The growing market competitiveness has been pressing companies to improve the efficiency and effectiveness of their customer relationship management strategies. Specifically, for customer acquisition, the direct marketing approaches have been attaining a prominent role due to their promotion of innovation and customization. These techniques represent a differentiating factor to the company since they seek to design personalized solutions and experiences. Despite the plethora of research articles on data mining methodologies applications to business cases regarding customer relationship management, there is a lack of documented applications of these techniques in leads data. In this sense, we propose a methodology that aims to improve efficiency on the different stages of leads management (capture, enhance, nurture, tracking, assess and convert), as well as support decision-making regarding the segmentation of leads. This research suggests the application of data mining techniques in the optimization of leads management processes, from capture to conversion, with the objective of improving customer conversion effectiveness. A case study in the telecommunications industry was developed, in which the proposed methodologies were applied. Afterwards, several opportunities for improvement were identified and the respective short and long-term solutions were suggested. Lastly, the results were presented and discussed in depth.

Original languageEnglish
JournalProceedings of International Conference on Computers and Industrial Engineering, CIE
Publication statusPublished - 2019
Event49th International Conference on Computers and Industrial Engineering, CIE 2019 - Beijing, China
Duration: 18 Oct 201921 Oct 2019


  • Data mining
  • Lead management
  • Machine learning
  • Predictive models


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