Growing concern for user privacy has led to the rise of privacy-first measures in contextual advertising, with the aim of protecting users' personal information. Cookies have long been a primary tool for audience targeting, but their use is being restricted, the most noteworthy example being Google's announced plans to phase them out entirely in 2024. With major browsers blocking or limiting third-party cookies, advertisers will need new ways to target audiences.
One result of the demise of cookies will be a renewed focus on broad contextual segment targeting based on site content. Contextual based targeting was a mainstay of digital advertising in the days before more detailed targeting methods were developed. The problem with contextual targeting on its own is that it lacks the type of personalization that resonates with users. With cookie-based targeting being phased out, we have even started to see some content distribution platforms reduce their publisher inventory, since they have no effective way to target users on sites that are not highly specialized into particular audience segments.
While a cookieless environment presents some challenges, there are also opportunities for innovation and creativity that will allow advertisers to reach audiences effectively, while maintaining user privacy and creating a more beneficial experience for all parties.
Fortunately for advertisers, a branch of Artificial Intelligence (AI) known as machine learning has progressed to the point where it can play a crucial role in empowering audience targeting without cookies. By analyzing vast amounts of data and recognizing patterns at a scale and depth not previously possible, AI algorithms can predict consumer behavior and create addressable audiences on the fly.
Unlike traditional targeting methods that rely on predefined categories, AI-powered contextual advertising goes beyond content and considers contextual signals such as user actions, browser metrics, device type, local time, weather, and whatever other data points are available. All of this information can be analyzed, weighted and matched to vast stores of past behavioral data in real time. This privacy-safe approach allows advertisers to understand consumer intent and identify their position in the sales funnel, and then deliver messages tailored to that individual.
Additionally, AI-powered contextual solutions can even extend beyond the pre-click phase. They can predict post-click behavior, enabling advertisers to optimize campaigns all the way to the final sale. By determining the likelihood of conversion and adjusting creatives accordingly, advertisers can dynamically maximize their return on investment.
This is where Nativo Predictive Audiences comes into play, offering a solution that leverages the power of machine learning, along with a scale and reach able to provide the volume of training data required to achieve effective audience targeting and real time optimization in a privacy-first manner, and without cookies.
Nativo Predictive Audiences’ demonstrated performance lift over traditional third-party audience targeting has been impressive, with clients achieving notable results across all categories. On average, Nativo Predictive Audiences outperforms third-party data targeting with a 43% lift in viewable clickthrough rate and 48% lift in time spent on content.
Nativo’s Ad Platform has benefited from the foresight of its CEO, Justin Choi, who predicted the rise of ad blocking and advertising privacy concerns years before they became a reality. He long ago set his team on course to design a system that could effectively mitigate changes in the industry he knew would come at some point. As a result, Nativo is the most advanced and mature platform that allows brands to connect with target audiences, and move seamlessly into the privacy-first era.
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Nativo worked with a leading beauty retailer to create educational content, amplifying LGBTQ+ voices and beauty brands