UX case study: solving “what to watch” on OTT platforms

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Bootcamp

14 min readJun 23, 2022

Every day we make so many choices about what to wear, what to eat, what mode of transportation we are going to take and the list goes on and on. On a relaxing day, we try to minimize these choices and just want to chill and enjoy the stress-free time that we have and some of us like to binge-watch movies and series and enjoy, but when we open any OTT platform of our choice and see thousands of movies and series we feel overwhelmed and we try to find the perfect content which worth our time and scroll the OTT platform or browse the internet and after some time we find something to watch but this same thing happens every relaxing/free day which is not at all relaxing.

I am a victim of this and in this case study, I tried to solve this problem or make this less painful.

If you also have faced this problem, let me walk you through my case study and explain each step that I took to reach a solution.

I have used Netflix as a reference and I was in no way associated with Netflix at the time of working on this project.

👉Business Problem Statement

With the Pandamic and the Lockdown, there has been an increase in audience watching shows and movies on OTT platforms. People tend to get very confused with the large number of options available. An OTT app wants to make it easier for people to decide what to watch.

👉Research Problem Statement

Making the decision of what to watch on an OTT platform has become very confusing for the audience. As a UX designer you first want to understand the behavior of people trying to choose what they want to watch and the different factors that influence the decision. Plan a research to identify the behavioral patterns to help the business cater to users in an efficient way.

Design Process

I followed the double diamond design process

Discover - In this stage, I tried to understand the problem and understand the previous research finding upon the problem statement through secondary research and to validate the secondary research data I also did primary research.

Define — In this stage, I analyzed the data that I have collected from the Discover stage and tried to make sense from it.

Develop- In this stage, I worked on ideating different solutions and creating wireframes.

Deliver — In this stage, I tested my soluting and made improvements on the basis of user feedback.

Understanding the problem statements

After going through the Business and Research problem statement I understood how these two are dependent on each other. The business wants to enhance the user experience by making the decision of what to watch on the OTT platform easier and the users get confused with the variety of content present. To solve this business problem the first thing I need to do is to understand the users- their behavior, pattern, frustration, and satisfaction after watching the content on the OTT platform.

Framing 5 Ws — WHO, WHAT, WHEN, WHERE & WHY 1H-How questions

To analyze the problem statement, I followed 5W-1H method,

👉WHO

Who are my primary users?
Who are we solving for?

👉WHAT

What will be the impact on the users after solving the problem?
What factors affect the user decision making?

👉WHEN

When do people use the OTT platform?
When do people get confused or frustrated?

👉WHERE

Where can the business come to solve the problem?

👉WHY

Why does this problem needed to be addressed?
Why do people get confused?

👉HOW

How are the people making the decision?
How do people choose an OTT platform subscription?
How OTT platform recommendation works?

After having some direction from these questions, I moved on to Secondary Research.

Secondary Research (👩‍💻Desk Research)

Desk research will provide me with what has already been discovered in the problem area or how the problem is being defined by different research work and what patterns they have found and how research moves forward with their data.

👉Insights from the Secondary research:

1. How do people choose an OTT app subscription?

According to a data analysis study, it was found that three factors affect choosing the OTT platform.

Age — The results from age analytics suggested that it depends on the age group of the users, and what they want to see.

Netflix had overwhelming TV-MA films compared to other platforms.
Amazon Prime had almost even distribution of different maturity rating films.
Disney+ had no movies rated TV-MA and had only those rated TV-PG or TV-G.

Genre

Netflix has diverse content across all genres like drama, comedy, actions, etc.
Amazon Prime has also all types of genres.
Disney+ has mostly family, adventure, and animation films;

Genome-tag

Netflix and Amazon had a similar trend of having tags related to drama, comedy, and action while Disney+’s tags were more focused on animated films.

Other factors that affect subscribing to an OTT platform, are users subscribe to the OTT platforms attracts through more social media with 70.8%, and next 21.3% through television ads, and 7.4% through print media.

2. How the OTT platform recommendation system works

Movie Metadata

This helps the machine-learning algorithm to identify the title, genre, country, language, rating, runtime, plot, IMDb rating, and type kind of information, which helps the algorithm to recommend to the user the best content.

Data about Users

Data that describes a user’s viewing patterns, choices, likes, and dislikes. This data also includes location, language, watch duration, and UP/Downvotes.

