By Mateusz Jędrocha, Head of Upper-Funnel Solutions Development at RTB House
Earlier this summer, a year after the previous extension of the deadline for third-party cookies deprecation, Google announced it would be extending the demise of cookies on its market-leading Chrome browser until the second half of 2024.
While many marketers will welcome the news on the timeline extension, others fear that the further delay may once again act as a stopper on the forward momentum of the digital marketing industry. Indeed, last year, shortly after the previous announcement, there was a visible slowdown in the commitment to work on privacy-oriented marketing APIs across the ecosystem.
Despite the delay, however, forward-thinking brands are pivoting to ‘privacy-first’ approaches to their digital marketing, given that much of the open web is already ‘cookieless’. As always, advertisers need to figure out how to reach the right consumers with the right message depending on their business objectives. That said, in 2022, the challenges are greater than ever. Consumers are bombarded with thousands of advertising messages every day, both online and offline. And with more people using ad-blocking than ever before, it’s clear that consumers are looking for ways to shield themselves from advertising. So if consumers are already annoyed by digital advertising that’s not relevant to them, the loss of third-party cookies risks making this imprecise targeting even worse.
Fortunately, there is a solution. Marketers need to embrace cutting-edge technologies that enable them to get rid of cookies and, rather than see a drop in efficiency, actually deliver a significant improvement versus cookie-based marketing,
This is where Deep Learning comes in.
Into the Deep
Deep Learning is a subcategory of a much broader concept – machine learning. It involves the construction and use of neural networks in such a way that they mimic the human brain – i.e. it can learn and evolve strategies for itself based on the continuous flow of new information. Just like a human brain, Deep Learning can improve every time it does something, however Deep Learning is capable of performing more calculations within milliseconds than a human is able to in a lifetime!
Deep Learning therefore allows for huge volumes of data to be processed in a very short time. Already today, artificial intelligence in various forms supports decision-making engines of many marketing programs, including bidding for favorable advertising rates, analysis of online consumer behavior, and natural language processing.
Today, technology is able to support marketers in many of their daily duties and perform their previous work much faster and more precisely. Freed from repetitive tasks, marketers can thus focus on more creative and demanding aspects of their work.
So, what are the key benefits of leveraging Deep Learning in the field of marketing? Here are a few examples:
Improved personalisation and efficiency
There are two problems with retargeting today: what to offer and how to display it. Advertisers, in various ways, try to adapt the advertising message so that it feels personal and attractive to the customer. When you use Deep Learning in e-commerce, it learns from experience, resulting in a faster and more accurate identification of potential purchases.
Deep Learning can build and continuously rebuild profiles and adjust what is presented on a banner every time an ad is displayed. Algorithms determine what should be shown on each banner, adjusting the contents based on a customer’s responses to previous offers. The effectiveness of recommendations increases by up to 41 percent compared to campaigns that do not use Deep Learning.
By using Deep Learning marketers are ensuring their campaigns are more efficient than ever. This is crucial especially nowadays, when marketers’ budgets are under pressure and the push for measurable business benefits is larger than ever.
Exposing hidden insights
Deep Learning has made it possible to analyse ‘hidden’ consumer data. It is now possible to analyse the visit time on products and the sequence of visited subpages in a store. Using data, machines interpret exactly what users did at the store and thereby predict their actual purchase intentions. It is possible to determine which products users are most interested in, and in response, send them customized offers relevant to their current needs.
With all this data, the next step is deciding how to present an offer in an ad, and in what order. Deep Learning algorithms analyse offers and how attractive they are from a user’s perspective. This approach makes it possible to implement a strategy where there is otherwise no clear pattern for a particular group of users.
Ultimately, the potential uses of Deep Learning in everyday marketing activities will continue to grow wherever decisions are made on the basis of data. Future-facing advertisers are already exploring the potential of Deep Learning, while those waiting for solutions that replicate the functionality of cookies without improving user privacy risk diminishing returns from their investments in digital advertising.