Bayesian semiparametric customer base segmentation of mobile phone users based on longitudinal traffic data
Customer segmentation is one of the most important purposes of customer base analysis for telecommunication companies. Because companies accumulate very large amounts of data on customer behavior, segmentation is typically achieved by profiling and clustering traffic behavior jointly with demographi...
Saved in:
| Published in | Applied stochastic models in business and industry Vol. 31; no. 5; pp. 721 - 731 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Blackwell Publishing Ltd
01.09.2015
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 1524-1904 1526-4025 |
| DOI | 10.1002/asmb.2085 |
Cover
| Abstract | Customer segmentation is one of the most important purposes of customer base analysis for telecommunication companies. Because companies accumulate very large amounts of data on customer behavior, segmentation is typically achieved by profiling and clustering traffic behavior jointly with demographic data and contracts characteristics. Unfortunately, most algorithms and models used for segmentation do not take into account the longitudinal characteristics of data. In particular, in telecommunication traffic analysis, the importance of decreasing patterns of traffic in customers' lives is well known, and it is relevant to aggregate all clients with such a pattern, while other unknown clusters may be of interest for the marketing manager. Our approach to address this problem is based on specifying the distribution of functions as a mixture of a parametric hierarchical model describing the decreasing pattern segment and a nonparametric contamination that allows unanticipated curve shapes in subjects' traffic. The parametric component is chosen based on prior knowledge, while the contamination is characterized as a functional Dirichlet process. Copyright © 2014 John Wiley & Sons, Ltd. |
|---|---|
| AbstractList | Customer segmentation is one of the most important purposes of customer base analysis for telecommunication companies. Because companies accumulate very large amounts of data on customer behavior, segmentation is typically achieved by profiling and clustering traffic behavior jointly with demographic data and contracts characteristics. Unfortunately, most algorithms and models used for segmentation do not take into account the longitudinal characteristics of data. In particular, in telecommunication traffic analysis, the importance of decreasing patterns of traffic in customers' lives is well known, and it is relevant to aggregate all clients with such a pattern, while other unknown clusters may be of interest for the marketing manager. Our approach to address this problem is based on specifying the distribution of functions as a mixture of a parametric hierarchical model describing the decreasing pattern segment and a nonparametric contamination that allows unanticipated curve shapes in subjects' traffic. The parametric component is chosen based on prior knowledge, while the contamination is characterized as a functional Dirichlet process. Copyright © 2014 John Wiley & Sons, Ltd. Customer segmentation is one of the most important purposes of customer base analysis for telecommunication companies. Because companies accumulate very large amounts of data on customer behavior, segmentation is typically achieved by profiling and clustering traffic behavior jointly with demographic data and contracts characteristics. Unfortunately, most algorithms and models used for segmentation do not take into account the longitudinal characteristics of data. In particular, in telecommunication traffic analysis, the importance of decreasing patterns of traffic in customers' lives is well known, and it is relevant to aggregate all clients with such a pattern, while other unknown clusters may be of interest for the marketing manager. Our approach to address this problem is based on specifying the distribution of functions as a mixture of a parametric hierarchical model describing the decreasing pattern segment and a nonparametric contamination that allows unanticipated curve shapes in subjects' traffic. The parametric component is chosen based on prior knowledge, while the contamination is characterized as a functional Dirichlet process. |
