Repository logo
 
Publication

Telco customer top‐ups: Stream‐based multi‐target regression

2022-07-29Journal article Restricted access
Simple item page
dc.contributor.authorAlves, Pedro Miguel
dc.contributor.authorFilipe, Ricardo
dc.contributor.authorMalheiro, Benedita
dc.date.accessioned2024-03-11T11:27:10Z
dc.date.available2024-03-11T11:27:10Z
dc.date.issued2022-07-29
dc.description.abstractTelecommunication operators compete not only for new clients, but, above all, to maintain current ones. The modelling and prediction of the top-up behaviour of prepaid mobile subscribers allows operators to anticipate customer intentions and implement measures to strengthen customer relationship. This research explores a data set from a Portuguese operator, comprising 30 months of top-up events, to predict the top-up monthly frequency and average value of prepaid subscribers using offline and online multi-target regression algorithms. The offline techniques adopt a monthly sliding window, whereas the online techniques use an event sliding window. Experiments were performed to determine the most promising set of features, analyse the accuracy of the offline and online regressors and the impact of sliding window dimension. The results show that online regression outperforms the offline counterparts. The best accuracy was achieved with adaptive model rules and a sliding window of 500 000 events (approximately 5 months). Finally, the predicted top-up monthly frequency and average value of each subscriber were converted to individual date and value intervals, which can be used by the operator to identify early signs of subscriber disengagement and immediately take pre-emptive measures.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationAlves, P. M., Filipe, R. Â., & Malheiro, B. (2023). Telco customer top-ups: Stream-based multi-target regression. Expert Systems, 40(5), e13111. https://doi.org/10.1111/exsy.13111pt_PT
dc.identifier.doi10.1111/exsy.13111pt_PT
dc.identifier.issn0266-4720
dc.identifier.urihttp://hdl.handle.net/10400.22/25144
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherJohn Wiley & Sonspt_PT
dc.relationINESC TEC- Institute for Systems and Computer Engineering, Technology and Science
dc.relation.ispartofseries;e13111
dc.relation.publisherversionhttps://onlinelibrary.wiley.com/doi/10.1111/exsy.13111pt_PT
dc.subjectmulti-target regressionpt_PT
dc.subjectstream processingpt_PT
dc.subjectstream processingpt_PT
dc.subjecttelcopt_PT
dc.subjecttop-up predictionpt_PT
dc.titleTelco customer top‐ups: Stream‐based multi‐target regressionpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleINESC TEC- Institute for Systems and Computer Engineering, Technology and Science
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F50014%2F2020/PT
oaire.citation.conferencePlaceHoboken, New Jerseypt_PT
oaire.citation.endPage14pt_PT
oaire.citation.issue5pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleExpert Systemspt_PT
oaire.citation.volume40pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameFilipe
person.familyNameBENEDITA CAMPOS NEVES MALHEIRO
person.givenNameRicardo
person.givenNameMARIA
person.identifier.ciencia-id2E1C-7AC6-0220
person.identifier.ciencia-id7A15-08FC-4430
person.identifier.orcid0000-0003-4438-4176
person.identifier.orcid0000-0001-9083-4292
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication8df4b2d7-7f64-48e8-ba85-a87157be569a
relation.isAuthorOfPublicationbabd4fda-654a-4b59-952d-6113eebbb308
relation.isAuthorOfPublication.latestForDiscoverybabd4fda-654a-4b59-952d-6113eebbb308
relation.isProjectOfPublication5efdbedb-4666-4d5b-94f0-0a938b0d5ce4
relation.isProjectOfPublication.latestForDiscovery5efdbedb-4666-4d5b-94f0-0a938b0d5ce4

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Telco_customer_top-ups_Stream-based_multi-target_r.pdf
Size:
2.1 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

ISEP – LSA – Artigos