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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-1889
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dc.contributor.authorSchatzmann, Anders-
dc.contributor.authorHeitz, Christoph-
dc.contributor.authorMünch, Thomas-
dc.date.accessioned2018-03-27T14:48:01Z-
dc.date.available2018-03-27T14:48:01Z-
dc.date.issued2014-
dc.identifier.isbn978-3-938137-57-4de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/4316-
dc.description.abstractWithin quantitative marketing, churn prediction on a single customer level has become a major issue. An extensive body of literature shows that, today, churn prediction is mainly based on structured CRM data. However, in the past years, more and more digitized customer text data has become available, originating from emails, surveys or scripts of phone calls. To date, this data source remains vastly untapped for churn prediction, and corresponding methods are rarely described in literature. Filling this gap, we present a method for estimating churn probabilities directly from text data, by adopting classical text mining methods and combining them with state-of-the-art statistical prediction modelling. We transform every customer text document into a vector in a high-dimensional word space, after applying text mining pre-processing steps such as removal of stop words, stemming and word selection. The churn probability is then estimated by statistical modelling, using random forest models. We applied these methods to customer text data of a major Swiss telecommunication provider, with data originating from transcripts of phone calls between customers and call-centre agents. In addition to the analysis of the text data, a similar churn prediction was performed for the same customers, based on structured CRM data. This second approach serves as a benchmark for the text data churn prediction, and is performed by using random forest on the structured CRM data which contains more than 300 variables. Comparing the churn prediction based on text data to classical churn prediction based on structured CRM data, we found that the churn prediction based on text data performs as well as the prediction using structured CRM data. Furthermore we found that by combining both structured and text data, the prediction accuracy can be increased up to 10%. These results show clearly that text data contains valuable information and should be considered for churn estimation.de_CH
dc.language.isoende_CH
dc.publisherFachhochschule Münsterde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectChurnde_CH
dc.subjectChurn predictionde_CH
dc.subjectText miningde_CH
dc.subjectText datade_CH
dc.subjectRandom forestde_CH
dc.subjectCRMde_CH
dc.subject.ddc658.8: Marketingmanagementde_CH
dc.titleChurn prediction based on text mining and CRM data analysisde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
zhaw.publisher.placeMünsterde_CH
dc.identifier.doi10.21256/zhaw-1889-
zhaw.conference.details13th International Science-to-Business Marketing Conference: «Cross Organizational Value Creation», Winterthur, 2-4 June 2014de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end310de_CH
zhaw.pages.start296de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewNot specifiedde_CH
zhaw.title.proceedingsConference proceedings of the 13th international science-to-business marketing conference on cross organizational value creationde_CH
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Schatzmann, A., Heitz, C., & Münch, T. (2014). Churn prediction based on text mining and CRM data analysis [Conference paper]. Conference Proceedings of the 13th International Science-to-Business Marketing Conference on Cross Organizational Value Creation, 296–310. https://doi.org/10.21256/zhaw-1889
Schatzmann, A., Heitz, C. and Münch, T. (2014) ‘Churn prediction based on text mining and CRM data analysis’, in Conference proceedings of the 13th international science-to-business marketing conference on cross organizational value creation. Münster: Fachhochschule Münster, pp. 296–310. Available at: https://doi.org/10.21256/zhaw-1889.
A. Schatzmann, C. Heitz, and T. Münch, “Churn prediction based on text mining and CRM data analysis,” in Conference proceedings of the 13th international science-to-business marketing conference on cross organizational value creation, 2014, pp. 296–310. doi: 10.21256/zhaw-1889.
SCHATZMANN, Anders, Christoph HEITZ und Thomas MÜNCH, 2014. Churn prediction based on text mining and CRM data analysis. In: Conference proceedings of the 13th international science-to-business marketing conference on cross organizational value creation. Conference paper. Münster: Fachhochschule Münster. 2014. S. 296–310. ISBN 978-3-938137-57-4
Schatzmann, Anders, Christoph Heitz, and Thomas Münch. 2014. “Churn Prediction Based on Text Mining and CRM Data Analysis.” Conference paper. In Conference Proceedings of the 13th International Science-to-Business Marketing Conference on Cross Organizational Value Creation, 296–310. Münster: Fachhochschule Münster. https://doi.org/10.21256/zhaw-1889.
Schatzmann, Anders, et al. “Churn Prediction Based on Text Mining and CRM Data Analysis.” Conference Proceedings of the 13th International Science-to-Business Marketing Conference on Cross Organizational Value Creation, Fachhochschule Münster, 2014, pp. 296–310, https://doi.org/10.21256/zhaw-1889.


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