Case study
Cineflux
Helping people to curate film watch lists and find new films
UX
UI
Mobile app
Case study
Cineflux
Helping people to curate film watch lists and find new films
UX
UI
Mobile app
Case study
Cineflux
Helping people to curate film watch lists and find new films
UX
UI
Mobile app
Summary
People watch movies alone or with others, at home or in cinemas. With numerous streaming options and cinema releases, choosing a film can be overwhelming. To address this, I designed a mobile app.
Film choices depend on various factors like tastes, mood, environment, opinions, time, and availability. While AI recommendations help, they often rely on personal tastes and can be predictable. For this project I also did not want to try and create another social network.
User interviews and competitor analysis revealed that mood significantly influences film choices. Enthusiastic film-watchers enjoy curating watchlists and sharing knowledge.
I designed and tested three key flows, which rated well for ease of use. However, some users found them slightly inefficient and lacking in value compared to existing products. Not all proposed features were tested, and feedback highlighted areas for improvement to better meet user needs.
Methodology
User interviews
Competitor analysis
Personas
Finding opportunities (POVs/HMWs)
Defining the solution & features
Information architecture
User flows
Low fidelity wireframes & initial testing
Brand design
High fidelity wireframes
Prototyping & usability testing
Revisions
Summary
People watch movies alone or with others, at home or in cinemas. With numerous streaming options and cinema releases, choosing a film can be overwhelming. To address this, I designed a mobile app.
Film choices depend on various factors like tastes, mood, environment, opinions, time, and availability. While AI recommendations help, they often rely on personal tastes and can be predictable. For this project I also did not want to try and create another social network.
User interviews and competitor analysis revealed that mood significantly influences film choices. Enthusiastic film-watchers enjoy curating watchlists and sharing knowledge.
I designed and tested three key flows, which rated well for ease of use. However, some users found them slightly inefficient and lacking in value compared to existing products. Not all proposed features were tested, and feedback highlighted areas for improvement to better meet user needs.
Methodology
User interviews
Competitor analysis
Personas
Finding opportunities (POVs/HMWs)
Defining the solution & features
Information architecture
User flows
Low fidelity wireframes & initial testing
Brand design
High fidelity wireframes
Prototyping & usability testing
Revisions
User research
I chose to interview 5 people remotely for around 20 - 45 minutes each:
Ages ranging from 20s to 30s, mixed gender
Mixture of students and professionals
I had three main areas of focus in my questioning:
Factors that influence choice of film to watch
Social dynamics that influence choice for group watches
How new films to watch are found
User research
I chose to interview 5 people remotely for around 20 - 45 minutes each:
Ages ranging from 20s to 30s, mixed gender
Mixture of students and professionals
I had three main areas of focus in my questioning:
Factors that influence choice of film to watch
Social dynamics that influence choice for group watches
How new films to watch are found
User research
I chose to interview 5 people remotely for around 20 - 45 minutes each:
Ages ranging from 20s to 30s, mixed gender
Mixture of students and professionals
I had three main areas of focus in my questioning:
Factors that influence choice of film to watch
Social dynamics that influence choice for group watches
How new films to watch are found
Findings
Engagement
Participants fell into two categories based on their level of engagement with film:
Casual watchers
These are people for whom trends/popularity & social media drives the selection of films. They judge films on the basis of genre and analyse the positives, rather than dwell on the negative aspects.
Film buffs
These are people that use specific criteria/sources for recommendations. They follow specific filmmakers and trusted experts, curate watchlists, see film as a way to teach culture, and analyse more deeply.
Mood & mindset
The time of day, environment & current feelings influence choice of film. In addition, subjective expectations of a film can influence the reception of that film.
Availability
The availability of films on streaming services or in physical cinemas drive a lot of film watching decisions.
Social dynamics
People may watch films they otherwise wouldn't when with friends. They may even enjoy bad movies ironically in such a situation. Furthermore, most like to discuss the films afterwards.
Findings
Engagement
Participants fell into two categories based on their level of engagement with film:
Casual watchers
These are people for whom trends/popularity & social media drives the selection of films. They judge films on the basis of genre and analyse the positives, rather than dwell on the negative aspects.
Film buffs
These are people that use specific criteria/sources for recommendations. They follow specific filmmakers and trusted experts, curate watchlists, see film as a way to teach culture, and analyse more deeply.
Mood & mindset
The time of day, environment & current feelings influence choice of film. In addition, subjective expectations of a film can influence the reception of that film.
Availability
The availability of films on streaming services or in physical cinemas drive a lot of film watching decisions.
Social dynamics
People may watch films they otherwise wouldn't when with friends. They may even enjoy bad movies ironically in such a situation. Furthermore, most like to discuss the films afterwards.
