Manifold Learning to Identify Consumer Profiles in Real Consumption Data

• DOI: 10.5220/0007832400230031
• Publication Date: 2019-08-09T05:19:53Z
• Authors: Diego Perez; Marta Rivera-Alba; Alberto Sanchez-C...
• Keywords: 05 social sciences; 0506 political science

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 consumption in our sample.