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)