By Mateusz Rumiński, VP of Product, PrimeAudience
Personalisation, the process of using data to serve ads that are both relevant and meaningful to the target audience, has been proven to deliver genuine competitive advantage to brands across all industries. According to recent McKinsey research, personalised advertising has the potential to generate anything from 5 to 15% in additional revenue, and boost marketing return on investment (ROI) by anything from 10 to 30%.
However, with the gradual phasing-out of third-party cookies and heightened sensitivity around the use of personal data, many of the signals traditionally needed to target advertising at an individual level are moving beyond the reach of advertisers. With a brand’s reputation potentially on the line, staying on the right side of international data-privacy laws as well as consumer sentiment has never been so important, making personalisation trickier.
In addition, from a creative perspective, personalisation isn’t straightforward. For example, when does personalisation start to feel ‘creepy’ to the consumer? Does that emotional tipping point differ for different types of consumer?
Almost paradoxically, despite their concerns around privacy, personalisation is in demand among consumers. 70% of consumers believe the ads they receive do not align with their interests, 71% now actively expect to receive personalised advertising and 76% are frustrated when they don’t get it. So how can brands and agencies deliver the tailored experience consumers are looking for while simultaneously navigating the constantly-shifting data privacy landscape?
Getting personal
Right now, based on a typical (not ‘logged-in’) customer journey online, ad tech algorithms can potentially collect a large amount of data, including:
- URL of visited websites
- Geolocation
- Context of sites visited
- User device
- User browser
- User operating system
- Behavioural habits of other users with similar tastes
- Previous interactions with the ads
- Topics API signals, and more.
AI can be helpful for connecting the dots and deriving conclusions from overwhelming amounts of collected data that would be impossible for the human brain to process. Machine Learning and Deep Learning algorithms are able to find hidden layers of insight in user behaviours.
For example, based on the data points mentioned above, algorithms can determine:
- The expected CTR/VCR
- What type of banner will be the most effective (video/display)
- The best creative to serve
- Which product should be displayed on the banner
- What placement on the publisher’s website offers the best value-to-price ratio.
In the cookieless world, these algorithms should still be able to provide answers to the above questions. The main difference will be that users will no longer be identified as individuals across different domains. Fortunately, the latest proposals from Google mean that this will not be necessary in order to deliver highly personalised advertising.
Targeted but protected
Part of Google’s Privacy Sandbox, the Protected Audience API allows the analysis of granular user behaviour from a single website and the utilisation of this information to group similar users into cohorts. A DSP can then target specific anonymised cohorts as opposed to individual users.
From the outset, the Protected Audience API, renamed Turtledove and then FLEDGE, was immediately associated with retargeting applications, but later, Google indicated that it’s not the only use case for this API. It’s also a powerful tool for building interest groups for upper-funnel funnel applications as a behavioural targeting technology. Tools such as these will be essential post-cookie.
For example, if a number of people on the general news site are interested in electric cars—searching for rankings or reviews of specific models—Protected Audience API allows ad tech companies partnering with the news website to gather together these users and create a group such as “electric vehicle enthusiasts”. These users can later be targeted across the internet while remaining anonymous.
Another positive aspect is preventing data leakage. In today’s ecosystem, there is both massive data leakage violating user privacy and data loss in the cookie matching process. Recent AdExchanger research suggests that more than 50% of users are “lost” in this process. The Protected Audience API makes it possible to reach 100% of users added to these groups because data is stored on the user’s device and does not require any matching.
The impact
Protected Audience API maintains the personalization and user experience of current advertising methods while ensuring the security of user data. The ads themselves are expected to look very similar to those that users are accustomed to now.
As the world of digital advertising enters the next era, personalization will continue to play a huge role in achieving great marketing results. Now, marketers must begin to understand its implications and work with ad tech ecosystem players to create successful personalised advertising that’s ready for the future.
