Market Trends and Customer Segmentation for Data of Electronic Retail Store

  • Carlos Rodriguez-PardoEmail author
  • Miguel A. PatricioEmail author
  • Antonio BerlangaEmail author
  • Jose M. MolinaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10334)


Data analysis is comprised of a set of processes that allows a key support for making better decisions. The ability to analyse data in the field of retail trade allows companies to obtain valuable information such as understanding the profile of customers who demand a particular type of product, optimizing the price of certain products, identifying customers interested in such products and analysing the best way to approach them. This paper will present results obtained during the development of an analysis process on the data of an electronic retail store. The analysis will show the results obtained and validated by end users using different visualization techniques. Finally, the result of applying client segmentation using self-organized maps and the interpretation of their results in a visual way will be discussed.


Postal Code Visualization Technique Price Range Data Mining Method Individual Customer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was partially funded by projects MINECO TEC2014-57022-C2-2-R, TEC2012-37832-C02-01.


  1. 1.
    Hebert, D., Anderson, B., Olinsky, A., Hardin, J.M.: Time series data mining: a retail application. Int. J. Bus. Anal. (IJBAN) 1(4), 51–68 (2014)CrossRefGoogle Scholar
  2. 2.
    Cisco: Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2015–2020. Technical report (2016)Google Scholar
  3. 3.
    Muley, M., Joshi, A.: Application of data mining techniques for customer segmentation in real time business intelligence. Int. J. Innov. Res. Adv. Eng. 2(4), 106–109 (2014)Google Scholar
  4. 4.
    Ismail, M., Ibrahim, M.M., Sanusi, Z.M., Nat, M.: Data Mining in Electronic Commerce: Benefits and Challenges, pp. 501–509, December 2015Google Scholar
  5. 5.
    Cuadros, A.J., Domínguez, V.E.: Customer segmentation model based on value generation for marketing strategies formulation. Estudios Gerenciales 30(130), 25–30 (2014)CrossRefGoogle Scholar
  6. 6.
    Dzobo, O., Alvehag, K., Gaunt, C.T., Herman, R.: Multi-dimensional customer segmentation model for power system reliability-worth analysis. Int. J. Electr. Power Energy Syst. 62, 532–539 (2014)CrossRefGoogle Scholar
  7. 7.
    Floh, A., Zauner, A., Koller, M., Rusch, T.: Customer segmentation using unobserved heterogeneity in the perceived-value-loyalty-intentions link. J. Bus. Res. 67(5), 974–982 (2014)CrossRefGoogle Scholar
  8. 8.
    Hosseini, S.Y., Ziaei Bideh, A.: A data mining approach for segmentation-based importance-performance analysis (SOM-BPNN-IPA): a new framework for developing customer retention strategies. Serv. Bus. 8(2), 295–312 (2014)CrossRefGoogle Scholar
  9. 9.
    Tam, N.T., Song, I.: Big data visualization. In: Kim, K., Joukov, N. (eds.) ICISA 2016. LNEE, vol. 376, pp. 399–408. Springer, Singapore (2016)CrossRefGoogle Scholar
  10. 10.
    Chi, E.H.: A taxonomy of visualization techniques using the data state reference model. In: Proceedings of the IEEE Symposium on Information Visualization 2000, INFOVIS 2000, vol. 94301(Table 2), pp. 69–75 (2000)Google Scholar
  11. 11.
    Dos Santos, S., Brodlie, K.: Gaining understanding of multivariate and multidimensional data through visualization. Comput. Graph. (Pergamon) 28(3), 311–325 (2004)CrossRefGoogle Scholar
  12. 12.
    Langseth, J., Aref, F., Alarcon, J., Lindner, W.: Real-time data visualization of streaming data (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Applied Artificial Intelligence GroupUniversity Carlos III of MadridMadridSpain

Personalised recommendations