Multichannel Marketing Attribution Using Markov Chains

Lukáš Kakalejč, Jozef Bucko (), Paulo A. A. Resende and Martina Ferencova ()
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Lukáš Kakalejč: 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

Abstract: 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: C25 L81 M31 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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