Reelfriends

Reelfriends

Client

Personal

Personal (Bootcamp)

Contribution

Research, UX, UI, Prototypes

Year

2021

Duration

10 Weeks

Tools

Figma, Miro, Maze

Client

Personal

Personal (Bootcamp)

Contribution

Research, UX, UI, Prototypes

Year

2021

Duration

10 Weeks

Tools

Figma, Miro, Maze

Client

Reelfriends

Personal (Bootcamp)

Contribution

Research, UX, UI, Prototypes

Year

2021

Duration

10 Weeks

Tools

Figma, Miro, Maze

Objective.
“To explore a problem space you’re passionate about, and design a solution for it. Weekly assignments will culminate in a mid-course design critique, and the final project demo to be delivered to the class.”
Project Guidelines, Syllabus, General Assembly.
Objective.
“To explore a problem space you’re passionate about, and design a solution for it. Weekly assignments will culminate in a mid-course design critique, and the final project demo to be delivered to the class.”
Project Guidelines, Syllabus, General Assembly.

Personal inspiration. I love film. Outside of design, it is my next great passion. I wanted this project to be focused on film in some way.

Personal inspiration. I love film. Outside of design, it is my next great passion. I wanted this project to be focused on film in some way.

Outside influence. During the COVID-19 pandemic we found ourselves “sheltering-in-place”. With little to do (safely), watching TV shows and movies played a dominant role in every day life.

Outside influence. During the COVID-19 pandemic we found ourselves “sheltering-in-place”. With little to do (safely), watching TV shows and movies played a dominant role in every day life.

A changing landscape. With the closure of movie theaters, and the desire for studios to launch their own streaming services, there was an endless amount of content to watch, at any time.

A changing landscape. With the closure of movie theaters, and the desire for studios to launch their own streaming services, there was an endless amount of content to watch, at any time.

4 user interviews

4 user interviews

30 minutes

10 questions

25 - 40 ages

10+ hrs/wk

2+ subscriptions

30 minutes

10 questions

25 - 40 ages

10+ hrs/wk

2+ subscriptions

30 minutes

10 questions

25 - 40 ages

10+ hrs/wk

2+ subscriptions

“It’s hard for me to decide what to watch”

“It’s hard for me to decide what to watch”

“I find it difficult to figure out what to watch”

“I find it difficult to figure out what to watch”

“I usually “doom-scroll” trying to find something to watch”

“I usually “doom-scroll” trying to find something to watch”

“I worry about time, and picking the wrong thing”

“I worry about time, and picking the wrong thing”

Decision making

Too much time is wasted making a choice

Decision making

Too much time is wasted making a choice

Recommendations

The best are from friends

Recommendations

The best are from friends

Platforms

No preferences or trust

Platforms

No preferences or trust

Watchlists

Rarely useful, and often aspirational

Watchlists

Rarely useful, and often aspirational

General/habits

Choice depends on mood

General/habits

Choice depends on mood

In a study of 2,000 Americans,

In a study of 2,000 Americans,

30 minutes spent deciding what to watch

30 minutes spent deciding what to watch

58% struggle to track what to watch

58% struggle to track what to watch

56% turn off the TV and do something else

56% turn off the TV and do something else

55% opt for re-watching a show or movie

55% opt for re-watching a show or movie

Problem statement. Streaming movie watchers need a better way to receive recommendations so that they can spend more time watching, and less time scrolling.

Problem statement. Streaming movie watchers need a better way to receive recommendations so that they can spend more time watching, and less time scrolling.

How-might-we statement. How might we provide better recommendations to streaming movie watchers?

How-might-we statement. How might we provide better recommendations to streaming movie watchers?

Proposed solution. An app to help users give, receive, and track recommendations.

Proposed solution. An app to help users give, receive, and track recommendations.

Most

Most

Features necessary to support core functionality

Features necessary to support core functionality

Give Recommendation

Give Recommendation

Receive Recommendations

Receive Recommendations

Friends List

Friends List

Movie Details Page

Movie Details Page

“Mood” & Genre

“Mood” & Genre

Account

Account

Should

Should

Ideal enhancements that would help reduce barriers

Ideal enhancements that would help reduce barriers

Platform Filter

Platform Filter

Direct Play

Direct Play

Connect Services

Connect Services

Connect your TV

Connect your TV

History

History

Account

Could

Could

Additional features that may add value for some users

Additional features that may add value for some users

“Why?”

“Why?”

