Applicable models of customer analytics for a retail company in Mexico

Research output: Contribution to journalConference article

Abstract

Big Data has become a worldwide tendency, having strong presence in the technological sphere as well as an increased growth in all market sectors. In this final evaluation project, extensive exploration on Big Data literature, predictive analytics, client analytics, as well as that of tools and technologies associated with the compilation and processing of mass data was undertaken. Furthermore, the elaboration of a data analysis model is being performed for a company within the ambit of the retail sector in Mexico. The abovementioned, through the development of a pilot test, which consists of three general stages, identification of the client, validation and refinement of the model and a forecast of new clients. The pilot test implies the formulation, refinement and reading of data of mass scale. This is undertaken by making use of three analyses: discriminant analysis, hierarchical cluster and k -media cluster. The methodology employed is DMADV, which is used where there is a need of designing or a re-designing of products and/or processes or, such as this case, the process of data analysis. As a result, a model capable of identifying 5 different segments, which the potential of providing analytical capacities in order to know, grow, monitor and maintain e-commerce clients within the retail business of Mexico.

Original languageEnglish
Pages (from-to)2446-2462
Number of pages17
JournalProceedings of the International Conference on Industrial Engineering and Operations Management
Issue numberJuly
Publication statusPublished - 1 Jul 2019
Event3rd Eu International Conference on Industrial Engineering and Operations Management,IEOM 2019 - Pilsen, Czech Republic
Duration: 23 Jul 201926 Jul 2019

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All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Management Science and Operations Research
  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

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