Predicting the consumer's purchase intention of durable goods: An attribute-level analysis

Abstract

Recently, Retail 4.0 is progressively demanding the accurate prediction of consumer's purchase intention. In this regard, an attribute level decision support prediction model has been developed for providing an influential e-commerce platform to the customers. In order to build the prediction model, brands' social perception score and reviews' polarity are computed from social network mining and sentiment analysis, respectively. Afterward, an appropriate regression analysis and suitable instances have been identified for each attribute to predict the appropriate product attributes. One of the key findings, the camera attributes: sensor, display, and image stabilization pursue the customer attention at the end of the search. The outcomes of this analysis can be beneficial to e-commerce retailers and prepare an efficient search platform for the customers to obtain the desired durable goods in an adorable form. Finally, the sensitivity analysis has also been performed to test the robustness of the proposed model.