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Authors: Diego Perez ; Marta Rivera-Alba and Alberto Sanchez-Carralero

Affiliation: Research, Clarity AI, New York and U.S.A.

Keyword(s): Manifold Learning, Big Data, Consumer Data, Econometrics, Consumption Profiles.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Analytics ; Computer Vision, Visualization and Computer Graphics ; Data Analytics ; Data Engineering ; Databases and Data Security ; Databases and Information Systems Integration ; Enterprise Information Systems ; General Data Visualization ; Information and Scientific Visualization ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Large Scale Databases ; Symbolic Systems

Abstract: Precise and comprehensive analysis of individual consumption is key to marketers and policy makers. Traditionally, people’s consumption profiles have been approximated by household surveys. Although insightful and complete, household surveys suffer from some biases and inaccuracies. To compensate for some of those biases, we propose a new approach to compute and analyze consumer profiles based on millions of purchase transactions collected by a personal financial manager. Since this new kind of data sources requires new analysis methods, in this paper we propose the use of manifold learning techniques to visualize the whole data set at once, demonstrating how these techniques can cluster consumers in more meaningful groups than demographics alone. These unsupervised behavior-based clusters allow us to draw more educated hypotheses that we could otherwise miss. As an example, we will specifically discuss the characteristics of individuals with high housing and recreation cons umption in our sample. (More)

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Paper citation in several formats:
Perez, D.; Rivera-Alba, M. and Sanchez-Carralero, A. (2019). Manifold Learning to Identify Consumer Profiles in Real Consumption Data. In Proceedings of the 8th International Conference on Data Science, Technology and Applications - DATA; ISBN 978-989-758-377-3; ISSN 2184-285X, SciTePress, pages 23-31. DOI: 10.5220/0007832400230031

@conference{data19,
author={Diego Perez. and Marta Rivera{-}Alba. and Alberto Sanchez{-}Carralero.},
title={Manifold Learning to Identify Consumer Profiles in Real Consumption Data},
booktitle={Proceedings of the 8th International Conference on Data Science, Technology and Applications - DATA},
year={2019},
pages={23-31},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007832400230031},
isbn={978-989-758-377-3},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Data Science, Technology and Applications - DATA
TI - Manifold Learning to Identify Consumer Profiles in Real Consumption Data
SN - 978-989-758-377-3
IS - 2184-285X
AU - Perez, D.
AU - Rivera-Alba, M.
AU - Sanchez-Carralero, A.
PY - 2019
SP - 23
EP - 31
DO - 10.5220/0007832400230031
PB - SciTePress