Competitor analysis
Netflix
Netflix
Letterboxd
Letterboxd
MovieSwipe
MovieSwipe
Movie Match
Movie Match
Matched
MovieSwipe
MovieSwipe
Characteristics
Tinder-style interfaces are commonly used to generate suggestions, but these are inefficient when starting from scratch
Personalisation is a key feature - recommendations are generated based on previous choices
Categorisation is present but often excessive and not particularly meaningful
Demographics - some apps provide excessive detail about films, while others provide little, seemingly targeting two disparate demographics with nothing in the middle
Apps often include a social curation element, which may be targeted towards certain kinds of social groups, such as couples or friend groups
Most apps will tell you the streaming service availability of each film
Defining the target audience
From user interviews I was able to define two archetypal personas, each with challenges, frustrations and goals. These are their goals:
Hoss, the film buff
Wants to more easily curate a list of interesting films to watch and select an appropriate film from it to watch at any given opportunity
Wants to deepen and broaden his knowledge of film and film history
Wants to broaden his film watching horizons, as feels like he is missing out on potential gems
Wants to find more films by filmmakers whose work he has enjoyed
Wants to share his film suggestions and knowledge with his immediate friends
Amy, the social movie-watcher
Wants to find new, popular/trending movies or TV series that would provide a good social watching experience with her friends
Wants to find interesting new movies and TV series that are popular/trending so she can be part of the conversation
Wants to understand what her friends are interested in watching together, so she can organise mutually satisfying group watches with them
Wants to bond with her friends over group watching experiences
Defining the target audience
From user interviews I was able to define two archetypal personas, each with challenges, frustrations and goals. These are their goals:
Hoss, the film buff
Wants to more easily curate a list of interesting films to watch and select an appropriate film from it to watch at any given opportunity
Wants to deepen and broaden his knowledge of film and film history
Wants to broaden his film watching horizons, as feels like he is missing out on potential gems
Wants to find more films by filmmakers whose work he has enjoyed
Wants to share his film suggestions and knowledge with his immediate friends
Amy, the social movie-watcher
Wants to find new, popular/trending movies or TV series that would provide a good social watching experience with her friends
Wants to find interesting new movies and TV series that are popular/trending so she can be part of the conversation
Wants to understand what her friends are interested in watching together, so she can organise mutually satisfying group watches with them
Wants to bond with her friends over group watching experiences
Finding opportunities
From POVs/HMWs it be became clear that were some opportunities to create an improved experience to:
Help users to find interesting and satisfying films to watch that they hadn’t seen before
Help users to curate and track watchlists
Help users to find films to watch that fulfil the audience’s current psychological wants and needs
Finding opportunities
From POVs/HMWs it be became clear that were some opportunities to create an improved experience to:
Help users to find interesting and satisfying films to watch that they hadn’t seen before
Help users to curate and track watchlists
Help users to find films to watch that fulfil the audience’s current psychological wants and needs
Defining the solution & features
For an MVP, I decided that the solution needed to include two core functions:
Watchlist curation
Suggestion generation, based on previous films, mood and other criteria
I designed a basic IA, splitting by those two core functions. Within those core functions I prioritised the following subfeatures accordingly:
Required for MVP
Basic information about films (e.g. title, year, cast/crew, critical ratings, poster etc)
Watchlist filtering - the ability to filter watchlist based on criteria such as mood
Next in priority
Watchlist sharing & collaboration
Ability to explore/trace films by cast/crew, genres
See streaming/cinema availability of films
Further down the line
Automatic suggestion of films based on time of day, inferring mood
TV series
Deeper contextual information about films, providing film education
Ability to explore/trace films by movement
Defining the solution & features
For an MVP, I decided that the solution needed to include two core functions:
Watchlist curation
Suggestion generation, based on previous films, mood and other criteria
I designed a basic IA, splitting by those two core functions. Within those core functions I prioritised the following subfeatures accordingly:
Required for MVP
Basic information about films (e.g. title, year, cast/crew, critical ratings, poster etc)
Watchlist filtering - the ability to filter watchlist based on criteria such as mood
Next in priority
Watchlist sharing & collaboration
Ability to explore/trace films by cast/crew, genres
See streaming/cinema availability of films
Further down the line
Automatic suggestion of films based on time of day, inferring mood
TV series
Deeper contextual information about films, providing film education
Ability to explore/trace films by movement
Key flows
I diagrammed key flows unique and core to the solution:
Adding films to watchlist
Selecting a film to watch based on suggestions
Diagramming the flows enabled me to resolve specific implementation details, in a way that made sense to the user.