| Author | Scarpa, Bruno |
| Author_xml | – sequence: 1 givenname: Bruno surname: Scarpa fullname: Scarpa, Bruno email: Correspondence to: Bruno Scarpa, Department of Statistical Sciences, University of Padua, Via Cesare Battisti 241, 35121 Padua, Italy., scarpa@stat.unipd.it organization: Department of Statistical Sciences, University of Padua, Via Cesare Battisti 241, 35121, Padua, Italy |
| BookMark | eNp9kEtP3TAQRi1EJR7tov_Ay3YR8COJkyXQ8hIFIVq16sYaxxNqSOJb2xHcf48vF3VRCVYeac73WXN2yObkJyTkI2d7nDGxD3E0e4I11QbZ5pWoi5KJavN5LgvesnKL7MR4xxjnpeLb5P4QlhgdTDTi6BYQYMQUXEe7OSY_YqAGIubl7YhTguT8RH1PR2_cgHTxJ_9O54ghPnOW5vXgp1uXZusmGGgK0Pe5zkKC9-RdD0PEDy_vLvlx_PX70WlxcXVydnRwUXSyZFVRS2GtaAwzVpSMW45QccEbVjfGSBSdUg20DeeVhN4okU9ppeSitKxvje3lLvm07l0E_3fGmPToYofDABP6OWquasGUUK3I6Oc12gUfY8BeL4IbISw1Z3olVK-E6pXQzO7_x3ZubSQf6Ya3Eg9Z1vL1an1w8-3wJVGsEy4mfPyXgHCvayVVpX9enujz09-_1PWXWl_KJ-X_mk0 |
| CitedBy_id | crossref_primary_10_1016_j_telpol_2015_09_004 |
| Cites_doi | 10.1093/biomet/92.2.419 10.1111/j.1541-0420.2007.00829.x 10.1111/1467-9868.00342 10.1002/9780470316801 10.1016/j.tele.2013.08.006 10.1111/j.1541-0420.2007.00761.x 10.1214/aos/1176342360 10.1002/0470863692 10.1080/01621459.1995.10476550 10.1111/j.1541-0420.2008.01163.x 10.1111/1467-9868.00265 10.1093/biomet/83.2.275 10.4018/jssoe.2011070101 10.1007/978-1-4757-7107-7 10.1108/03090560710737552 10.1080/01621459.2013.866564 10.1016/j.eswa.2008.02.021 10.1214/088342305000000016 10.1198/016214501753381913 10.1002/9781119207863 10.1214/aos/1176342752 10.1198/jasa.2009.0001 10.1093/bioinformatics/18.9.1194 10.1016/j.tele.2013.08.004 10.1177/0092070300281006 10.1016/j.tele.2013.04.004 10.1093/biomet/asn054 |
| ContentType | Journal Article |
| Copyright | Copyright © 2014 John Wiley & Sons, Ltd. |
| Copyright_xml | – notice: Copyright © 2014 John Wiley & Sons, Ltd. |
| DBID | BSCLL AAYXX CITATION 7SC 7SP 7TA 8FD JG9 JQ2 L7M L~C L~D |
| DOI | 10.1002/asmb.2085 |
| DatabaseName | Istex CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Materials Business File Technology Research Database Materials Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | CrossRef Materials Research Database Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace Materials Business File Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | CrossRef Materials Research Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Mathematics |
| EISSN | 1526-4025 |
| EndPage | 731 |
| ExternalDocumentID | 10_1002_asmb_2085 ASMB2085 ark_67375_WNG_JHZX7QD6_N |
| Genre | article |
| GroupedDBID | .3N .GA .Y3 05W 0R~ 10A 1L6 1OB 1OC 23M 31~ 33P 3SF 3WU 4.4 50Y 50Z 51W 51X 52M 52N 52O 52P 52S 52T 52U 52W 52X 5GY 5VS 66C 702 7PT 8-0 8-1 8-3 8-4 8-5 8UM 8VB 930 A03 AAESR AAEVG AAHQN AAMMB AAMNL AANHP AANLZ AAONW AASGY AAXRX AAYCA AAZKR ABCQN ABCUV ABEML ABIJN ABJNI ABPVW ACAHQ ACBWZ ACCZN ACGFS ACIWK ACPOU ACRPL ACSCC ACXBN ACXQS ACYXJ ADBBV ADEOM ADIZJ ADKYN ADMGS ADNMO ADOZA ADXAS ADZMN AEFGJ AEIGN AEIMD AEMOZ AENEX AEUYR AEYWJ AFBPY AFFPM AFGKR AFWVQ AFZJQ AGHNM AGQPQ AGXDD AGYGG AHBTC AHQJS AIDQK AIDYY AITYG AIURR AJXKR AKVCP ALAGY ALMA_UNASSIGNED_HOLDINGS ALUQN ALVPJ AMBMR AMVHM AMYDB ATUGU AUFTA AZBYB AZFZN AZVAB BAFTC BDRZF BFHJK BHBCM BMNLL BMXJE BNHUX BROTX BRXPI BSCLL BY8 CS3 D-E D-F DCZOG DPXWK DR2 DRFUL DRSTM EBA EBO EBR EBS EBU EJD EMK EPL F00 F01 F04 FEDTE G-S G.N GNP GODZA H.T H.X HF~ HGLYW HHZ HVGLF HZ~ IX1 J0M JPC K1G KQQ LATKE LAW LC2 LC3 LEEKS LH4 LITHE LOXES LP6 LP7 LUTES LW6 LYRES MEWTI MK4 MRFUL MRSTM MSFUL MSSTM MXFUL MXSTM N04 N05 N9A NF~ O66 O9- OIG P2P P2W P2X P4D Q.N Q11 QB0 QRW QWB R.K ROL RX1 RYL SUPJJ TH9 UB1 W8V W99 WBKPD WIH WIK WJL WOHZO WQJ WXSBR WYISQ XBAML XG1 XPP XV2 YHZ ZL0 ~IA ~WT AAYXX CITATION 7SC 7SP 7TA 8FD JG9 JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c3405-632dd28b0bd2401d1ea51218068bb3e2c778a981153afb72011933124d0f9bdf3 |
