Subgroup evaluation and decision support for a direct  mailing marketing problem

PA Flach; D Gamberger

Subgroup evaluation and decision support for a direct mailing marketing problem

PA Flach
, D Gamberger

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

Abstract

In this work we use ROC (Receiver Operating Characteristic) analysis to evaluate customer subgroups detected by different machine learning approaches in a marketing database. A direct mailing model with a marginal cost per mailing and an average expected profit per new customer has been assumed. In order to identify optimal mailing strategies for different marketing situations, we introduce the normalised profit curve, which extends the ROC curve by not only identifying the optimal subgroup in a given context, but also indicating the expected profit. In this sense, the analysis presents a link between data mining and decision support.

Translated title of the contribution	Subgroup evaluation and decision support for a direct mailing marketing problem
Original language	English
Title of host publication	Unknown
Editors	Christophe Giraud-Carrier, Nada Lavrac, Steve Moyle
Publisher	ECML/PKDD'01 workshop notes
Pages	45 - 56
Number of pages	11
Publication status	Published - Sept 2001

Bibliographical note

Conference Proceedings/Title of Journal: Integrating Aspects of Data Mining, Decision Support and Meta-Learning

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http://www.cs.bris.ac.uk/Publications/pub_info.jsp?id=1000561

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Cite this

@inproceedings{8a7882da8da748aeaa2f169ee6040fb1,

title = "Subgroup evaluation and decision support for a direct mailing marketing problem",

abstract = "In this work we use ROC (Receiver Operating Characteristic) analysis to evaluate customer subgroups detected by different machine learning approaches in a marketing database. A direct mailing model with a marginal cost per mailing and an average expected profit per new customer has been assumed. In order to identify optimal mailing strategies for different marketing situations, we introduce the normalised profit curve, which extends the ROC curve by not only identifying the optimal subgroup in a given context, but also indicating the expected profit. In this sense, the analysis presents a link between data mining and decision support. ",

author = "PA Flach and D Gamberger",

note = "Conference Proceedings/Title of Journal: Integrating Aspects of Data Mining, Decision Support and Meta-Learning",

year = "2001",

month = sep,

language = "English",

pages = "45 -- 56",

editor = "Christophe Giraud-Carrier and Nada Lavrac and Steve Moyle",

booktitle = "Unknown",

publisher = "ECML/PKDD'01 workshop notes",

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TY - GEN

T1 - Subgroup evaluation and decision support for a direct mailing marketing problem

AU - Flach, PA

AU - Gamberger, D

N1 - Conference Proceedings/Title of Journal: Integrating Aspects of Data Mining, Decision Support and Meta-Learning

PY - 2001/9

Y1 - 2001/9

N2 - In this work we use ROC (Receiver Operating Characteristic) analysis to evaluate customer subgroups detected by different machine learning approaches in a marketing database. A direct mailing model with a marginal cost per mailing and an average expected profit per new customer has been assumed. In order to identify optimal mailing strategies for different marketing situations, we introduce the normalised profit curve, which extends the ROC curve by not only identifying the optimal subgroup in a given context, but also indicating the expected profit. In this sense, the analysis presents a link between data mining and decision support.

AB - In this work we use ROC (Receiver Operating Characteristic) analysis to evaluate customer subgroups detected by different machine learning approaches in a marketing database. A direct mailing model with a marginal cost per mailing and an average expected profit per new customer has been assumed. In order to identify optimal mailing strategies for different marketing situations, we introduce the normalised profit curve, which extends the ROC curve by not only identifying the optimal subgroup in a given context, but also indicating the expected profit. In this sense, the analysis presents a link between data mining and decision support.

M3 - Conference Contribution (Conference Proceeding)

SP - 45

EP - 56

BT - Unknown

A2 - Giraud-Carrier, Christophe

A2 - Lavrac, Nada

A2 - Moyle, Steve

PB - ECML/PKDD'01 workshop notes