[FC Seminar series at UniBz] - TODAY - Seminar of Alexander Tuzhilin, 7th March 2008, 14:30-15:30

TODAY!

I am pleased to invite you to the following seminar of the Faculty of
Computer Science of Bolzano-Bozen.

The seminar takes place at P.za Sernesi, 1, room D101

For the complete list of the Faculty Seminar Series 2007/2008 and
additional information on how to reach us, please visit the web site

http://www.unibz.it/inf/csseminars_1/index.html?LanguageID=EN

07.03.08 14:30-15:30 - Free University of Bolzano-Bozen, P.za Sernesi,
1, room D101

Segmenting Customer Bases in Personalization Applications Using Direct
Grouping and Micro-Targeting Approaches

<http://www.unibz.it/printerversions/newsfull.html?LanguageID=EN&content
id=5241&pageid=11&PV=TRUE&SHOW_CMD=FALSE>

Alexander Tuzhilin, Stern School of Business, New York University

Abstract
It is crucial to segment customers intelligently in order to offer them
more targeted and personalized products and services. Traditionally,
customer segmentation is achieved using statistics-based methods that
compute a set of statistics from the customer data and group customers
into segments by applying clustering algorithms. In this talk an
alternative direct grouping approach is presented that groups customers
not based on computed statistics, but in terms of optimally combining
transactional data of several customers to build a predictive data
mining model of customer behavior for each segment. Then building
customer segments becomes a combinatorial optimization problem of
finding the best partitioning of the customer base into disjoint groups
that collectively yield the best performance of predicting customer
behavior across the constructed segments. It is shown that finding an
optimal customer partition is NP-hard, and several suboptimal direct
grouping segmentation methods are proposed and empirically compared
among themselves and also against traditional statistics-based
segmentation and 1-to-1 methods across multiple experimental conditions.
Also, a micro-targeting method is proposed as an extension of the direct
grouping method that builds predictive models of customer behavior not
on the segments of customers but rather on the customer-product groups.
It is shown empirically that micro-targeting significantly outperforms
the direct grouping and statistics-based segmentation methods across
multiple experimental conditions and that it generates predominately
small-sized segments, thus providing additional support for the
micro-targeting approach to personalization.

Joint work with Tianyi Jiang

CV:
Alexander Tuzhilin is a Professor of Information Systems and NEC Faculty
Fellow at the Stern School of Business, NYU. He received Ph.D. in
Computer Science from the Courant Institute of Mathematical Sciences,
NYU. His current research interests include knowledge discovery in
databases, personalization, recommendation, and CRM technologies. He
published widely in leading CS and IS journals and conference
proceedings. Dr. Tuzhilin served on program and organizing committees of
numerous CS and IS conferences, including as a Program Co-Chair of the
Third IEEE International Conference on Data Mining. He also served on
the Editorial Boards of the IEEE Transactions on Knowledge and Data
Engineering, the Data Mining and Knowledge Discovery Journal, the
INFORMS Journal on Computing (as an Area Editor), the Electronic
Commerce Research Journal and the Journal of the Association of
Information Systems. Results of Dr. Tuzhilin's various academic and
industrial activities were described in major media publications,
including The New York Times, The Wall Street Journal, Business Week,
The Financial Times and The Los Angeles Times.

Reference person: Francesco Ricci <mailto:%20Francesco.Ricci(a)unibz.it>

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