Deep learning-driven intelligent pricing model in retail: from sales forecasting to dynamic price optimization

Abstract

Under the wave of the digital era, the retail industry is facing unprecedented fierce competition and a rapidly changing market environment. In this context, developing smart and efficient pricing strategies has become a top priority in the industry. Faced with this challenge, traditional pricing methods are inadequate due to their slow response, insufficient adaptability to instant changes in the market, and over-reliance on historical data and human experience. In response to this urgent need, this study aims to design an intelligent pricing model rooted in deep learning to enhance the vitality and competitiveness of the retail industry. The emerging solution adopted in this article combines Temporal Fusion Transformer (TFT), Ensemble of Simplified RNNs (ES-RNN), and dynamic attention mechanisms, aiming to accurately capture and analyze complex time series data through these advanced technologies. TFT processes multivariate and multi-level data, ES-RNN technology integrates multiple simple versions of recurrent neural networks to enhance predictive power, and the dynamic attention mechanism allows the model to dynamically weight the importance of different points in the time series, thereby improving the effectiveness of feature extraction. Test experimental results on four different data sets show that our models all show excellent performance, and the accuracy of predicted product sales far exceeds traditional models. In addition, with its ability to dynamically adjust pricing, the model demonstrates excellent stability and adaptability amid market fluctuations. This research not only promotes the intelligent transformation of retail pricing strategies, but also provides a more strategic tool for enterprises to compete for market share.