A Bayesian based approach for analyzing customer's online sales data to identify weights of product attributes

© 2022 Elsevier LtdE-commerce websites include large volume of online customer data regarding customer preferences. This study puts forward a novel Bayesian methodology to estimate the impact of product attributes on customer satisfaction by analyzing online data, so that product designers and market researchers are facilitated in their decision making processes. This method proves that valuable information on customer insights can be provided even if we have only data of overall customer satisfaction score and product attribute characteristics. The unknown data are acquired via statistical methods such that non-parametric density estimation is utilized to estimate distributions of satisfaction scores of product attributes. The impacts of product attributes on customer satisfaction are considered as weights of mixture kernel distributions and posterior distributions of weights are simulated with Markov Chain Monte Carlo method. The applicability of the proposed methodology is demonstrated by a case study, in which online data of mobile phone market are analyzed