| IEDL.DBID | DR2 |
| ISSN | 1524-1904 |
| IngestDate | Fri Sep 05 10:02:08 EDT 2025 Thu Apr 24 22:54:49 EDT 2025 Wed Oct 01 05:04:17 EDT 2025 Wed Aug 20 07:26:18 EDT 2025 Sun Sep 21 06:29:33 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 5 |
| Language | English |
| License | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c3405-632dd28b0bd2401d1ea51218068bb3e2c778a981153afb72011933124d0f9bdf3 |
| Notes | ArticleID:ASMB2085 istex:3B9290D69DEBBCB870D77B9E58B081049BFCC7BD ark:/67375/WNG-JHZX7QD6-N ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| PQID | 1762072792 |
| PQPubID | 23500 |
| PageCount | 11 |
| ParticipantIDs | proquest_miscellaneous_1762072792 crossref_primary_10_1002_asmb_2085 crossref_citationtrail_10_1002_asmb_2085 wiley_primary_10_1002_asmb_2085_ASMB2085 istex_primary_ark_67375_WNG_JHZX7QD6_N |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2015-09 September/October 2015 2015-09-00 20150901 |
| PublicationDateYYYYMMDD | 2015-09-01 |
| PublicationDate_xml | – month: 09 year: 2015 text: 2015-09 |
| PublicationDecade | 2010 |
| PublicationTitle | Applied stochastic models in business and industry |
| PublicationTitleAlternate | Appl. Stochastic Models Bus. Ind |
| PublicationYear | 2015 |
| Publisher | Blackwell Publishing Ltd |
| Publisher_xml | – name: Blackwell Publishing Ltd |
| References | Scarpa B, Dunson DB. Bayesian hierarchical functional data analysis via contaminated informative priors. Biometrics 2009; 65:772-780. Medvedovic M, Sivaganesan S. Bayesian infinite mixture model based clustering of gene expression profiles. Bioinformatics 2002; 18(9):1194-1206. Hamka F, Bouwman H, de Reuver M, Kroesen M. Mobile customer segmentation based on smartphone measurement. Telematics and Informatics 2014; 31:220-227. Sheth JN, Sisodia RS, Sharma A. The antecedents and consequences of customer-centric marketing. Journal of the Academy of Marketing Science 2000; 28:55-66. Escobar MD, West M. Bayesian density estimation and inference using mixtures. Journal of the American Statistical Association 1995; 90:577-588. James GM. Generalized linear models with functional predictors. Journal of the Royal Statistical Scoiety, Series B 2002; 64:411-432. Sell A, Mezei J, Walden P. An attitude-based latent class segmentation analysis of mobile phone users. Telematics and Informatics 2014; 31:209-219. McDonald M, Dunbar I. Market segmentation. How to do it, how to profit from it 4th Edition. John Wiley & Sons: Chichester, 2012. Behseta S, Kass RE, Wallstrom GL. Hierarchical models for assessing variability among functions. Biometrika 2005; 92:419-434. Bigelow JL, Dunson DB. Bayesian adaptive regression splines for hierarchical data. Biometrics 2007; 63:724-732. Sethuraman J. A constructive definition of Dirichlet priors. Statistica Sinica 1994; 4:639-650. Durante D, Scarpa B, Dunson D. Locally adaptive factor processes for multivariate time series. Journal of Machine Learning Research 2014; 15:1493-1522. Berry MJA, Linoff G. Data Mining Techniques: For Marketing, Sales, and Customer Support. John Wiley & Sons: New York, 1997. Thompson WK, Rosen O. A Bayesian model for sparse functional data. Biometrics 2008; 64:54-63. Ngui EWT, Xiu L, Chau DCK. Application of data mining techniques in customer relationship management: a literature review and classification. Expert System with Applications 2009; 36:2592-2602. Stephens