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How Cooperative Game Theory can be utilised to enhance marketing analytics attribution

Matthews, James (2016) How Cooperative Game Theory can be utilised to enhance marketing analytics attribution. Masters thesis, Dublin, National College of Ireland.

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Abstract

Marketing analytics and attribution modelling enable businesses and organisations to measure the true performance of how all online channels work together to generate revenue, sales and increase market growth. The usage of marketing analytics and attribution however remains extremely low, which results in a marginalisation of the marketing industry. As businesses and organisations shift significant amounts of their marketing budget towards digital channel advertising, the necessity for marketing analytics and attribution is ever important.

The marketing industry is currently using Last Click analysis to measure online channel performance which assigns the credit of a conversion and sale to the last
marketing channel a customer has interacted with. Research shows Last Click analysis to be an insufficient method to measure campaign effectiveness and performance. Cooperative Game Theory attribution using Shapley Value is shown to be an optimum methodology and technique to employ in order to conduct attribution analysis. An enhanced method of Cooperative Game Theory attribution using a k-order Markov chain can evaluate the online conversion path a user takes prior to purchasing, thus enabling a business to quantify the individual contribution each marketing channel brings in driving conversion and generating revenue.

The author has conducted benchmark analyses that examine the performance of Last Click attribution against that of a Cooperative Game Theory attribution model. The evaluated results show that Cooperative Game Theory attribution out performs Last Click attribution and enhances marketing analytics by unequivocally showing the true added business value that each marketing channel brings in driving conversion and generating revenue.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science

H Social Sciences > HF Commerce > Marketing > e Marketing
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: CAOIMHE NI MHAICIN
Date Deposited: 03 Dec 2016 12:09
Last Modified: 03 Dec 2016 12:09
URI: http://trap.ncirl.ie/id/eprint/2492

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