In the last two years, the industry has been working hard to find alternative solutions to the deprecation of the third-party cookie. AI technology, predictive analytics, and machine learning have been touted as potential solutions to revive those mechanisms essential for marketers to connect with their consumers.

The third-party cookie’s inescapable date with destiny – albeit deferred – will mark a watershed moment in the history of user-based marketing. However, the age of identity isn’t necessarily coming to an end. While some industry experts have predicted the end of identity-based targeting, what we are more likely to see is a less dramatic shift from precision marketing to prediction marketing.

Predictive capabilities are useful tools not only for Google and other big technology companies. When applied to marketing, they can become a vital part of a marketer’s toolkit as they strive to make the most of brand-consumer interactions. So what is predictive marketing, how does it work and why is it the way forward?

Taking the guesswork out of marketing

Predictive marketing gives digital advertising a new source of power. For over two decades, its main fuel has been third-party cookies; with marketers able to plug in their target audiences and obtain real-time clues about how and where they can be reached. With this mechanism now disappearing – alongside the privacy updates from mobile tech players, such as Apple – marketers are facing the challenge of data becoming increasingly scarce and anonymised.

Keeping the marketing engine going requires alternative ways to classify and structure user preferences and engagements. This will result in a change of approach to marketing, with marketers and industry players harnessing algorithm-generated insights and audiences built on probabilistic product interactions and purchase intent.

Building on first party data

While first-party data provides a logical answer to the deprecation of third-party identifiers, unlocking its true value brings challenges, with scale being the most critical. Although leveraging data from consenting users enables a supply of valuable privacy-friendly insight, granularity, integrity, and reach can vary. Some users opt to share information only partially, for example by omitting age and gender, while others prefer to stay anonymous.

Often the result is limited visibility for marketers and publishers, leaving both without the essential understanding of their audiences needed to create accurate profiles and deliver relevant and engaging user experiences. Predictive capabilities present a unique scalable opportunity to significantly expand the scope of first-party data and fill in the gaps. In many cases, results are even better than using conventional methods, as logical user attributes often trump declared ones.

Exploring the mechanics behind predictive marketing

Beyond the fundamentals of data orchestration (i.e. the coordination and consolidation of fragmented data) predictive technology is about maximising the value of data.

Publishers can maximise the value of their data, working on the basis of ‘ground truths’ about known users and on-site activity, through AI-powered algorithms that identify patterns for individuals with certain traits, which can be applied to similar users. This allows for better tailored offerings and messages while expanding audience reach. This insight can be applied to known and unknown users, whether they are logged in or not. By combining data from consenting users with contextual information in real-time, impressions can be made addressable even when user signals are lacking.

Tapping into machine learning capabilities and predictive technology helps all sides to embark early on emerging trends. Through analysis of real-time and historic data, predictive machine learning tools can detect patterns that allow marketers and publishers to forecast what users will be interested in next. By seizing this advantage, publishers can diversify their offerings and improve content recommendations, while advertisers can increase the chance of conversion from the first impression.

Predictive technology can go one step further to facilitate privacy-friendly data collaboration. Adding a predictive layer to clean-room technology enables publishers and advertisers to integrate their data and match audiences – not only for simple retargeting use cases, but also for prospecting and targeted brand engagement cases. This expands the reach of highly-relevant audiences by multiples and creates value for all parties involved.

 Why going predictive is the best way forward

For publishers and marketers, ensuring relevant and positive digital experiences is essential to connect with their consumers and build loyalty, as well as driving revenue from user engagement. But it’s also worth noting other longer-term benefits for the industry at large.

In the era of predictive marketing, AI technology facilitates the computation of complex data processing tasks, such as compiling audience profiles, with high accuracy and efficiency rates, outperforming humans. Companies that leverage these capabilities will have a higher chance of success in making valuable connections with their audience. 

At measurement level, the efficacy of algorithms will be measured by comparing predicted conversions with actual performance, allowing marketers to close the gap between their targeting efforts and the desired outcomes.

And in the not-so-distant future, purchasing decisions will be made by AI tools, and marketers will need to find ways to influence and market to machines as well as users.