M. Dealing with label switching in mixture models. Journal of the Royal Statistical Society B 2000; 62:795-809. Janusz G. Data mining and complex telecommunications problems modeling. Journal of Telecommunications and Information Technology 2003; 3:115-120. Rodriguez A, Dunson DB, Gelfand AE. Bayesian nonparametric functional analysis through density estimation. Biometrika 2009; 96:149-162. Ferguson TS. Prior distributions on spaces of probability measures. The Annals of Statistics 1974; 2:615-629. Bigelow JL, Dunson DB. Bayesian semiparametric joint models for functional predictors. Journal of the Americal Statistical Association 2009; 104:26-36. Sell A, Walden P, Carlsson C. Segmentation matters: an exploratory study of mobile service users. International Journal of Systems and Service-Oriented Engineering 2011; 2:1-17. Ferguson TS. A Bayesian analysis of some nonparametric problems. The Annals of Statistics 1973; 1:209-230. Bush CA, MacEachern SN. A semiparametric Bayesian model for randomised block designs. Biometrika 1996; 83:275-285. Jasra A, Holmes CC, Stephens DA. Markov chain Monte Carlo methods and the label switching problem in Bayesian mixture modeling. Statistical Science 2005; 20:50-67. Quinn L, Hines T, Bennison D. Making sense of market segmentation: a fashion retailing case. European Journal of Marketing 2007; 41:439-465. Vanden Abeele M, Antheunis ML, Schouten AP. Me, myself and my mobile: a segmentation of youths based on their attitudes towards the mobile phone as a status instrument. Telematics and Informatics 2014; 31:194-208. Scarpa B, Dunson DB. Enriched stick breaking processes for functional data. Journal of the American Statistical Association 2014; 109:647-660. Rossi PE, Allenby GM, McCulloch R. Bayesian Statistics and Marketing. John Wiley & Sons: New York, 2005. Azzalini A, Scarpa B. Analisi Dei Dati e Data Mining. Oxford University Press: New York, 2012. Ke C, Wang Y. Semiparametric nonlinear mixed-effects models and their application (with discussion). Journal of the American Statistical Association 2001; 96:1272-1298. Kaufman L, Rousseeuw PJ Finding Groups in Data: An Introduction to Cluster Analysis. Wiley: New York, 1990. Ramsay JO, Silverman BW. Functional Data Analysis. Springer: New York, 1997. 2011; 2 2009; 65 2002; 18 2000; 28 1995; 90 2012 1997 2005; 20 2005 1974; 2 2009; 36 2009; 96 2014; 109 1990 2002; 64 1996; 83 2003; 3 2000; 62 2014; 15 2005; 92 2007; 41 2007; 63 2008; 64 1973; 1 2001; 96 2009; 104 1994; 4 2014; 31 Janusz G (e_1_2_6_3_1) 2003; 3 e_1_2_6_32_1 e_1_2_6_10_1 e_1_2_6_31_1 e_1_2_6_30_1 e_1_2_6_19_1 e_1_2_6_13_1 e_1_2_6_14_1 e_1_2_6_11_1 e_1_2_6_12_1 e_1_2_6_17_1 e_1_2_6_18_1 e_1_2_6_15_1 e_1_2_6_16_1 Sethuraman J (e_1_2_6_27_1) 1994; 4 e_1_2_6_21_1 e_1_2_6_20_1 Berry MJA (e_1_2_6_4_1) 1997 Azzalini A (e_1_2_6_5_1) 2012 e_1_2_6_9_1 e_1_2_6_8_1 e_1_2_6_7_1 e_1_2_6_6_1 e_1_2_6_25_1 Durante D (e_1_2_6_33_1) 2014; 15 e_1_2_6_24_1 e_1_2_6_23_1 e_1_2_6_2_1 e_1_2_6_22_1 e_1_2_6_29_1 e_1_2_6_28_1 e_1_2_6_26_1 |
| References_xml | – reference: Vanden Abeele M, Antheunis ML, Schouten AP. Me, myself and my mobile: a segmentation of youths based on their attitudes towards the mobile phone as a status instrument. Telematics and Informatics 2014; 31:194-208. – reference: Behseta S, Kass RE, Wallstrom GL. Hierarchical models for assessing variability among functions. Biometrika 2005; 92:419-434. – reference: McDonald M, Dunbar I. Market segmentation. How to do it, how to profit from it 4th Edition. John Wiley & Sons: Chichester, 2012. – reference: Sheth JN, Sisodia RS, Sharma A. The antecedents and consequences of customer-centric marketing. Journal of the Academy of Marketing Science 2000; 28:55-66. – reference: Jasra A, Holmes CC, Stephens DA. Markov chain Monte Carlo methods and the label switching problem in Bayesian mixture modeling. Statistical