Sales segmentation for a mobile phone service through logistic regression algorithm

The research aims to describe the CRISP-DM method to identify optimal customer groups that are likely to migrate from a prepaid to postpaid plan in order to formulate an improvement plan in call management by sorting the database. The logistic regression model was applied to analyze the characterist...

Full description

Autores:
Viloria, Amelec
Wang, Guojun
Gaitan, Mercedes
Tipo de recurso:
Article of journal
Fecha de publicación:
2020
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/7758
Acceso en línea:
https://hdl.handle.net/11323/7758
https://doi.org/10.1007/978-981-32-9889-7_3
https://repositorio.cuc.edu.co/
Palabra clave:
Call center
CRISP-DM
Logistic regression model.
Rights
openAccess
License
Attribution-NonCommercial-NoDerivatives 4.0 International
id RCUC2_a2a81bf995efe8e5364e93df03412286
oai_identifier_str oai:repositorio.cuc.edu.co:11323/7758
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Sales segmentation for a mobile phone service through logistic regression algorithm
title Sales segmentation for a mobile phone service through logistic regression algorithm
spellingShingle Sales segmentation for a mobile phone service through logistic regression algorithm
Call center
CRISP-DM
Logistic regression model.
title_short Sales segmentation for a mobile phone service through logistic regression algorithm
title_full Sales segmentation for a mobile phone service through logistic regression algorithm
title_fullStr Sales segmentation for a mobile phone service through logistic regression algorithm
title_full_unstemmed Sales segmentation for a mobile phone service through logistic regression algorithm
title_sort Sales segmentation for a mobile phone service through logistic regression algorithm
dc.creator.fl_str_mv Viloria, Amelec
Wang, Guojun
Gaitan, Mercedes
dc.contributor.author.spa.fl_str_mv Viloria, Amelec
Wang, Guojun
Gaitan, Mercedes
dc.subject.spa.fl_str_mv Call center
CRISP-DM
Logistic regression model.
topic Call center
CRISP-DM
Logistic regression model.
description The research aims to describe the CRISP-DM method to identify optimal customer groups that are likely to migrate from a prepaid to postpaid plan in order to formulate an improvement plan in call management by sorting the database. The logistic regression model was applied to analyze the characteristics generated by the purchase of different services. In this sense, groups differentiated by their probability of sales success (migrating from a prepaid to postpaid plan) were found, as segments that reflect needs and characteristics that allow to design marketing actions focused on the objective of increasing the effectiveness, contactability, and sales.
publishDate 2020
dc.date.issued.none.fl_str_mv 2020
dc.date.accessioned.none.fl_str_mv 2021-01-22T23:49:38Z
dc.date.available.none.fl_str_mv 2021-01-22T23:49:38Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/7758
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1007/978-981-32-9889-7_3
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
url https://hdl.handle.net/11323/7758
https://doi.org/10.1007/978-981-32-9889-7_3
https://repositorio.cuc.edu.co/
identifier_str_mv Corporación Universidad de la Costa
REDICUC - Repositorio CUC
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv 1. Pineda Lezama, O., Gómez Dorta, R.: Techniques of multivariate statistical analysis: an application for the Honduran banking sector. Innov.: J. Sci. Technol. 5(2), 61–75 (2017)
2. Viloria, A., Lis-Gutierrez, J.P., Gaitán-Angulo, M., Godoy, A.R.M., Moreno, G.C., Kamatkar, S.J.: Methodology for the design of a student pattern recognition tool to facilitate the teaching—learning process through knowledge data discovery (Big Data). In: Tan, Y., Shi, Y., Tang, Q. (Eds.) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol. 10943. Springer, Cham (2018)
