Predicting the Consumer‟s Product Purchase Intention Using Regression Analysis at Attribute Level

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2019 by IJCTT Journal
Volume-67 Issue-9
Year of Publication : 2019
Authors :  K Radha, V Karthik , K Manish
  10.14445/22312803/IJCTT-V67I9P103

MLA

MLA Style: K Radha, V Karthik , K Manish  "Predicting the Consumer‟s Product Purchase Intention Using Regression Analysis at Attribute Level" International Journal of Engineering Trends and Technology 67.9 (2019):11-20.

APA Style K Radha, V Karthik , K Manish. Predicting the Consumer‟s Product Purchase Intention Using Regression Analysis at Attribute Level International Journal of Engineering Trends and Technology, 67(9),11-20.

Abstract
Recently, Retail 4.0 is higher demand for accurate prediction of consumer’s purchase intention. In this regard, an attribute level decision support prediction model has been created for providing an influential online shopping platform to the customers. In order to build the prediction model, brand’s social reviews’ polarity are calculated from social network mining and sentiment analysis, respectively. Afterward, an appropriate regression analysis and required instances have been found for each attribute to predict the appropriate product stats. One of the key findings, the camera attributes: sensor, display, and image stabilization make the customer attention at the end of the search. The outcomes of this analysis can be profitable to online retailers and prepare an efficient platform for the customers to obtain the desired goods. Finally, the sensitivity analysis has also been done to test the robustness of the applied model.

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Keywords
attribute level decision support prediction model, regression analysis, social network mining, sentiment analysis, e-commerce retailers