Science 2005; 20:50-67. – reference: Escobar MD, West M. Bayesian density estimation and inference using mixtures. Journal of the American Statistical Association 1995; 90:577-588. – reference: Bush CA, MacEachern SN. A semiparametric Bayesian model for randomised block designs. Biometrika 1996; 83:275-285. – reference: Sethuraman J. A constructive definition of Dirichlet priors. Statistica Sinica 1994; 4:639-650. – reference: Rossi PE, Allenby GM, McCulloch R. Bayesian Statistics and Marketing. John Wiley & Sons: New York, 2005. – reference: Scarpa B, Dunson DB. Bayesian hierarchical functional data analysis via contaminated informative priors. Biometrics 2009; 65:772-780. – reference: Scarpa B, Dunson DB. Enriched stick breaking processes for functional data. Journal of the American Statistical Association 2014; 109:647-660. – reference: Sell A, Walden P, Carlsson C. Segmentation matters: an exploratory study of mobile service users. International Journal of Systems and Service-Oriented Engineering 2011; 2:1-17. – reference: Durante D, Scarpa B, Dunson D. Locally adaptive factor processes for multivariate time series. Journal of Machine Learning Research 2014; 15:1493-1522. – reference: Bigelow JL, Dunson DB. Bayesian semiparametric joint models for functional predictors. Journal of the Americal Statistical Association 2009; 104:26-36. – reference: Berry MJA, Linoff G. Data Mining Techniques: For Marketing, Sales, and Customer Support. John Wiley & Sons: New York, 1997. – reference: Ke C, Wang Y. Semiparametric nonlinear mixed-effects models and their application (with discussion). Journal of the American Statistical Association 2001; 96:1272-1298. – reference: James GM. Generalized linear models with functional predictors. Journal of the Royal Statistical Scoiety, Series B 2002; 64:411-432. – reference: Medvedovic M, Sivaganesan S. Bayesian infinite mixture model based clustering of gene expression profiles. Bioinformatics 2002; 18(9):1194-1206. – reference: Rodriguez A, Dunson DB, Gelfand AE. Bayesian nonparametric functional analysis through density estimation. Biometrika 2009; 96:149-162. – reference: Ngui EWT, Xiu L, Chau DCK. Application of data mining techniques in customer relationship management: a literature review and classification. Expert System with Applications 2009; 36:2592-2602. – reference: Ramsay JO, Silverman BW. Functional Data Analysis. Springer: New York, 1997. – reference: Kaufman L, Rousseeuw PJ Finding Groups in Data: An Introduction to Cluster Analysis. Wiley: New York, 1990. – reference: Ferguson TS. A Bayesian analysis of some nonparametric problems. The Annals of Statistics 1973; 1:209-230. – reference: Ferguson TS. Prior distributions on spaces of probability measures. The Annals of Statistics 1974; 2:615-629. – reference: Azzalini A, Scarpa B. Analisi Dei Dati e Data Mining. Oxford University Press: New York, 2012. – reference: Thompson WK, Rosen O. A Bayesian model for sparse functional data. Biometrics 2008; 64:54-63. – reference: Quinn L, Hines T, Bennison D. Making sense of market segmentation: a fashion retailing case. European Journal of Marketing 2007; 41:439-465. – reference: Sell A, Mezei J, Walden P. An attitude-based latent class segmentation analysis of mobile phone users. Telematics and Informatics 2014; 31:209-219. – reference: Hamka F, Bouwman H, de Reuver M, Kroesen M. Mobile customer segmentation based on smartphone measurement. Telematics and Informatics 2014; 31:220-227. – reference: Bigelow JL, Dunson DB. Bayesian adaptive regression splines for hierarchical data. Biometrics 2007; 63:724-732. – reference: Stephens M. Dealing with label switching in mixture models. Journal of the Royal Statistical Society B 2000; 62:795-809. – reference: Janusz G. Data mining and complex telecommunications problems modeling. Journal of