Monodense Deep Neural Model for Determining Item Price Elasticity
Summary: arXiv:2603.29261v1 Announce Type: cross
Item Price Elasticity is a critical metric used to quantify the responsiveness of consumer demand to changes in item prices. This capability enables businesses to devise effective pricing strategies and optimize their revenue management processes. Various sectors, including store retail, e-commerce, and consumer goods, rely heavily on elasticity information derived from historical sales and pricing data. Understanding elasticity provides businesses with insights into purchasing behavior across different items, sensitivity to discounts, and the demand elasticity of various departments.
This information holds particular significance for competitive markets and resource-constrained businesses aiming to maximize profitability and market share. Additionally, price elasticity analysis unveils historical shifts in consumer responsiveness over time, offering vital strategic insights.
Research Overview
In this paper, we introduce a novel framework for modeling item-level price elasticity using large-scale transactional datasets. Our proposed approach has the capability to function effectively even in the absence of treatment control settings. To test this framework, we employ several machine learning-based algorithms, including our newly proposed Monodense deep neural network. The primary methodologies used in our study include:
- Monodense-DL network: A hybrid neural network architecture that incorporates embedding, dense, and Monodense layers, designed for enhanced performance in elasticity estimation.
- DML: A double machine learning setting utilizing regression models to improve accuracy in estimating price elasticity.
- LGBM: The Light Gradient Boosting Model, known for its efficiency and speed in handling large datasets.
Methodology and Evaluation
We evaluated our model on a diverse set of multi-category retail data, encompassing millions of transactions. The evaluation was conducted using a robust back testing framework, allowing us to rigorously assess the model’s performance.
Our experimental results underscore the superiority of the Monodense deep neural network model compared to other prevalent machine learning methods. The findings reveal that the Monodense network not only improves the accuracy of price elasticity estimates but also enhances the interpretability of the results, making it a valuable tool for businesses looking to refine their pricing strategies.
Conclusion
The development of the Monodense deep neural model represents a significant advancement in the field of price elasticity estimation. By leveraging large-scale transactional data, our framework provides businesses with the insights needed to navigate complex market dynamics effectively. This research not only contributes to the academic literature on price elasticity but also offers practical implications for industry stakeholders seeking to optimize pricing strategies and improve revenue management.
In conclusion, the Monodense deep neural network stands as a promising solution for understanding consumer demand responsiveness, thereby empowering businesses to make informed decisions in an increasingly competitive landscape.