Why some products compete and others don't: A competitive attribution model from customer perspective

Competitive intelligence uses information collected about competitors to derive better managerial insights. In this study, we focus on identifying the competitors and detecting the competitive dimensions concurrently. To achieve this goal, we propose an aspect-level competitive attribution model (a variation of the topic model) to leverage consumer-reviewed products and their review texts. To better analyze product relations and the underlying competitive aspects, we consider consumer limited attention when modeling consumers' preferences and introduce a background aspect to filter out the trivial and maintain the valuable competition-related information in review texts. We validate this approach using a dataset of 785 products reviewed by 15,669 consumers in the auto industry. Based on the empirical experiments, we show that our model can accurately infer high-quality competitive segments and decipher competition-related aspects corresponding to these segments. To highlight differences, we conduct comparisons and find our approach outperforms the benchmark models meaningfully in the literature when predicting consumers' online behaviors.