To maximise monetisation, operators and video service providers need to be strategic with the FAST channels they add to their portfolio and leverage enhanced discoverability tools. They need to cater to the specific preferences of end users to enhance viewer engagement while boosting their ad opportunities. This article will explore some of the key elements to successfully launching a FAST channel offering.

How to implement a user-driven FAST channels offering

The proliferation of FAST (Free Ad-Supported TV) channels is undeniable. However, merely presenting hundreds of these channels to users can lead to a paradox of choice. This often results in viewers feeling overwhelmed, potentially not watching any channels, or even worse, turning off the TV or watching something on another service. Therefore, it’s crucial for service providers to curate a FAST offering that aligns with audience preferences, bolstered by effective discoverability tools. Moreover, considerations around distribution, advertising, and content alignment are paramount when launching a FAST channel.

For a distinctive experience, leveraging the niche content typically found in FAST is essential. It’s logical to assume that a viewer who watched Formula 1 content via a Pay TV channel might be interested in a motorsport-themed FAST channel. Similarly, a fan of ‘MasterChef’ would likely gravitate towards cooking channels. By tapping into viewer preferences, service providers can craft a compelling value proposition, target a specific audience segment, and subsequently boost advertising revenues. After all, tailored content often commands higher cost per mille (CPM) rates.

Advertising plays a pivotal role in the FAST ecosystem. With the surge in FAST channels, there’s an expansion in content inventory. Both service providers and advertisers need a streamlined strategy to match ads with appropriate audience segments, ensuring expansive reach. Advertisers aim to engage the majority of their target audience, making this a critical factor in their investment decisions. Furthermore, the relevance of ads to the audience is essential. To truly maximise revenue, a solution that consolidates demand from various sources and offers a broad audience reach is vital. This approach can package available ad slots to achieve optimal monetisation. However, service providers must tread carefully to ensure their audience data isn’t exploited by digital advertising behemoths. In today’s age, where addressable advertising is mainstream, there are numerous TV-centric solutions tailored to addressing this concern for service providers.

Personalising the FAST channel experience

The media domain is in a state of flux, ushering in a heightened demand for top-tier, user-centric TV experiences. Generative AI for content discovery, and increasingly, content creation, appears to be the solution the industry is leaning towards. Such AI-driven recommendations empower service providers to suggest FAST channels that resonate with viewer inclinations.

Looking ahead, the vision is for AI-powered personalised FAST channels. These channels would offer a bespoke experience, where each viewer, upon tuning in, encounters content curated to their individual tastes. This evolution promises a future where content aligns seamlessly with each viewer’s unique preferences.

Conclusion

Research shows that Europeans engaged in 50% more FAST content hours in Q3 2022 than the same quarter the previous year. The rise in FAST channel consumption has resulted in an explosion of new channels. To stand out in today’s highly competitive FAST market, content providers must deliver a compelling, user-driven experience to viewers.

By adopting AI-driven content discovery, AI-based personalised FAST channels, and targeted TV advertising solutions, service providers can drive viewers to the content they want to watch, segment audience data, and offer targeted ads.

Targeted advertising has become a must-have strategy for every Pay TV operator and broadcaster. Netflix is the last streaming giant to recently announce that it will soon launch its own advertising tier. So, how can operators utilise targeted TV advertising in the best way possible? This article reviews strategic steps that operators can take to effectively implement AI/ML techniques in addressable TV advertising.

Key steps on the targeted TV advertising journey 

Leveraging AI/ML for audience segmentation is a multi-phased process. The more accurate the segmentation, the smaller the audience for the targeted ad. By starting with simple use-cases and building up to granular segmentation, operators can gain more experience in targeted TV advertising, increase their knowledge of TV audiences, and ultimately, boost their revenues.

One of the first steps on the targeted TV advertising path is serving ads based on geolocation. While this may sound like a straightforward segment, where information can simply be pulled from the database, AI and ML are highly involved with achieving accurate and granular location-based targeting. Extracting accurate data from viewers who are watching content on the go, sharing credentials, and constantly moving between addresses is quite challenging without the proper AI/ML techniques in place. For this reason, the sheer ability of leveraging AI to target TV viewers based on geolocation is a huge game-changer, and geolocation addresses a little over half of the advertisers’ targeting ambitions, making it a must-have segment for operators. By utilising geolocation, operators can optimise the effectiveness of advertising and gain advertisers’ trust — as well as additional revenue opportunities.

The next tier of segmentation is usage-based. With this approach, operators can offer advertisers information about viewers who have already seen their ad on traditional broadcast TV — using their specific viewing history. This enables advertisers to either target or exclude certain individuals, based on their prior exposure to a certain campaign. ML allows operators to establish those segments with good granularity. Once the segments are in place, each ad can target a different, highly relevant audience.

Eventually, usage-based advertising enables operators to optimise their ads’ effectiveness and generate additional advertising opportunities by running multiple personalised commercials during a programme break. In addition, it also helps advertisers target “unreachable” audiences while they are watching VOD content.

Moreover, by using AI and ML, operators can deduce segments such as age, gender, and other demographic properties from the usage data. These segments are in high demand from advertisers. AI and ML allow operators to break down usage-based data and help advertisers deliver commercials oriented toward specific age groups or household structures. For example, operators might determine that the age group of a particular viewer is a teenager based on a viewing history of titles such as “Sponge Bob Square Pants” and “Saturday Night Live.” Operators can determine this by comparing the viewing history with a panel group and by applying ML techniques.

To extract more value from the data, and address insights that are notably based on changes in behavioral patterns over time, operators need to add a time axis. Advanced ML and AI techniques can identify pattern changes that point to life-altering events, such as starting primary school, retiring from work, going on maternity leave, and more. Eventually, gaining a deeper understanding of viewers and household life changes enables advertisers to deliver highly relevant ads.

Next steps: Putting the segmentation to work 

 Once operators have a well-established targeted TV advertising solution that can exploit the demand for mainstream segments, their next step on the targeted TV advertising journey is to put the segments to work. As the accuracy and granularity of the segments increases, the audience per segment becomes smaller. Sometimes video ads for highly granular audiences are too expensive to produce. In other words, operators might not gain the most out of the segmentation. A simple way to embrace granular segmentation is through banner ads and animated gifs. The cost of producing these is minimal and, combined with the advanced segmentation capabilities driven by AI and ML, they can allow for simple, quick, but highly effective advertising.

Additionally, Pay TV operators can leverage AI and ML to promote their own content. Statista found that ads are the most common way Americans and Canadians discover new movies and TV shows. Operators already have a large amount of data on usage consumption. The advanced capabilities of AI and ML algorithms can enable very accurate segmentation to effectively reach the desired audience.

Start simple and then expand

 There is no better time than now to adopt targeted TV advertising. This burgeoning technology opens up new and expanded opportunities for Pay TV operators, allowing them to strengthen their position in today’s ultra-competitive TV market.

By starting with the simpler forms of AI and ML-based targeted TV advertising, such as geolocation and usage-driven ads, operators can quickly react to market demands. Then, operators may move on to a higher level of audience segmentation. By applying the latest AI and ML techniques to TV data, operators can deliver targeted TV ads that enable increased viewer engagement and create additional monetisation.