Latin America. Today, content is available almost anywhere and through multiple channels, which has created a deeply fragmented media landscape.
If we add Artificial Intelligence to this, we are faced with a veritable tsunami of options that users must give themselves when browsing, along with an overwhelming number of spaces where broadcasters and advertisers look for their audience. How can consumers orient themselves in this avalanche of content? And how can radio continue to be the main option for those listening from their vehicles?
At Xperi, we believe that selective, contextual, and content-focused discovery is key. The machine learning-powered technology we have developed for our connected car platform, DTS AutoStage, demonstrates how AI can be a true ally for broadcasters, delivering better engagement, content loyalty, and better metrics for broadcasters of today and tomorrow, as well as for automakers.
How? First, because DTS AutoStage can link linear radio with other broadcaster-generated content and more than that, it can select music metadata such as detailed artist profiles, connect with local communities (such as events in the area), and more. In this way, it enriches the user's listening experience in the car. But this is only the beginning.
Because DTS AutoStage is globally connected to tens of thousands of radio stations, it is able to listen to (and learn from) broadcasters, enabling an understanding that goes beyond music labels such as rock and country. Genres of music can mean very different things depending on the states or countries. For example, the term "country" means something very different in Austin, Texas, than it does in Topeka, Kansas.
Beyond gender
With its machine learning technology, DTS AutoStage offers a future where the platform can analyze music characteristics, not just genre, including playlists, record songs and contextualize them, add metadata and behavioral preferences used to identify and describe listening material, and thereby create a profile of the radio station. This is achieved by considering the station's sound mix, the analysis of the audience from the car and the recording of other key indicators of the station, which may even be more accurate than what the broadcaster itself knows.
This contextualization is very detailed, as each piece of music can have up to 250 attributes associated with it. Some are just specific data, such as where the song was recorded. But, there are also other qualitative and quantitative indicators, such as beats per minute, the mood of the music and the tempo. Combine this with the complex profile that the system can create based on each listener's preferences, and machine learning instantly and intuitively enables highly accurate and valuable recommendations for listeners.
Let's say you're going on a road trip and you're interested in a specific sports team: the platform will recognize your tastes and suggest relevant sports content on the radio in real time. Or maybe you don't like country music, but you like rap, then it will take you directly to listen to "Houdini" by Eminem, on a station classified as "country", but the system knows that the station has a much more nuanced proposal; or, if it knows your location, it gives you breaking news in your area. This is different from genre filters that would have kept you away from that country radio station, causing you to miss your favorite rap song.
The radio of the future might be so aware of your actual preferences that it will automatically tune in to the content it knows is your priority. You won't have to look for it anymore, it will do it for you.
"The radio of the future could be so aware of your real preferences that it will automatically tune in to the content it knows is your priority."
Integrated and loyal
With the benefits that AI brings, station-related content can be displayed directly on the radio interface, eliminating the friction that often occurs when listeners are redirected to information outside the station. In this case, the digital material is presented contextually and is smoothly visualized and integrated into the system.
Technically, this concept is known as "zero-layer UI" user interface design, and automakers are already beginning to redesign their systems in this way.
BMW's Intelligent Suggest system and Mercedes-Benz's "layer zero" MBUX user interface are just two examples of how AI can be used to offer context-based recommendations, remove submenus and display relevant information on the main screen. At Xperi, we envision a near future where DTS AutoStage enhances the elements that make broadcasters unique and different from other audio content providers.
This has a significant impact on the user experience. In these cases, the listener inside the vehicle will be as integrated and loyal as possible in the radio ecosystem, and highly receptive to the relevant advertising content to which they are precisely directed. Additionally, the platform can return that data to the broadcaster immediately, allowing for close tracking and performance metrics that contribute to better decision-making for program directors and DJs.
With DTS AutoStage at its core, this dashboard of the future delivers a next-generation, next-level in-car radio user experience (UX) that could completely revolutionize the search and discovery paradigm, ensuring that radio not only stays relevant, but also innovates beyond other media in the connected car ecosystem.
Written by Joe D'Angelo, SVP - Commercial Strategy & Partnerships • Xperi Broadcast Business Dev & Mgmt.
