research-article

Public Access

Portrait of an Online Shopper: Understanding and Predicting Consumer Behavior

Authors:

Kristina Lerman,

Luca Maria Aiello,

Mihajlo Grbovic,

Nemanja Djuric,

Vladan RadosavljevicAuthors Info & Claims

WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining

Pages 205 - 214

https://doi.org/10.1145/2835776.2835831

Published: 08 February 2016 Publication History

Abstract

Consumer spending accounts for a large fraction of economic footprint of modern countries. Increasingly, consumer activity is moving to the web, where digital receipts of online purchases provide valuable data sources detailing consumer behavior. We consider such data extracted from emails and combined with with consumers' demographic information, which we use to characterize, model, and predict purchasing behavior. We analyze such behavior of consumers in different age and gender groups, and find interesting, actionable patterns that can be used to improve ad targeting systems. For example, we found that the amount of money spent on online purchases grows sharply with age, peaking in the late 30s, while shoppers from wealthy areas tend to purchase more expensive items and buy them more frequently. Furthermore, we look at the influence of social connections on purchasing habits, as well as at the temporal dynamics of online shopping where we discovered daily and weekly behavioral patterns. Finally, we build a model to predict when shoppers are most likely to make a purchase and how much will they spend, showing improvement over baseline approaches. The presented results paint a clear picture of a modern online shopper, and allow better understanding of consumer behavior that can help improve marketing efforts and make shopping more pleasant and efficient experience for online customers.

References

[1]

Combination of multiple classifiers for the customer's purchase behavior prediction. Decision Support Systems, 34(2):167 -- 175, 2003.

Digital Library

[2]

Understanding gender-based differences in consumer e-commerce adoption. Commun. Association for Information Systems, 26, 2005.

[3]

L. M. Aiello, A. Barrat, C. Cattuto, G. Ruffo, and R. Schifanella. Link creation and profile alignment in the aNobii social network. In SocialCom, 2010.

Digital Library

[4]

L. M. Aiello, A. Barrat, R. Schifanella, C. Cattuto, B. Markines, and F. Menczer. Friendship prediction and homophily in social media. ACM Trans. Web, 6(2):9:1--9:33, Jun 2012.

Digital Library

[5]

A. Anagnostopoulos, R. Kumar, and M. Mahdian. Influence and correlation in social networks. In KDD, pp. 7--15, 2008.

Digital Library

[6]

S. Bellman, G. L. Lohse, and E. J. Johnson. Predictors of online buying behavior. Commun. ACM, 42(12):32--38, 1999.

Digital Library

[7]

A. Bhatnagar, S. Misra, and H. R. Rao. On risk, convenience, and internet shopping behavior. Commun. ACM, 43(11):98--105, Nov. 2000.

Digital Library

[8]

J.-S. Chiou and C.-C. Ting. Will you spend more money and time on internet shopping when the product and situation are right? Computers in Human Behavior, 27(1):203--208, 2011.

Digital Library

[9]

D. Crandall, D. Cosley, D. Huttenlocher, J. Kleinberg, and S. Suri. Feedback effects between similarity and social influence in online communities. In KDD'08, pp. 160--168, 2008.

Digital Library

[10]

R. R. Dholakia. Going shopping: key determinants of shopping behaviors and motivations. Int. J. Retail & Distribution Management, 27(4):154--165, 1999.

[11]

R. R. Dholakia. Going shopping: key determinants of shopping behaviors and motivations. Int. J. Retail and Distribution Management, 27:154--165, 1999.

[12]

N. Djuric, V. Radosavljevic, M. Grbovic, and N. Bhamidipati. Hidden conditional random fields with deep user embeddings for ad targeting. In ICDM, pp. 779--784, 2014.

Digital Library

[13]

R. Y. Du and W. A. Kamakura. Where did all that money go? understanding how consumers allocate their consumption budget. J. Marketing, 72(6):109--131, 2008.

[14]

W. N. Evans and T. J. Moore. Liquidity, economic activity, and mortality. Review of Economics and Statistics, 94(2):400--418, Jan. 2011.

