By Anda Tanchuma, Senior Product Manager at Viaccess-Orca
As pay-TV operators and service providers look to boost their monetization, targeted TV advertising is gaining significant traction. Even Netflix has surrendered to the trend, joining the rest of the other streaming giants in the AVOD world by launching its own advertising tier.
AI-driven targeted advertising, in particular, is an effective way to improve TV monetization for operators and service providers, while alleviating reliance on legacy, controversial, and third-party data-based targeting techniques. By leveraging an AI/ML-based approach to targeted advertising, and by monitoring the TV user experience, operators can tap into new revenue streams.
The Case for Targeted TV Advertising
Recent research shows substantial growth in the SVOD market. Global SVOD subscriptions will increase from 1.21 billion in 2021 to reach 1.68 billion by 2027, an impressive growth of 39%. Global revenues, meanwhile, will reach $132 billion.
While global consumption of SVOD services rises, growth is even higher in the AVOD space. According to Statista, AVOD spending in the U.S. is set to surge in the next few years, and surpass $24.2 billion by 2025, as new services are launched domestically and internationally. The pivot toward AVOD will have a significant impact around the world, opening up additional monetization avenues for operators and service providers.
Must-Have Targeted TV Advertising Features
Targeted TV advertising provides operators with a foundation for content monetization, but its success relies on attaining valuable, in-depth insights into viewers’ behaviors and preferences.
Using AI/ML-based targeted TV advertising techniques, pay-TV operators and service providers can accurately segment audiences based on viewing interests, household composition, life moment events, demographics, and more, to drive higher engagement, increase viewing time, and maximize revenues.
So, what key features should operators look for in a targeted TV advertising solution?
It is imperative that the solution supports all platforms, devices, operating systems, content forms (i.e., VOD, linear, time-shift TV), viewer touchpoints, delivery methods (i.e., IPTV, OTT, cable, DTH, DVB), and a diverse range of ad types. This will ensure that operators and service providers can reach as many viewers as possible and, in turn, effectively increase their revenues.
Leveraging advanced data analytics is also key to the success of targeted TV advertising (see Fig. 1). By leveraging usage data, operators and service providers can create personalized recommendations to drive higher engagement and increase viewing time. Data analytics facilitates far more opportunities to display ads and a higher revenue potential. In addition, the same usage data can be leveraged to make a unique set of profiles and household segmentations, improving campaign targeting by matching every ad with the desired audience.
Figure 1: Operators can better understand the composition of households with AI-based data analytics and insights.
Another important aspect of targeted TV advertising is maintaining compliance with regulations and keeping operators’ data safe. For instance, in many European countries General Data Protection Regulation (GDPR) must be considered. By putting into place a consent management system operators can ensure that only the users who have given their consent will receive targeted ads. This doesn’t mean that other users don’t get ads. They will still receive ads aimed at general audiences or contextual targeting ads, targeting the content they are consuming while the ads are being displayed. However, their personal data and viewing habits cannot be used to associate them to viewer segments.
When it comes to data privacy, it is vital for operators to keep their data in a walled garden. This will ensure that operators’ segments cannot be targeted by third-party vendors. Data is valuable, and only operators should benefit from it.
Implementing AI/ML-Based Targeted TV Advertising
Utilizing AI/ML for audience segmentation is a multi-phased process. Operators can start small and then expand as required. For instance, operators and service providers can begin serving ads based on geolocation. With this approach, AI and ML techniques are utilized to accurately extract data from viewers watching content on the go, sharing credentials, and moving between addresses.
The next step might involve offering usage-based targeted advertising based on viewers’ specific viewing history. This approach allows advertisers to either target or exclude certain individuals, based on their prior exposure to a certain campaign.
Eventually, operators can build up to granular segmentation. With this method of targeted TV advertising, operators use ML to establish viewer segments with exceptional granularity. Granular segmentation empowers advertisers to deliver a different targeted ad to a highly relevant audience that has been segmented by viewing history. A simple way for operators and service providers to adopt granular segmentation is through banner ads and animated gifs that are inexpensive to produce but highly effective thanks to the use of AI and ML.
Improving Monetization With User Experience Monitoring
When implementing targeted TV advertising, it is imperative that service providers ensure and maintain an outstanding quality. A poor-quality ad will create viewer dissatisfaction, churn, and a decrease in the ARPU. However, delivering high-quality targeted TV advertising can be challenging given the complexity of the technical architecture for video streaming. There are many different software and network components involved in video streaming, making it difficult for operators and service providers to understand where quality of experience and quality of service issues are happening.
By monitoring the TV user experience on subscribers’ connected devices — including smartphones, tablets, PCs, set-top boxes, and smart TVs — operators and service providers can gain insights into how end users are experiencing playback on their video streaming services, including targeted TV ads. Having real-time access to critical data allows operators to see video streaming issues from the subscribers’ perspective and, in turn, make appropriate UX improvements.
Some of the critical data points that need to be collected include video start failure, time taken to start the playback, rebuffering, playback error, stream quality, what version of the application viewers are running, the CDN being used, video resolution, zapping time, network bandwidth compared with player bandwidth, and more.
Once the data is collected, comprehension is essential; operators must be able to rapidly interpret data. Moreover, most operators today are delivering video streaming services to a massive volume of viewers and devices, meaning scalability can become an issue as they are accumulating a significant amount of data. However, with a cloud SaaS solution, operators can effectively scale up their service to address surges in demand.
Conclusion
With billions of potential revenues available, now is the time to embrace targeted TV advertising. Leveraging the latest AI-based data analytics and UX monitoring techniques, operators can deliver targeted TV ads that improve viewer engagement, reduce churn, extend subscriber viewing times, and boost monetization.
