Multichannel Marketing Attribution Using Markov Chains
Paulo A. A. Resende and
Martina Ferencova ()
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Lukáš Kakalejč: Technical University of Košice, Slovakia
Jozef Bucko: Technical University of Košice, Slovakia
Paulo A. A. Resende: University of Brasília, Brasil
Martina Ferencova: Constitutional Court of the Slovak Republic, Slovakia
Journal of Applied Management and Investments, 2018, vol. 7, issue 1, 49-60
The objective of this paper is to analyze the data of a selected company using Markov chains. The data about online customer journeys were analyzed. The authors found that Markov model decreases the credit assigned to channels favored by last-touch heuristic models and assigns more credit to channels favored by first-touch or linear heuristic models. By using Markov order estimator GDL the authors also found that order 4 was the most suitable for analysis of buyer journeys. Approximately 40% of revenue was generated by journeys with less than 5 interactions and thus indecisive customers have small incremental effect on the overall conversions.
Keywords: attribution modeling; multichannel attribution; Markov chains; digital analysis; web analytics (search for similar items in EconPapers)
JEL-codes: M31 L81 C25 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:ods:journl:v:7:y:2018:i:1:p:49-60
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