Helping people find strong connections for compatible and happy living

Compatibility in Cohabitation

My Role: Product Designer
Product viability & direction, research, ideations, wireframes, user flows, usability testing, UI, design system creation and management

Project Type: Conceptual Project
Project Completion: November 2022
Project Duration: 4 weeks

CoHabit Case Study Hero Image
Case Study Contents:

In cities like NYC and LA, more than

45% of adults live in
doubled up* households

*Households having 1+ adults in addition to the head of household and partner, such as an adult child living at home or two related or unrelated families residing together

Living with others significantly increases the possibility for social friction due to mismatching expectations and can ultimately have serious impact on resident quality of life, and mental, physical, and financial health.

“I would just like leave for the entire day and I would avoid coming back for stuff because I like didn’t want to see [my roommate]”

❌   The Problem

In the era of technology and online connection, it’s still difficult to find good relationships and even harder to find someone you trust in your own home.

People turn to social media and open discussions to find potential house-mates, but have trouble evaluating compatability. We have Hinge for online dating, Bumble BFF to find friends, and LinkedIn for professional networking.
So, why not a centralized location to connect people looking for arguably one of the most impactful relationships?

THE 
GOAL

CoHabit is a new app that alleviates long-term domicile stress by helping users find compatible housemates.

CoHabit needs an end-to-end design of a mobile app that captures user preferences, match compatible users, and offers a space for them to get to know each other before increasing commitment levels.

Understanding the User
Research Finding #1
Location-based Facebook groups are currently the most popular place to look for available rooms and potential housemates.
Win!!! This gave me an extensive amount of unstructured data to analyze to further understand the housemate search before diving into user interviews
Facebook posts identified two main user types:

THE LISTER: 
Someone who has an available room and is looking for a housemate to fill it

These individuals share:

  • Information about the apartment and the available room
  • Themselves and their current housemates
  • What type of person they are looking for to fill the room

THE SEEKER: 
Someone who actively searching for a room

These individuals share:

  • Personal details about themselves and their lifestyle preferences
  • What type of space they are looking for (i.e. location, size, etc)
Research Finding #2
Most people bring up the same 5-10 characteristics when asked how they define a "compatible roommate".
Research revealed things that certain lifestyle choices were “non-negotiables” whereas others were “nice-to-haves”. This data is incredibly important in informing the compatibility algorithm.
Research Finding #3
The lister spends hours looking through responses to internally evaluate and compare roommates.
Although the housemate searching process is generally <2 months, listers spend weeks finding, speaking to, and evaluating candidates to find the best possible fit within the timeframe.
Research Finding #4
Room seekers have trouble sifting through hundreds of long listings
Room-seekers struggle with weeding through listings that do not meet their non-negotiable requirements such as location, rent, pet-friendly, etc.
Research Finding #5
Everyone hates realtor spam and is looking to rent to/from a “real person”
More and more, popular housemate searching platforms are over run with regular and realtor spam.
Ideating on the Solution
The research yielded many implications for designs. The main implication is summed up by the following statement:
HOW MIGHT WE...
create a platform that takes the guesswork, back & forth, and time out of finding a compatible roommate and/or the perfect room?
Learning from the past: Who does compatibility best? Naturally, I thought of the heavy hitters of the past decade - dating apps.
Dating app’s in-depth profiles can help us in a few ways:
The first hurdle was to understand profiles and the profile creation process in the context of cohabitation
Let’s talk profile creation
I previously determined that in-depth profiles could fuel compatibility matching thus reducing the energy of finding a compatible roommate. This leaves the product heavily reliant on a large volume of user inputted data.
SO... HOW MIGHT WE...
Ask (and encourage) users to fill out a considerable amount of information AND mitigate against abandonment?
Benchmarked across multiple dating app onboarding flows, my initial onboarding flow was deceptively simple and familiar:
1
Single sign-on (SSO) and verification helps combat realtor spam
When looking for rooms/roommates, people want to rent from individuals and currently spend time filtering through realtor-spam on FB and Craigslist.
2
20 profile questions asked in onboarding were considerations based off of research and then grouped into “required” vs “nice to have” questions via card sorting
To combat flow exhaustion and abandonment, only absolutely necessary information (like name, age) were moved to the “required” group.
3
Profile questions were further split into 3 major sections and ordered by importance
Users are given the opportunity to skip full sections of “nice to have” questions to reduce time to value (TTV) and get product exposure.
4
Each section starts with an entry page to show users what is to come
This copy encourages users to fill out the upcoming section, but provides enough information so users can comfortably decide to skip the section.
5
Each screen only included one question at a time
This improves digestability and visual overload.
6
Progress bars and skip buttons are clearly labeled
Due to the nature of the long form, we wanted to make sure that users never felt stuck within the form.
Although the flow is simple and all users COULD complete the task when asked, usability interviews showed that the onboarding experience needed to be optimized to truly minimize abandonment rates.
6/7
Could complete onboarding task easily without hesitation or assistance
4/7
Likely to complete FULL onboarding process
4/7
Understand product value proposition compared to competitors or current methods of room-mate searching
9:34
Average amount of time it takes for the user to create FB post for roommate search

