Analysis of Brand Effects in Data-Driven Design Based on Online Reviews

Abstract

Recently, online user-generated data have emerged as a valuable source for consumer product research. However, most studies have neglected the brand effect, although it is a significant factor in conventional market research. This paper demonstrates the importance of brands in data-driven design using online reviews. Specifically, this study utilizes game theory and suggests a game setting representing market competition. Elements of the game are determined based on online data analysis. The proposed approach consists of four stages. The first stage divides online customers into different segments and analyzes them to extract the feature importance of each brand in each segment. The importance is based on the positive term frequency of features, and it becomes the customer's partial utility for each feature. The second stage defines the specification of product candidates and calculates their costs. This study refers to real market datasets (bill of materials) available online. At this point, the game is all set. The third stage finds the Nash equilibrium of the designed game, and the final stage compares the optimal strategy for a product portfolio with and without brand consideration. The suggested approach was tested on smartphone reviews from Amazon. The result shows that the lack of brand consideration leads a company to choose a non-optimal product strategy, illustrating the significance of the brand factor.