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Marketing Knowledge Management Model

  • Teresa Guarda
  • Maria Fernanda Augusto
  • Marcelo León
  • Hugo Pérez
  • Washington Torres
  • Walter Orozco
  • Jacqueline Bacilio
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 721)

Abstract

Database marketing (DBM) refers to the use of information databases to support marketing activities in order to obtain useful information to establish and maintain a profitable interaction with the customer. This work focuses the failures of traditional approaches to the database marketing, proposing the use of techniques from artificial intelligence, in the context of business intelligence in marketing areas. Based in literature review, it’s explored a vision for the systemic use of methods and techniques of data mining in projects of DBM, and proposed a conceptual model that combines DBM activities with appropriate data mining techniques, contributing to efficiency and effectiveness of database marketing projects.

Keywords

Database marketing Data mining Business intelligence Knowledge discovery from databases 

References

  1. 1.
    Lusch, R., Vargo, S.: The Service-Dominant Logic of Marketing: Dialog, Debate, and Directions. Routledge, Abingdon (2014)Google Scholar
  2. 2.
    Khodakarami, F., Chan, Y.: Exploring the role of customer relationship management (CRM) systems in customer knowledge creation. Inf. Manag. 51(1), 27–42 (2014)CrossRefGoogle Scholar
  3. 3.
    Wedel, M., Kannan, P.: Marketing analytics for data-rich environments. J. Mark. 80(6), 97–121 (2016)CrossRefGoogle Scholar
  4. 4.
    Kasemsap, K.: The role of data mining for business intelligence in knowledge management. In: Integration of Data Mining in Business Intelligence Systems, pp. 12–33 (2015)Google Scholar
  5. 5.
    Kingyens, A., Paradi, J., Tam, F.: Bankruptcy prediction of companies in the retail-apparel industry using data envelopment analysis. In: Advances in Efficiency and Productivity, pp. 299–329. Springer International Publishing (2016)Google Scholar
  6. 6.
    Wheeler, R., Aitken, S.: Multiple algorithms for fraud detection. Knowl.-Based Syst. 13(2), 93–99 (2004)Google Scholar
  7. 7.
    Silva, A., Cortez, P., Santos, M., Gomes, L., Neves, J.: Multiple organ failure diagnosis using adverse events and neural networks. In: Proceedings of 6th International Conference on Enterprise Information Systems – ICEIS, vol. 2 (2004)Google Scholar
  8. 8.
    Shaw, M., Subramaniam, C., Tan, G., Welge, M.: Knowledge management and data mining for marketing. Decis. Support Syst. 31, 127–137 (2001)CrossRefGoogle Scholar
  9. 9.
    Guarda, T., Augusto, M.F., L., Sousa, A., Silva, C., Costa, A.: Database Marketing Tools for SMEs (2014)Google Scholar
  10. 10.
    Lee, J.: A study on the data mining preprocessing tool for efficient database marketing. J. Digital Convergence 12(11), 257–264 (2014)CrossRefGoogle Scholar
  11. 11.
    Oliveira, T., Coelho, V., Souza, M., Boava, D., Boava, F., Coelho, I., Coelho, B.: A hybrid variable neighborhood search algorithm for targeted offers in direct marketing. Electron. Notes Discrete Math. 47, 205–212 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Aguinis, H., Forcum, L., Joo, H.: Using market basket analysis in management research. J. Manag. 39(7), 1799–1824 (2013)Google Scholar
  13. 13.
    McDonald, M., Wilson, H.: Marketing Plans: How to Prepare Them, How to Profit from Them. Wiley, Chichester (2016)CrossRefGoogle Scholar
  14. 14.
    Consulting, C.: Database Marketing Standards for the Retail Industry. Retail Target Marketing System Inc (1996)Google Scholar
  15. 15.
    Babin, B., Zikmund, W.: Exploring Marketing Research. Cengage Learning, Boston (2015)Google Scholar
  16. 16.
    Rodriguez, M., Peterson, R.M., Ajjan, H.: CRM/social media technology: impact on customer orientation process and organizational sales performance. In: Ideas in Marketing: Finding the New and Polishing the Old. Springer, Cham (2015)Google Scholar
  17. 17.
    Pinto, F., Guarda, T., Gago, P.: A framework proposal for ontologies usage in marketing databases. In: International Conference on Model and Data Engineering, pp. 31–41. Springer (2011)Google Scholar
  18. 18.
    Mining, W.: Data Mining: Concepts and Techniques. Morgan Kaufmann, Burlington (2006)Google Scholar
  19. 19.
    Piatetsky-Shapiro, G. (ed.): Advances in Knowledge Discovery and Data Mining Uthurusamy, 21st edn. AAAI Press, Menlo Park (1996)Google Scholar
  20. 20.
    Elder, J.: Handbook of Statistical Analysis and Data Mining Applications. Academic Press, Cambridge (2009)zbMATHGoogle Scholar
  21. 21.
    Gonçalves, J., Faria, B.M., Reis, L.P., Carvalho, V., Rocha, Á.: Data mining and electronic devices applied to quality of life related to health data. In: 2015 10th Iberian Conference on Information Systems and Technologies (CISTI), pp. 1–4. IEEE (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Teresa Guarda
    • 1
    • 2
    • 3
  • Maria Fernanda Augusto
    • 1
    • 3
  • Marcelo León
    • 1
    • 3
  • Hugo Pérez
    • 3
  • Washington Torres
    • 1
  • Walter Orozco
    • 1
  • Jacqueline Bacilio
    • 1
  1. 1.Universidad Estatal Península de Santa Elena – UPSELa LibertadEcuador
  2. 2.Algoritmi CentreMinho UniversityBragaPortugal
  3. 3.Universidad de las Fuerzas Armadas-ESPESangolqui, QuitoEcuador

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