Classifying Spending Behavior using Socio-Mobile Data
Name
SocialCom_Journal_SingleColumn.pdf
Description
Main article
Size
171.51 KB
Format
Adobe PDF
Checksum (MD5)
c344037d8c42eecaf7a0c3ffefc4dca7
Author(s)
Singh, Vivek Kumar
Freeman, Laura
Lepri, Bruno
Pentland, Alex Paul
Date Issued
June 2013
Journal
Human Journal
Publisher
Academy of Science and Engineering
Citation
Singh, Vivek K., Laura Freeman, Bruno Lepri, Alex (Sandy) Pentland. "1
Classifying Spending Behavior using Socio-Mobile Data ." Human Journal 2: 2 (2013) pp.99-111.
Version
Author's final manuscript
Abstract
Human spending behavior is essentially social. This work motivates and grounds the use of mobile phone based social interaction features for classifying spending behavior. Using a data set involving 52 adults (26 couples) living in a community for over a year, we find that social behavior measured via face-to-face interaction, call, and SMS logs, can be used to predict the spending behavior for couples in terms of their propensity to explore diverse businesses, become loyal customers, and overspend. Our results show that mobile phone based social interaction patterns can provide more predictive power on spending behavior than personality based features. Interestingly, we find that more social couples also tend to overspend. Obtaining such insights about couple level spending behavior via novel social-computing frameworks can be of vital importance to economists, marketing professionals, and policy makers.
MIT Department
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
http://ojs.scienceengineering.org/index.php/human/article/view/76
