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

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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Identifiers

ORCID for Bart Baesens: orcid.org/0000-0002-5831-5668

ORCID for Christophe Mues: orcid.org/0000-0002-6289-5490

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