Telecommunications and Information Technology 2003; 3:115-120. – volume: 96 start-page: 149 year: 2009 end-page: 162 article-title: Bayesian nonparametric functional analysis through density estimation publication-title: Biometrika – year: 2005 – volume: 15 start-page: 1493 year: 2014 end-page: 1522 article-title: Locally adaptive factor processes for multivariate time series publication-title: Journal of Machine Learning Research – volume: 109 start-page: 647 year: 2014 end-page: 660 article-title: Enriched stick breaking processes for functional data publication-title: Journal of the American Statistical Association – volume: 63 start-page: 724 year: 2007 end-page: 732 article-title: Bayesian adaptive regression splines for hierarchical data publication-title: Biometrics – volume: 18 start-page: 1194 issue: 9 year: 2002 end-page: 1206 article-title: Bayesian infinite mixture model based clustering of gene expression profiles publication-title: Bioinformatics – volume: 90 start-page: 577 year: 1995 end-page: 588 article-title: Bayesian density estimation and inference using mixtures publication-title: Journal of the American Statistical Association – volume: 31 start-page: 194 year: 2014 end-page: 208 article-title: Me, myself and my mobile: a segmentation of youths based on their attitudes towards the mobile phone as a status instrument publication-title: Telematics and Informatics – volume: 31 start-page: 209 year: 2014 end-page: 219 article-title: An attitude‐based latent class segmentation analysis of mobile phone users publication-title: Telematics and Informatics – volume: 31 start-page: 220 year: 2014 end-page: 227 article-title: Mobile customer segmentation based on smartphone measurement publication-title: Telematics and Informatics – volume: 83 start-page: 275 year: 1996 end-page: 285 article-title: A semiparametric Bayesian model for randomised block designs publication-title: Biometrika – volume: 20 start-page: 50 year: 2005 end-page: 67 article-title: Markov chain Monte Carlo methods and the label switching problem in Bayesian mixture modeling publication-title: Statistical Science – volume: 64 start-page: 54 year: 2008 end-page: 63 article-title: A Bayesian model for sparse functional data publication-title: Biometrics – year: 1990 – year: 2012 – volume: 65 start-page: 772 year: 2009 end-page: 780 article-title: Bayesian hierarchical functional data analysis via contaminated informative priors publication-title: Biometrics – volume: 64 start-page: 411 year: 2002 end-page: 432 article-title: Generalized linear models with functional predictors publication-title: Journal of the Royal Statistical Scoiety, Series B – volume: 104 start-page: 26 year: 2009 end-page: 36 article-title: Bayesian semiparametric joint models for functional predictors publication-title: Journal of the Americal Statistical Association – volume: 92 start-page: 419 year: 2005 end-page: 434 article-title: Hierarchical models for assessing variability among functions publication-title: Biometrika – volume: 96 start-page: 1272 year: 2001 end-page: 1298 article-title: Semiparametric nonlinear mixed‐effects models and their application (with discussion) publication-title: Journal of the American Statistical Association – volume: 28 start-page: 55 year: 2000 end-page: 66 article-title: The antecedents and consequences of customer‐centric marketing publication-title: Journal of the Academy of Marketing Science – volume: 3 start-page: 115 year: 2003 end-page: 120 article-title: Data mining and complex telecommunications problems modeling publication-title: Journal of Telecommunications and Information Technology – volume: 2 start-page: 1 year: 2011 end-page: 17 article-title: Segmentation matters: an exploratory study of mobile service users publication-title: International Journal of Systems and Service‐Oriented