, M. Anandhavalli Muniasamy
Copyright: © 2022 |Pages: 20
ISBN13: 9781668453865|ISBN10: 166845386X|ISBN13 Softcover: 9781668453872|EISBN13: 9781668453889
DOI: 10.4018/978-1-6684-5386-5.ch003
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MLA

Alquhtani, Salma Abdulaziz, and M. Anandhavalli Muniasamy. "Development of Effective Electronic Customer Relationship Management (ECRM) Model by the Applications of Web Intelligence Analytics." Building a Brand Image Through Electronic Customer Relationship Management, edited by Arshi Naim and Sandeep Kumar Kautish, IGI Global, 2022, pp. 44-63. https://doi.org/10.4018/978-1-6684-5386-5.ch003

APA

Alquhtani, S. A. & Muniasamy, M. (2022). Development of Effective Electronic Customer Relationship Management (ECRM) Model by the Applications of Web Intelligence Analytics. In A. Naim & S. Kautish (Eds.), Building a Brand Image Through Electronic Customer Relationship Management (pp. 44-63). IGI Global. https://doi.org/10.4018/978-1-6684-5386-5.ch003

Chicago

Alquhtani, Salma Abdulaziz, and M. Anandhavalli Muniasamy. "Development of Effective Electronic Customer Relationship Management (ECRM) Model by the Applications of Web Intelligence Analytics." In Building a Brand Image Through Electronic Customer Relationship Management, edited by Arshi Naim and Sandeep Kumar Kautish, 44-63. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-6684-5386-5.ch003

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Abstract

Analysis of customer relationships based on their satisfaction is a practical and motivating success factor for the growth of every company. Web intelligence describes the scientific development that uses information technology and artificial intelligence for new frameworks, services, and products provided by the web. This chapter aims to present the model of analyzing the users' sentiments from their online reviews on an e-commerce platform using machine-learning classifiers, namely naive bayes, logistic regression, support vector machine, and neural network. For data analysis, latent semantic analysis has been applied to examine the most frequent words used in online reviews. Finally, customers' interest in online shopping analysis has been performed to classify the customers' sentiments from their posted reviews on the e-commerce platform. In addition, the authors compared the performance results of these classifiers on the e-commerce dataset. The results reveal that the naive bayes classifier has performed better than all the other three classifiers.

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