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Measuring the diffusion of marketing messages across a social network

Research output: Contribution to journalJournal articlepeer-review

<mark>Journal publication date</mark>11/2012
<mark>Journal</mark>Journal of Direct, Data and Digital Marketing Practice
Issue number2
Number of pages34
Pages (from-to)97-130
Publication StatusPublished
Early online date26/11/12
<mark>Original language</mark>English


The last few years have seen significant investment in social media as an advertising, marketing and customer outreach opportunity. In the US alone, in 2010, almost $1.7 bn was spent by advertisers on social media marketing, with 53 per cent specifically allocated to Facebook 1. Due to the explicit links that users maintain with each other, social media platforms are perceived as a highly suited environment for network-based marketing: word-of-mouth marketing, diffusion of innovation, or buzz and viral marketing 2 all aim to take advantage of the relationships between users to facilitate the spread of awareness or adoption. In order to predetermine the effectiveness of such campaigns, it is important to be able to estimate potential return on investment. In particular, the ability to model existing networks, track the propagation of marketing messages and estimate customer exposures and impressions are essential for this purpose. A wide range of techniques to measure notions such as user engagement on such platforms have been developed and there also exists a significant amount of research on modelling contagion and diffusion in network-based environments that can be exploited to generally refine an overall marketing strategy. However, the structure and properties of different social media platforms introduce various constraints on both the means via which data propagate and the visibility of content and nodes, constraints that must be taken into account when modelling or measuring the impact of social media campaigns. Perfect information about exposures within a given graph to a given message will not be available and as such it is important to investigate and define methodologies for diffusion monitoring that are suited to specific platforms.