From Static to Dynamic: Revolutionizing Restaurant Pricing Strategies

Abstract

The restaurant industry has a problem with the changing customer demand that it cannot match with its fixed operational capacity, leading to loss of revenue and inefficient use of resources. Traditional static pricing models are not capable of adjusting to real-time demand changes and other contextual factors, thus resulting in suboptimal profitability. This study explores the implementation of dynamic pricing strategies that integrate machine learning, data science, and revenue management principles. It features demand forecasting models (ARIMA, Prophet, LSTM), multi-layered pricing engines with rule-based, elasticity-based, and reinforcement learning approaches, and intelligent table allocation algorithms. The literature from peer-reviewed journals (2003-2025) suggests that dynamic transparent pricing results in revenue increasing by 5-30% and waste reduction by 50%, with customer satisfaction at the same level. The research gaps are the limited number of longitudinal studies, cross-cultural research, and the applicability of small restaurants.