A Data-Driven Game Theory Approach for Optimizing Sales Strategies in Food SMEs - M. Imron Mas'ud; Misbach Munir; Matheus Nugroho; Nuriyanto (Universitas Islam Negeri Sultan Syarif Kasim Riau, 2026)
This study aims to develop an optimal sales strategy for food SMEs by applying a data-driven game theory approach while examining its managerial implications for productivity improvement. Productivity is interpreted as improved business performance through more effective resource allocation and increased sales efficiency rather than as a direct operational productivity measure. The research employs a quantitative approach integrating descriptive analysis, validity and reliability testing using SPSS, and game theory modeling solved with POM-QM. Primary data were collected through structured questionnaires from 33 customers of a food service SME, evaluating attributes including price, taste, portion, presentation, and promotion. The novelty of this study lies in integrating empirical customer perception data into game-theoretic payoff matrices to support strategic decision-making in SMEs. Unlike previous studies relying on descriptive, AHP, or regression-based approaches, this research demonstrates a transparent and practical data-driven game theory framework for sales strategy optimization. The results show a saddle point with a game value of 1, indicating a pure strategy equilibrium. Promotion emerges as the dominant strategy for both competing menu products, with a probability of 1, suggesting that intensive promotional efforts provide the most stable and optimal outcome. These findings indicate that while price and taste remain important, promotion is the key strategic lever for improving sales performance and productivity. The study contributes to the literature by demonstrating the practical application of data-driven game theory to support SME decision-making and enhance marketing efficiency, competitiveness, and business productivity.