Why is Discovery so hard to implement for video services?

Last week, Google said it was trying to tackle one of the hardest problems on the internet -- video Discovery.

Looking at consumer video services (Netflix, Hulu, Amazon and even GoogleTV) and their second screen counterparts (Matcha, Fanhattan, BuddyTV, etc), the admission of the challenge is painfully evident in the user interface the consumer faces and the result of the Discovery process.

But let's back up a bit first. What is Discovery? How does it relate to Search and Recommendation? I think we will find wide agreement that the concept of Search is one where you know what you are looking for and are trying to find it. Now this can be more complex than "Where can I find a legal version of Mission Impossible: Ghost Protocol that I can watch in my living room right now?" (which itself can be challenging in today's service offerings).  It is not usually as complex as the problem Shazam solves in the music industry ("what is the name of that song that sounds like..."), but can be difficult (I know the actor who was in the movie or what it was about). Search is decidedly a "lean forward" experience, and as most of us have found out over the last 5 years, it incredibly difficult to implement on a 10-foot remote experience, with various virtual keyboards or fancy remotes trying to help us solve this problem.



Recommendation is also relatively straight forward as a concept. Usually, it starts with features I have already seen that I like or genres I know I like and then asking a friend or a service to recommend something similar in hopes that the movie or TV show may also appeal. The simplest approach here is what we all know as the "Amazon" approach (people who watched that movie, also watched this movie). It can also be incredibly complex, taking into account your social network, what is currently popular, what movies or TV shows you have already seen, and what genres you like.

So what is Discovery? In this blog, I have often defined it by the service's ability to suggest relevant and interesting content to the user in a very simple, "lean back" user experience. In the real world, this might be akin to the difference between "do you have filet mignon?" (Search) vs. "I heard the fresh lobster here is amazing" (Recommendation) vs. the chef preparing a tasting menu of courses based on some consideration of what you don't like/are allergic to and his particular culinary skills (Discovery). Discovery suggests that you are going to end up watching something you didn't know you were looking for, but will very likely enjoy.

So why is this so hard to accomplish that even the mighty Google is struggling with it? The challenge is very likely in the detailed nuances of our likes and dislikes of videos. For example, "Space Balls" and "Aliens" are both science fiction, but hardly even remotely similar movies. Additionally, you may have liked the latest Mission Impossible because it is a spy movie, or an action movie, or a Tom Cruise movie--or it could have been you actually prefer movies where there is suspense, drama, gunfire, intrigue, and scantily clad heroines--but that may be difficult for you to describe yourself, but you know what you like when you see it. Lately, some of the video services have started to attack this nuance a little more directly by featuring complex genre options such as "goofy family comedies" (Netflix) or "epic heists" (Fanhattan).

Another contributing problem is the user interface, or more broadly, the user experience. For example, imagine the PC market before the mouse and graphical interface--no amount of amazing desktop publishing algorithm could solve a problem without a change in the way the user interacts with the program.

So let's consider what we are trying to accomplish again: a "lean back" experience akin to the way we surf channels these days in the living room, but that quickly and easily delivers something for us to sample with ever increasing probability of success. This very likely means the majority of Discovery experiences will need to take place on a 2-foot remote (tablet/smartphone), especially since its more mature siblings Search and Recommendation are likely to be more effective there.

Let's look at what might be important in that user interface. "Lean back" implies quick and easy, without much thought. Let's agree here that we want to find something worth sampling at least in 3 clicks/gestures. Additionally, let's consider the famous Columbia & Stanford University "When Choice is Demotivating" which concluded that consumers faced with 6 choices had a reasonable buy rate, but when faced with 36 choices, those same consumers shut down and walked away. So, as we look for a good UI, let's agree that any presentation that requires a significant amount of processing (by our brain), a significant number of clicks/typing/gestures, or presents too many choices at any given point is not going to work well.

So what is out there then?

Netflix said recently in their tech blog that 75% of their streamed content is the result of their recommendations. I think this is more likely a factor of the consumer use case for Netflix (I am bored and want to watch something, almost anything) than an indication of their algorithm's success. While the recently implemented Just For Kids vs. Adult UX is a good start, the recommendation service still struggles because there is no distinction of which members of the household are using the service at any given point, giving me recommendations on Dinosaur content and Gossip Girl and suggesting spy thrillers to my 8-year old son and wife. Further, their UI is based on a concept of rows of choices related to an algorithm choice ("Top 10 for this account", "Popular with Members Like Me", "Like: a recent title I watched") that presents at least 18 choices on the screen at once with a scrolling access to 12 rows (72 choices).  Too much.

Hulu has an anemic "Featured" (stuff someone is paying them to put in front of you) and "Most Popular" (think Top 40 radio) set of categories. At first glance you seem to be presented with only 9 choices, but the scrolling begs you to look down through row after row, presenting hundreds of choices.

Amazon has been absent on the iPad, with only their web browser as a 2-foot interface (no app), and the experience is absolutely painful. The company that built the recommendation culture has fallen flat here. To be fair, their PS3 experience has "Best of Prime", "Popular Movies" and "Popular TV", but that doesn't match with my expectations from them.

Vudu comes up significantly short with only "Top Picks" as a real category on the iPad experience. Their PS3 experience is more like Amazon's (popular rental titles, popular purchases, etc).

BuddyTV, a popular second screen app known for enabling you to Simply control your DirecTV and AT&T set top box and to recommend shows on right now from Netflix, Amazon or your channel service provider, does have a rather cool seeding process for their algorithm (asking you your preferences on a short list of movies and integrating your Facebook Likes to try to guess what you like) and presents them directly to you with only 5 initial choices (good). They do fall prey again to the scrolling process (seemingly endless choice) and separated visual choices for OTT video services from the channel line-up (oddly enough).

Fanhattan, a well-regarded second screen app known for its ability to provide tons of Stimulating content about a movie or TV show, does provide a decent filterable genre and category approach, including things like what your friends most or recently liked, but its "similar to this movie" recommendation feature is buried deep in the UI and while a powerful way to discover content, is too complex ro be "lean back" and get you there in 3 clicks/gestures.

Matcha is an app that is designed to be a "2nd screen as your 1st screen" recommendation experience, linking directly to your Netflix, Hulu and Facebook, and in theory launching you directly to those services when you pick a feature (except for Amazon, since they do not have an app). They actually do a slightly better job than Netflix, but they fall prey to the "row UI" approach, cluttering your decision field with 3 rows of 6 choices at any given time and oddly burying the recommendations row down below the initial screen, but at least limit the rows to 5 in numbers (but seemingly infinite choice to the right). They do a very good job of indicating the logic behind some of their recommendations (showing small thumbnails of Facebook friends if they have seen it or the Rotten Tomatoes and IMDB logos and ratings for the most popular content.


So apparently implementing Discovery is very hard, as the great list of companies above haven't really licked the UI/UX yet, and despite claims on performance of the recommendations themselves, the average consumer would not rave about the results either.

I think you will see two major efforts in this space in the near future:
  1) a continuous effort to improve the UI so that consumers can be presented with multiple paths to a recommendation (genre, friends, popular, new), but that allows a limited number of clicks/gestures to get to an increasingly better set of results quickly, and

  2) further effort on the algorithms themselves to better harness the nuances of the videos we like to watch and integrating that information more seamlessly with input from our social networks, our stated preferences, and external events (new releases, movie awards, etc).

I am sure all of us wish Netflix and the 2nd screen apps the best of luck in solving this since all of us would have a happier 37 hours in our week...

@ChuckParkerTech
www.chuckparker.tv