Tips to optimize on demand TV services
According to a recent report by Ericsson ConsumerLab, over a third of international TV viewing is now on demand. We know that there’s never been more choice available, but how do we decide what to watch? On Demand services offer a range of ways to find something interesting, but viewing habits remain conservative, as highlighted by the rise of box set binge viewing.
Helping viewers to discover new and interesting programming is becoming an increasingly serious challenge for On Demand providers. If this doesn’t happen, the potential of vast pools of programming will remain unrealised and viewers may gravitate away from services.
Finding Known Programmes
TV schedules are still the bedrock of broadcaster services such as iPlayer and All 4. Viewers understand these and they provide an effective way of catching up with known programmes. Searching by programme names is also well-understood and catered for, so if I want to see the latest episode of The Apprentice or just browse what was on last night, I should be able to. There are always ways of improving options through design and functionality, but the real challenge is broadening viewer appetites and helping them to discover new programmes they weren’t necessarily aware of.
Improving the Browsing Experience
Browsing by genre is a common way of seeking inspiration. I may need intellectual stimulation, so be in the mood for a documentary, or just looking for light relief through comedy. However, you soon tire of going back and forth, and each genre can encompass a wide range of tastes and topics. It’s an imperfect solution and viewers can spend more time looking for programming than actually watch it.
Collections around a topical theme or a strong feature of a popular programme can encourage new viewing
Providing a narrower set of options via a collection of themed programmes can be helpful. Doing this around a topical subject, (e.g. First World War commemoration), or a strong theme within a popular programme (e.g. the family comedy associated with Family Guy or The Simpsons) may encourage viewers to investigate something new. However, there are limits to what it can achieve as a collection can still encompass a broad range of programming and it takes continual effort to curate a range of interesting and timely options.
For all their shortcomings, directories are likely to be around for a long time, so design effort to make them feel more engaging and the navigation less clunky is valuable. Greater dynamism and a richer visual experience are a key to reducing the perceived effort, making the user feel they’re not continually moving up and down through a series of discrete pages. This is increasingly evident with services such as Amazon Instant Video, which are moving towards long pages of programming, using roll-overs to allow viewers to quickly scan numerous options without having to leave a page.
Roll-overs allow viewers to quickly scan numerous options
Good design can also help to ensure that the right information about a programme or film is shown while users are scanning options, rather than having to dig for it. There are well-established conventions about what information to provide, including title, genre, duration, and perhaps a short synopsis, but this feels like an opportunity to do more. Viewer research to understand more about the decision-making process could unlock a fresh approach.
Building on Buzz
The value of buzz in deciding what to watch was recognised as particularly powerful by the UK communications regulator Ofcom in a recent report . This can come from word-of-mouth recommendations from friends and family, or via social media and other sources. The implicit stamp of quality that these recommendations bring (particularly when from somebody aware of your taste), makes them invaluable.
Recommendation via social media will become increasingly powerful, but needs to integrate into On Demand designs more effectively. For example, appearing at the point a decision is being made, rather than when they pop up in Facebook or Twitter. A formatted recommendation link (provided by ITV Player amongst others) can be helpful but may not be seen at the time when choices are being made. A social feature which offers recommendations at the time of choosing will ultimately be more successful.
Social media will become increasingly important if it is better matched with viewing behaviour
Highlighting recently added and most viewed programmes are less personal ways of building a buzz. However, they’re great for introducing new unexpected options to viewers, and they give a reason for viewing. They are likely to remain a prominent feature of most services as a result.
Pushing The Boundaries
System-generated recommendations can bring relevant content to the viewer rather than making them seek it out. Viewers aren’t interested in spending time up front to indicate preferences, so they work best when based on previous viewing. However, their effectiveness is limited. The previously mentioned Ericsson Consumerlab study across 20 countries found that viewers felt that “recommendations are simply not smart or personal enough”, so there is clearly some way to go.
Many recommendation engines will suggest a similar programme to one you’ve already watched (and it does need to be what you’ve watched rather than another family member). This can be helpful, but because it’s from a single data point it may not capture the essence of what it was that was so appealing. Viewing preferences also vary over time, context and by device your viewing on. For example, the appeal of binge watching a series at home at the weekend is unlikely to be replicated on the bus to work on Monday morning. There are smarter recommendation engines out there which can untease these elements, although they’ve not necessarily made it to major On Demand services yet.
Recommendations based on a single programme viewed are an imperfect solution
Appeal is also multi-faceted. What makes a new series appealing may not be easily summed up by factors such as genre, actors, mood and duration. It could also be something quirky or a personal connection, such as being based on a book you’ve read or in a city you’ve visited. This can be difficult to define, as shown by the music recommendation engine used by Pandora, which draws on 450 attributes to recommend a piece of music for you, based on what you’ve just listened to. If we’re going to nudge viewers towards great undiscovered content we’re going to need to understand more about how they become aware of programmes in their peripheral vision and how they are drawn to them. Some of this can be understood from data. For example, Netflix recently used data analysis to discover the tipping point where a viewer becomes hooked on a series. However, data analysis is better at understanding current behaviour rather than predicting the future. We need to study the viewer, and understand their fundamental drivers to do that.
Studying users in their own environment helps us to better predict evolving viewer needs
Ethnographic research and diary studies are a powerful and complementary tool for getting there and provide the most powerful starting point for innovation, as was recognised recently by the Harvard Business Review. This involves studying viewer decision-making processes in the natural environment, building up a rich picture of it and any influences. Diary studies, to capture these moments are becoming easier as smart phones are a great tool for quickly taking photos, indicating location and reporting back. There are also secondary benefits to this. For example, during a diary study for an On Demand service we quickly built up a strong sense of tablet app orientation preferences when looking for programmes via the screen shots that participants had shared with us.
As the shift from linear viewing online continues it’s going to become increasingly important to draw viewers to new programming they wouldn’t necessarily have found themselves. This is a real challenge, but there are three powerful tools to help; more engaging design to encourage browsing, more use of data to understand behaviour patterns with existing services and more research with viewers to understand and map decision making around new content discovery. Effective use of all three will help viewers go beyond box sets on demand.