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Channel attribution modelling using clickstream data from an online store
Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In marketing, behaviour of users is analysed in order to discover which channels (for instance TV, Social media etc.) are important for increasing the user’s intention to buy a product. The search for better channel attribution models than the common last-click model is of major concern for the industry of marketing. In this thesis, a probabilistic model for channel attribution has been developed, and this model is demonstrated to be more data-driven than the conventional last- click model. The modelling includes an attempt to include the time aspect in the modelling which have not been done in previous research. Our model is based on studying different sequence length and computing conditional probabilities of conversion by using logistic regression models. A clickstream dataset from an online store was analysed using the proposed model. This thesis has revealed proof of that the last-click model is not optimal for conducting these kinds of analyses. 

Place, publisher, year, edition, pages
2017. , p. 42
Keywords [en]
Clickstream data, Channel attribution, Logistic regression, Variable selection, Data driven marketing
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-139318ISRN: LIU-IDA/STAT-A--17/008—SEOAI: oai:DiVA.org:liu-139318DiVA, id: diva2:1121025
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Kaplan
Subject / course
Statistics
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Examiners
Available from: 2017-08-08 Created: 2017-07-07 Last updated: 2017-08-08Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf