Online behavioural targeting is one of the most popular business strategies on the display advertising today. It is based primarily on analysing web user behavioural data with the usage of machine learning techniques with the aim to optimise web advertising. Being able to identify “unknown” and “first time seen” customers is of high importance in online advertising since a successful guess could identify “possible prospects” who would be more likely to purchase an advertisement’s product. By identifying prospective customers, online advertisers may be able to optimise campaign performance, maximise their revenue as well as deliver advertisements tailored to a variety of user interests.
According to statistics published by the Internet Advertising Bureau online advertisers in the UK have spent more than 8.6 billion UK-pounds in 2016 on behavioural targeted advertising a figure which grew 16.4% compared to 2015. The estimate represents steady growth rates of about 20% from 2010 through 2016. Behavioural targeting and customer prospecting are both promising and challenging aspects in display advertising. Promising since the more information of user behavioural activity exists the better targeted advertisements could be delivered to end users and challenging since display advertising is a rather complex ecosystem which involves multiple interested parties such as end users, advertisers, publishers, and ad platforms. The size of data generated and collected from any involved parties is significantly large: Billions of websites requests every day trigger millions of advertisements that are finally displayed to millions of users.
Digital advertisers attract increasing traffic on their websites aiming for certain user marketing actions, more commonly, accomplishing an online purchase. This action is recorded as a conversion. There are two ways for viewing an advert upon arrival on an affiliate ad-friendly website. Firstly, by clicking on the advert and immediately buying and/or by viewing an advert and waiting for a future return and a possible purchase. The journey of a user throughout several websites can be represented as a series of events with intermediate temporal durations. This can be interpreted into a “workflow” of variant length which may or may not convert at its final stages.
Research has shown that workflow behaviours with such a distinct event-duration coupling can be formalised over a general theory of time be graph-represented, monitored and explained effectively using Case-based Reasoning techniques. The research questions on top of the online marketing business model are twofold – One: which metric features in terms of evaluating an online campaign performance are mostly important and -Two: based on the set of identified metrics what is the profile of an ad viewer who is keen to make a purchase.
In such way by analysing and classifying past behavioural observations among ad viewers, could allow marketers to identify future prospect customers more effectively. The research handles a challenging area in the online display advertising marketplace, this of customer prospecting. Customer prospecting identifies web users who are likely to purchase a product after seeing an advertisement. We developed a process mining methodology based on an advertising campaign implemented by an ad network provider. We collected and analysed campaign data that contained audience demographic information and audience behavioural segments to predict whether a user who had no previous seen an advert is likely to convert.
For more details: http://ceur-ws.org/Vol-2028/paper14.pdf
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