Big data in the retail world: customer segmentation and recommender system in a European supermarket chain

Authors

DOI:

https://doi.org/10.26439/ing.ind2022.n.5808

Keywords:

retail, segmentation, recommender system, big data, machine learning

Abstract

In this research, we want to present the concepts and techniques used in a big data project for a European supermarket company, through a customer segmentation proposal, using the k-means algorithm, and a recommender system, via Light FM library. The main conclusions include the importance of adequately defining the problem to be solved, the correct use of the big data infrastructure, the relevance of the exploratory analysis of the dataset and its pre-processing, as well as the use of the TDSP methodology (Team Data Science Process), oriented to big data projects.

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Author Biography

  • César Rogelio Cam Gensollen, Universidad de Lima, Facultad de Ingeniería y Arquitectura, Lima, Perú

    Candidato a doctor en Business Administration por la Universidad ESAN. Máster en Big Data Engineer por la Universidad de Barcelona. Magíster en Investigación en Ciencias de la Administración por la Universidad ESAN. MBA por la Universidad Peruana de Ciencias Aplicadas. Ingeniero industrial por la Universidad de Lima. Asimismo, completó la diplomatura de Estudio en Estadística Aplicada por la Pontificia Universidad Católica del Perú. Posee especializaciones en Dirección Comercial y Liderazgo por el PAD de la Universidad de Piura, así como un diploma de especialización en Big Data & Analytics por la Universidad Nacional de Ingeniería. Ha complementado su formación académica certificándose en Scrum Máster e ITIL 4. Cuenta con más de treinta años de experiencia profesional y ha sido director de importantes empresas del sector B2B.

References

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Published

2022-04-22

Issue

2022: Edición Especial

Section

Artículos

How to Cite

Big data in the retail world: customer segmentation and recommender system in a European supermarket chain. (2022). Ingeniería Industrial, 189-216. https://doi.org/10.26439/ing.ind2022.n.5808