Key flows
I diagrammed key flows unique and core to the solution:
Adding films to watchlist
Selecting a film to watch based on suggestions
Diagramming the flows enabled me to resolve specific implementation details, in a way that made sense to the user.
Low fidelity prototyping & testing
I initially created some simple low fidelity wireframes to illustrate the concept, and tested these one of my original interview subjects, and shared with other designers to solicit feedback. The feedback from testing/critique:
The lack of a first-time experience for new users
Confusion about some of the language used in the UI and the relationship between UI elements in the search screen
More information wanted for the films displayed in the UI
Low fidelity prototyping & testing
I initially created some simple low fidelity wireframes to illustrate the concept, and tested these one of my original interview subjects, and shared with other designers to solicit feedback. The feedback from testing/critique:
The lack of a first-time experience for new users
Confusion about some of the language used in the UI and the relationship between UI elements in the search screen
More information wanted for the films displayed in the UI
Brand design
For this project I designed a brand and design system: Cineflux.
Brand design
For this project I designed a brand and design system: Cineflux.
High fidelity prototyping & testing
With the initial feedback from the low fidelity wireframes, I fleshed them out into a high fidelity interactive prototype of key flows:
Adding first film to watchlist
Looking up a specific film to add to watchlist
Getting suggestions for a film to watch based on watchlist
These flows were then tested with 5 potential users, including one film buff. The results:
Users rated the flows relatively highly for ease of use (5-7 out of 7 for ease of use)
Lower ratings were due to flows feeling cumbersome or too inefficient
Roughly half the users considered the app useful to them in its current form; the others felt like it needed to provide more value over other solutions
High fidelity prototyping & testing
With the initial feedback from the low fidelity wireframes, I fleshed them out into a high fidelity interactive prototype of key flows:
Adding first film to watchlist
Looking up a specific film to add to watchlist
Getting suggestions for a film to watch based on watchlist
These flows were then tested with 5 potential users, including one film buff. The results:
Users rated the flows relatively highly for ease of use (5-7 out of 7 for ease of use)
Lower ratings were due to flows feeling cumbersome or too inefficient
Roughly half the users considered the app useful to them in its current form; the others felt like it needed to provide more value over other solutions
Priority revisions
Verbiage used in some places wasn’t super clear to some users
→ Changed verbiage to be more consistent and clear
More expressive filtering and clarity in filtering was desired; filter navigation was inconsistent and confusing
→ Reworked filter screens to be more organised and easier to navigate, with additional filter categories to account for the audience
Most users wished for the ability to retain watched movies in watchlist (a history)
→ Reworked watchlists to retain already watched films, and toggle between showing just unwatched films, just watched films or all
Users consistently asked for more information on each film to be visible throughout the app, especially critical ratings
→ Added more information about each film wherever they are displayed throughout the app
Some users found the Tinder-style interface for suggestions to be inefficient as they already knew so many films
→ Enabled users to switch between a Tinder-like view of suggestions and a list view of suggestions
Priority revisions
Verbiage used in some places wasn’t super clear to some users
→ Changed verbiage to be more consistent and clear
More expressive filtering and clarity in filtering was desired; filter navigation was inconsistent and confusing
→ Reworked filter screens to be more organised and easier to navigate, with additional filter categories to account for the audience
Most users wished for the ability to retain watched movies in watchlist (a history)
→ Reworked watchlists to retain already watched films, and toggle between showing just unwatched films, just watched films or all
Users consistently asked for more information on each film to be visible throughout the app, especially critical ratings
→ Added more information about each film wherever they are displayed throughout the app
Some users found the Tinder-style interface for suggestions to be inefficient as they already knew so many films
→ Enabled users to switch between a Tinder-like view of suggestions and a list view of suggestions
The future
The aforementioned priority revisions are “quick wins” - more detailed work should be done in future to improve this design:
Sharing should be iterated on and tested - including view-only sharing of watchlists and potentially the group curation of watchlists
Adding more information about each film
Iteration to surface trending/popular categories of films, as one of the target personas uses this
Iteration to surface categories and filters earlier in flows - as test users found flows inefficient
As it stands the currently prototyped design targets the film buff persona more than the social film-watcher persona - further revisions in line with feedback and the other features proposed could be used to rebalance this.
The future
The aforementioned priority revisions are “quick wins” - more detailed work should be done in future to improve this design:
Sharing should be iterated on and tested - including view-only sharing of watchlists and potentially the group curation of watchlists
Adding more information about each film
Iteration to surface trending/popular categories of films, as one of the target personas uses this
Iteration to surface categories and filters earlier in flows - as test users found flows inefficient
As it stands the currently prototyped design targets the film buff persona more than the social film-watcher persona - further revisions in line with feedback and the other features proposed could be used to rebalance this.
