[2603.29261] Monodense Deep Neural Model for Determining Item Price Elasticity

Computer Science > Machine Learning arXiv:2603.29261 (cs) [Submitted on 31 Mar 2026] Title:Monodense Deep Neural Model for Determining Item Price Elasticity Authors:Lakshya Garg, Sai Yaswanth, Deep Narayan Mishra, Karthik Kumaran, Anupriya Sharma, Mayank Uniyal View a PDF of the paper titled Monodense Deep Neural Model for Determining Item Price Elasticity, by Lakshya Garg and 5 other authors View PDF HTML (experimental) Abstract:Item Price Elasticity is used to quantify the responsiveness of consumer demand to changes in item prices, enabling businesses to create pricing strategies and optimize revenue management. Sectors such as store retail, e-commerce, and consumer goods rely on elasticity information derived from historical sales and pricing data. This elasticity provides an understanding of purchasing behavior across different items, consumer discount sensitivity, and demand elastic departments. This information is particularly valuable for competitive markets and resource-constrained businesses decision making which aims to maximize profitability and market share. Price elasticity also uncovers historical shifts in consumer responsiveness over time. In this paper, we model item-level price elasticity using large-scale transactional datasets, by proposing a novel elasticity estimation framework which has the capability to work in an absence of treatment control setting. We test this framework by using Machine learning based algorithms listed below, including our newl...