Title: Marketing channel attribution modelling: Markov chain analysis

Authors: Kunal Mehta; Ekta Singhal

Addresses: Manager Data Analytics, Marketing, Publicis Sapient, Gurugram, India ' Faculty of Marketing, Fortune Institute of International Business, New Delhi, India

Abstract: With the advent of digital era the business landscape has evolved drastically thereby impacting all the marketing and advertising activities. Advertisers employ multiple channels to reach the customers on digital platform. Now the challenge has come up to design methodology to attribute conversions to these multiple channels in order to measure ROI (return on investment) and optimise the allocation of media budget. The problem gets compounded on digital platform where people tend to visit multiple times through multiple channels before each conversion. Conventional models of first touch, last touch and linear attribution do not give statistically complete picture, but at the same time, there are not enough resources outside which helps to implement a model like Markov attribution model to get statistically sound attribution and analysis of conversions. This paper aims to provide a high-level overview of different attribution models provided within some of the most prominent tools like Adobe Analytics and Google Analytics. At the same time the paper builds the case for more statistically sound model like 'Markov analysis' to showcase how and why it is better than traditional models.

Keywords: marketing strategies; marketing channel; digital platform; channel attribution model; Markov analysis; web analytics.

DOI: 10.1504/IJICBM.2020.109344

International Journal of Indian Culture and Business Management, 2020 Vol.21 No.1, pp.63 - 77

Received: 15 Apr 2019
Accepted: 24 Jul 2019
Published online: 03 Sep 2020 *

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