Last Updated on: 13th October 2023, 11:16 am
Everyone loves a good challenge, but some are tougher to tackle than others. For marketers, the big one is the personalization paradox. 71% of consumers prefer personalized ad content, but 74% are also concerned about how advertisers are actually using their personal data. This creates a problem for marketers. They want to reach out to their customers with personalized content but often rely on tracking technologies that are considered invasive. Fortunately, there is an answer to this particular paradox. Brands can use data-driven marketing to leverage large anonymized datasets that provide rich personalization at scale.
In this article, you will learn:
- Why the personalization paradox is such a headache for brands
- How the way we use data is changing as third-party cookies are retired
- What methods brands will need to adapt to take advantage of the cookieless future
- Why Deep Learning will be a key tool for the privacy-first advertising world
Table of Contents:
- Why brands need to balance personalization and privacy
- Big Tech is responding to consumer concerns
- Privacy-friendly personalization leverages data to use a holistic advertising approach
- Deep Learning makes it easier to understand complicated datasets
Why brands need to balance personalization and privacy
Before we go into the how, let’s discuss the why. Programmatic advertising enabled automated ads. This was great because it helped users find new products, but some advertisers took it too far by adopting an overly aggressive strategy.
Over time, users became constantly bombarded with ads. Whether it’s on Instagram or the open web, users are inundated with advertising. This has caused ad fatigue to the point where 82% of users simply ignore online ads. To counter this, brands have increasingly opted for personalized ads that highlight products, or even the features of a product, that a specific user is interested in.
This was fine until users began to realize that the bike they looked at one time was following them around the internet for the next month, and began to question why. The answer came in the form of tracking cookies. These third-party cookies collect vast amounts of data about the sites a user visits, and they can even track a user across multiple websites. This was useful for personalization, but ultimately it damaged user trust in advertisers, creating a significant pushback against this kind of technology.
Big Tech is responding to consumer concerns
In response, Google announced that it will be retiring third-party tracking cookies from the Chrome browser – originally planned in 2022, but then postponed to 2023, and then 2024. In its place, Google Chrome is trialing several solutions from the Google Chrome Privacy Sandbox. These will allow advertisers to connect with groups of users with shared interests, without needing to know the identity of any specific individual.
Specifically, Google Chrome has been trialing FLEDGE. This approach allows buyers to group users into specific cohorts which share the same interests. The advertiser can buy impressions targeted to this group without ever knowing who a specific individual is.
There are still significant limitations to FLEDGE, specifically in terms of industry participation. The industry isn’t just relying on the Privacy Sandbox; alternative methods are also in the works. For example, IAB Labs has been working on Seller Defined Audiences (SDAs) to give publishers a way to provide meaningful information about their inventory and audiences to downstream buyers through a standardized system. While promising, our own tests with SDAs have identified two key flaws. The first is that there are still no long-term signs that quality assurance will be sufficient to make these signals meaningful, and the second is that it does not adequately prevent fingerprinting techniques.
This is a common theme for new cookieless tracking technologies: there is a promise but no guarantee for advertisers that they will be as effective as the old methods. Thus, advertisers face a stark choice: Do they double down on fingerprinting and other “tested” technologies, ignoring user privacy concerns, or do they work to adapt their methodologies to a privacy-friendly personalization approach, using a combination of classic concepts like contextual marketing and modern Deep Learning algorithms?
In our view, the answer is clear. If advertisers continue to ignore user privacy, the pushback against ads is only going to worsen. We run the risk of losing control of the environment as users retreat behind ad-blockers or states legislate against these technologies without broader industry participation.
That’s why we propose taking a data-driven approach to personalization.
Privacy-friendly personalization leverages data to use a holistic advertising approach
Cookies were useful precisely because they provided advertisers with a rich dataset. This made it easy for ad buyers to find users to connect with. However, there are other ways to do this. They simply require interpreting different kinds of data and combining multiple approaches.
For example, you might be able to use publisher data to successfully conduct a contextual marketing campaign on publications where you know users interested in your product will frequent. RTB House has had significant success using ContextAI to conduct these sorts of campaigns.
You could then continue the conversation through a variety of methods. For example, you might use first-party cookies to remind users who return to your site about products they interacted with last time. This same technology can also be used to construct interest groups in FLEDGE, which can then be used to connect with users without needing cross-site identifiers.
This approach enables deep personalization without violating a user’s privacy. Most importantly, it can help avoid the uncanny feeling that certain products are chasing you online. Over the long term, brands that proactively address user privacy concerns will have a significant advantage over those that rely on invasive tracking methodologies.
This will eventually change the advertising environment, and companies that are best able to process complicated, even unstructured, datasets will begin to have an advantage.
Deep Learning makes it easier to understand complicated datasets
One of the ways to do this, and the way we do it here at RTB House, is to take advantage of Deep Learning algorithms. This tool passes data through layers and uses a neural net inspired by the structure of the human brain to draw increasingly complicated conclusions. This allows the algorithm to learn as more data is fed into it and to handle complicated diverse datasets.
We use Deep Learning in every single one of our advertising solutions. This has positioned RTB House to thrive in the cookieless future, and we can help your company do the same.
To talk more about the privacy paradox and how you can adopt a data-driven marketing approach, reach out to our team today.
