Abstract
Online e-commerce sites track user behavior through use of in-house analytics or by integrating with third party platforms such as Google Analytics. Understanding user behavioral data assists with strategies for user retention, buy-in loyalty and optimizing objective completions. One of the more difficult problems though is understanding user intent that can be dynamic or built over time. Knowing user intent is key to enabling user conversions - the term used to denote completion of a particular goal. Current industry approaches for intent inference have an inherent disadvantage of having the need for embedded tracking code per site-sections as well as the inability to track user’s intent over longer periods. In this paper, we present our work on mining dynamic as well as evolving user’s intents, using a latent multi-topic estimation approach over user’s web browsing activity. Further, based on the intent patterns, we look at generating association rules that model purchasing behavior. Our studies show that users typically go through multiple states of intent behavior, dependent on key features of products under consideration. We test the behavioral model by coupling it with Google Analytics platform to augment a re-marketing campaign, analyzing purchasing behavior changes. We prove statistically that user conversions are possible, provided purchase category dependent associations are effectively used.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
KPMG: Truth about online consumers, 2017 Global Online Consumer Report (2017)
Google Analytics. https://analytics.google.com/analytics/web
Microsoft Azure Analytics. https://azure.microsoft.com/en-us/product-categories/analytics/
Kumar, A.H., John, S.F., Senith, S.: A study on factors influencing consumer buying behavior in cosmetic products. Int. J. Sci. Res. Publ. 4(9), 6 (2014)
Day, D., Gan, B., Gendall, P., Esslemont, D.: Predicting purchase behavior. Market. Bull. 2, 18–30 (1991). Article 3
Bell, S., Bala, K.: Learning visual similarity for product design with convolutional neural networks. ACM Trans. Graph. (TOG) 34(4) (2015). SIGGRAPH 2015. Article no 98
Cesar, A.C.: Impact of Consumer Attitude in Predicting Purchasing Behaviour (2007)
Arulkumar, S., Kannaiah, D.: Predicting purchase intention of online consumers using discriminant analysis approach. Eur. J. Bus. Manag. 7(4), 319–324 (2015)
Pal, S.: Know your buyer: a predictive approach to understand online buyer’s behavior’, white paper, Happiest Minds
Banerjee, N., Chakraborty, D., Joshi, A., Mittal, S., Rai, A., Ravindran, B.: Towards analyzing micro-blogs for detection and classification of real-time intentions. In: International Conference on Web and Social Media (2012)
Rose, D.E., Levinson, D.: Understanding user goals in web search. In: Proceedings of the 13th Conference on World Wide Web (2004)
Ioanas, E., Stoica, I.: Social media and its impact on consumers behavior. Int. J. Econ. Pract. Theor. 4(2), 295–303 (2014)
Guo, S., Wang, M., Leskovec, J.: The role of social networks in online shopping: information passing, price of trust, and consumer choice. In: Proceedings of the 12th ACM Conference on Electronic Commerce, pp. 157–166 (2011)
Sathish, S., Patankar, A., Neema, N.: Semantics-based browsing using latent topic warped indexes. In: International Conference on Semantic Computing, ICSC 2016
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Finkel, J.R., Manning, C.D.: Nested named entity recognition. In: Conference on Empirical Methods in Natural Language Processing, vol. 1, pp. 141–150 (2009)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of the 20th VLDB Conference, Chile (1994)
Sathish, S., Patankar, A., Priyodit, N.: Enabling multi-topic and cross-language browsing using web-semantics service. In: International Conference on Web Services (ICWS) (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Sathish, S.K., Patankar, A. (2019). Intent Based Association Modeling for E-commerce. In: Métais, E., Meziane, F., Vadera, S., Sugumaran, V., Saraee, M. (eds) Natural Language Processing and Information Systems. NLDB 2019. Lecture Notes in Computer Science(), vol 11608. Springer, Cham. https://doi.org/10.1007/978-3-030-23281-8_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-23281-8_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-23280-1
Online ISBN: 978-3-030-23281-8
eBook Packages: Computer ScienceComputer Science (R0)