User tracking consists in registering the activity of users as they interact with one or more websites so that such activity can be related with specific, uniquely identified users. Counting unique visitors of websites is an essential activity in order to perform web analytics, since many web analytics metrics depend on individuals counted only once (e.g., new visitors, return visitors, etc.).
Web analytics provides the measurement model to digital marketing, letting to analyse and measure the effectiveness of advertisement campaigns. Data gathered by applying web analytics (e.g., number of persons that have visualised a banner) is typically compared against key performance indicators (e.g., reach of a campaign), and used to improve the audience response to marketing campaigns (e.g., move the banner to a site with more audience). The most significant KPIs depend on counting unique visitors.
Uniquely identifying users is also needed for performing behavioural targeting, which involves tracking of the online activities of users in order to deliver tailored ads to them.
The technique most used for uniquely identifying users from captured web activity is the one that combines cookies and web bugs. This technique is being affected by several factors, such as, strict privacy restrictions implemented by web browsers, or the use of new devices for navigating the web that do not support cookies (e.g., many set-top boxes and certain video game consoles). Furthermore, several security programmes, such the antispyware ones, remove cookies periodically, making it difficult to trace recurring visits to websites. Thus, these security measures, enabled to protect the privacy of users, affect basic aggregated metrics obtained with web analytics, from which valuable business insights can be derived, such as the number of unique visitors of a website, or the bounce rate.
An alternative to cookies for uniquely identifying users consists of capturing distinctive technical attributes of the system used by such users to navigate the web (i.e., their browser fingerprint). While the effectiveness of this technique has been demonstrated, such technique is not entirely accurate, since browser fingerprint is built from attributes that evolve over time. Thus, changes in values of fingerprint attributes lead to incorrectly accounting new users.
In summary, existing techniques for counting unique visitors are losing effectiveness, because of privacy restrictions and new devices for navigating the web. The fingerprinting technique deals with such restrictions and devices, but is quite sensible to changes in the attributes of the web browser, which leads to counting unique visitors imprecisely. The paper "Detecting browser fingerprint evolution for identifying unique users" describes an algorithm, based on the fingerprinting technique, which allows identifying unique visitors accurately, regardless changes in browser attributes. For doing so, such algorithm is able to detect the evolution of fingerprints, therefore, effectively grouping distinct fingerprints that correspond to the same user.
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