Engineering – year: 1997 – volume: 1 start-page: 209 year: 1973 end-page: 230 article-title: A Bayesian analysis of some nonparametric problems publication-title: The Annals of Statistics – volume: 41 start-page: 439 year: 2007 end-page: 465 article-title: Making sense of market segmentation: a fashion retailing case publication-title: European Journal of Marketing – volume: 36 start-page: 2592 year: 2009 end-page: 2602 article-title: Application of data mining techniques in customer relationship management: a literature review and classification publication-title: Expert System with Applications – volume: 62 start-page: 795 year: 2000 end-page: 809 article-title: Dealing with label switching in mixture models publication-title: Journal of the Royal Statistical Society B – volume: 2 start-page: 615 year: 1974 end-page: 629 article-title: Prior distributions on spaces of probability measures publication-title: The Annals of Statistics – volume: 4 start-page: 639 year: 1994 end-page: 650 article-title: A constructive definition of Dirichlet priors publication-title: Statistica Sinica – ident: e_1_2_6_15_1 doi: 10.1093/biomet/92.2.419 – ident: e_1_2_6_19_1 doi: 10.1111/j.1541-0420.2007.00829.x – ident: e_1_2_6_17_1 doi: 10.1111/1467-9868.00342 – ident: e_1_2_6_32_1 doi: 10.1002/9780470316801 – ident: e_1_2_6_7_1 doi: 10.1016/j.tele.2013.08.006 – ident: e_1_2_6_18_1 doi: 10.1111/j.1541-0420.2007.00761.x – ident: e_1_2_6_21_1 doi: 10.1214/aos/1176342360 – ident: e_1_2_6_6_1 doi: 10.1002/0470863692 – ident: e_1_2_6_28_1 doi: 10.1080/01621459.1995.10476550 – ident: e_1_2_6_25_1 doi: 10.1111/j.1541-0420.2008.01163.x – volume-title: Data Mining Techniques: For Marketing, Sales, and Customer Support year: 1997 ident: e_1_2_6_4_1 – ident: e_1_2_6_29_1 doi: 10.1111/1467-9868.00265 – volume: 3 start-page: 115 year: 2003 ident: e_1_2_6_3_1 article-title: Data mining and complex telecommunications problems modeling publication-title: Journal of Telecommunications and Information Technology – ident: e_1_2_6_23_1 doi: 10.1093/biomet/83.2.275 – volume-title: Analisi Dei Dati e Data Mining year: 2012 ident: e_1_2_6_5_1 – ident: e_1_2_6_11_1 doi: 10.4018/jssoe.2011070101 – ident: e_1_2_6_14_1 doi: 10.1007/978-1-4757-7107-7 – ident: e_1_2_6_13_1 doi: 10.1108/03090560710737552 – ident: e_1_2_6_26_1 doi: 10.1080/01621459.2013.866564 – ident: e_1_2_6_8_1 doi: 10.1016/j.eswa.2008.02.021 – ident: e_1_2_6_30_1 doi: 10.1214/088342305000000016 – ident: e_1_2_6_16_1 doi: 10.1198/016214501753381913 – ident: e_1_2_6_2_1 doi: 10.1002/9781119207863 – ident: e_1_2_6_22_1 doi: 10.1214/aos/1176342752 – ident: e_1_2_6_20_1 doi: 10.1198/jasa.2009.0001 – volume: 4 start-page: 639 year: 1994 ident: e_1_2_6_27_1 article-title: A constructive definition of Dirichlet priors publication-title: Statistica Sinica – volume: 15 start-page: 1493 year: 2014 ident: e_1_2_6_33_1 article-title: Locally adaptive factor processes for multivariate time series publication-title: Journal of Machine Learning Research – ident: e_1_2_6_31_1 doi: 10.1093/bioinformatics/18.9.1194 – ident: e_1_2_6_10_1 doi: 10.1016/j.tele.2013.08.004 – ident: e_1_2_6_12_1 doi: 10.1177/0092070300281006 – ident: e_1_2_6_9_1 doi: 10.1016/j.tele.2013.04.004 – ident: e_1_2_6_24_1 doi: 10.1093/biomet/asn054 |
| SSID | ssj0011471 |
| Score | 2.0091603 |
| Snippet | Customer segmentation is one of the most important purposes of customer base analysis for telecommunication companies. Because companies accumulate very large... |
| SourceID | proquest crossref wiley istex |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 721 |
| SubjectTerms | Algorithms Bayesian nonparametrics Contamination customer profiling Dirichlet problem functional clustering functional data analysis Mathematical models Segmentation statistical methods for customer relationship management Telecommunications telephone traffic Traffic engineering Traffic flow |
