Process-Driven AI for Behavioral Analytics In Display Ads

Online behavioral targeting is one of the most popular business strategies on the display advertising today.

Forbes defines behavioral targeting as using information about Web users to ensure ads are shown to the right consumers at the right time. It is based primarily on analyzing web user behavioral data with the usage of machine learning techniques with the aim to optimize 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 optimize campaign performance, maximize their revenue as well as deliver advertisements tailored to a variety of user interests.

According to statistics published by the Internet Advertising Bureau, UK online advertisers spent more than UK£8.6 billion in 2016 on behavioral targeted advertising a figure which grew 16.4% compared to 2015.

The estimate represents steady growth rates of about 20% from 2010 through 2016.

Behavioral targeting and customer prospecting are both promising and challenging aspects in display advertising.

Promising since the more information of user behavioral 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 formalized 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 –


Which metric features in terms of evaluating an online campaign performance are mostly important


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 analyzed campaign data that contained audience demographic information and audience behavioral segments to predict whether a user who had no previous seen an advert is likely to convert.

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