Refining pricing strategies of retail firms through game theory models

Abstract

Efficient price setting is crucial for ecommerce and retail businesses, yet implementing pricing strategies pose a challenge. Traditional consumer-oriented pricing approaches based on price sensitivity may not be effective for today as markets are diverse and competitive as well as firms are looking for innovation. This research aims to demonstrate the benefits of analyzing returns of price strategizing processes, regardless of product price elasticity. Pricing among different retail firms, inherently a game-like process, and achieving win-win outcomes should be the goal for a stable market. The objectives include conducting a theoretical study on game theory models applicable to price strategizing automation, implementing these models using computer programs and analyzing the potential gains through comparison with other pricing strategies. Additionally, a proposal for a pricing framework and digital infrastructure for retail businesses will be presented. The methodology involves a comprehensive literature review of game theory models in the domain, evaluating their practicality and suitability for retail contexts using examples and critical analysis. This research primarily explores the application of game theory models to analyze pricing decisions in retail markets. The Bertrand competition model and the game of product differentiation are employed to understand price dynamics, market equilibrium, and product positioning. Mixed Nash Equilibrium (MNE) is utilized to identify optimal strategies, considering the strategic interactions among retailers. Various methods, such as backward induction, linear programming, and Monte Carlo simulation, are applied to calculate MNE based on market characteristics. The study aims to simulate customer behavior using Monte Carlo simulation and analyze strategic interactions among retail firms using game theory models. Stochastic customer behavior is modeled, and lognormal distributions are employed to represent firms’ strategies. The Monte Carlo simulation generates payoffs for each firm, which are then inputted into the game theory model to identify optimal strategies and equilibrium points. Fictitious play and the Moran process are employed to analyze dynamic strategy evolution over time. The findings provide valuable insights into the competitive landscape and decision-making processes in the retail industry, assisting firms in formulating effective pricing strategies for achieving desired outcomes.