Alpaca was a tech start-up with the goal of disrupting the appartment rental space. Over its lifetime we helped over 8 million roommates, renters, and landlords through the process of finding a home. While I had a host of projects and responsibilities as the sole product designer, on this page I want to focus on how I built our first product.
Update: Alpaca was acquired by boompay in 2021.
Summary
During our time at the MIT delta v accelerator we decided to explore three distinct product ideas to help renters find appartments. With the help of user interviews and user testings, I designed prototypes that helped us decide which path we wanted to pursue.
Background
Alpaca Inc owns the biggest rental and apartment groups on Facebook with more than 8 million users and 400 groups. During our research, we found that a lot of users struggle to find apartments and that’s when we thought we could help them by solving one of their biggest pain points and making the apartment hunt an easier and safer process.
Research
We started with our research in NYC, where we visited around 30 apartments and went through the process of finding an apartment, reaching out to our Facebook group users and learning about their struggles during our focus groups and one-on-ones with agents and renters.
Prototypes
Using the interviews and information we’ve gathered during research, I designed three products that resolved three main pain points from renters and agents:
Chatbot + Webviews: Increase transparancy for renter's by showing them all the relevant information about an apartment. The process starts via a chatbot that is connected to our Facebook pages thereby allowing for a seamless onboarding for our users.
Live showings: Save time by visiting the apartment online where you can also ask the agents questions.
Collaboration tool: A perfectly organized tool with the relevant information of all the apartments visited - users can share this tool with their partner and comment or highlight the apartments they liked best.
Conclusion
After 4 weeks of testing MVPs of the live showing and chatbot prototype, we found that the Chatbot was the most successful given the amount of interaction, viewings, and applications collected. We also analyzed the chatbot conversations and gathered more information through another round of user interviews to improve the existing user flow and experience, by decreasing the time invested when providing search preferences, misunderstandings by our chatbot, and a more aesthetic look.
