iPhone Users Shun Trackers: Dialling up Data-Driven Marketing in a Privacy-First World
The advertising world has waited with bated breath to understand how iPhone users will respond to an opt-in approach to data tracking. The results are in and they’re not looking good. 96% of iPhone users said ‘no thanks’ to apps following them around the web. Of course, these are very early days – many do not yet have the latest iOS update installed, and behavior can change over time.
However, it does reflect one current and growing trend: consumers are looking for more control over their data. Whether app tracking becomes more accepted or not, the advertising ecosystem is changing as consumers increasingly value privacy.
Browsers are now set to lose the third-party cookie, and across every part of the ad ecosystem questions are being asked about how to respond to the demand for greater user privacy.
Privacy is a watchword that will soon go far beyond these first frontiers, spelling the end of any kind of behavioral tracking, as consumers demand anonymity and respect online. Advertisers are facing the end days of identity-based behavioral tracking.
However, this does not spell disaster for data-driven marketing – the core tenet of most digital advertisers. There are other, plentiful sources of data that do not rely on tracking or identity that can target customers to the same effect. The advertising ecosystem will be complex, with FLoCs, walled gardens and first-party data all part of the picture when reaching consumers. One key avenue for advertisers beyond these solutions will be contextual targeting – but not as we know it now. Next-generation contextual targeting, with a sharp focus on the data that matters, is where the future lies for reaching the wider web in a precise and effective manner.
Reaching the wider web is imperative for advertisers. Yes, walled gardens can provide insight on customers (though perhaps the days of individualistic tracking are coming to an end) and are a core part of many digital marketing campaigns. But most people spend significant proportions of their time on the web outside of logged-in environments. Meanwhile, Nano’s own data shows us that over 55% of UK internet users are already in environments without third-party cookies, such as Safari or Firefox.
Fuelled by machine learning, we can still take a data-driven approach to reaching those people in the right way, with the most effective ads. What’s more – this means your brand is delivering ads in line with consumer appetite: intelligently targeted, without needing to know what school they went to or their mother’s occupation.
So what data does matter to next gen contextual targeting?
Live intent: or, how a person arrived at a webpage. Whether by searches or clicking through from another article, understanding what they’re looking for can help assess whether they’re right for your brand. An individual looking for articles to remove tough stains might respond well to an ad for stain remover, for example. There is no need for ‘historical’ data in this equation – knowing what they need now is more important than understanding their personal shopping history.
Context: going beyond keywords, data can tell us the underlying content of the entities on the page. Knowing the sentiment of the piece and its brand safety – according to the guidelines of your brand – while using content blocking technology over keyword-based methodology is key to a data-driven and effective approach to contextual targeting. IAS found in a study in 2020 that 73% of UK consumers said that the sentiment of the content on a page impacts their feelings toward brands advertising on it.
Technology can identify where, for example, discussions of COVID-19 are perfectly brand safe – for example in content pushing employers to offer more flexibility to staff. Understanding sentiment could also give a new opportunity in competitor conquesting. If a person is reading around ‘mobile phone battery issues’ and a given article is negative about a certain device, this may well be a good time for a competitor brand to push their model.
Past performance: while we can’t track the user, we can track our ads. This means both looking at how different iterations have performed for the campaign and combining that with an understanding of how on-page inventory has performed in the past, be that viewability, dwell time, or click-through rate. Machine learning can in this way quickly give a deep understanding of which creative to place where, to ensure the best results for any specific campaign.
These are just some of the layered points of data that machine learning can unite to create a complex and layered view of an ad placement – optimizing on the content on the page, the intent of the visitor and campaign outcomes.
Thus, with zero tracking, a full picture begins to emerge that can drive positive brand perception and crucially gain customer attention. These are the metrics that matter – and marketers who focus on the quality and effectiveness of their campaigns will quickly see that next gen contextual targeting is a key solution in a privacy-first world.
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