Using Favorite Data to Analyze Asymmetric Competition: Machine Learning Models

Abstract User-generated data enable businesses to derive competitive intelligence from the perspective of customers. With the advent of favorite data, we propose a sparse biterm-based Dirichlet process model and bipartite graph model with a random walk algorithm to analyze asymmetric competition. Through investigating the asymmetric market structure, representativeness degree of each entity, and competition network, the proposed machine learning models provide managerial insights into market competition. Based on 832,897 customers’ favorite lists, we empirically employ the proposed models to analyze the competition among the 2,204 car models in China's automotive market. Drawing on the activities of many customers in the market, our models enhance firms’ understanding of how the market is segmented by customers, which segments are most popular, and how entities compete with each other within the competition network. We can also inform managers of the segments within which a product competes, the representativeness of its competitors, and the leaders in each segment. The empirical results demonstrate that our models are practical in deriving insights about asymmetric competition for managers.