119 research outputs found
A Special Issue on Statistical Challenges and Opportunities in Electronic Commerce Research
This special issue is a product of the First Interdisciplinary Symposium on
Statistical Challenges and Opportunities in Electronic Commerce Research, which
took place on May 22--23, 2005, at the Robert H. Smith School of Business,
University of Maryland, College Park
(\url{www.smith.umd.edu/dit/statschallenges/}). The symposium brought together,
for the first time, researchers from statistics, information systems, and
related fields, all of whom work or are interested in empirical research
related to electronic commerce. The goal of the symposium was to cross the
borders, discuss joint research opportunities, expose this field and its
statistical challenges, and promote collaboration between the different fields.Comment: Published at http://dx.doi.org/10.1214/088342306000000178 in the
Statistical Science (http://www.imstat.org/sts/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Functional Data Analysis in Electronic Commerce Research
This paper describes opportunities and challenges of using functional data
analysis (FDA) for the exploration and analysis of data originating from
electronic commerce (eCommerce). We discuss the special data structures that
arise in the online environment and why FDA is a natural approach for
representing and analyzing such data. The paper reviews several FDA methods and
motivates their usefulness in eCommerce research by providing a glimpse into
new domain insights that they allow. We argue that the wedding of eCommerce
with FDA leads to innovations both in statistical methodology, due to the
challenges and complications that arise in eCommerce data, and in online
research, by being able to ask (and subsequently answer) new research questions
that classical statistical methods are not able to address, and also by
expanding on research questions beyond the ones traditionally asked in the
offline environment. We describe several applications originating from online
transactions which are new to the statistics literature, and point out
statistical challenges accompanied by some solutions. We also discuss some
promising future directions for joint research efforts between researchers in
eCommerce and statistics.Comment: Published at http://dx.doi.org/10.1214/088342306000000132 in the
Statistical Science (http://www.imstat.org/sts/) by the Institute of
Mathematical Statistics (http://www.imstat.org
E-loyalty networks in online auctions
Creating a loyal customer base is one of the most important, and at the same
time, most difficult tasks a company faces. Creating loyalty online (e-loyalty)
is especially difficult since customers can ``switch'' to a competitor with the
click of a mouse. In this paper we investigate e-loyalty in online auctions.
Using a unique data set of over 30,000 auctions from one of the main
consumer-to-consumer online auction houses, we propose a novel measure of
e-loyalty via the associated network of transactions between bidders and
sellers. Using a bipartite network of bidder and seller nodes, two nodes are
linked when a bidder purchases from a seller and the number of repeat-purchases
determines the strength of that link. We employ ideas from functional principal
component analysis to derive, from this network, the loyalty distribution which
measures the perceived loyalty of every individual seller, and associated
loyalty scores which summarize this distribution in a parsimonious way. We then
investigate the effect of loyalty on the outcome of an auction. In doing so, we
are confronted with several statistical challenges in that standard statistical
models lead to a misrepresentation of the data and a violation of the model
assumptions. The reason is that loyalty networks result in an extreme
clustering of the data, with few high-volume sellers accounting for most of the
individual transactions. We investigate several remedies to the clustering
problem and conclude that loyalty networks consist of very distinct segments
that can best be understood individually.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS310 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
The BARISTA: A model for bid arrivals in online auctions
The arrival process of bidders and bids in online auctions is important for
studying and modeling supply and demand in the online marketplace. A popular
assumption in the online auction literature is that a Poisson bidder arrival
process is a reasonable approximation. This approximation underlies theoretical
derivations, statistical models and simulations used in field studies. However,
when it comes to the bid arrivals, empirical research has shown that the
process is far from Poisson, with early bidding and last-moment bids taking
place. An additional feature that has been reported by various authors is an
apparent self-similarity in the bid arrival process. Despite the wide evidence
for the changing bidding intensities and the self-similarity, there has been no
rigorous attempt at developing a model that adequately approximates bid
arrivals and accounts for these features. The goal of this paper is to
introduce a family of distributions that well-approximate the bid time
distribution in hard-close auctions. We call this the BARISTA process (Bid
ARrivals In STAges) because of its ability to generate different intensities at
different stages. We describe the properties of this model, show how to
simulate bid arrivals from it, and how to use it for estimation and inference.
