An intersection of artificial intelligence and data science, machine learning has propelled digital advertising into a new era, in which enhanced automation and detailed data can be utilised to elevate advertising campaigns. With the deprecation of third-party cookies drawing nearer, we examine how machine learning is transforming targeted advertising and personalisation for consumers.

Enhanced targeting in the post-cookie world?

As the phase out of third-party-cookies draws closer, advertisers are scrambling to establish alternative methods of targeting customers without compromising privacy. As a result, contextual advertising is reemerging as a popular method of understanding audiences without the use of personal identifiers. Through data such as keywords, page types, and media channels, advertisers can understand the context of the page and provide the most relevant and engaging communications without the use of cookies. By using advanced algorithms, machine learning can process vast amounts of contextual data to identify patterns and predict behaviour on an amplified scale, helping advertisers to segment their audiences into more detailed and nuanced categories. By better understanding audiences, advertisers can ensure they are served the most relevant and timely ads, increasing the probability of click-throughs and engagement.

Additionally, these capabilities can enhance another developing area of digital advertising: programmatic. While programmatic advertising has developed substantially over the last decade, the medium is yet to be adopted on a universal scale. Through machine learning, programmatic advertising can be supercharged, making the process of picking and placing suitable adverts in relevant places much faster. With automation increased and the need for human intervention reduced, brands and marketers can be afforded more time and resources for other critical elements of their campaigns, such as creative.

Balancing personalisation with privacy

As privacy regulations continue to evolve, adaptability is critical. Through adaptive machine learning, advertisers can maintain pace with legislators while continuing to analyse their audience at scale. It is, however, vital that advertisers do not lose sight of the individual experience: research from McKinsey & Company found that 71% of consumers expect companies to deliver personalised customer experiences. With 6 in 10 consumers actively avoiding brands which do not offer personalised experiences, it’s clear that meeting privacy standards without compromising personalisation is imperative for brands and advertisers. 

Fortunately, machine learning can be utilised to, not only deliver, but augment personalised experiences. In e-commerce, for example, a brand can use detailed analysis of its customers’ data to eliminate “popularity bias” – the practice of showing its most popular products regardless of relevancy to the customer – to recommend products based on more detailed data. Through these heightened data analysis capabilities, advertisers can eliminate guesswork to provide consumers with hyper-personalised experiences that encourage purchase intent and forge strong B2C relationships.

Full steam ahead?

With the many benefits machine learning can offer, it may be tempting to view it as a one-size-fits-all solution to see advertisers through the end of cookies and beyond. Nonetheless, there are some potential barriers that advertisers will need to overcome to maximise the emerging technology. For example, the cost of developing and testing machine learning is substantial, reducing its availability to the biggest players in the advertising space, like Meta and Amazon. Additionally, the environmental impact of artificial intelligence and machine learning cannot be overlooked, with reports that training just one AI model can emit over 626,000 pounds of carbon dioxide equivalent. With digital advertising advancing towards sustainability, the emissions machine learning currently generates pose a new challenge to reducing the industry’s already substantial carbon footprint.

With the technology behind machine learning still in development, it is difficult to predict how exactly it will reshape the advertising industry following the deprecation of cookies. When it comes to personalisation and ad targeting, however, machine learning is already being recognised as a powerful tool. With the right approach, advertisers can utilise machine learning to maximise their datasets for accurate and relevant personalised experiences for customers.