Apartment Search Website

Background

A major apartment search website was in the process of redesigning the site and wanted to ensure that the design was based on a solid understanding of their users’ needs.

The wanted to get an understanding of their users and insight into whether there were any distinct user clusters; specifically they wanted to determine what drives users’ decisions around apartment search, what priorities users have when searching for apartments, and the specific search process employed by different users.

The also wanted to determine the best possible categories for organizing different apartment characteristics that users may be interested in sorting and / or filtering search results by as well as appropriate locations for individual characteristics within said categories in order to provide the most intuitive and easy-to-use experience possible for the site.

Process

To determine the appropriate organization for the site’s filter options, I conducted a card sort study with 301 participants using the WebSort online card sorting application. The client had conducted some prior research into common search strings, which were used to generate the cards. After normalizing the card sort data, I used the average linkage cluster analysis functionality within WebSort to help interpret the data.

Contextual interviews were conducted with 24 respondents. Respondents were located in Atlanta, GA; Birmingham, AL; Charlotte, NC; Toronto, ON; and San Diego, CA. During these contextual interviews, I spoke to the respondents about how long they had been searching for an apartment, what tools and services they make use of, what their priorities are when looking for a place to live, etc.  When possible, I also had them walk us through a search for an apartment.

Findings and Recommendations

From the card sort study, I was able to provide recommendations for overall categories for filtering options:

• Pricing
• Location / Distance
• Rooms and Layout
• Community Amenities
• Apartment Features
• Payment and Lease Options
• Nearby Transportation
• Spanish Speaking Apartments
• Accessible Apartments
• Senior Living
• Corporate Apartments
• Military Apartments

User types emerged based on people’s apartment hunting behaviors. These clusters tended to be based on two variables:  their standards and willingness to compromise on them, and their experience and proficiency at the task of searching for an apartment. From this, my team and I were able to build a robust set of user types.