Empirical Studies In Online Retail Operations And Dynamic Pricing

This dissertation studies empirical problems in retail operations management and dynamic pricing through three essays. The first essay studies the financial impact of offering faster delivery in online retail. Using econometric policy analysis framework, we study a quasi-experimental setting in which a group of U.S. customers for a large apparel retailer experienced a reduction in delivery time due to the opening of a new distribution center (DC). We show that faster delivery increased sales growth by 0.58% per week following the opening of the new DC, with the effect varying inversely with respect to distance from the new DC. The second essay studies the design of free shipping threshold policy in online retail using transaction data from a major online apparel retailer. We develop models of customer demand and product return behavior that are consistent with empirical data to determine the optimal level of free shipping threshold. In particular, we incorporate a behavior called order padding, in which customers deliberately inflate their orders to qualify for free shipping, and its effect on product return. We analyze the model to show that a free shipping threshold policy is most effective when the retailer faces high product margin, low shipping revenue, low product return probability, and when order padding does not cause customers to delay future purchase. The third essay studies practical issues in large-scale multiproduct dynamic pricing. We partner with a Major League Baseball (MLB) franchise to develop a demand model for its single-game tickets. The demand model is then used to evaluate the effectiveness of dynamic pricing policies. The demand model indicates that due to various practical constraints in pricing, the franchise was unable to benefit from the use of dynamic pricing. We address these issues and use simulation to show that revenue improvement of up to 15% can be achieved through the effective use of dynamic pricing. We also show that a properly calibrated fixed pricing policy based on a detailed demand model can achieve similar levels of revenue improvement as the optimal dynamic pricing policy

NCART: Neural Classification and Regression Tree for Tabular Data

Deep learning models have become popular in the analysis of tabular data, as they address the limitations of decision trees and enable valuable applications like semi-supervised learning, online learning, and transfer learning. However, these deep-learning approaches often encounter a trade-off. On one hand, they can be computationally expensive when dealing with large-scale or high-dimensional datasets. On the other hand, they may lack interpretability and may not be suitable for small-scale datasets. In this study, we propose a novel interpretable neural network called Neural Classification and Regression Tree (NCART) to overcome these challenges. NCART is a modified version of Residual Networks that replaces fully-connected layers with multiple differentiable oblivious decision trees. By integrating decision trees into the architecture, NCART maintains its interpretability while benefiting from the end-to-end capabilities of neural networks. The simplicity of the NCART architecture makes it well-suited for datasets of varying sizes and reduces computational costs compared to state-of-the-art deep learning models. Extensive numerical experiments demonstrate the superior performance of NCART compared to existing deep learning models, establishing it as a strong competitor to tree-based models

Analyze the Robustness of Classifiers under Label Noise

This study explores the robustness of label noise classifiers, aiming to enhance model resilience against noisy data in complex real-world scenarios. Label noise in supervised learning, characterized by erroneous or imprecise labels, significantly impairs model performance. This research focuses on the increasingly pertinent issue of label noise's impact on practical applications. Addressing the prevalent challenge of inaccurate training data labels, we integrate adversarial machine learning (AML) and importance reweighting techniques. Our approach involves employing convolutional neural networks (CNN) as the foundational model, with an emphasis on parameter adjustment for individual training samples. This strategy is designed to heighten the model's focus on samples critically influencing performance.Comment: 21 pages, 11 figure