3. According to the Accenture survey

70% of Indians are Frustrated with the OTT viewing experience.
Around 46% of those surveyed indicated they spend more than six minutes searching for something to watch.
Twenty-two percent of consumers globally said they use four or more services, versus 33% of whom said they subscribe to just one.

4. The Unlimited Choices

Each streaming platform has on average 2000-plus movies and 500-plus TV shows. Across all the major platforms (Netflix, Amazon Prime, and Hotstar), there are more than 50,000 movies and 12,000 TV shows, or 20,000 seasons and 6,00,000 episodes, to choose from.

Primary Research

Taking the insights from my secondary research as the foundation, I moved forward with my primary research to find qualitative data.

👉YOU ≠ USER

I should not consider myself as the user because my solution will be biased on the basis of my experience and every person have different interaction and experience with the ott platform.

👉Defining the user

👉Finding the users

In order to find different sets of users I took a survey and circulated it on different social media platforms and after a few days of analyzing the data of the survey, I selected 4 users for the interview. I tried to avoid the people whom I already know to avoid biases.

👉Interview Questions

Before the interview I prepared some questions so that I would not miss anything important.

Do you use OTT platform?
Which OTT platform do you use?And why?
On which device do you watch the OTT content?
Is there any factor you like in any specific app that you really like and help you to make decision?
How much time do you spend on OOT platform on a day and over a week?
Does your account has multiple user?
What section of the app you frequently use-> like, mylist,download etc?
When was the last time you used that platform?
What did you watch?
How did you find the content to watch?
Were you happy or satisfied after watching it?
What was the length of the content?
Do you watch movies/series alone or with family/friends?
If you watch the content with family or friends ,so how would you all decide what to watch?
Do you like movies/series suggestion from family or friends ?
If yes how would you rate your experience?
Do you take movies/series suggestion from social media trends?
If yes how would you rate your experience?
Do you like to share your personal experience/ recommendation on social media?
Does social media trends affect your choices or your previous history of content?
Would you spend more time to watch trending content , so that you can write review about it on social media?
Do you use app recommendation ?
How often do you use it?
Do you think it always recommend you the right content?
What kind of content do you like?Do you like to watch same kind/ genre of content ?
Do you like the recommendation section of the app with segregation of romance, thriller etc.
Do you feel with your past history you get good recommendation ? if noy ,Why?
Do you use the info section of the app?
Do you feel some movie character does affect your decision-making?

👉 Scenario-based questions

Can you describe a scenario when after a hectic day you opened the app?

•How would you choose the content?

•How would you filter the content?

•Does time affect your choice?

•Does your mood affect your choice?

2. Suppose one day you got 5 good movies recommendation from your friend?

How would you proceed to watch it?
In which order you would watch it?

3. Tell me a scenario when you did not get anything to watch?

👉Insights from the User Interviews

Users don’t feel the recommendation systems work correctly because of the several OTT platforms they use and there is no way to combine these OTT platform data and make the recommendation system works better.
Users usually follow their friends or family's recommendations and not the app's recommendations.
Users feel the filter could be improved by adding more options like time duration etc.
Users find it difficult to arrange the content in their saved list.
Users ask the recommendation from their friends/family on a particular app so that they don’t need to buy a new subscription to a different app.
Users want to share and receive recommendations with friends or family in a more simplified way.
Users love recommendations from friends and family but they usually forget the recommended movies/series after some days and ask again for the recommendation.

Analyzing the data

After interviewing the users, I had a lot of data but to make sense of the data I needed to find some pattern in it, for that I created user sets.

👉User Sets: After carefully analyzing the data, I segregated them into groups on the basis of user behavior and pattern.

The Trend Follower- These users follow the latest trends and love to share their views on social media. The duration of the content doesn’t matter to them.
The Time passer — These users use the OTT app to relax and for a limited duration of time and usually watch quality content recommended by friends or family, if they don’t have any recommendation or time they re-watch their favorite movie or series.

👉Empathy mapping

To understand the user sets data, I created Empathy Map to understand what my users say, think, feel, and do.

👉Personas

After the empathy map was done, I used the user data to create a fictional character called persona, to understand and relate to the user sets I have created. These users' persona will help to understand the users of the two sets, their objectives, motivations, and frustration.

Define and Ideate

After understanding the user's frustration and pain point, I tried to come up with all the solutions that can solve these problems and created some how might we questions.

Q: How might we help users to choose content quickly without scrolling all day.

By improving the filter option.
By combining the watch history of all the OTT services that the user is using and improving the recommendation system.