[15]

S. Farag, T. Schwanen, M. Dijst, and J. Faber. Shopping online and/or in-store? a structural equation model of the relationships between e-shopping and in-store shopping. Transportation Research Part A: Policy and Practice, 41(2):125--141, 2007.

[16]

S. L. Feld. The focused organization of social ties. The American Journal of Sociology, 86(5):1015--1035, 1981.

[17]

E. Garbarino and M. Strahilevitz. Gender differences in the perceived risk of buying online and the effects of receiving a site recommendation. J. Business Research, 57(7):768--775, 2004.

[18]

M. Grbovic and S. Vucetic. Generating ad targeting rules using sparse principal component analysis with constraints. In WWW Companion, pp. 283--284, 2014.

Digital Library

[19]

S. Guo, M. Wang, and J. Leskovec. The role of social networks in online shopping: Information passing, price of trust, and consumer choice. In EC, pp. 157--166, 2011.

Digital Library

[20]

V. Gupta, D. Varshney, H. Jhamtani, D. Kedia, and S. Karwa. Identifying purchase intent from social posts. In ICWSM, 2014.

[21]

T. Hansen, J. Moller Jensen, and H. Stubbe Solgaard. Predicting online grocery buying intention: a comparison of the theory of reasoned action and the theory of planned behavior. Int. J. Information Management, 24(6):539--550, 2004.

Digital Library

[22]

C. R. Hayhoe, L. J. Leach, P. R. Turner, M. J. Bruin, and F. C. Lawrence. Differences in spending habits and credit use of college students. J. Consumer Affairs, 34(1):113--133, 2000.

[23]

J. A. Horrigan. Online shopping. Pew Internet & American Life Project Report, 36, 2008.

[24]

T.-K. Hui and D. Wan. Factors affecting internet shopping behaviour in singapore: gender and educational issues. Int. J. Consumer Studies, 31(3):310--316, 2007.

[25]

J.-H. Kang and K. Lerman. Using lists to measure homophily on twitter. In AAAI workshop on Intelligent Techniques for Web Personalization and Recommendation, July 2012.

[26]

F. Kooti, L. M. Aiello, M. Grbovic, K. Lerman, and A. Mantrach. Evolution of conversations in the age of email overload. In WWW, pp. 603--613, 2015.

Digital Library

[27]

J. Leskovec, L. A. Adamic, and B. A. Huberman. The dynamics of viral marketing. ACM Trans. Web, 1(1), May 2007.

Digital Library

[28]

G. Linden, B. Smith, and J. York. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 7(1):76--80, Jan. 2003.

Digital Library

[29]

M. McPherson, L. Smith-Lovin, and J. M. Cook. Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27(1):415--444, 2001.

[30]

A. L. Montgomery, S. Li, K. Srinivasan, and J. C. Liechty. Predicting online purchase conversion using web path analysis. Technical report, 2002.

[31]

P. A. Pavlou and M. Fygenson. Understanding and predicting electronic commerce adoption: An extension of the theory of planned behavior. MIS quarterly, pp. 115--143, 2006.

Digital Library

[32]

T. Perea y Monsuwé, B. G. Dellaert, and K. De Ruyter. What drives consumers to shop online? a literature review. Int. J. Service Industry Management, 15(1):102--121, 2004.

[33]

T. Rodrigues, F. Benevenuto, M. Cha, K. Gummadi, and V. Almeida. On word-of-mouth based discovery of the web. In IMC, pp. 381--396, 2011.

Digital Library

[34]

S. Senecal, P. J. Kalczynski, and J. Nantel. Consumers' decision-making process and their online shopping behavior: a clickstream analysis. J. Business Research, 58(11):1599--1608, 2005.

[35]

C. R. Shalizi and A. C. Thomas. Homophily and contagion are generically confounded in observational social network studies. Sociological Methods and Research, 40(2):211--239, 2011.

[36]

C. Sismeiro and R. E. Bucklin. Modeling purchase behavior at an e-commerce web site: A task-completion approach. J. Marketing Research, 41(3):306--323, 2004.

[37]

S. Sobolevsky, I. Sitko, R. Tachet des Combes, B. Hawelka, J. Murillo Arias, and C. Ratti. Cities through the prism of people's spending behavior. arXiv, 2015.

