Research plan for Apart-mate
Apartmate is a cognitive AI experience that aids frequent movers in the tedious process of finding new housing in an uncharted city in a short amount of time. With our groups limited knowledge as to the actual direct users who would benefit from such an app, we needed to figure out the demands of the user to know which housing venue to stay at. From our standpoint as graduate students, the task of having to relocate within a matter of days is an infamous part of the job process. And this is a very similar situation for most workers and families throughout the country. However, before we delved right into designing this app, we needed to figure out where the specific problem within apartment hunting lies, and who is it affecting.
We first started by generating a user frame, which is a really useful tool for narrowing down your idea in terms of end users and elements within your app. By cross listing our speculated audience with our app’s abilities, we noticed that of our speculated user most were not in our target scope, and that when it came time to creating the problem statement we would need to present focus on the users that reaped the most benefit from the app, which by the end of our user frame was: college students, graduates, and frequent movers. Thus, in moving forward our app would be targeted towards these people.
Next we moved on to creating a business frame, which gives us an idea of which preexisting groups would our app interface with, and which features of our app would provide benefit to these groups. When cross referencing external groups with our own app features, the more shared benefits results in more opportunity for income. Originally when performing our first iteration of the business frame it was apparent that our application features were not spanning a majority of all groups. Thus we then brainstormed more applications that our app could implement to span even more groups such as implementing a VR system for users to tour locations before actually going there. We also came up with cost comparison maps for users who wanted to save a lot of money in their stay, and amenity awareness so that businesses would be more satisfied to see that their amenities were advertised more. At this point we had a target user, target groups to interface with, and multiple features our app would implement.
In our problem frame it was a matter of compressing our user frame and business frame together, and by mapping them, we were able to generate the problem. By starting with the target users we know that of the that most if not all would benefit from savings, and that a cost effectiveness mapping was key. While most others can also benefit from our amenity ability in terms of having better value from their housing investments. Then we were finally able to narrow our problem statement down to, understanding the struggles of finding housing on short notice from new graduates, interns, and frequent movers, though a new app and digital service.
The research objective of our group is defined in terms of figuring out the most tedious parts of finding housing on short notice. Also, conduct searches for data in the US that would link certain characteristics to more demand in housing. Given that we would be able to better tailor the app for these specific users.
As for secondary research we will be utilizing data sources such as pew research center to collect, specifically we need a way of relating the population density of certain areas to the amount of hiring that is in the area, this way we can tailor part of our application to business people by providing clear and cost effective housing for them. Thus, links from the US census could provide excellent data in this context. Another secondary research topic is in terms of figuring out the general atmosphere targeted by users in our frame, to which data that provides how housing is affected by social media; an outlet for users to talk about prime locations could prove useful in a learning algorithm to determine the quality or type of atmosphere. Furthermore, with a user that is constantly providing feedback to their own personal aesthetic the cognitive AI gets better at finding suitable locations, which are only as effective as the research we are providing it with.
