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Domain knowledge integration in data mining for churn and customer lifetime value modelling: new approaches and applications

Domain knowledge integration in data mining for churn and customer lifetime value modelling: new approaches and applications
Domain knowledge integration in data mining for churn and customer lifetime value modelling: new approaches and applications
The evaluation of the relationship with the customer and related benefits has become a
key point for a company’s competitive advantage. Consequently, interest in key
concepts, such as customer lifetime value and churn has increased over the years.
However, the complexity of building, interpreting and applying customer lifetime value
and churn models, creates obstacles for their implementation by companies. A proposed
qualitative study demonstrates how companies implement and evaluate the importance
of these key concepts, including the use of data mining and domain knowledge,
emphasising and justifying the need of more interpretable and acceptable models.
Supporting the idea of generating acceptable models, one of the main contributions of
this research is to show how domain knowledge can be integrated as part of the data
mining process when predicting churn and customer lifetime value. This is done
through, firstly, the evaluation of signs in regression models and secondly, the analysis
of rules’ monotonicity in decision tables. Decision tables are used for contrasting
extracted knowledge, in this case from a decision tree model. An algorithm is presented,
which allows verification of whether the knowledge contained in a decision table is in
accordance with domain knowledge. In the case of churn, both approaches are applied
to two telecom data sets, in order to empirically demonstrate how domain knowledge
can facilitate the interpretability of results. In the case of customer lifetime value, both
approaches are applied to a catalogue company data set, also demonstrating the
interpretability of results provided by the domain knowledge application. Finally, a
backtesting framework is proposed for churn evaluation, enabling the validation and
monitoring process for the generated churn models.
de Oliveira Lima, Elen
8f1ffca1-e1b6-45fb-a571-e7c5e77aa7d7
de Oliveira Lima, Elen
8f1ffca1-e1b6-45fb-a571-e7c5e77aa7d7
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Mues, Christophe
07438e46-bad6-48ba-8f56-f945bc2ff934

de Oliveira Lima, Elen (2009) Domain knowledge integration in data mining for churn and customer lifetime value modelling: new approaches and applications. University of Southampton, School of Management, Doctoral Thesis, 239pp.

Record type: Thesis (Doctoral)

Abstract

The evaluation of the relationship with the customer and related benefits has become a
key point for a company’s competitive advantage. Consequently, interest in key
concepts, such as customer lifetime value and churn has increased over the years.
However, the complexity of building, interpreting and applying customer lifetime value
and churn models, creates obstacles for their implementation by companies. A proposed
qualitative study demonstrates how companies implement and evaluate the importance
of these key concepts, including the use of data mining and domain knowledge,
emphasising and justifying the need of more interpretable and acceptable models.
Supporting the idea of generating acceptable models, one of the main contributions of
this research is to show how domain knowledge can be integrated as part of the data
mining process when predicting churn and customer lifetime value. This is done
through, firstly, the evaluation of signs in regression models and secondly, the analysis
of rules’ monotonicity in decision tables. Decision tables are used for contrasting
extracted knowledge, in this case from a decision tree model. An algorithm is presented,
which allows verification of whether the knowledge contained in a decision table is in
accordance with domain knowledge. In the case of churn, both approaches are applied
to two telecom data sets, in order to empirically demonstrate how domain knowledge
can facilitate the interpretability of results. In the case of customer lifetime value, both
approaches are applied to a catalogue company data set, also demonstrating the
interpretability of results provided by the domain knowledge application. Finally, a
backtesting framework is proposed for churn evaluation, enabling the validation and
monitoring process for the generated churn models.

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More information

Published date: January 2009
Organisations: University of Southampton

Identifiers

Local EPrints ID: 65692
URI: http://eprints.soton.ac.uk/id/eprint/65692
PURE UUID: e23dd2fe-2d53-443c-96ef-66b51012fb03
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668
ORCID for Christophe Mues: ORCID iD orcid.org/0000-0002-6289-5490

Catalogue record

Date deposited: 17 Mar 2009
Last modified: 14 Mar 2024 02:49

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Contributors

Author: Elen de Oliveira Lima
Thesis advisor: Bart Baesens ORCID iD
Thesis advisor: Christophe Mues ORCID iD

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