Hinweis

Dieser Eintrag befindet sich in Bearbeitung, die Daten wurden noch nicht validiert.

Hinweis

Dies ist nicht die aktuellste Version von diesem Eintrag. Die aktuellste Version finden Sie unter: https://www.research-collection.ethz.ch/handle/20.500.11850/404486

Zur Kurzanzeige

dc.contributor.author
Hatt, Tobias
dc.contributor.author
Feuerriegel, Stefan
dc.date.accessioned
2020-03-12T07:00:15Z
dc.date.available
2020-03-12T07:00:15Z
dc.date.issued
2020-04
dc.identifier.uri
http://hdl.handle.net/20.500.11850/404486.1
dc.description.abstract
Most users leave e-commerce websites with no purchase. Hence, it is important for website owners to detect users at risk of exiting and intervene early (e. g., adapting website content or offering price promotions). Prior approaches make widespread use of clickstream data; however, state-of-the-art algorithms only model the sequence of web pages visited and not the time spent on them. In this paper, we develop a novel Markov modulated marked point process (M3PP) model for detecting users at risk of exiting with no purchase from clickstream data. It accommodates clickstream data in a holistic manner: our proposed M3PP models both the sequence of pages visited and the temporal dynamics between them, i. e., the time spent on pages. This is achieved by a continuoustime marked point process. Different from previous Markovian clickstream models, our M3PP is the first model in which the continuous nature of time is considered. The marked point process is modulated by a continuous-time Markov process in order to account for different latent shopping phases. As a secondary contribution, we suggest a risk assessment framework. Rather than predicting future page visits, we compute a user’s risk of exiting with no purchase. For this purpose, we build upon sequential hypothesis testing in order to suggest a risk score for user exits. Our computational experiments draw upon real-world clickstream data provided by a large online retailer. Based on this, we find that state-of-the-art algorithms are consistently outperformed by our M3PP model in terms of both AUROC (+6.24 percentage points) and so-called time of early warning (+12.93 %). Accordingly, our M3PP model allows for timely detections of user exits and thus provides sufficient time for e-commerce website owners to trigger dynamic online interventions.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.subject
Clickstream data
en_US
dc.subject
Marked point process
en_US
dc.subject
Continuous-time Markov process
en_US
dc.subject
Risk scoring
en_US
dc.subject
Online user behavior
en_US
dc.title
Early Detection of User Exits from Clickstream Data: A Markov Modulated Marked Point Process Model
en_US
dc.type
Conference Paper
dc.rights.license
In Copyright - Non-Commercial Use Permitted
ethz.size
11 p. accepted version
en_US
ethz.version.deposit
acceptedVersion
en_US
ethz.event
International World Wide Web Conference (WWW 2020)
en_US
ethz.event.location
Taipei, Taiwan
en_US
ethz.event.date
April 20-24, 2020
en_US
ethz.publication.place
Taipei, Taiwan
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.::09623 - Feuerriegel, Stefan / Feuerriegel, Stefan
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.::09623 - Feuerriegel, Stefan / Feuerriegel, Stefan
en_US
ethz.date.deposited
2020-01-11T13:59:29Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/389527
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/404336
ethz.COinS
ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.atitle=Early%20Detection%20of%20User%20Exits%20from%20Clickstream%20Data:%20A%20Markov%20Modulated%20Marked%20Point%20Process%20Model&rft.date=2020-04&rft.au=Hatt,%20Tobias&Feuerriegel,%20Stefan&rft.genre=proceeding&rft.btitle=Early%20Detection%20of%20User%20Exits%20from%20Clickstream%20Data:%20A%20Markov%20Modulated%20Marked%20Point%20Process%20Model
 Suchen via SFX

Dateien zu diesem Eintrag

DateienGrößeFormatIm Viewer öffnen

Zu diesem Eintrag gibt es keine Dateien.

Publikationstyp

Zur Kurzanzeige