Segmentation of sales for a mobile phone service through CART classification tree algorithm

The work consisted of detailing the CRISP-DM method in order to identify optimal groups of customers who are more likely to migrate from a prepaid to postpaid option in order to formulate an improvement plan for in call management by sorting the database. Classification models were applied to analyz...

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:11323/7769
Acceso en línea:
https://hdl.handle.net/11323/7769
https://doi.org/10.1007/978-981-32-9889-7_7
https://repositorio.cuc.edu.co/
Palabra clave:
Call center
CRISP-DM CART
classification tree algorithm
Rights
openAccess
License
Attribution-NonCommercial-NoDerivatives 4.0 International
id RCUC2_40c9106d84bf5ae4db9db257fce9659c
oai_identifier_str oai:repositorio.cuc.edu.co:11323/7769
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Segmentation of sales for a mobile phone service through CART classification tree algorithm
title Segmentation of sales for a mobile phone service through CART classification tree algorithm
spellingShingle Segmentation of sales for a mobile phone service through CART classification tree algorithm
Call center
CRISP-DM CART
classification tree algorithm
title_short Segmentation of sales for a mobile phone service through CART classification tree algorithm
title_full Segmentation of sales for a mobile phone service through CART classification tree algorithm
title_fullStr Segmentation of sales for a mobile phone service through CART classification tree algorithm
title_full_unstemmed Segmentation of sales for a mobile phone service through CART classification tree algorithm
title_sort Segmentation of sales for a mobile phone service through CART classification tree 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 CART
classification tree algorithm
topic Call center
CRISP-DM CART
classification tree algorithm
description The work consisted of detailing the CRISP-DM method in order to identify optimal groups of customers who are more likely to migrate from a prepaid to postpaid option in order to formulate an improvement plan for in call management by sorting the database. Classification models were applied to analyze the characteristics generated by the purchase of the different services. The CART Classification Tree algorithm. As a result, groups differentiated by probabilities of sales success (migrate from a prepaid to postpaid plan) were found, segments that reflect particular needs and characteristics to design marketing actions focused on the objective of increasing the effectiveness rate, contact information, and sales increase.
publishDate 2020
dc.date.issued.none.fl_str_mv 2020
dc.date.accessioned.none.fl_str_mv 2021-01-27T14:38:25Z
dc.date.available.none.fl_str_mv 2021-01-27T14:38:25Z
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
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dc.type.content.spa.fl_str_mv Text
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dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
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dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/7769
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1007/978-981-32-9889-7_7
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/11323/7769
https://doi.org/10.1007/978-981-32-9889-7_7
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. Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., et al.: Gene ontology: tool for the unification of biology. Nat. Genet. 25(1), 25 (2000)
2. Bouveyron, C., Girard, S., Schmid, C.: High-dimensional discriminant analysis. Commun. Stat. Theory Methods 36(14), 2607–2623 (2007)
3. Left, 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)
4. Pineda Lezama, O., Gómez Dorta, R.: Techniques of multivariate statistical analysis: an application for the Honduran banking sector. Innovate: J. Sci. Technol. 5(2), 61–75 (2017)
5. Viloria, A., Lis-Gutierrez, JP., 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)
6. Duke Oliva, E., Finch Chaparro, C.: Measuring the perception of service quality education by students AAUCTU Duitama. Free Criterion magazine, vol. 10, no. 16, (January–July 2012)
7. Yao, L: The present situation and development tendency of higher education quality evaluation in western countries. Priv. Educ. Beef. (2006)
8. 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)
9. Guo, Y., Hastie, T., Tibshirani, R.: Regularized linear discriminant analysis and its application in microarrays. Biostatistics 8(1), 86–100 (2006)
10. Wickramarachchi, D., Robertson, B., Reale, M., Price, C., Brown, J.: HHCART: an oblique decision tree. Comput. Stat. Data Anal. 96, 12–23 (2016)
11. Hong, S., Yang, D., Park, B., et al.: An efficient intra-mode decision method for HEVC. SIViP 10(6), 1–9 (2016)
12. Ramezanpour, M., Zargari, F.: Fast CU size and prediction mode decision method for HEVC encoder based on spatial features. SIViP 10(7), 1233–1240 (2016)
13. 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)
14. 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)
15. 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)
16. 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)
17. 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)
18. 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)
19. 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 1935, 16 (2019)
20. Li, Y.Z., Zhang, N., Liu, F., et al.: Application of apriori algorithm based on information theory optimization in traffic accident analysis [J]. Inf. Syst. Eng. 10, 80–84 (2016)
21. Singh, G., Sachdeva, S.N., Pal, M.: M5 model tree based predictive modeling of road accidents on non-urban sections of highways in India [J]. Accid. Anal. Prev. 96, 108 (2016)
22. Lombardi, D.A., Horrey, W.J., Courtney, T.K.: Age-related differences in fatal intersection crashes in the United States [J]. Accid. Anal. Prev. 99(Pt A), 20 (2016)
23. Liu, Z.Q., Wang, L., Zhang, A.H.: Study on the mechanism of traffic accidents in wusongtian highway based on bayesian model [J]. J. Chongqing Univ. Technol. 1, 43–49 (2018)
