ARTIFICIAL INTELLIGENCE INTEGRATION IN RETAIL: IMPACT ON ASSORTMENT MANAGEMENT AND DYNAMIC PRICING
DOI:
https://doi.org/10.5281/zenodo.20049026Keywords:
Artificial Intelligence, Retail Pricing, Dynamic Pricing, Revenue Optimization, Machine LearningAbstract
In today’s rapidly evolving retail environment, pricing strategies have become more complex and require
innovative approaches to maintain competitiveness.
The emergence of Artificial Intelligence (AI) has created new opportunities to improve pricing practices. AI enables
retailers to analyze large volumes of data, identify patterns, and make more accurate and timely pricing decisions. By
using data-driven insights, retailers can dynamically adjust prices according to market conditions, customer preferences,
and competitor actions.
As a result, AI-based pricing strategies enhance operational efficiency, improve responsiveness to market changes,
and increase overall profitability. This technological advancement represents a significant shift from traditional pricing
models toward more intelligent and adaptive retail pricing systems
References
Chenavaz, R., & Dimitrov, S. (2025). Artificial intelligence and dynamic pricing: A systematic literature review. Economic
Research.
Nowak, M., & Pawłowska-Nowak, B. (2024). Machine learning in e-commerce pricing strategies. Applied Sciences.
Heger, J., & Klein, R. (2024). Assortment optimization: A review. OR Spectrum.
Sun, W., Udwani, R., & Shen, X. (2024). Dynamic assortment optimization under uncertainty. arXiv preprint.
Kim, J., & Oh, S. (2025). Joint pricing and assortment optimization. arXiv preprint.
Roosta, S., et al. (2025). Reinforcement learning for retail pricing and inventory management. PLOS ONE.
Aschersleben, J., & Steiner, W. (2024). Bayesian demand estimation in pricing. OR Spectrum.
Sprenkels, S., Atan, Z., & Adan, I. (2025). Multi-product pricing models. Optimization Letters