3. Lee, A., Taylor, P., Kalpathy-Cramer, J.: A Tufail Machine learning has arrived! Ophthalmology 124, 1726–1728 (2017)
4. Yao, L.: The present situation and development tendency of higher education quality evaluation in Western Countries, p. 2006. Educ. Beef, Priv (2006)
5. Gregorutti, B., Michel, B., Saint-Pierre, P.: Grouped variable importance with random forests and application to multiple functional data analysis. Comput. Stat. Data Anal. 90, 15–35 (2015)
6. Guo, Y., Hastie, T., Tibshirani, R.: Regularized linear discriminant analysis and its application in microarrays. Biostatistics 8(1), 86–100 (2006)
7. Wickramarachchi, D., Robertson, B., Reale, M., Price, C., Brown, J.: HHCART: an oblique decision tree. Comput. Stat. Data Anal. 96, 12–23 (2016)
8. Hong, S., Yang, D., Park, B., et al.: An efficient intra-mode decision method for HEVC. SIViP 10(6), 1–9 (2016)
9. Kavzoglu, T., Sahin, E.K., Colkesen, I.: An assessment of multivariate and bivariate approaches in landslide susceptibility mapping: a case study of Duzkoy district. Nat. Hazards 76, 471–496 (2015)
10. Lei, J., Sun, Z., Gu, Z., Zhu, T., Ling, N., Wu, F.: Simplified search algorithm for explicit wedgelet signalization mode in 3D-HEVC. In: 2017 IEEE International Conference on Multimedia and Expo (ICME), Hong Kong, pp. 805–810 (2017)
11. Zhao, J., Zhao, X., Zhang, W., et al.: An efficient depth modeling mode decision algorithm for 3D-HEVC depth map coding. Optik Int. J. Light Electron Opt. 127(24), 12048–12055 (2016)
12. Aghdam, I.N., Varzandeh, M.H.M., Pradhan, B.: Landslide susceptibility mapping using an ensemble statistical index (Wi) and adaptive neuro-fuzzy inference system (ANFIS) model at Alborz Mountains (Iran). Environ. Earth Sci. 75, 553 (2016)
13. Liu, P., He, G., Xue, S., Li, Y.: A fast mode selection for depth modelling modes of intra depth coding in 3D-HEVC. In: 2016 Visual Communications and Image Processing (VCIP), Chengdu, pp. 1–4 (2016)
14. Guo, R., He, G., Li, Y., Wang, K.: Fast algorithm for prediction unit and mode decisions of intra depth coding in 3D-HEVC. In: 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, pp. 1121–1125 (2016)
15. Bonerge Pineda Lezama, O., Varela Izquierdo, N., Pérez Fernández, D., Gómez Dorta, R.L., Viloria, A., Romero Marín, L.: Models of multivariate regression for labor accidents in different production sectors: comparative study. In: Tan, Y., Shi, Y., Tang, Q. (Eds.) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol. 10943. Springer, Cham (2018)
16. Izquierdo, N.V., Lezama, O.B.P., Dorta, R.G., Viloria, A., Deras, I., Hernández-Fernández, L.: Fuzzy logic applied to the performance evaluation. honduran coffee sector case. In: Tan, Y., Shi, Y., Tang, Q. (Eds.) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science, vol. 10942. Springer, Cham (2018)
17. Yao, Y., Zhao, X., Du, H., Zhang, Y., Zhang, G., Rong, J.: Classification of fatigued and drunk driving based on decision tree methods: a simulator study. Int. J. Environ. Res. Public Health 2019, 16 (1935)
18. Li, Y.Z., Zhang, N., Liu, F., et al.: Application of apriori algorithm based on information theory optimization in traffic accident analysis. Inf. Syst. Eng. 10, 80–84 (2016)
19. Lombardi, D.A., Horrey, W.J., Courtney, T.K.: Age-related differences in fatal intersection crashes in the United States. Accid. Anal. Prev. 99(Pt A), 20 (2016)
20. Liu, Z.Q., Wang, L., Zhang, A.H.: Study on the mechanism of traffic accidents in wusongtian highway based on bayesian model. J. Chongqing Univ. Technol. 1, 43–49 (2018)
21. Markov, Z., Russell, I.: An introduction to the WEKA data mining system[C]. In: Sigcse Conference on Innovation & Technology in Computer Science Education, pp. 367–368. ACM (2006)
22. Gao, Y., Dong, X., Tian, F.: Analysis of current situation of highway traffic safety and management countermeasures. China Saf. Prod. Sci. Technol. 11(10), 110–115 (2015)
23. Amelec, V.: Validation of strategies to reduce exhausted shelf products in a pharmaceutical chain. Adv. Sci. Lett. 21(5), 1403–1405 (2015)
24. Amelec, V.: Increased efficiency in a company of development of technological solutions in the areas commercial and of consultancy. Adv. Sci. Lett. 21(5), 1406–1408 (2015)
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spelling Viloria, Amelecfc29d54ed3c7d39e34b3d61c512ace8fWang, Guojun10b01e2aa5df7e090742606ffc979818Gaitan, Mercedesdcf0f652ddd45d60b5ea2b4009f6b44d2021-01-22T23:49:38Z2021-01-22T23:49:38Z2020https://hdl.handle.net/11323/7758https://doi.org/10.1007/978-981-32-9889-7_3Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The research aims to describe the CRISP-DM method to identify optimal customer groups that are likely to migrate from a prepaid to postpaid plan in order to formulate an improvement plan in call management by sorting the database. The logistic regression model was applied to analyze the characteristics generated by the purchase of different services. In this sense, groups differentiated by their probability of sales success (migrating from a prepaid to postpaid plan) were found, as segments that reflect needs and characteristics that allow to design marketing actions focused on the objective of increasing the effectiveness, contactability, and sales.application/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Smart Innovation, Systems and Technologieshttps://link.springer.com/chapter/10.1007/978-981-32-9889-7_3Call centerCRISP-DMLogistic regression model.Sales segmentation for a mobile phone service through logistic regression algorithmArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1. Pineda Lezama, O., Gómez Dorta, R.: Techniques of multivariate statistical analysis: an application for the Honduran banking sector. Innov.: J. Sci. Technol. 5(2), 61–75 (2017)2. Viloria, A., Lis-Gutierrez, J.P., Gaitán-Angulo, M., Godoy, A.R.M., Moreno, G.C., Kamatkar, S.J.: Methodology for the design of a student pattern recognition tool to facilitate the teaching—learning process through knowledge data discovery (Big Data). In: Tan, Y., Shi, Y., Tang, Q. (Eds.) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol. 10943. Springer, Cham (2018)3. Lee, A., Taylor, P., Kalpathy-Cramer, J.: A Tufail Machine learning has arrived! Ophthalmology 124, 1726–1728 (2017)4. Yao, L.: The present situation and development tendency of higher education quality evaluation in Western Countries, p. 2006. Educ. Beef, Priv (2006)5. Gregorutti, B., Michel, B., Saint-Pierre, P.: Grouped variable importance with random forests and application to multiple functional data analysis. Comput. Stat. Data Anal. 90, 15–35 (2015)6. Guo, Y., Hastie, T., Tibshirani, R.: Regularized linear discriminant analysis and its application in microarrays. Biostatistics 8(1), 86–100 (2006)7. Wickramarachchi, D., Robertson, B., Reale, M., Price, C., Brown, J.: HHCART: an oblique decision tree. Comput. Stat. Data Anal. 96, 12–23 (2016)8. Hong, S., Yang, D., Park, B., et al.: An efficient intra-mode decision method for HEVC. SIViP 10(6), 1–9 (2016)9. Kavzoglu, T., Sahin, E.K., Colkesen, I.: An assessment of multivariate and bivariate approaches in landslide susceptibility mapping: a case study of Duzkoy district. Nat. Hazards 76, 471–496 (2015)10. Lei, J., Sun, Z., Gu, Z., Zhu, T., Ling, N., Wu, F.: Simplified search algorithm for explicit wedgelet signalization mode in 3D-HEVC. In: 2017 IEEE International Conference on Multimedia and Expo (ICME), Hong Kong, pp. 805–810 (2017)11. Zhao, J., Zhao, X., Zhang, W., et al.: An efficient depth modeling mode decision algorithm for 3D-HEVC depth map coding. Optik Int. J. Light Electron Opt. 127(24), 12048–12055 (2016)12. Aghdam, I.N., Varzandeh, M.H.M., Pradhan, B.: Landslide susceptibility mapping using an ensemble statistical index (Wi) and adaptive neuro-fuzzy inference system (ANFIS) model at Alborz Mountains (Iran). Environ. Earth Sci. 75, 553 (2016)13. Liu, P., He, G., Xue, S., Li, Y.: A fast mode selection for depth modelling modes of intra depth coding in 3D-HEVC. In: 2016 Visual Communications and Image Processing (VCIP), Chengdu, pp. 1–4 (2016)14. Guo, R., He, G., Li, Y., Wang, K.: Fast algorithm for prediction unit and mode decisions of intra depth coding in 3D-HEVC. In: 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, pp. 1121–1125 (2016)15. Bonerge Pineda Lezama, O., Varela Izquierdo, N., Pérez Fernández, D., Gómez Dorta, R.L., Viloria, A., Romero Marín, L.: Models of multivariate regression for labor accidents in different production sectors: comparative study. In: Tan, Y., Shi, Y., Tang, Q. (Eds.) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol. 10943. Springer, Cham (2018)16. Izquierdo, N.V., Lezama, O.B.P., Dorta, R.G., Viloria, A., Deras, I., Hernández-Fernández, L.: Fuzzy logic applied to the performance evaluation. honduran coffee sector case. In: Tan, Y., Shi, Y., Tang, Q. (Eds.) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science, vol. 10942. Springer, Cham (2018)17. Yao, Y., Zhao, X., Du, H., Zhang, Y., Zhang, G., Rong, J.: Classification of fatigued and drunk driving based on decision tree methods: a simulator study. Int. J. Environ. Res. Public Health 2019, 16 (1935)18. Li, Y.Z., Zhang, N., Liu, F., et al.: Application of apriori algorithm based on information theory optimization in traffic accident analysis. Inf. Syst. Eng. 10, 80–84 (2016)19. Lombardi, D.A., Horrey, W.J., Courtney, T.K.: Age-related differences in fatal intersection crashes in the United States. Accid. Anal. Prev. 99(Pt A), 20 (2016)20. Liu, Z.Q., Wang, L., Zhang, A.H.: Study on the mechanism of traffic accidents in wusongtian highway based on bayesian model. J. Chongqing Univ. Technol. 1, 43–49 (2018)21. Markov, Z., Russell, I.: An introduction to the WEKA data mining system[C]. In: Sigcse Conference on Innovation & Technology in Computer Science Education, pp. 367–368. ACM (2006)22. Gao, Y., Dong, X., Tian, F.: Analysis of current situation of highway traffic safety and management countermeasures. China Saf. Prod. Sci. Technol. 11(10), 110–115 (2015)23. Amelec, V.: Validation of strategies to reduce exhausted shelf products in a pharmaceutical chain. Adv. Sci. Lett. 21(5), 1403–1405 (2015)24. Amelec, V.: Increased efficiency in a company of development of technological solutions in the areas commercial and of consultancy. Adv. Sci. Lett. 21(5), 1406–1408 (2015)LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstream/11323/7758/3/license.txte30e9215131d99561d40d6b0abbe9badMD53open accessORIGINALSales segmentation for a mobile phone service through logistic regression algorithm.pdfSales segmentation for a mobile phone service through logistic regression algorithm.pdfapplication/pdf93454https://repositorio.cuc.edu.co/bitstream/11323/7758/1/Sales%20segmentation%20for%20a%20mobile%20phone%20service%20through%20logistic%20regression%20algorithm.pdf59dd2a68c6aa098bbabc67e1957711aeMD51open accessCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.cuc.edu.co/bitstream/11323/7758/2/license_rdf4460e5956bc1d1639be9ae6146a50347MD52open accessTHUMBNAILSales segmentation for a mobile phone service through logistic regression algorithm.pdf.jpgSales segmentation for a mobile phone service through logistic regression 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