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Selection models for information channels: Applications in multichannel digital marketing
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
2015 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

In all digital marketing efforts, different information channels must be selected and used to reach customers. In this thesis, data describing the interactions that members of the loyalty program of a Nordic airline company have had with three information channels is used to estimate four selection models. These multinomial logistic regression models have the purpose of selecting which channel(s) best suits a given member. The models are evaluated and the one that best fits the given situation is examined in detail. It is found that the estimated models have satisfactory predictive power in estimating which channel best suits a given customer, and that this method can have useful practical applications.

Place, publisher, year, edition, pages
2015. , 31 p.
Keyword [en]
multinomial logistic regression; predictions; model estimation; customer engagement
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-254907OAI: oai:DiVA.org:uu-254907DiVA: diva2:820195
External cooperation
Nordiskt flygbolag
Subject / course
Statistics
Educational program
Freestanding course
Supervisors
Examiners
Available from: 2015-06-30 Created: 2015-06-11 Last updated: 2015-06-30Bibliographically approved

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Selection models for information channels - Eckerdal Winberg(5338 kB)225 downloads
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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • 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