Dark and grey traffic - a major problem for online marketing and digital analytics
In the evolving landscape of online marketing and digital analytics, accurately tracking and analyzing website traffic has become one of the biggest challenges. Two increasingly emerging and particularly sensitive topics are dark traffic and grey traffic.
Both terms refer to visits that are either invisible or do not allow data activation and therefore cannot be operationalized by personalization or CX/UX-optimization tools. The impact of this can be profound and lead to skewed data, ineffective marketing strategies and many missed optimization opportunities.
What is dark traffic?
Dark traffic refers to website visits that do not appear in analytics tools because users did not give tracking consent.
This issue arises from privacy regulations, such as the General Data ProtectionRegulation (GDPR) in the European Union or the California Consumer Privacy Act (CCPA). Websites must obtain the explicit consent of users before recording and tracking their ongoing activities including possible personal identifiable data. Without giving consent, a visit still takes place, but it is not recorded.
Note: Consent-free web tracking can be made possible if – in addition to other requirements – only sessions are tracked but without setting and tracking a visitor’s ID – a simple fallback to avoid dark traffic.
However, since the visitor ID is required for many online marketing methods, such as customer journey analyses, touchpoint attribution, LTV and RFM modeling, etc., these approaches become less effective.
Dark traffic, therefore, leads to data gaps of unknown size. Without sufficiently complete tracking data, marketing strategies can be misdirected, lead to incorrect assumptions about user preferences, and reduce the effectiveness and analysis of campaigns: The user experience within the dark traffic segment is completely unknown.
Increasing share of grey traffic
Grey traffic, on the other hand, refers to visits that are recorded in basic tracking but do not give consent for cookies from other tools.
This means that the vast majority of personalization tools such as Customer Data Platforms (CDPs), UX/CX optimization, smart onsite searches, etc. are not operationalizing grey traffic at all. As long data activation is provided by these tools, no user-individual personalization can be displayed for grey traffic.
Thus, common optimization tools can operationalize only part of the total traffic. Also, the problem is not solved by using only 1st party cookies which is misleadingly called “cookie-less”, at most the problem is alleviated a little.
Grey traffic is therefore a problem for all online marketing activities that are implemented with consent-based tools.
Impact of dark and grey traffic
Both dark and grey traffic lead to incomplete or distorted data sets that challenge the core of data-driven marketing strategies. With dark traffic, the problem is the underreporting of traffic, with data gaps and a distorted view of user behavior.
Grey traffic is at least visible, yet presents a different challenge: Optimization cannot be delivered. Depending on the case, this affects “only” 30% but often 50-80% of the entire traffic in e-commerce.
Solution approaches
To overcome these challenges and to serve 100% of the traffic with an optimized UX/CX, a solution must work independently of cookies and consent.
One approach to at least avoid dark traffic is the use of server-side tracking or (possibly additional) consent-free tracking limited to session tracking without visitor IDs and with privacy compliant tools.
Due to this, contextual targeting is getting more popular in performance marketing. The targeting then delivers ads and content based on the context of the ad placement, e.g. the type of page, visiting target group, type of content, keywords, etc. While the approach is less precise than a user-specific approach, it allows for greater reach.
From an onsite optimization perspective, immediate data activation for the current user is necessary. This requires real-time analysis of every visit and every click. Analyzing the available data points including anonymous data points from the user agent, such as device type, geography, referrer, day, time, browser, operating system, etc., as well as the clicks made within a session in real time, allows to identify a look-alike-audience per user and click from the own data history.
This look-alike-audience must be
- large enough to be statistical significant, but it must also be
- small enough to be relevant for the current user.
Identifying a look-alike-audience based on the current, situational user behavior and datapoints, allows to predict the most likely most relevant products or topics, which have to be displayed immediately in milliseconds.
This approach requires the ability to perform complex real-time analyses and an interface for real-time data activation towards the current business user. It is paramount to always display relevant and personalized content up from the first impression of a visit and then continuously adapt it with each further click.
Conclusion
The problem of dark and grey traffic poses major challenges for online marketing and digital analysis. It undermines, complicates or even prevents the analysis and effectiveness of marketing strategies and the optimization of the user experience.
It is time for new, modern methods in online marketing and e-commerce that meet the requirements of data protection as well as ensuring continuous optimization and, above all, getting the most out of the data provided.
