GetAgent is a new startup that makes choosing an estate agent really easy. Essentially, the services provides access to estate agent performance data so that users can see which agents perform better than others.
I was asked by the founders to review the current iteration of the website and it’s usage and look for ways to improve the user experience and the value of the product. This boiled down to two main things:
The data is the centrepiece of the service. The data is also complex and open to interpretation, so we needed to find ways to make the data useful and simple at the same time.
Currently the site uses a 5-star rating system to indicate those agent who do better than others. The problem with this is that 1) there is little granularity as so many agents get 4- and 5-star ratings and 2) there is no indication of how that rating is achieved. Indeed, different users have different priorities, and this calls for a more dynamic scoring system which would take into consideration the priorities of each user so that estate agent recommendations are much more useful.
A dynamic scoring system is about how we interpret the data on behalf of the user. For example, if estate agent may be shown to take a long time to sell a property. This could be bad for sellers who need to sell fast and don’t care so much about the final selling price. But for user’s who aren’t in a hurry, a slow agent could be good if the house sells for their original asking price.
To solve problems these problems, we need to find out from each user what their priorities are. This would allow us to tailor the data and the scoring system so that estate agents who are a better match score higher than those who aren’t. The website is already doing that, it’s just a matter of asking these questions at just the right time during the user’s visit on the site.
A very effective way to tailor the data for users is to detect their location right off the bat. I proposed that using services like GeoIP and HTML5 Geolocation API we could immediately increase the value of the data. A user who sees a list of estate agents for the whole of the UK won’t be as engaged as a user who sees a list of agents who are clearly just down the road from where they live. Making the data more relevant is critical to getting users stuck in.
The fundamental premise of this service is that it enables people to use comparisons to find the best agents. It should therefore be a guiding principle that any data provided should be comparative, that it should enable the user to reach some kind of conclusion between two or more agents. So wherever there is data, it should not relate to only one agent. This principle is adhered to, for example, on the estate agent profile page, where the extra “average for all postcodes” dimension pits the agent's performance against all agents for that postcode so that the user can get a sense of relative performance.
Now add some code and we have a basic prototype:
But for now it’s not without the following issues:
Well there aren’t any just yet. These are early design explorations are scheduled to go into development in March 2015. Analytics has been set up to monitor the effectiveness of the new user journeys and site structure and there will be further iterations as results come in and as we dive into ethnographic research.