Elector Relationship Management: Concepts, Practices and Technological Support

  • Jalal BoussaidEmail author
  • Hassan Azdimousa
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 92)


Election abstention is generally high during political campaigns and it is widely recognized that one of the ways to promote successful elections is to put in place mechanisms to track the assessment of their success.

To support electoral campaigns, it is essential to gain knowledge about citizen-electors. This knowledge will enable the adoption of adequate and effective actions and decisions to closely monitor elector behavior. For such procedures to be possible, this paper proposes an Elector Relationship Management (ERM) system. This system will support the ERM concept and practices and will be implemented using the concepts and technology infrastructure supporting Business Intelligence systems. The concept, practice and architecture of the ERM system is presented in this article and its main purpose is to provide a technological tool that helps political parties to acquire the essential knowledge to the decision-making process. The prototype of the ERM system proposed, once implemented, will be validated by the execution of a set of demonstration cases in different political parties in Morocco.


Costumer Relationship Management Business Intelligence Elector Relationship Management (ERM) Data warehousing Data mining 


  1. 1.
    Klaukka, G., van der Staak, S., Valladares, J.: The changing nature of political parties and representation. In: International Institute for Democracy and Electoral Assistance (International IDEA), The Global State of Democracy: Exploring Democracy’s Resilience, pp. 98–118 (2017)Google Scholar
  2. 2.
    Bréchon, P.: La signification de l’abstention électorale, Texte rédigé à l’occasion d’un séminaire doctoral à l’Université libre de Bruxelles (ULB) (2010)Google Scholar
  3. 3.
    Adams, J., Dow, J., Merrill, S.: The political consequences of alienation-based and indifference-based voter abstention: applications to presidential elections. Polit. Behav. 28(1), 65–86 (2006)CrossRefGoogle Scholar
  4. 4.
    Muxel, A.: La vague de l’abstention, Science Po, Centre de recherche (2014)Google Scholar
  5. 5.
    Schellong, A., Langenberg, T.: Managing citizen relationships in disasters: Hurricane Wilma, 311 and Miami –Dade County. In: Proceedings of the 40th Hawai International Conference on System Sciences, Hawai, USA (2007)Google Scholar
  6. 6.
    Janssen, M., Wangenaar, R.: Customer relationship management in e-government: a dutch survey. In: European Conference on E-Government, 1–2 October, St Catherine (2005)Google Scholar
  7. 7.
    Zamanian, M., Khaji, M.R., Emamian, S.M.S.: The value chain of citizen relationship management (CzRM): a framework for improvement. Afr. J BM. 5(22), 8909–8917 (2011)Google Scholar
  8. 8.
    Fayerman, M.: Customer Relationship Management, New Directions for Institutional Research, vol. 113, pp. 57–67 (2002)Google Scholar
  9. 9.
    Payne, A.: Handbook of CRM. Achieving Excellence in Customer Management. Elsevier-BH (Butterworth-Heinemann) (2006)Google Scholar
  10. 10.
    Mishra, A.: Customer relationship management: implementation process perspective. Acta Polytechnica Hungarica 6, 83–99 (2009)Google Scholar
  11. 11.
    Hahnke, J.: The Critical Phase of the CRM Lifecycle. Without CRM Analytics, Your Customer Won’t Even Know You’re There (2001).
  12. 12.
    Wu, J.: Customer relationship management in practice: a case study of hi-tech company from China. In: International Conference on Service Systems and Service Management, 30 June–2 July 2008, pp. 1–6. IEEE Computer Society (2008)Google Scholar
  13. 13.
    Yu, J.: Customer Relationship Management in Practice: a Case Study of Hi-Tech from China. IEEE Computer Society (2008)Google Scholar
  14. 14.
    Bull, C.: Strategic issues in a customer relationship management (CRM) implementation. Bus. Process Manage. J. 9(5), 592–602 (2003)CrossRefGoogle Scholar
  15. 15.
    Fogelman, F.: CRM Analytique, l’apport du data mining. In: Bennani, Y., Viennet, E. (eds.) Apprentissage Artificiel & Fouille de Données, Volume: Revue des Nouvelles Technologies de l’Information, RNTI-A-2. Cépaduès, pp 15–34 (2007)Google Scholar
  16. 16.
    Inmon, W.H.: Building the Data Warehouse, 2nd edn. Wiley, New York (1996)Google Scholar
  17. 17.
    Kimball, R., Reeves, L., Thornthwaite, W.: The Data Warehouse Lifecycle Toolkit: Expert Methods for Designing, Developing, and Deploying Data Warehouses. Wiley (1998)Google Scholar
  18. 18.
    Negash, S., Gray, P.: Business intelligence. In: Ninth Americas Conference on Information Systems (2003)Google Scholar
  19. 19.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers (2001)Google Scholar
  20. 20.
    Inmon, W.H.: Building the Data Warehouse. Wiley (2005)Google Scholar
  21. 21.
    Cunningham, C., Song, I.Y., Chen, P.P.: Business Intelligence: Data Warehouse Design to Support Customer Relationship Management Analyses, ACM - The Guide (2004)Google Scholar
  22. 22.
    Thomsen, E.: OLAP Solutions. Building Multidimensional Information Systems. Wiley (2002)Google Scholar
  23. 23.
    Berry, M.J.A., Linoff, G.S.: Data Mining Techniques for Marketing, Sales, and Customer Relationship Management. Wiley (2004)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Ibn Tofail UniversityKenitraMorocco

Personalised recommendations