| Title | Bayesian semiparametric customer base segmentation of mobile phone users based on longitudinal traffic data |
| URI | https://api.istex.fr/ark:/67375/WNG-JHZX7QD6-N/fulltext.pdf https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fasmb.2085 https://www.proquest.com/docview/1762072792 |
| Volume | 31 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVEBS databaseName: Business Source Ultimate customDbUrl: eissn: 1526-4025 dateEnd: 20241003 omitProxy: false ssIdentifier: ssj0011471 issn: 1524-1904 databaseCode: AKVCP dateStart: 19990401 isFulltext: true titleUrlDefault: https://search.ebscohost.com/login.aspx?authtype=ip,uid&profile=ehost&defaultdb=bsu providerName: EBSCOhost – providerCode: PRVWIB databaseName: Wiley Online Library - Core collection (SURFmarket) issn: 1524-1904 databaseCode: DR2 dateStart: 19960101 customDbUrl: isFulltext: true eissn: 1526-4025 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0011471 providerName: Wiley-Blackwell |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3da9RAEB9K-6IPtVWLZ2tZpYgvuSbZfNKn65dHyx2t2nqIsOxXpFxzVy4XsP3rO5NcohUF6VtgJ2QzuzPz293Z3wDsSE_JFJ2vQ2DYCVIr0aQS7qhYe64JUk9XbPuDYdS_CE5G4WgJ9pq7MDU_RLvhRpZR-WsycKmK3V-kobLIVZcqTKL_9XhYHdF-aqmjEObXi63QDxwMekHDKuT6u-2bD2LRCqn15wOg-TtcreLN8TP43vS0TjMZd8u56uq7P0gcH_kra7C6wKGsV0-cdViyk-fwdNCSuBYvYLwvby3dsWSFza-IIzyn8lua6RIRY25njEIgNv7IFxeYJmyasXyqsBOMct4toz2QopIzDJuvp1QeqTRUiovNZ5L4Kxglqb6Ei-OjLwd9Z1GbwdEcMZ4Tcd8YP1GuMogJPONZidDBS9woUYpbX8dxItME8SaXmYoJZqScI5gwbpYqk_ENWJ5gP14BCxCSJkmauZFFj5JqSRT4HJdRXMVW2rADH5pREnpBXE71M65FTbnsC9KfIP114F0relOzdfxN6H011K2EnI0pvS0OxdfhR3HS_zaKzw8jMezA22YuCDQ6OkmREzstC-FhCHFj4l7EzlUj--_Pid7nwT49vP5_0U14ghoL61y2LViez0r7BsHPXG3DSu_08uBsu5rt99ReAuY |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Za9wwEB5yPCR96JGkdHtFCSX0xRvb8gl9SZqmm2MX2iZ0KRShyyFsvFvWa2j76ztjr52DFkLfDBrj8Uij-SSNvgF4Iz0lU5x8HQLDTpBaiS6VcEfF2nNNkHq6YtvvD6LeeXA8DIcL8K65C1PzQ7QbbuQZ1XxNDk4b0rvXrKGyyFWXSkwuwjKdz5FbHnxuyaMQ6NfLrdAPHAx7QcMr5Pq77au3otEyGfbnLah5E7BWEefwEXxvdK0TTUbdcqa6-vcdGsf__ZnH8HAORdlePXaewIIdr8GDfsvjWqzDaF_-snTNkhU2vySa8JwqcGmmSwSNuZ0yioLYeJHP7zCN2SRj-UShFozS3i2jbZCikjMMm68mVCGpNFSNi82mkigsGOWpbsD54Yez9z1nXp7B0RxhnhNx3xg_Ua4yCAs841mJ6MFL3ChRiltfx3Ei0wQhJ5eZiglppJwjnjBuliqT8aewNEY9ngELEJUmSZq5kcVJJdWSWPA5rqS4iq20YQfeNt0k9Jy7nEpoXImaddkXZD9B9uvAdiv6oybs-JvQTtXXrYScjijDLQ7F18FHcdz7Now_HURi0IGtZjAI9Ds6TJFjOykL4WEUcWOiX0Tlqq799-fE3pf-Pj08v7_oJqz0zvqn4vRocPICVtF6YZ3a9hKWZtPSvkIsNFOvqyH_B8mRBWo |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3ri9NAEB_OFsT74FuuPlcR8Ut6STZP_HT1rLXa4uuwiLDsK8fRpj2aBtS_3pmkiVYUxG-BnZDJ7M7Ob3dnfwPwWHpKpjj5OgSGnSC1El0q4Y6KteeaIPV0xbY_mUajk2A8C2d78Ky5C1PzQ7QbbuQZ1XxNDn5ussOfpKGyyFWfKkxegG4Q4eqKENH7ljsKcX692gr9wMGoFzS0Qq5_2L66E4y6ZNevO0jzV7xaBZzhFfjSqFrnmcz75Ub19fffWBz_81-uwuUtEGVH9ci5Bnt2eR32Jy2La3ED5gP5zdIlS1bY_IxIwnOqv6WZLhEy5nbNKAZi42m-vcG0ZKuM5SuFSjBKereMNkGKSs4wbF6sqD5SaagWF9usJRFYMMpSvQknwxcfn4-cbXEGR3MEeU7EfWP8RLnKICjwjGclYgcvcaNEKW59HceJTBMEnFxmKiackXKOaMK4WapMxm9BZ4l6HAALEJMmSZq5kcUpJdWSOPA5rqO4iq20YQ-eNr0k9Ja5nApoLETNuewLsp8g-_XgUSt6XtN1_EnoSdXVrYRczym_LQ7Fp-lLMR59nsXvjiMx7cHDZiwI9Do6SpFLuyoL4WEMcWMiX0Tlqp79--fE0YfJgB5u_7voA7j49ngo3ryavr4Dl9B4YZ3Xdhc6m3Vp7yEQ2qj71YD_Ab4iBBk |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Bayesian+semiparametric+customer+base+segmentation+of+mobile+phone+users+based+on+longitudinal+traffic+data&rft.jtitle=Applied+stochastic+models+in+business+and+industry&rft.au=Scarpa%2C+Bruno&rft.date=2015-09-01&rft.issn=1524-1904&rft.eissn=1526-4025&rft.volume=31&rft.issue=5&rft.spage=721&rft.epage=731&rft_id=info:doi/10.1002%2Fasmb.2085&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1524-1904&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1524-1904&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1524-1904&client=summon |