[38]

P. Sorce, V. Perotti, and S. Widrick. Attitude and age differences in online buying. Int. J. Retail & Distribution Management, 33(2):122--132, 2005.

[39]

W. R. Swinyard and S. M. Smith. Why people (don't) shop online: A lifestyle study of the internet consumer. Psychology & Marketing, 20(7):567, 2003.

[40]

W. R. Swinyard and S. M. Smith. Activities, interests, and opinions of online shoppers and non-shoppers. International Business and Economics Research Journal, 3(4):37--48, 2011.

[41]

M. Tabatabaei. Online shopping perceptions of offline shoppers. Issues in Information Systems, 10(2):22--26, 2009.

[42]

T. S. Teo. Attitudes toward online shopping and the internet. Behaviour & Information Technology, 21(4):259--271, 2002.

[43]

F. Ulbrich, T. Christensen, and L. Stankus. Gender-specific on-line shopping preferences. Electronic Commerce Research, 11(2):181--199, 2011.

Digital Library

[44]

D. Van den Poel and W. Buckinx. Predicting online-purchasing behaviour. European Journal of Operational Research, 166(2):557--575, 2005.

[45]

C. Van Slyke, C. L. Comunale, and F. Belanger. Gender differences in perceptions of web-based shopping. Commun. ACM, 45(8):82--86, Aug. 2002.

Digital Library

[46]

M. Wolfinbarger and M. C. Gilly. Shopping online for freedom, control, and fun. California Management Review, 43(2):34--55, 2001.

[47]

M. Zaman and M. Y. W. Meng. Internet shopping adoption: A comparative study on city and regional consumers. In ANZMAC, pp. 2421--2428. Deakin University, 2002.

[48]

Y. Zhang and M. Pennacchiotti. Predicting purchase behaviors from social media. In WWW, pp. 1521--1532, 2013.

Digital Library

[49]

J. McAuley, R. Pandey, J. Leskovec. Inferring networks of substitutable and complementary products. In ACM SIGKDD, pp. 785--794, 2015.

Digital Library

[50]

M. Grbovic, V. Radosavljevic, N. Djuric, N. Bhamidipati, J. Savla, V. Bhagwan, and D. Sharp E-commerce in Your Inbox: Product Recommendations at Scale. In ACM SIGKDD, pp. 1809--1818, 2015.

Digital Library

Cited By

Zhang ZWei ZMa L(2024)UBRMTC: User Behavior Recognition Model With Transaction CharacterIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.325722711:2(1589-1601)Online publication date: Apr-2024
https://doi.org/10.1109/TCSS.2023.3257227
Predić BĆirić MStoimenov L(2023)Business Purchase Prediction Based on XAI and LSTM Neural NetworksElectronics10.3390/electronics1221451012:21(4510)Online publication date: 2-Nov-2023
https://doi.org/10.3390/electronics12214510
Ćirić MPredić BStojanović DĆirić I(2023)Single and Multiple Separate LSTM Neural Networks for Multiple Output Feature Purchase PredictionElectronics10.3390/electronics1212261612:12(2616)Online publication date: 10-Jun-2023
https://doi.org/10.3390/electronics12122616
Show More Cited By

Index Terms

Portrait of an Online Shopper: Understanding and Predicting Consumer Behavior
1. Information systems
  1. World Wide Web
    1. Web applications
      1. Internet communications tools

Recommendations

Demographic factors in assessing perceived risk in online shopping
ICEC '11: Proceedings of the 13th International Conference on Electronic Commerce

Research in online shopping was the focus of many studies as the age of electronic commerce began in the late 1990's. More recently, research in this area has declined, even as shopping on the Internet continues to increase and now dominates some ...
Patterns of Online Grocery Shopping in India: An Empirical Study
ICONIAAC '14: Proceedings of the 2014 International Conference on Interdisciplinary Advances in Applied Computing

India has a large and growing population using internet services for various needs. In the early stage of development IRCTC has created a huge mass of Indian population dependent on internet for online booking of railway ticket. In recent times, there ...
Online Shopping Behavior: A Study of Factors Influencing Consumer Satisfaction on Online viz-a-viz Conventional Store Shopping

