COLAB5 - Web App


YourRoomz is a web-application that enables users to efficiently find the missing pieces that complement their existing living-room furniture by meeting them at their current stage of the home furnishing journey.


Problem Background

Unless someone is willing to hire an interior designer, furnishing  a living-space can be a confusing and drawn-out process. While countless sources of design inspiration exist across Pinterest, Instagram, Houzz, etc, attempting to reverse-engineer and replicate a model home is a wasted effort for most people, as doing so would likely require them to start from scratch. 

This is reasonable for anyone that wants to create their long-term living space, but what about someone that has furniture they’re not willing to part with? Their design possibilities are ultimately limited by what they already have. 

The problem is that most interior design resources start from the same stage of the home-furnishing journey: the beginning. Without any personalized guidance, these people are often left to play the guessing-game of finding items that they hope will look okay with their existing furniture.

Research & Validation

To validate this problem, we initially interviewed six potential users. Four had recently furnished a new living space, two were in the process of doing so, and one was a few weeks away from moving. Three lived in areas where online furniture shopping is available. 

Four out of the six said they used, or would be using, furniture they already had to furnish their space. All three of the interviewees in online shopping markets said they had difficulty finding products online that they felt would fit the furniture they already have. Thinking about scope, we also asked them which room they spend the most effort furnishing, to which they all responded: the living room. 

Some relevant quotes included:

 “I don’t actually know how [my living room] should look in totality. I would love some advising in some form of how to tie this all together.”

“I struggled with putting enough items to make the home look good and not putting enough to make it look choked".

“When I go on Wayfair I’m overwhelmed by everything I see. I almost want a 3D reality that allows me to view what I already have in my room and see how these other items fit in with it.”

Understanding that our research size was rather small, we also looked at the broader interior design market for further verification:

As a result of the COVID-19 pandemic, 2020 saw a significant increase in consumer spending being allocated to their homes. According to Factset, the Q4 YoY increase for Home Improvement Retail was 18.0% and 7.6% for Home Furnishing.

This YoY increase in home spending might not be an anomaly, however. In a poll performed by McKinsey, 30% of responders said they planned on “[splurging] on items for their home after the pandemic.” For comparison, 30 percent said “they will spend more on in-person restaurant dining, out-of-home entertainment, and travel”.

More specific to the problem we’re trying to solve, according to, there is significant monthly search volume for interior design services and ideas (June 2020) that suggests people are seeking online design solutions:

o   “Interior design ideas”: 49.5k searches

o   “Interior design for small house”: 14.8k

o   “Online interior design course”: 14.8k

o   “Online interior design”: 12.1k

o   “Interior design app”: 4k

Landing on the solution 

With our findings from our initial interviews, we were already set on limiting the scope of our solution to providing users product recommendations for their living room. The next steps were then to figure out how we could learn about our users’ existing living room setup, how we could determine that certain products were complements to their setup, and where we would find these products.   

Capturing User Data

In the ideal future-state,  we would include a feature that allows the user to use their phone cameras to feed us information about their furniture, dimensions, styles, etc., but given the time and resource constraints, we had to devise a feasible solution. We settled on the idea of having users provide certain details about their living room and existing furniture that we felt would be necessary for providing recommendations. As a fail-safe, we’d show them images of varying styles of their existing  furniture categories and allow them to select the one that most closely resembles what they have. 

We tested this idea with users via low and high fidelity designs, thinking it could be too complicated, but received consistent, positive feedback, which led us to pursue this solution.

Product Recommendations

We then had to establish a methodology for utilizing the user’s data to provide recommendations. We initially considered using what’s referred to as the “60/30/10” rule - an interior design framework that allocates the color distribution of a room amongst 3 colors - but had to pivot due to scope. Instead, we created a hierarchy of room features and product categories based on how heavily they dictate the design of the living room, from wall-color all the way down to decorative pillows, that we’d use to determine what furniture items should match vs what should be a complementary color. We’d use the user’s “similar furniture” selection to determine the products that would be stylistic matches.

Product Sources

We considered several sources, such as Wayfair and Ikea, but ultimately settled on Amazon when we found an API that provided the necessary product data for our solution. Amazon also has an affiliate marketing API we could transition to post-launch, which would then be our initial source of revenue. 


YourRoomz is a web-application that enables users to efficiently find the missing pieces that complement their existing living-room furniture by meeting them at their current stage of the home furnishing journey.

YourRoomz consists of 8 features:

Landing Page/Login: Users can use the application via their Google, Twitter, or GitHub accounts

Room Selection: Users pick what room they want recommendations for and provide the  wall and floor colors of that room  (currently limited to Living Room)

Pre-existing Item Selection: Users provide living room furniture they already have and their details (color, material, select stylistically similar image)

Pre-existing Items Summary: Summary view of all the user’s pre-existing items for that room 

Choose recommendation category: User selects any furniture categories they want recommendations for (default is every category the user doesn’t already have)

Product Recommendations: User views product recommendations based on the data they provided for all the categories that were selected on the previous screen. They can filter product category recommendations  by price, view product ratings, and add multiple product images to a single view so they can get an idea of how they will look together

Add to cart: User selects items they want to purchase, which takes them to a checkout screen with product links to Amazon

User profile: Summary view of all the rooms the user has submitted and received recommendations for

Low Fidelity vs High Fidelity

Next steps 

There are several metrics we want to track post-launch:


  • Website visits
  • Impressions
  • Time on page
  • Bounce rate and what screen users typically leave on
  • Percentage of new vs returning users


  • Want to eventually track cost of acquisition 


  • Initially revenue stream will be transition to Amazon affiliate API


  • Customer lifetime value
  • Retention/churn rates
  • Product usage
  • Time it takes users to go from first to final screens


  • User satisfaction
  • Helpfulness

We ultimately want to confirm that we’re reducing the time it currently takes users to find complementary furniture online. We also want to confirm recommendations are helpful.

Our team also has a backlog of future-state deliverables we want to consider, including modifying the designs for mobile, transitioning to the Amazon affiliate API, and adding additional product sources, but want to analyze the post-launch feedback for other possible action items before moving forward.


Product Manager Learnings:

Pat Quinn

  • How to test a hypothesis in order to validate demand
  • How to filter a broad problem down to a solution by incorporating user feedback and technical feasibility
  • That trusting and empowering teammates to perform their role in their own unique way is a core value of high-functioning teams

Designer Learnings:

Olayinka Fadare

  • Learned how to be intentional about constant communication with my team members to ensure we are always in sync.
  • I experienced first-hand the value of team members sharing ideas with each other.
  • Carried out usability tests and used feedback from users on how to improve the product

Developer Learnings:

Roy Anger

  • Developed and shipped an application working in a multi-disciplinary team using agile project management methods
  • Experienced the need to develop features and with the the ideas and needs of the project and the design
  • Learned and experienced properly scoping features and the project for the timelines involved, and addressing how to decide on what features to focus on

Developers Learnings:


Full Team Learning

We learned how to collaborate effectively - taking input from all 3 product functions - in order to devise a user-centric solution for a broad problem. This required us to consider many trade-offs as a team, which ultimately became easier and easier as we were all aligned on the same goal: delivering user value. We also learned how to work efficiently across multiple time-zones and support each other in whatever ways we could.