We’re living deep in the experience economy. In 1999, Joe Pine and Joe Gilmore brought out a seminal book on experience innovation, and two decades later, they’re about to release a new edition.
Nowhere is this ‘experience economy’ more relevant than in TV and entertainment: Netflix invented binge-watching, setting the standard for time spent digesting new content, episode after episode and for hours on end. But the shift in the experience economy today says that people won’t passively accept the experiences they’re given anymore. They want greater control over what they’re watching – which means more time spent seeing content they love and less time spent searching for it.
Now the rest of the TV world is catching up. Reducing the time spent searching for content and increasing viewer engagement rates are key KPIs for TV providers. Keeping your user’s eyes on your content is a way for you to measure how satisfied they are – it’s your NPS.
So how will your experience skip the endless browsing to serve users your unique content seamlessly, and stand out from the rest to bump up your NPS?
Here are four ways to speed up content discovery for four different user personas – serving them the right recommendations and improving NPS through smooth UX and smartly applied data.
Your first user is Lucas, and he’s looking for a show, a topic, trend or public figure you don’t have matching content for. He has some time to spare, so he doesn’t want a result dead-end. In fact, no one wants to hit that dreaded ‘zero results found’… if you don’t have exactly what Lucas is looking for, whether it’s a programme or a person, make sure you can take him in a direction he might enjoy – even if you don’t have the exact word-for-word match.
This is the first crucial step towards salvation for TV service providers. As more content floods the market, it gets more difficult for viewers to find what they’re looking for. Make it your imperative to give Lucas recommendations such as trending topics or the latest news to test and learn how he engages with that content, then improve what you serve – or see him quickly ‘exit’.
How can you test the stickiness of your content? Through an accurate understanding of what video content is made up of; identifying the faces, objects, and content snippets that users prefer. Machine learning algorithms can help companies with this mission to identify images, audio and text – cross-referencing millions of clips and content sources and combining them into meaningful categories for your user’s content discovery.
Now you have some insights, don’t only use word-for-word search criteria. Instead, make a metadata search engine that applies the data your user willingly gives to you: dig inside what they watch instead of what they type, and use their search terms to serve up content featuring similar topics, interests, people, story points or moods – detected through speech and advanced moving image recognition.
Meet Zara, a diehard football fan from Manchester, UK. Based on understanding what’s inside the content Zara watches, you now don’t only know that she engages with the latest news in a broad category like ‘sports’ or even ‘football’. You understand which team or even player she prefers, as well as the specific matches she plays all the way through, and moments she reruns. You start to build a richer picture of what she wants to watch. But that’s not all.
Based on understanding her watching habits, you also better understand who she is… after all, we are greater than the sum of our postcode, age bracket, and gender.
Zara isn’t a demographic, she’s a human being. She’s a football junkie, a Liverpool FC fan, a Virgil van Dijk lover and she prefers to skip certain scenes when she watches reruns, rather than sitting through the whole match. She then wants to watch every post-match interview with Virgil; keeping tabs on the non-sports shows where he makes an appearance too.
She also appreciates the details. As well as enriching search, you can also enrich her watching experience, helping her skim through the programme detail with a film-strip summary so she can easily navigate her personal content highlights over a longer segment.
But many users won’t even bother to use the search bar before they hit exit, like your third user, Anil. And why should he? Streaming at its best means a) it’s seamless, and b) it intuits what a user wants to watch next – even when they don’t know themselves yet. If you do this well, serving recommendations means not pushing content that you want Anil to watch – but content that he would enjoy.
So spread your discovery paths beyond the main menu to keep him with you. Is he approaching the end of watching the Oscars ceremony? Serve him a shortlist of similar items he can skip to. These next-best-items can work two-fold, recommending content similar to what Anil has just watched, and content relevant to his profile. Picture this as a next step; a behind-the-scenes interview with the ‘best picture’ winning Director in a talk show that aired a few days ago. .
Then, does he generally prefer to snack on content rather than binge? Instead of letting him pause and replay small segments of the award ceremony, stitch them together into a string of shorter content snippets you know he will enjoy.
Once you understand what your user wants through tapping into this metadata via their watching habits, you can turn their home-screen into a custom dashboard featuring visual news feeds, snackable content reels, current and undiscovered favourite shows.
This is a screen of personal highlights that potentially cuts out any unnecessary steps for your final user, Sophie. Instead of a single recommendation on a specific topic, she wants a quick overview of all the news and entertainment she missed whilst away on holiday.
This home screen doesn’t only dynamically reflect the topics or interests Sophie wants to watch – but in the right sequence and size so she can consume it the way she wants to watch it, right as she switches on. Get this content mix right and she will see your home screen as something of a personalised social media feed – a go-to of what’s going on based on her unique video content fingerprint.
It follows that visuals on the home screen – in fact, any screen – should be engaging and relevant for Sophie: this is her primary way to decide which video to watch and whether she sticks or switches the channel.
The next era in the experience economy is already here, and there’s still space for service providers to delve deeper into the TV choices of every user, depending on their unique preferences. This not only means making sure content shows up at any user interaction. It means seeing that their next recommendation mirrors and intuits what they want to watch on a more relevant level.
Compressing and improving content discovery for TV viewers is not a strategic imperative for tomorrow; it’s a survival need for service providers today.