Nicolas Christou

English

Research Papers in Economics

JSS Journal of Statistical Software
February 2011, Volume 39, Book Review 1. http://www.jstatsoft.org/
Reviewer: Nicolas Christou
University of California, Los Angeles
Statistical Methods in e-Commerce Research
Wolfgang Jank, Galit Shmueli
John Wiley & Sons, Hoboken, NJ, 2008.
ISBN 978-0-470-1202-5. 430 pp. USD 105.00.
http://www.wiley.com/WileyCDA/WileyTitle/productCd-0470120126.html
Electronic commerce (e-commerce) has become part of our everyday lives. Using the internet
(clicking, buying, selling, rating) generates a vast amount of data. These data can be recorded
and stored and can provide important information that it is not available in the oine world.
The eld of statistics can play a major role in the development of methods for empirical
research related to electronic commerce.
The idea for writing this book started at a conference held at the University of Maryland in
May 2005. The title of the conference was\Statistical Challenges and Opportunities in Elec-
tronic Commerce Research". In this book more than 30 researchers present very interesting
ideas and challenges that arise from the substantial amount of data collected from today's
online buying, selling, advertising, investing, etc. Today, there is a need for statisticians to
collect, clean, and make sense of the massive amount of data generated on the internet. This
book is unique in that it attempts to narrow the gap between statistics and e-commerce. To
achieve this, the authors present the challenges that arise in e-commerce data, propose novel
statistical techniques to tackle the problem, and suggest future research in this eld. This
book does an excellent job in addressing these goals and highlighting the ability of statistics
to facilitate e-commerce research. Additionally, it can be used not only by researchers, but
also by instructors for advanced undergraduate and graduate courses. I believe this book can
be an invaluable tool in educating statisticians and computer scientists in the many areas of
research that e-commerce has to oer.
The book consists of three sections. Five articles from the rst section provide an overview
of the e-commerce research challenges. One can understand the importance of statistics in
e-commerce applications by understanding the oine world when using data from the online
world. By using online data we can understand consumer behavior by answering questions on
economic, marketing, and information systems issues. The digital divide in the United States
(and around the world) presents another potential research problem. Despite the prevalent
use of internet there are still low-income, rural, and small-town communities that have not
joined the online world, and therefore the study on how the geographical location aect e-
commerce is important. The privacy of data gathered online is another issue that requires2 Statistical Methods in e-Commerce Research
new computational and statistical technologies to protect online data from the invasion of
databases by commercial and other queries. The last chapter of the rst section discusses the
enormous data and research problems created on Wikipedia.
The second section of the book examines some e-commerce applications. The topics covered
include price dynamics using online auction data, web usability, internet rms' performance
and survival, changing of regression coecients over time by examining pooled cross-sectional
data, and optimization of search engine marketing bidding strategies.
The last section presents new methods for e-commerce data. The methods described are
clustering e-commerce data into groups with data points in each group similar to each other;
functional data analysis using online auction data; price process in online auctions using
growth models such as exponential, logarithmic, logistic, and reected-logistic models, and
models describing bidders arrival and departure in online auctions. In particular, I nd the
discussion of spatial models in e-commerce to be interesting. Despite the belief that online
markets are geography-independent, spatial models can dramatically increase the performance
of models that explore the spatial correlation exhibited in the data. The authors use data
from online mortgage leads and compare the performance of three models: a non-spatial
model, a static spatial model, and a dynamic spatial model (the static model uses all the
data at once compared to the dynamic model which updates its parameters as consumers join
an online business over time). The spatial models easily outperform the classical regression
model, with the dynamic spatial model giving the best predictions. The last three chapters
of this section deal with dierential equations to model the price dynamics in online auctions,
understanding the purchasing power of consumers (also called wallet estimation), and use of
randomized response in e-commerce.
There is no doubt that internet-based data applications will continue to grow and cover many
areas of the science spectrum. For example, during the 2008 swine u epidemic, Ginsberg
et al. (2009) at Google Inc. were able to forecast on real time the percentage of inuenza-
like illness symptoms by collecting Google search results for \u symptoms, inuenza, u
treatments, etc." Google's forecast was two weeks ahead compared to actual data from the
Center for Disease Control (CDC).
Interesting articles about e-commerce applications also appear on various websites such as
http://radar.oreilly.com/, e.g., Loukides (2010). Today's data scientists must be able to
use statistics and computer science to make e-commerce data tell their story. UC Berkeley's
professor Hal Varian's quote\The ability to take data { to be able to understand it, to process
it, to extract value from it, to visualize it, to communicate it { that's going to be a hugely
important skill in the next decades,"(see McKinsey & Company 2009) summarizes nicely the
importance for the development of new tools to tackle the problem of internet-based data.
In closing, I believe this book is a great reference for statisticians who are interested in
analyzing data from online transactions and it should be consulted for its useful ideas and
proposed research topics.
References
Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L (2009).\Detecting
Inuenza Epidemics Using Search Engine Query Data." Nature, 457, 1012{1014.Journal of Statistical Software { Book Reviews 3
Loukides M (2010). \What is Data Science?" O'Reilly Radar. URL http://radar.oreilly.
com/2010/06/what-is-data-science.html.
McKinsey & Company (2009). \Hal Varian on How the Web Challenges Managers." The
McKinsey Quarterly. URL http://www.mckinseyquarterly.com/Hal_Varian_on_how_
the_Web_challenges_managers_2286.
Reviewer:
Nicolas Christou
University of California, Los Angeles
Department of Statistics
8125 Math Sciences Bldg.
Los Angeles, CA 90095, United States of America
E-mail: nchristo@stat.ucla.edu
http://www.stat.ucla.edu/~nchristo/
Journal of Statistical Software http://www.jstatsoft.org/
published by the American Statistical Association http://www.amstat.org/
Volume 39, Book Review 1 Published: 2011-03-01
February 2011

Statistical Methods in e-Commerce Research

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