There’s no doubt that this move, which follows in the footsteps of other browsers, including Apple’s Safari, is greatly challenging for the digital media industry. In fact, we should have been waving goodbye to cookies over the next few months, but the complexity of the project last year led Google to delay the phase-out until late 2023.

The battle for attention

At the same time as this technological challenge, brands are facing a new battle for attention. The growth of digital media has been a double-edged sword for brands, with ever greater numbers of small and medium businesses advertising for the first time. While that’s happening, consumers are enjoying more ways than ever to spend their time. From virtual reality to unlimited streaming services, the media industry continues to offer ever greater choices for consumers to spend their time – both commercial and ad-free.

It seems like a perfect storm. But while the end of cookies marks the death of the old world of digital advertising, marketers shouldn’t panic. Advances in alternative ad technologies – often making use of artificial intelligence – will enable advertisers in the post-cookie age to make their campaigns more effective, without the privacy headache of cookies.

An alternative solution

A number of new approaches to allow programmatic advertising to continue without cookies are in the works. But it is contextual signals, based on anonymous interest cohorts, that are quickly becoming the best data point to maximise metrics like attention. 

Contextual signals don’t make use of personal data, or track users between websites. That means they avoid not just the privacy concerns of cookies, but also the issue of being shown the same ad repeatedly. This is a major aspect of “bombardment”, identified as a key threat to public trust in advertising in a 2019 report by the UK’s Advertising Association.

Instead, contextual signals use non-user-specific metadata from bid requests. As a campaign runs, this data sheds light on which ad buys are most likely to lead to conversions. But to manually analyse this data and make budget allocation adjustments as circumstances change is a Herculean task for anyone. The complexities of digital media buying also result in campaigns that are challenging to scale, due to the need to monitor increased numbers of interdependent items. 

AI in action

This is where AI comes in. With AI, advertisers can build a bespoke learning data set for any given campaign, which then evolves as it learns. As a result, it can make choices that get the best results from a series of variables, therefore delivering the strongest performance. The AI can automatically reallocate budgets in real time, making it much more achievable to scale a campaign. 

This approach means that rather than over-targeting a predetermined set of users (remarketing, for example), a campaign can continually move towards buys that are most effective in capturing the attention of users – leading to greater conversion rates and boosting return on ad spend. 

AI can also make use of other sources of data, such as a brand’s opted-in customer relationship management data or measurement and attribution data, and incorporate custom metrics beyond ROAS (Return On Ad Spend). This gives advertisers greater control of their advertising and allows them to plan campaigns to meet specific business objectives.

A stronger digital media ecosystem

For example, luxury beauty brand Charlotte Tilbury recently utilised an AI designed to be customisable to the digital marketing needs of the brand. They faced problems common in the luxury market, where the relatively small number of (high-spending) consumers means ad campaigns often generate fewer higher value conversions than in other sectors. That then leads to a lack of data that can inform and improve campaign performance and scale. 

Because of this, the AI solution took into account data that is generated in abundance but which legacy approaches to optimisation are unable to utilise: upper funnel signals such as clicks, site visits and ad viewability. The results were significant: a 60% growth in the conversion rate of the brand’s acquisition campaigns, and a 29% drop in average cost per acquisition.

Marketers don’t need to shed a tear for the demise of cookies: the capabilities of AI, when used with contextual signals and first-party data, will let them measure and optimise the performance of their campaigns more effectively than ever before. Along with positive developments in areas such as optimising digital creative, this means a digital media ecosystem that offers better results for advertisers and a better experience for consumers.

The post From Contextual to Attention – How AI is Changing Success Measurement appeared first on PerformanceIN.

Contextual signals, though free or personal data, still use data to ensure that ads are being served to the relevant consumers. And the entire industry is trying to figure out how to make this data more effective for decisioning. 

Contextual signals provide a truly privacy-first approach to advertising, as they don’t utilise any personal data or track users around the web. This enables advertisers to move away from third-party cookies, allowing them to leverage their first-party data such as segments as additional information in the bidding process. Removing this reliance on third-party user data will put advertisers in good stead for the privacy-centric world we now live in. And one of the most effective ways that advertisers can get results in this world is through artificial intelligence (AI).

Contextual intelligence

Though AI can benefit the entire digital advertising space – particularly once third-party cookies have departed – it can play a huge role in taking contextual experiences to the next level.

For advertisers, AI can be thought of as a technology that gets the best results from a set of variables, especially the contextual information of a bid request.

A reliance on behavioural targeting in the wake of the deprecation of third-party cookies will have a huge impact on ROI, so it’s key for advertisers to be open to targeting ads via fewer data intensive avenues. 

When data-abundant contextual signals are coupled with machine learning, the efficiency of campaigns can actually be enhanced beyond the capabilities of traditional approaches, and provide consumers with a more friendly experience overall.

AI can be used to gather the contextual information of a bid request. Having fewer data segments available to target means that the performance of ads is going to be more reliant on learning the optimal bidding function for particular campaigns, rather than targeting “lookalikes”. 

The AI enables advertisers to deliver highly-relevant ads without the need for personal data, finding patterns to engage with the right types of consumers and maximise return on investment (ROI). 

Without the support of AI, it will be more difficult to make the most of the contextual signals available in bid requests to deliver ads that grab the attention of consumers, regardless of other key factors like creative. 

An intelligent future

AI is imperative to the future of digital advertising. With the increasingly privacy-conscious landscape, AI can ensure that advertisers realise consumer expectations in a more seamless manner, while ensuring that the experiences delivered to those consumers are privacy-friendly.

In this post-cookie world, AI can collate the non-user-specific metadata that exists in abundance within bid requests, and use this to better align brands and consumers contextually, without ever tracking users across sites. 

Sophisticated privacy-preserving AI technology requires no interaction with the consumer at all and, thanks to being able to make more of metadata than broken cookie data, is far more powerful and effective than using legacy systems to deliver and optimise advertising. 

Rather than over-targeting to a rigid set of people, AI can learn from a pool of unlabelled signals building a learning data set specific to each campaign, which itself evolves progressively as the campaign evolves, shedding those variables that do not create performance and welcoming new ones that do. It can get conversions from more people which, in turn, boosts a campaign’s ROI by leveraging those signals to determine the exact price to pay for an impression with regards to its computed probability to convert. 

A machine for the post-cookie world

Though it can’t be denied that the deprecation of cookies will hit the industry in a big way, the cookieless world of digital advertising will still provide advertisers with plenty of opportunities to reach consumers, and one of the best ways to do that is through AI-powered ad decisioning.

As we wave goodbye to the third-party cookie, driving ROI will become increasingly dependent on AI solutions that don’t rely on any personal data or cross-site behavioural analysis. There’s a great opportunity for advertisers to utilise the technology to deliver advertising in a way that reduces friction between consumers and brands.

The privacy-centric world of digital advertising can use customisable AI to deliver powerful ad decisioning that works with non-user-specific contextual signals for targeting, moving away from user tracking and profiling. 

It may not always seem like it, but the future of digital advertising is something to be positive about. The technology exists to unlock audience attention at scale, while leaving behind the intrusive legacy technology that we have all become accustomed to. And that technology is artificial intelligence.

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