Online shopping is a recent phenomenon in the field of E-Business with promising results indicating revolutionary change in shopping in the world. Accessibility and convenience are the key drivers for a major shift to online shopping. Due to ever ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining

February 2016

746 pages

ISBN:9781450337168

DOI:10.1145/2835776

General Chairs:
Paul N. Bennett
Microsoft Research
,
Vanja Josifovski
Pinterest
,
Program Chairs:
Jennifer Neville
Purdue University
,
Filip Radlinski
Microsoft

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 February 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

AFOSR
DARPA

Conference

WSDM 2016

Sponsor:

WSDM 2016: Ninth ACM International Conference on Web Search and Data Mining

February 22 - 25, 2016

California, San Francisco, USA

Acceptance Rates

WSDM '16 Paper Acceptance Rate 67 of 368 submissions, 18%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

46
Total Citations
View Citations
3,533
Total Downloads

Downloads (Last 12 months)507
Downloads (Last 6 weeks)59

Reflects downloads up to 27 Jul 2024

Other Metrics

View Author Metrics

Citations

Cited By

Zhang ZWei ZMa L(2024)UBRMTC: User Behavior Recognition Model With Transaction CharacterIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.325722711:2(1589-1601)Online publication date: Apr-2024
https://doi.org/10.1109/TCSS.2023.3257227
Predić BĆirić MStoimenov L(2023)Business Purchase Prediction Based on XAI and LSTM Neural NetworksElectronics10.3390/electronics1221451012:21(4510)Online publication date: 2-Nov-2023
https://doi.org/10.3390/electronics12214510
Ćirić MPredić BStojanović DĆirić I(2023)Single and Multiple Separate LSTM Neural Networks for Multiple Output Feature Purchase PredictionElectronics10.3390/electronics1212261612:12(2616)Online publication date: 10-Jun-2023
https://doi.org/10.3390/electronics12122616
Sekandari EAghaei I(2023)Exploring Must-know Criteria for Effective Customer Segmentation in Online Market Using AHPReview of Marketing Science10.1515/roms-2023-002721:1(271-294)Online publication date: 13-Jul-2023
https://doi.org/10.1515/roms-2023-0027
Xu EYu ZSun ZGuo BYao L(2023)Modeling Within-Basket Auxiliary Item Recommendation with Matchability and UbiquityACM Transactions on Intelligent Systems and Technology10.1145/357415714:3(1-19)Online publication date: 17-Feb-2023
https://dl.acm.org/doi/10.1145/3574157
Zhong RHan SWang Z(2023)Developing personas for live streaming commerce platforms with user survey dataUniversal Access in the Information Society10.1007/s10209-023-00996-xOnline publication date: 27-Apr-2023
https://doi.org/10.1007/s10209-023-00996-x
Wibowo WMuhaimin AAbdul-Rahman S(2023)Predicting Internet Usage for Digital Finance Services: Multitarget Classification Using Vector Generalized Additive Model with SMOTE-NCData Science and Emerging Technologies10.1007/978-981-99-0741-0_35(494-504)Online publication date: 1-Apr-2023
https://doi.org/10.1007/978-981-99-0741-0_35
Tahali S(2022)La réservation d’hôtel en ligne, quels déterminants pour le touriste : Cas du pourtour transfrontalier du LuxembourgProjectics / Proyéctica / Projectique10.3917/proj.031.0127n°31:1(127-145)Online publication date: 15-Jun-2022
https://doi.org/10.3917/proj.031.0127
Aljukhadar MSenecal S(2022)Targeting the very important buyers VIB: A cluster analysis approachCogent Business & Management10.1080/23311975.2022.20884589:1Online publication date: 15-Jun-2022
https://doi.org/10.1080/23311975.2022.2088458
Srinivasarao USharaff A(2022)Email thread sentiment sequence identification using PLSA clustering algorithmExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.116475193:COnline publication date: 1-May-2022
https://dl.acm.org/doi/10.1016/j.eswa.2021.116475
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents