The University of Southampton
University of Southampton Institutional Repository

A new SOM-based method for profile generation: theory and an application in direct marketing

A new SOM-based method for profile generation: theory and an application in direct marketing
A new SOM-based method for profile generation: theory and an application in direct marketing
The field of direct marketing is constantly searching for new data mining techniques in order to analyze the increasing available amount of data. Self-organizing maps (SOM) have been widely applied and discussed in the literature, since they give the possibility to reduce the complexity of a high dimensional attribute space while providing a powerful visual exploration facility. Combined with clustering techniques and the extraction of the so called salient dimensions, it is possible for a direct marketer to gain a high level insight about a dataset of prospects. In this paper, a SOM-based profile generator is presented, consisting of a generic method leading to value adding and business-oriented profiles for targeting individuals with predefined characteristics. Moreover, the proposed method is applied in detail to a concrete case study from the concert industry. The performance of the method is then illustrated and discussed and possible future research tracks are outlined.
data mining, customer profiling, som, direct marketing
0377-2217
199-209
Seret, Alex
34884644-0660-4bce-9a0d-97c99492b892
Verbraken, Thomas
40def165-29ac-4a4d-8820-f434ea123b96
Versailles, Sebastien
73dbcc0c-cd6b-4cd6-b81d-f646a077809a
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Seret, Alex
34884644-0660-4bce-9a0d-97c99492b892
Verbraken, Thomas
40def165-29ac-4a4d-8820-f434ea123b96
Versailles, Sebastien
73dbcc0c-cd6b-4cd6-b81d-f646a077809a
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0

Seret, Alex, Verbraken, Thomas, Versailles, Sebastien and Baesens, Bart (2012) A new SOM-based method for profile generation: theory and an application in direct marketing. European Journal of Operational Research, 220 (1), 199-209. (doi:10.1016/j.ejor.2012.01.044).

Record type: Article

Abstract

The field of direct marketing is constantly searching for new data mining techniques in order to analyze the increasing available amount of data. Self-organizing maps (SOM) have been widely applied and discussed in the literature, since they give the possibility to reduce the complexity of a high dimensional attribute space while providing a powerful visual exploration facility. Combined with clustering techniques and the extraction of the so called salient dimensions, it is possible for a direct marketer to gain a high level insight about a dataset of prospects. In this paper, a SOM-based profile generator is presented, consisting of a generic method leading to value adding and business-oriented profiles for targeting individuals with predefined characteristics. Moreover, the proposed method is applied in detail to a concrete case study from the concert industry. The performance of the method is then illustrated and discussed and possible future research tracks are outlined.

This record has no associated files available for download.

More information

Published date: 1 July 2012
Keywords: data mining, customer profiling, som, direct marketing
Organisations: Southampton Business School

Identifiers

Local EPrints ID: 336466
URI: http://eprints.soton.ac.uk/id/eprint/336466
ISSN: 0377-2217
PURE UUID: 21a3310f-1908-40b1-b5fe-a2fda50a3490
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668

Catalogue record

Date deposited: 27 Mar 2012 10:56
Last modified: 15 Mar 2024 03:20

Export record

Altmetrics

Contributors

Author: Alex Seret
Author: Thomas Verbraken
Author: Sebastien Versailles
Author: Bart Baesens ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×