How AI can reshape TV

TV content today is a cultural zeitgeist that unfolds in real-time, to be consumed anytime. People are fed up with 5-to-12-minute unskippable ad breaks, search attempts yielding zero results, and putting up with hours-long programmes for the sake of finding the few minutes they want to watch.

But although the likes of Netflix and Disney have upended the industry, giving birth to a generation of cord-cutters and cord-nevers, TV viewership is still at a two-year high in the US, and video services struggle to take hold of programming genres like talk shows and live sports - content which injects a much-needed ‘did-you-see-this?’ watercooler effect among people lost in the midst of a Stranger Things marathon.

The topics of TV, the seamlessness of streaming
TV’s to-the-moment, topical and snackable nature, its long-standing institutional trust, and its distribution of high quality content from many sources, are its strengths - what sets it apart from challengers.

What traditional TV service providers miss and that digital-first does best are the agile, user-driven capabilities to buffer, test and release new services as fast as tastes change. This is a critical blind spot.

TV service providers still have an opportunity to reinvent themselves and harness the best of both of these worlds, but they need to act fast.

I’d like to propose 3 ways that TV service providers can play to their strengths and use their resources wisely, to reclaim and sustain a significant slice of the attention economy.

In doing so, they address three elephants in the TV room:

• Better understanding what’s inside programmes to deliver superior user experiences, by serving existing content in new ways that viewers genuinely want to pay for
• Creating richer, more scalable targeting capabilities for advertisers, based on user trust and value
• Making smart use of limited resources by using Artificial Intelligence to interpret vast amounts of video content, and automatically cross-reference it with viewing data for deeper insights

Easier in-content search
One low-threshold way to add value for viewers is to help them find existing content in new ways, through synthesised metadata. Imagine not only knowing the programmes your viewers previously watched but the topics, TV personalities and even the parts of the episodes they prefer. By using a smart search engine to recognise faces and voices inside video, TV services can offer

• Deeper search criteria such as subjects and featured people
• The ability to search for segments and snippets of content as opposed to entire programmes, say for example only Formula One highlights within an hour-long sports update
• A treasure-trove of content they wouldn’t normally find, alongside the convenience of finding what they were looking for with greater granularity, by going beyond word-for-word search terms.

This rich layer of metadata is only a way of reading and surfacing the wealth of licensed TV content service providers already have. Through recognition across faces, texts, speech and object extraction, as well as on-screen overlay elements such as nametags and credits, viewers can tune in anytime to receive the right string of videos or clips, in the right measure.

• Webinar Martin Prins joins the panel of Engaging users and boosting advertising with AI.

Snackable video services
Finding the right content through richer metadata can be built into how you want to consume it. Have you got five minutes to catch up on Coachella festival highlights, before you head to work? Or do you have three hours to binge-watch all of Beyoncé’s best moments?

Snackable content capabilities use smart models to scrape the seconds or minutes you don’t want to miss over an entire weekend of live coverage, a series of episodes or a movie marathon, and stitch them back together based on preferred topics, search terms, or previously watched videos.

TV providers need to forego monolithic experiences where users need to skip scenes or fast-forward to the clip they’re looking for, and instead anticipate and serve right-size content for every occasion - stitched together in real-time based on a user’s ever-shifting behaviours and multi-faceted tastes.

This is something we don’t see currently in our regular TV subscriptions, but which users gravitate towards YouTube for. What if traditional TV players were to evolve into smart stations for snackable content like YouTube, without the annoying minutes-long broadcast TV ad breaks… what if instead, service providers could go beyond serving demographically-driven ads, to help enrich user profiles based on what was inside the content they consume?

Topic-based profile enrichment
In the struggle to stay relevant, it’s time for TV to offer more meaningful, trust-based ways to target users based on deep content analysis. By creating smart segmentation using implicit metadata like what’s inside content, as opposed to solely relying on explicit Personally Identifiable Information, brands are able to divulge and harness genuine impressions and interactions, profiling by personality and interest - ‘Formula One fans’ or ‘Beyoncé lovers’ - as well as, or instead of, demographics and geolocation.

This serves as a safeguard from crossing any tricky regulatory red tape, as well as offering a stronger proposition to both advertisers and users based on clear shared value: what is someone watching? What does it really say about them?

Content-based profile enrichment doesn’t only mean acquiring new, sustainable sources of revenue; it gives TV service providers the opportunity to educate the market about what the TV advertising ecosystem should be all about: content-led consumer behaviour, tailored experiences and applying AI-driven insights across in-video aspects, instead of a spray-and-pray ad-serving approach to entire swathes of the population.

From linear broadcasting to user-driven resequencing
Broadcasters and TV service providers don’t have to go it alone in their bid to take apart their content, reassemble it, and deliver new kinds of value. Rather than spending billions on proprietary technologies to do this, in-content insights can be brought to scale through working with applied AI specialists. These accessible, purpose-built machine learning models can then retrieve deeper intelligence in an existing backlog of video, by cross-referencing voices, languages, faces, and texts with one another, then activating them where needed.

It’s time for TV service providers to deconstruct and re-sequence their content, to serve up right-size, on-topic experiences. From smarter search to snackable content and richer profiles, the players who differentiate through their content metadata will manage stay relevant for expectant audiences against the odds, without the need to reinvent the wheel.

Martin Prins is product owner at Media Distillery.