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Simulating Competitive Market Dynamics: Gaining an Edge Through Predictive Analytics
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Author(s): Parihar Suresh Dahake (Ramdeobaba University, Nagpur, India), Pragati Parihar Dahake (VMV Commerce, JMT Arts, and JJP Science College, Nagpur, India)and Ruchi Sao (Institute of Management, Nirma University, Ahmedabad, India)
Copyright: 2026
Pages: 52
Source title:
Navigating Simulations in Marketing for Strategic Success
Source Author(s)/Editor(s): Andreas Masouras (Neapolis University Pafos, Cyprus)and Marcos Komodromos (University of Nicosia, Cyprus)
DOI: 10.4018/979-8-3373-3141-6.ch009
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Abstract
This study examines competitive market dynamics through the lens of predictive analytics, where multiple predictors, including machine learning models and service providers, compete for user attention and influence decisions. Users' choices feed back into the predictors' learning process, creating a complex adaptive loop that influences the quality of predictions. A theoretical model is proposed, treating both user and predictor decisions as probabilistic outcomes shaped by their historical states and actions. Findings reveal that optimal competition enhances predictive performance, while excessive or insufficient competition degrades it. This framework reflects behaviours akin to multi-agent competitive games. To validate the model, numerical experiments are presented in regression, classification, and consensus-clipping scenarios. Further, the study applies this concept to retail analytics, proposing a data-driven benchmarking framework. Using model-based clustering and competitive learning, it segments stores based on performance dynamics and delivers tailored recommendations through optimization techniques. This work provides strategic insights for both regulators and businesses seeking to refine their prediction systems and enhance market competitiveness.
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