Q: How might we help the user to organize the saved content.

By using the folder structure and adding a title, and genre, and having the link of the folder if they want they can share it with their friends or the public and have a comment section.

Q: How might we help the user to share their recommendation to others’ on a particular OTT platform without relying on the memory.

We could make user profiles private or public. If the profile is private they can share their recommendation folder link and if the profile is public everyone can see the person's likes and dislikes.

2. We could make a shared list where a user can create a list and add other users or share with other users and they all can see all the content in that folder link and add more content, like a common recommendation and when a new recommendation comes the user will get a notification.

After writing down all the problems and the possible solutions in HMW(How Might We) step, I decided to solve only two problems that are

•How might we help the user to share their recommendation with others on a particular OTT platform without relying on the memory.

•How might we help the user to organize the saved content.

👉Reasons to pick this problem

Most of my users rely on friends or family recommendations.
Sometimes friends and family recommendations are scattered on different OTT platforms and users don’t prefer a new subscription for some recommendations.
Users feel unorganized in “My List” section as there is no way to organize the content on the basis of genre or mood.

Wireframing

After picking the problems that I was going to solve , I drew a rough wireframes so that I can quickly draw my ideas before I forget.

👉Low Fidelity Wireframe

UI and Prototype

For UI design I took some screenshots of the Netflix app and added my changes.

Original screenshot

These are the original screenshot I took of Netflix.

What I did in the UI

👉 For organizing the saved content

In the menu down below I have removed the download option and added list-icon so that users can navigate to the “My Lists” section easily.

2. For segregating the list I have added a folder structure so that users can create any list and add the movies/series of their choices.

3. When a user clicks on the add-to list, they will get an option that shows the available lists that the user has created, or if they want they can create a new list.

I have followed Jacob’s law where I used the reference of create board of the Pinterest app.

👉 Making Sharing Easy

To make the sharing easy, I have added a copy link of the folder, and users can directly copy the link and send it to their friends/family when they ask for a recommendation.
I have also added a share option where users can send the list to their friend's email id at once and users can also give access to the folder to their selected friends and their friends can also start adding the movies/series to the recommendation list.
Users can also add messages to their friends to let them know what they are sharing.
A red dot or “New” will appear when a new recommendation would come.
Users have also the option to download the folder at once without downloading it one by one.
Users have the flexibility to rename the list or change the photo of the list or remove the list.

I have followed Jacob’s law where I have used the sharing feature of Google Docs as a reference .

Usability testing

During the usability testing, I tried to observe the user’s reactions when I presented my UI prototype. I prepared a set of questions to ask users in order to know what they understand and if they find the flow of the screen useful or difficult to navigate.

👩User 1

Liked: She liked the “My List” option and she also realized that she uses the same list/board creation on Pintrest and She also loved the recommendation sharing.
Improvement: She had a doubt if she is sharing a common list with her friends and they added some recommendations that she had already watched ,will she still get the notification.

👨User 2

Liked: He liked the new features in the “My Lists” and said he would use the recommendations sharing with his friends.
Improvement: He said while sharing the recommendations, we could also add a date and time in the calendar like when they are planning to watch the movie marathon.

🧑User 3

Liked: He liked the overall changes and he said friends recommendation list will save him a lot of time as he usually search the recommended movies/series by himself and sometimes he forget the name and ask the friend again.

👱‍♀️User 4

Liked: She also liked the new features in the “My List” and said it will be really useful for her.
Improvement: She said in the list section there are no options to differentiate the genre of the movie and creating different lists for different genres and sharing with friends will be tough.

👉Insights from the usability testing

Segregation of genre inside the list.
If the user has already watched the movie that his friend recommended in the recommendation list, he should not get the notification for it.

👉Changes made in the UI after the usability test

To solve the segregation problem I have added a sliding category that users can use and change the genre as they want and this sliding feature is also the same in other apps so users are comfortable using it.

This concludes the usability testing and my project as well🥳

👉Key learnings-

I learned the importance of secondary and primary research.

2. I learned how primary research data helps to validate secondary research.

3. I learned how to prioritize the right problem and the solution I am going to follow.

4. It is important to understand the problem and then come up with the solution, rather than coming up with a solution and assuming that it will solve the problem.

5. I learned how usability testing can help me to improve my solution.

👉Key Un-learning

I unlearnt to ask open-ended questions.
I unlearnt to keep my personal experiences aside and focus on the user’s experience.