24. Markov, Z, Russell, I.: An introduction to the WEKA data mining system [C]. In: Sigcse Conference on Innovation & Technology in Computer Science Education. ACM, pp. 367–368 (2006)
25. Gao, Y., Dong, X., Tian, F.: Analysis of current situation of highway traffic safety and management countermeasures [J]. China Saf. Prod. Sci. Technol. 11(10), 110–115 (2015)
26. Amelec, V.: Validation of strategies to reduce exhausted shelf products in a pharmaceutical chain. Adv. Sci. Lett. 21(5), 1403–1405 (2015)
27. 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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dc.source.spa.fl_str_mv Smart Innovation, Systems and Technologies
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spelling Viloria, Amelecfc29d54ed3c7d39e34b3d61c512ace8fWang, Guojun10b01e2aa5df7e090742606ffc979818Gaitan, Mercedesdcf0f652ddd45d60b5ea2b4009f6b44d2021-01-27T14:38:25Z2021-01-27T14:38:25Z2020https://hdl.handle.net/11323/7769https://doi.org/10.1007/978-981-32-9889-7_7Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The work consisted of detailing the CRISP-DM method in order to identify optimal groups of customers who are more likely to migrate from a prepaid to postpaid option in order to formulate an improvement plan for in call management by sorting the database. Classification models were applied to analyze the characteristics generated by the purchase of the different services. The CART Classification Tree algorithm. As a result, groups differentiated by probabilities of sales success (migrate from a prepaid to postpaid plan) were found, segments that reflect particular needs and characteristics to design marketing actions focused on the objective of increasing the effectiveness rate, contact information, and sales increase.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_7Call centerCRISP-DM CARTclassification tree algorithmSegmentation of sales for a mobile phone service through CART classification tree 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. Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., et al.: Gene ontology: tool for the unification of biology. Nat. Genet. 25(1), 25 (2000)2. Bouveyron, C., Girard, S., Schmid, C.: High-dimensional discriminant analysis. Commun. Stat. Theory Methods 36(14), 2607–2623 (2007)3. Left, 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)4. Pineda Lezama, O., Gómez Dorta, R.: Techniques of multivariate statistical analysis: an application for the Honduran banking sector. Innovate: J. Sci. Technol. 5(2), 61–75 (2017)5. Viloria, A., Lis-Gutierrez, JP., 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)6. Duke Oliva, E., Finch Chaparro, C.: Measuring the perception of service quality education by students AAUCTU Duitama. Free Criterion magazine, vol. 10, no. 16, (January–July 2012)7. Yao, L: The present situation and development tendency of higher education quality evaluation in western countries. Priv. Educ. Beef. (2006)8. 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)9. Guo, Y., Hastie, T., Tibshirani, R.: Regularized linear discriminant analysis and its application in microarrays. Biostatistics 8(1), 86–100 (2006)10. Wickramarachchi, D., Robertson, B., Reale, M., Price, C., Brown, J.: HHCART: an oblique decision tree. Comput. Stat. Data Anal. 96, 12–23 (2016)11. Hong, S., Yang, D., Park, B., et al.: An efficient intra-mode decision method for HEVC. SIViP 10(6), 1–9 (2016)12. Ramezanpour, M., Zargari, F.: Fast CU size and prediction mode decision method for HEVC encoder based on spatial features. SIViP 10(7), 1233–1240 (2016)13. 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)14. 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)15. 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)16. 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)17. 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)18. 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)19. 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 1935, 16 (2019)20. Li, Y.Z., Zhang, N., Liu, F., et al.: Application of apriori algorithm based on information theory optimization in traffic accident analysis [J]. Inf. Syst. Eng. 10, 80–84 (2016)21. Singh, G., Sachdeva, S.N., Pal, M.: M5 model tree based predictive modeling of road accidents on non-urban sections of highways in India [J]. Accid. Anal. Prev. 96, 108 (2016)22. Lombardi, D.A., Horrey, W.J., Courtney, T.K.: Age-related differences in fatal intersection crashes in the United States [J]. Accid. Anal. Prev. 99(Pt A), 20 (2016)23. Liu, Z.Q., Wang, L., Zhang, A.H.: Study on the mechanism of traffic accidents in wusongtian highway based on bayesian model [J]. J. Chongqing Univ. Technol. 1, 43–49 (2018)24. Markov, Z, Russell, I.: An introduction to the WEKA data mining system [C]. In: Sigcse Conference on Innovation & Technology in Computer Science Education. ACM, pp. 367–368 (2006)25. Gao, Y., Dong, X., Tian, F.: Analysis of current situation of highway traffic safety and management countermeasures [J]. China Saf. Prod. Sci. Technol. 11(10), 110–115 (2015)26. Amelec, V.: Validation of strategies to reduce exhausted shelf products in a pharmaceutical chain. Adv. Sci. Lett. 21(5), 1403–1405 (2015)27. 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)ORIGINALSegmentation of sales for a mobile phone service through CART classification tree algorithm.pdfSegmentation of sales for a mobile phone service through CART classification tree algorithm.pdfapplication/pdf93022https://repositorio.cuc.edu.co/bitstream/11323/7769/1/Segmentation%20of%20sales%20for%20a%20mobile%20phone%20service%20through%20CART%20classification%20tree%20algorithm.pdfa6ae9279e7c92dece53ee5d8978e8d02MD51open accessCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.cuc.edu.co/bitstream/11323/7769/2/license_rdf4460e5956bc1d1639be9ae6146a50347MD52open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstream/11323/7769/3/license.txte30e9215131d99561d40d6b0abbe9badMD53open accessTHUMBNAILSegmentation of sales for a mobile phone service through CART classification tree algorithm.pdf.jpgSegmentation of sales for a mobile phone service through CART 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