Research on the Optimization of Quality Differentiated Product Pricing Strategies and Sales Forecast Models Based on Strategic Customer Behaviors

Abstract

With the intensification of market competition and the diversification of consumer behavior, traditional pricing strategies and sales forecasting models have become unable to adapt to the complex and constantly changing market environment. Pricing strategies based on strategic customer behavior (BBP), stacking ensemble learning methods, Prophet-LSTM combined models, and multi-dimensional grey models combined with neural networks, among others, have become key technologies for optimizing sales forecasting and inventory management at present. This paper combines game theory, machine learning, and deep learning models to explore how the synergy of behavioral pricing strategies, sales forecasting models, and inventory optimization methods can enhance the market adaptability and profitability of enterprises. Research shows that the BBP model can optimize pricing strategies under certain conditions, but it may lead to a “lose-lose” situation in the case of information asymmetry. Stacking ensemble learning methods and Prophet-LSTM combined models can significantly improve the accuracy of sales forecasting, especially in market environments with large fluctuations in demand. The method combining multi-dimensional grey models and neural networks can provide relatively accurate prediction results in the case of incomplete or complex data. Finally, this paper proposes optimization suggestions, such as designing dynamic inventory frameworks and differentiated discount strategies based on real-time behavioral mechanisms, while outlining future trajectories for adaptive artificial intelligence in operations.