Modeling Seller Listing Strategies

In 12th International Workshop on Agent Mediated Electronic Commerce (AMEC-10) Toronto, Canada, May 2010
Modeling Seller Listing Strategies
Quang Duong, Neel Sundaresan, Zeqian Shen
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eBay Authors
Abstract

Online markets have enjoyed explosive growths and emerged as an important research topic in the field of electronic commerce. Researchers have mostly focused on studying consumer behavior and experience, while largely neglecting the seller side of these markets.

Our research addresses the problem of examining strategies sellers employ in listing their products on online market places. In particular, we introduce a Markov Chain model that captures and predicts seller listing behavior based on their present and past actions, their relative positions in the market, and market conditions. These features distinguish our approach from existing models that usually overlook the importance of historical information, as well as sellers’ interactions.

We choose to examine successful sellers on eBay, one of the most prominent online marketplaces, and empirically test our model framework using eBay’s data for fixed-priced items collected over a period of four and a half months.

This empirical study entails comparing our most complex history-dependent model’s predictive power against that of a semi-random behavior baseline model and our own history-independent model. The outcomes exhibit differences between different sellers in their listing strategies for different products, and validate our models’ capability in capturing seller behavior. Furthermore, the incorporation of historical information on seller actions in our model proves to improve its predictions of future behavior

Another publication from the same author: Nish Parikh

In proceedings of the Workshop on Log-based Personalization (the 4th WSCD workshop) at WSDM 2014

A Large Scale Query Logs Analysis for Assessing Personalization Opportunities in E-commerce Sites

Neel Sundaresan, Zitao Liu

Personalization offers the promise of improving online search and shopping experience. In this work, we perform a large scale analysis on the sample of eBay query logs, which involves 9.24 billion session data spanning 12 months (08/2012-07/2013) and address the following topics

(1) What user information is useful for personalization;

(2) Importance of per-query personalization

(3) Importance of recency in query prediction.

In this paper, we study these problems and provide some preliminary conclusions

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Another publication from the same category: Economics

Proceedings of the Sixteenth ACM Conference on Economics and Computation (EC '15). ACM, New York, NY, USA (2015)

Canary in the e-Commerce Coal Mine: Detecting and Predicting Poor Experiences Using Buyer-to-Seller Messages

Dimitriy Masterov, Uwe Mayer, Steve Tadelis

Reputation and feedback systems in online marketplaces are often biased, making it difficult to ascertain the quality of sellers. We use post-transaction, buyer-to-seller message traffic to detect signals of unsatisfactory transactions on eBay. We posit that a message sent after the item was paid for serves as a reliable indicator that the buyer may be unhappy with that purchase, particularly when the message included words associated with a negative experience. The fraction of a seller's message traffic that was negative predicts whether a buyer who transacts with this seller will stop purchasing on eBay, implying that platforms can use these messages as an additional signal of seller quality.