Movie of the Day

Movie of the Day

Made For You

Made For You

Friend Activity

Friend Activity

“Mood” & Genre

Account

Won't

Won't

Features that don’t address the problem statement

Features that don’t address the problem statement

Mark as Watched

Mark as Watched

Tweet Length Review

Tweet Length Review

Rate

Rate

Watchlist

Watchlist

Most Popular

Most Popular

Trending

Trending

New Releases

New Releases

Request a Recommendation. “As a User, I want to request a movie recommendation from my friends”

Request a Recommendation. “As a User, I want to request a movie recommendation from my friends”

Give a Recommendation. “As a User, I want to search a movie and recommend it to a friend”

Give a Recommendation. “As a User, I want to search a movie and recommend it to a friend”

Prioritized

The strongest recommendation only

Prioritized

The strongest recommendation only

Personal

Only personally relevant content

Personal

Only personally relevant content

Efficient

Utilitarian and fast

Efficient

Utilitarian and fast

Design Goals. The design solution was guided by three goals that directly reflected the key insights gained during the research phase.

Design Goals. The design solution was guided by three goals that directly reflected the key insights gained during the research phase.

Wireframes and Testing. Through multiple rounds of wireframes and usability tests, I was able to iterate through core elements such as layout, features, and flows.

Wireframes and Testing. Through multiple rounds of wireframes and usability tests, I was able to iterate through core elements such as layout, features, and flows.

Mood

Tags

Vague and unreliable

Personal and familiar

Mood

Tags

Vague and unreliable

Personal and familiar

Mood

Tags

Vague and unreliable

Personal and familiar

Mood

Tags

Vague and unreliable

Personal and familiar

Early insight. One of the key insights from the user interviews was that mood played a big factor in deciding what to watch. The designs addressed this by a) allowing users to add a mood to a recommendation request, and b) the ability to browse or search by mood.

Early insight. One of the key insights from the user interviews was that mood played a big factor in deciding what to watch. The designs addressed this by a) allowing users to add a mood to a recommendation request, and b) the ability to browse or search by mood.

Testing. During testing, all users were successful in using this feature, and reported that they clearly understood its function. However, at the end of every testing session I would solicit feedback from my users on the solution and the overall experience. When discussing the “mood” options, all users reported they weren’t interested in it as presented. Users felt this would likely be vague, unreliable and inherently untrustworthy. This mirrored what users reported in the initial interviews around recommendations provided by the streaming platforms they were using.

Testing. During testing, all users were successful in using this feature, and reported that they clearly understood its function. However, at the end of every testing session I would solicit feedback from my users on the solution and the overall experience. When discussing the “mood” options, all users reported they weren’t interested in it as presented. Users felt this would likely be vague, unreliable and inherently untrustworthy. This mirrored what users reported in the initial interviews around recommendations provided by the streaming platforms they were using.

Redesign. With this in mind, I removed the “mood” feature, and added a “tag” feature. This allows users to add three unique “tags” to each request or submission. Users can apply their own familiar vernacular to confidently express their preferences or attitudes, and it is specific to each interaction. This created a more personalized experience, and shifted this solution to one that supported and enhanced their existing social networks.

Redesign. With this in mind, I removed the “mood” feature, and added a “tag” feature. This allows users to add three unique “tags” to each request or submission. Users can apply their own familiar vernacular to confidently express their preferences or attitudes, and it is specific to each interaction. This created a more personalized experience, and shifted this solution to one that supported and enhanced their existing social networks.

Beginner mistakes. During tests users struggled with some choices I made. This immediately illustrated how important existing standards and patterns can be to a successful design.

Beginner mistakes. During tests users struggled with some choices I made. This immediately illustrated how important existing standards and patterns can be to a successful design.

  • Contrast. Highest contrast for key actions.

  • Familiarity. Users expect familiar patterns.

  • Usability. If a feature is shown, users expect to interact with it.

  • Copy. Natural language clarifies actions for users.

  • Contrast. Highest contrast for key actions.

  • Familiarity. Users expect familiar patterns.

  • Usability. If a feature is shown, users expect to interact with it.

  • Copy. Natural language clarifies actions for users.

  • Contrast. Highest contrast for key actions.

  • Familiarity. Users expect familiar patterns.

  • Usability. If a feature is shown, users expect to interact with it.

  • Copy. Natural language clarifies actions for users.

Design process. Insight into the iterative design process, and examples of changes made.

Design process. Insight into the iterative design process, and examples of changes made.

  • Early color, icon, and navigation concepts

  • Home, and Detail page concepts

  • Changing from a light to dark theme to reinforce the movie experience, and most likely use case

  • Changing from a light to dark theme to reinforce the movie experience, and most likely use case

  • Less exploration, more utility

  • Moved towards a darker, less distracting navigatioon

  • “Top Recommendation” concepts

  • Movie details page concepts

  • Early color, icon, and navigation concepts

  • Home, and Detail page concepts

  • Changing from a light to dark theme to reinforce the movie experience, and most likely use case

  • Changing from a light to dark theme to reinforce the movie experience, and most likely use case

  • Less exploration, more utility

  • Moved towards a darker, less distracting navigatioon

  • “Top Recommendation” concepts

  • Movie details page concepts

Onboarding.

Onboarding.

Top recommendation.

Top recommendation.

Direct play.

Direct play.