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Segmentation of Sales for a Mobile Phone Service Through CART Classification Tree Algorithm

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Proceedings of 6th International Conference on Big Data and Cloud Computing Challenges

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 164))

Abstract

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.

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References

  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)

    Article  Google Scholar 

  2. Bouveyron, C., Girard, S., Schmid, C.: High-dimensional discriminant analysis. Commun. Stat. Theory Methods 36(14), 2607–2623 (2007)

    Article  MathSciNet  Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  7. Yao, L: The present situation and development tendency of higher education quality evaluation in western countries. Priv. Educ. Beef. (2006)

    Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  9. Guo, Y., Hastie, T., Tibshirani, R.: Regularized linear discriminant analysis and its application in microarrays. Biostatistics 8(1), 86–100 (2006)

    Article  Google Scholar 

  10. Wickramarachchi, D., Robertson, B., Reale, M., Price, C., Brown, J.: HHCART: an oblique decision tree. Comput. Stat. Data Anal. 96, 12–23 (2016)

    Article  MathSciNet  Google Scholar 

  11. Hong, S., Yang, D., Park, B., et al.: An efficient intra-mode decision method for HEVC. SIViP 10(6), 1–9 (2016)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  26. Amelec, V.: Validation of strategies to reduce exhausted shelf products in a pharmaceutical chain. Adv. Sci. Lett. 21(5), 1403–1405 (2015)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Universidad de la Costa, St. 58 #66, Barranquilla, Atlántico, Colombia

    Amelec Viloria

  2. Guangzhou University, Guangzhou, China

    Guojun Wang

  3. Corporación Universitaria del Meta (UNIMETA), Villavicencio, Colombia

    Mercedes Gaitan

Authors
  1. Amelec Viloria
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  2. Guojun Wang
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  3. Mercedes Gaitan
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Corresponding author

Correspondence to Amelec Viloria .

Editor information

Editors and Affiliations

  1. School of Computing Science and Engineering, VIT, Chennai, India

    V. Vijayakumar

  2. School of Computing Science and Engineering, VIT, Chennai, India

    V. Neelanarayanan

  3. School of Computing and Engineering, University of Missouri-Kansas City, Kansas, MO, USA

    Praveen Rao

  4. Department of Computer Science, University of New Brunswick, Fredericton, NB, Canada

    Janet Light

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Viloria, A., Wang, G., Gaitan, M. (2020). Segmentation of Sales for a Mobile Phone Service Through CART Classification Tree Algorithm. In: Vijayakumar, V., Neelanarayanan, V., Rao, P., Light, J. (eds) Proceedings of 6th International Conference on Big Data and Cloud Computing Challenges. Smart Innovation, Systems and Technologies, vol 164. Springer, Singapore. https://doi.org/10.1007/978-981-32-9889-7_7

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