International Journal of Science, Engineering and TechnologyAuthors: Dr.S.Subalakshmi, Dr. S. Udhaya
Abstract: The rise of the internet has made pricing a much more dynamic process than ever before. In this paper, we propose an end-to-end solution to implement data-driven pricing optimizations through an integration of demand forecasting, price elasticity, and pricing optimization components. We build a multi-horizon forecasting model leveraging TFTs for accurate future demands forecasts, a product-level price elasticity model based on Bayesian structural time-series models, and a revenue maximization optimization engine to find optimal prices. Our methodology, trained on historical transaction data consisting of 5 million sales transactions for 10,000 SKUs over a 3-year period (2023-2025) achieved a revenue lift of 12.4% (p<0.01) in an A/B test versus two benchmark pricing methods such as cost-plus pricing (4.2% lift) and competitor-based pricing (6.1% lift). The paper concludes with a discussion of implementation challenges and practical guidelines for deploying algorithmic pricing in competitive retail environments.
DOI: https://doi.org/10.5281/zenodo.20415916
