research-article

A Deep Markov Model for Clickstream Analytics in Online Shopping

Authors:

Yilmazcan Ozyurt,

Stefan FeuerriegelAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 3071 - 3081

https://doi.org/10.1145/3485447.3512027

Published: 25 April 2022 Publication History

Abstract

Machine learning is widely used in e-commerce to analyze clickstream sessions and then to allocate marketing resources. Traditional neural learning can model long-term dependencies in clickstream data, yet it ignores the different shopping phases (i. e., goal-directed search vs. browsing) in user behavior as theorized by marketing research. In this paper, we develop a novel, theory-informed machine learning model to account for different shopping phases as defined in marketing theory. Specifically, we formalize a tailored attentive deep Markov model called ClickstreamDMM for predicting the risk of user exits without purchase in e-commerce web sessions. Our ClickstreamDMM combines (1) an attention network to learn long-term dependencies in clickstream data and (2) a latent variable model to capture different shopping phases (i. e., goal-directed search vs. browsing). Due to the interpretable structure, our ClickstreamDMM allows marketers to generate new insights on how shopping phases relate to actual purchase behavior. We evaluate our model using real-world clickstream data from a leading e-commerce platform consisting of 26,279 sessions with 250,287 page clicks. Thereby, we demonstrate that our model is effective in predicting user exits without purchase: compared to existing baselines, it achieves an improvement by 11.5 % in AUROC and 12.7 % in AUPRC. Overall, our model enables e-commerce platforms to detect users at the risk of exiting without purchase. Based on it, e-commerce platforms can then intervene with marketing resources to steer users toward purchasing.

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Cited By

Sakalauskas VKriksciuniene D(2024)Personalized Advertising in E-Commerce: Using Clickstream Data to Target High-Value CustomersAlgorithms10.3390/a1701002717:1(27)Online publication date: 10-Jan-2024
https://doi.org/10.3390/a17010027
Wen ZLin WLiu H(2023)Machine-Learning-Based Approach for Anonymous Online Customer Purchase Intentions Using Clickstream DataSystems10.3390/systems1105025511:5(255)Online publication date: 18-May-2023
https://doi.org/10.3390/systems11050255
Necula S(2023)Exploring the Impact of Time Spent Reading Product Information on E-Commerce Websites: A Machine Learning Approach to Analyze Consumer BehaviorBehavioral Sciences10.3390/bs1306043913:6(439)Online publication date: 23-May-2023
https://doi.org/10.3390/bs13060439
Show More Cited By

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A Deep Markov Model for Clickstream Analytics in Online Shopping

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Published In

cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Published: 25 April 2022

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WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

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Cited By

Sakalauskas VKriksciuniene D(2024)Personalized Advertising in E-Commerce: Using Clickstream Data to Target High-Value CustomersAlgorithms10.3390/a1701002717:1(27)Online publication date: 10-Jan-2024
https://doi.org/10.3390/a17010027
Wen ZLin WLiu H(2023)Machine-Learning-Based Approach for Anonymous Online Customer Purchase Intentions Using Clickstream DataSystems10.3390/systems1105025511:5(255)Online publication date: 18-May-2023
https://doi.org/10.3390/systems11050255
Necula S(2023)Exploring the Impact of Time Spent Reading Product Information on E-Commerce Websites: A Machine Learning Approach to Analyze Consumer BehaviorBehavioral Sciences10.3390/bs1306043913:6(439)Online publication date: 23-May-2023
https://doi.org/10.3390/bs13060439
Bhargavi MSinha ADesai JGarg NBhatnagar YMishra P(2022)Comparative Study of Consumer Purchasing and Decision Pattern Analysis using Pincer Search Based Data Mining Method2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT54827.2022.9984410(1-7)Online publication date: 3-Oct-2022
https://doi.org/10.1109/ICCCNT54827.2022.9984410

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