CONTACT: GEORGE MCCRORY
100 Old Public Library
Iowa City IA 52242
(319) 384-0012; fax (319) 384-0024
Release: Aug. 30, 2000
UI management sciences team wins data mining competition
IOWA CITY, Iowa -- What kinds of people are likely to buy insurance policies
for their trailers? The answer, submitted by assistant professor Nick Street
and Ph.D. candidate YongSeog Kim of the University of Iowa Henry B. Tippie
College of Business, helped the Iowa researchers win the CoIL Challenge, an
international data mining competition.
Street and Kim of the college's department of management sciences won the
description category of the CoIL Challenge, which was created to promote the
application of data mining technology to real world problems. Data mining
is a process of identifying valid and potentially useful patterns of information
drawn from a large volume of data and is being used by companies to make informed
decisions that affect the bottom line.
The competition consisted of two tasks derived from a Dutch insurance company's
direct marketing data. One was to predict which customers would be interested
in the policy, based on 86 variables including product usage data and socio-demographic
data derived from postal codes. The other was to describe why customers have
such a policy and how these customers are different from other people.
For the prediction task, Street and Kim used artificial neural networks
(a machine learning approach inspired by the structure of neurons in the brain)
and an evolutionary search method developed by Filippo Menczer, a UI assistant
professor of management sciences, to choose a subset of the 86 variables that
could reliably predict policy buyers. Their resulting model raised the expected
hit rate of prospective policy buyers from under six percent to over 14 percent,
a significant improvement that ranked with the top handful of entries in the
For the description task, Street and Kim combined information from an artificial
neural network with other data mining techniques to build a descriptive profile
of likely -- and unlikely -- customers. These models learn to spot relationships
in the data that may escape human inspection.
So who are the most likely customers to buy insurance for trailers? They
come from upper-middle-class families with more than one insured automobile,
and possibly other types of insurance policies. Bad bets include both extremes
of the income scale, farmers, and somewhat surprisingly, senior citizens.
Their entry was chosen as the winner from 41 entries submitted from around
the world by both academic and professional contestants, as judged on comprehensibility,
usefulness and actionability.
According to Stephan van Heusden, a marketing consultant from MSP Associates
who judged the contest, a good entry combined a description of the results
with a 'tool' to use the results. He said the UI team "clearly explained"
which steps preceded conclusions. "(They) may have discovered all conclusions
the others also found," but additionally, tried to interpret them, said
For more information, contact Street at (319) 335-1016 or firstname.lastname@example.org,
and see the CoIL challenge website at http://www.dcs.napier.ac.uk/coil/.