1:53
Average amount of time it takes for the user to finish segmented profile creation
4:28
Average amount of time it takes for the user to finish full profile creation
Not bad, but we can do better!
User Problem
Users don’t fully understand value proposition of product leading to underutilized profile creation and higher abandonment rates
Solution
Add informational carousel on app landing page to explain value proposition
  • 4-5 words in each headline increases scanability of CoHabit’s main value propositions
  • Sign In/Create Account CTAs are consistent on each screen to encourage users to enter the app at any time
  • Progression indicator to keep users in control
User Problem
Users tend to skip or express confusion around the same questions
Solution
Remove under-utilized questions to shorten profile creation
[V1] Unclear which input is for sleep vs wake and how to use this input type. Users express that it is unimportant in the housemate search process.

[V2] Added additional copy and changed UI to a slider for clarity around input style. Users still chose to skip question.

[V3] Eliminated the quiet hours question altogether to shorten profile creation.
Testing on high-fidelity prototypes with iterations were slightly more successful.
6/7
Could complete onboarding task easily without hesitation or assistance

Compared to 6/7 before
5/7
Likely to complete FULL onboarding process

Compared to 4/7 before
5/7
Understand product value proposition compared to competitors or current methods of room-mate searching

Compared to 4/7 before
But ultimately, I wanted to decrease time to value (TTV) and help users understand the value of completing the full profile creation process
Time to re-visit our deceptively simple, and familiar onboarding + profile creation flow.
The original onboarding flow takes the user 24-27 screens until they can enter the app
This works for currently dating apps because they have an extremely strong and well-known value proposition that users can’t find elsewhere.

Because CoHabit is a newer platform, I began to try and understand if TTV or immediate collection user inputted data was of higher value to the success of launch.

Ultimately, TTV weighed out as more important for launch as user data could always be collected at a later point.
Creating a deferred profile creation flow: This flow allows users to access the app content in 3 screens, but still encourages profile creation afterwards.
Users are granted browsing capabilities, however when they want to use the platform to pursue a main action (i.e. posting a room, inquiring about an open room, etc.), they are then prompted to fill out their profile creation.
This small tweak in the onboarding flow completely decreased abandonment in the onboarding process.
7/7
Likely to complete onboarding process

Compared to 5/7 before
6/7
Understand product value proposition compared to competitors or current methods of room-mate searching

Compared to 5/7 before
6/7
Likely to complete FULL profile creation process

Compared to 5/7 before
The final solution (for now...)
Bringing everything together, here is the final prototype
A Quick and Necessary Reflection
The importance of an effective design system
Building and maintaining an effective design system early on helped save hours if not days of time across multiple stages of this project. Most notably, it made rapid prototyping incredibly efficient and allowed for more time iteration and testing.
Respect the non-linear design process
Sometimes it’s necessary to go back to the drawing board and re-evaluate, even after solutions have been put forth and tested. Design decisions are never made in a vacuum and have to be weighed against other cross-functional factors.
CoHabit Web
Many users are used to searching for roommates on web platforms such as Facebook and Craigslist. CoHabit web is in the works to help people find compatible roommates regardless of which interface they prefer to work off of.
View Figma File (Coming Soon)
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