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

Alves, Pedro Miguel; Filipe, Ricardo Ângelo; Malheiro, Benedita

Publication

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

2022Journal article

dc.contributor.author	Alves, Pedro Miguel
dc.contributor.author	Filipe, Ricardo Ângelo
dc.contributor.author	Malheiro, Benedita
dc.date.accessioned	2023-01-13T10:41:04Z
dc.date.embargo	2033
dc.date.issued	2022
dc.description.abstract	Telecommunication 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.sponsorship	This work was partially supported by National Funds through FCT - Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UIDB/50014/2020.	pt_PT
dc.description.version	info:eu-repo/semantics/publishedVersion	pt_PT
dc.identifier.citation	Alves, P. M., Filipe, R. ^A., & Malheiro, B. (2022). Telco customer top-ups: Stream-based multi-target regression. Expert Systems, e13111. https://doi.org/10.1111/exsy.13111	pt_PT
dc.identifier.doi	10.1111/exsy.13111	pt_PT
dc.identifier.issn	1468-0394
dc.identifier.uri	http://hdl.handle.net/10400.22/21491
dc.language.iso	eng	pt_PT
dc.peerreviewed	yes	pt_PT
dc.publisher	John Wiley & Sons	pt_PT
dc.relation	INESC TEC- Institute for Systems and Computer Engineering, Technology and Science
dc.relation.publisherversion	https://doi.org/10.1111/exsy.13111	pt_PT
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/	pt_PT
dc.subject	Multi-Target Regression	pt_PT
dc.subject	Sliding Window	pt_PT
dc.subject	Stream processing	pt_PT
dc.subject	Telco	pt_PT
dc.subject	Top-up prediction	pt_PT
dc.title	Telco customer top‐ups: Stream‐based multi‐target regression	pt_PT
dc.type	journal article
dspace.entity.type	Publication
oaire.awardTitle	INESC TEC- Institute for Systems and Computer Engineering, Technology and Science
oaire.awardURI	info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50014%2F2020/PT
oaire.citation.conferencePlace	Hoboken, New Jersey, USA	pt_PT
oaire.citation.endPage	14	pt_PT
oaire.citation.startPage	1	pt_PT
oaire.citation.title	Expert Systems	pt_PT
oaire.fundingStream	6817 - DCRRNI ID
person.familyName	BENEDITA CAMPOS NEVES MALHEIRO
person.givenName	MARIA
person.identifier.ciencia-id	7A15-08FC-4430
person.identifier.orcid	0000-0001-9083-4292
project.funder.identifier	http://doi.org/10.13039/501100001871
project.funder.name	Fundação para a Ciência e a Tecnologia
rcaap.rights	closedAccess	pt_PT
rcaap.type	article	pt_PT
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