We illustrate its power and usefulness by fitting simulated and real data from
eBay.com. Finally, we show how a Poisson bidder arrival process relates to a
BARISTA bid arrival process.Comment: Published in at http://dx.doi.org/10.1214/07-AOAS117 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Smoothing sparse and unevenly sampled curves using semiparametric mixed models: An application to online auctions
Functional data analysis can be challenging when the functional objects are sampled only very sparsely and unevenly. Most approaches rely on smoothing to recover the underlying functional object from the data which can be difficult if the data is irregularly distributed. In this paper we present a new approach that can overcome this challenge. The approach is based on the ideas of mixed models. Specifically, we propose a semiparametric mixed model with boosting to recover the functional object. While the model can handle sparse and unevenly distributed data, it also results in conceptually more meaningful functional objects. In particular, we motivate our method within the framework of eBay's online auctions. Online auctions produce monotonic increasing price curves that are often correlated across two auctions. The semiparametric mixed model accounts for this correlation in a parsimonious way. It also estimates the underlying increasing trend from the data without imposing model-constraints. Our application shows that the resulting functional objects are conceptually more appealing. Moreover, when used to forecast the outcome of an online auction, our approach also results in more accurate price predictions compared to standard approaches. We illustrate our model on a set of 183 closed auctions for Palm M515 personal digital assistants
Bidder Migration and Its Price Effects on Auctions
Auctions are often not independent from each other, and the movement of bidders across different auctions is one of the key linkages. We propose different measures of bidder movements (which we call bidder migration in this paper) and how such migration affects the price outcome of later auctions. Moreover, we identify two potentially confounding effects: the learning effect where bidders learn to become more sophisticated bidders, hence driving down the price of later auctions; and the desperation effect where bidders, in a hope to obtain the product that they previous couldn’t win, tend to increase the prices. We empirically investigated these effects using bidding history data from eBay and Generalized Linear Model specifications. We further discussed potential applications of bidder migration for online auction platforms, such as bidder segmentation, dynamic promotions, and shill bidder detection. These bidder migration measures can be provided to internet auction sellers as a value-added service
Getting the Most out of Third Party Trust Seals: An Empirical Analysis
Electronic markets have successfully adopted third party trust seals as a self-regulatory mechanism to enhance consumer trust. While there exist many papers supporting the effectiveness of trust signals, interaction between trusts seals and contextual factors in e-commerce (e.g., value of shopping carts, number of trust seals displayed, shopper experience and retailer’s sales volume) is an underexplored area. In this study, we exploit a dataset of over a quarter million of online transactions across 493 online retailers collected from randomized field experiments. A large trust seal provider conducted the experiments and subsequently shared the dataset with us. Our main contribution is the demonstration of four variables moderating the effectiveness of trust seals on the likelihood of purchase completion. More specifically, our work shows that trust seals are more effective for small online retailers and new shoppers, thus serving as partial substitutes for both shopper experience and seller’s sales volume. Interestingly, we find that presence of too many (i.e., more than two) seals can lower the likelihood of purchase completion. Our findings also show that trust seals are more effective for higher value shopping carts but only in the latter stages of the shopping cycle. Finally, we discuss the implications of our findings for online retailers, third party certifiers, as well as for policy makers
Competition Between Auctions
Even though auctions are capturing an increasing share of commerce, they are typically treated in the theoretical economics literature as isolated. That is, an auction is typically treated as a single seller facing multiple buyers or as a single buyer facing multiple sellers. In this paper, we review the state of the art of competition between auctions. We consider three different types of competition: competition between auctions, competition between formats, and competition between auctioneers vying for auction traffic. We highlight the newest experimental, statistical and analytical methods in the analysis of competition between auctions
- …