Iris Yuster: Personalized TV Under Scrutiny

written by: Nicolas Bry

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iris-yuster-designerGuest post from Iris Yuster: An experienced designer, Yuster delivers innovative apps for mobile and multiscreen, leveraging personalized recommendations for content as well as for shopping.

What is unique about Iris is her ability to combine social understanding of user behaviour with interaction design (UX) skills. Nic Bry.

There are many video and second screen Apps aimed at creating a new watching experience, more personalized, social, engaging that also supports users multitasking behavior while watching TV. 
 
Apps such as Netflix, HBO, Zeebox, BBC iPlayer, network operators, cable service providers, etc., are facing this challenge and try to invent this new platform to be easy and enjoyable. From the users’ perspective this is an essential change; they invest a lot of effort and spend too much time finding something relevant to watch.

How can it be achieved? Users’ alternatives must be highly personalized and they need to be directed how to choose among myriad alternatives to make their “Watching Decision”. In this post I will describe how personalization and recommendations in TV Apps should influence and support the behavioral side of users’ “Watching Decision”. 

The “Watching Decision”

A provider of a TV-related App has to find the way to make it easy for users to decide which content to watch. The user needs to be confident that he is choosing the “right” alternative. How can we build this confidence? I use the term “Watching Decision” as an equivalent to “Online Buying Decision”, in order to describe the process that leads users to tap on “Buy”. According to very recent research (Kotler & Keller, 2012), a user passes through five stages to reach a “Buying Decision”:

  1. Problem or need recognition
  2. Information search
  3. Evaluation of alternatives
  4. Purchase decision
  5. Post purchase decision

The stages of information search and alternatives evaluation are critical. A user has to be able to quickly find relevant alternatives. Then he must get hints, as to which of those alternatives would be the most satisfying for him. It means that when a user turns on his video and second screen App he should find in front of him the most relevant information including: favorites channels, movies and TV shows. As there is a mass of information the ability to personalize the experience is critical to prevent service attrition.

1. Personalized TV

Knowing about users’ TV interests and habits enables the creation of personalized experience and increased “Watching Decision” rates. There are different aspects of personalization that are implemented in video and second screen Apps:

Recommendations

  1. Basic level such as “Top Ten” and “Most Popular”;
  2. Medium level such as “More like this” and
”People who watch this also watch…”;
  3. Advanced level such as “Your Favorites” and “Suggestions for you”;
  4. Social level such as “Your friends recommend…”. Friends and family recommendations are known as the most influential, although each user has a small number of social influences, not always with the same taste and interests.

2. Search – personalized search recognizes the user’s search intention by matching his interests and suggesting the most relevant results for him;

3. Content Enrichment – the ability to focus on specific TV-related content, relevant for a specific user and to provide direct access to it. This is in contrast to too basic or too much information;

4. Social activities – such as indicating what your friends are watching now, inviting them to watch with you, sharing comments and chat before, during and after watching a show.

The right combination and balance of all of these minimizes users’ efforts to find relevant content and increases the likelihood of a “Watching Decision”.

Are there any video and second screen Apps that have the right balance of all personalization aspects?  Let’s briefly analyze a few leading Apps and try to answer this question.

Netflix

The Netflix iPad App is focused on personalized VOD recommendations. It has various aspects including: “recently watched”, “my list” and “top picks” for a specific user (1, 1a) that enable direct access to some preferred content, and “popular on Netflix” (1a) which is a basic personalization aspect.

It would probably be better if the App presented the specific “top pick” above the “popular”, since it is a higher level of personalization that better supports the user’s decision. At the stage of alternative evaluation, the user taps on “More info” (2,3) and finds basic info with neither relevant enrichment nor any social elements.

The user can add it to “My list” or continue with his sequences of browsing by choosing other items from “More like this”. In addition, the stack method (“back” arrow on the left side) enables the user to return to a previous item without losing it or returns directly to the main screen (X button on the right side).

Looking again at the balance between personalization aspects there are some challenges such as:

  1. How do users deal with an extensive variety of recommendations? Is there any prioritization of users’ preferred ones?
  2. What about the missing areas such as social and content enrichment?

Netflix’ strength is the ability to support users’ “Watching Decision” by variety of personalized recommendations. It has an easy navigation method and direct access to relevant content, which creates a comfortable environment in which to make a decision.

Zeebox

Zeebox is a second screen App for TV programs that is focused on social engagement and content enrichment. The user can interact by sharing and chatting (3a) or can just follow others’ activities (3).

Related program topics are enhanced with Wikipedia or magazines (1a, 3b). The user, when connecting for the first time, defines his favorite content manually.

Then the main screen “My TV” of the App (1) includes news feeds related to the user’s favorite content. In the “Discover” screen (2) programs are sorted mostly by the basic personalization level: “Most Booked”, “Editor’s Picks” etc. and show the number of comments that users gave it.

Given all these social opportunities, I would like to raise two open questions:

  1. What is the goal of the main screen? Can it support user “Watching Decision”? Is it aimed at being purely entertainment and engagement after this decision was made?
  2. Social oriented and active users are obviously the targeted audience for this App. Still wondering about the other types of users? A paper that was published by Bradley Horowitz in 2006 describes the “pyramid model” of active users, and claims that only 1% of all users are active:

Zeebox offers a sophisticated solution for a specifically targeted audience. The navigation pattern supports discovery but on the other hand it may not close the “Watching Decision” funnel very clearly.

State of Affairs
 
Although Netflix and Zeebox represent leading Apps in terms of implementation of personalization aspects and support successful “Watching Decisions”, they are still not providing the “perfect” alternative to the video and second screen experience.
 
There are many more video and second screen Apps, such as from local operators to global content providers, focused mainly on providing multiple device control for their users. In some cases these Apps have basic recommendations that allow the discovery of the content, as in the example below:

This simplified implementation is missing many of the aspects of personalization already discussed here, and therefore, only rarely supporting users’ “Watching Decisions”. It is definitely not enough to integrate the recommendation engine and hope that it will do the work by itself.

In this competitive landscape, the winner has to balance “Content is King” and “The User is King”. Users need a personalized video and second screen experience that can fulfill different aspects of their personality and behavior. The trend is clear, but the optimal solution has yet to be created. I believe that a solution that offers a well-balanced implantation of personalization will generate usage and increase revenues.

 

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