Walking in the shoes of our users
We have recently been testing various aspects of our Go Jauntly app with members of the public. For those of you who don’t know about Go Jauntly check out gojauntly.com and for those in a hurry, Go Jauntly is a walking app which helps people discover walking routes, create their own and share outdoors adventures.
The app recently won a Civic Innovation Challenge in partnership with the Greater London Authority and the Mayor of London. We won the Active Travel category which was sponsored by Transport for London. As part of the challenge, we were lucky enough to gain access to Talk London and their excellent team, they are an online community for Londoners to have their say on issues affecting them.
As part of the challenge we introduced new features to the app that focused on travelling in and around London. These new features were formed around three key design hypotheses. The first was all around the potential for more commuter journeys to be walked or partly walked, the second looked at opportunities to improve the ‘walking for leisure’ experience of Londoners and the third was around how much we could learn more about the walking habits of Londoners.
How we tested our Hypotheses
Over the course of two months we attempted to prove or disprove our hypotheses, we surveyed 1437 Londoners via Talk London on their relationship with walking. We carried out two incentivised pilot studies on London walking content and a new station-to-station walking map. We ran in-app opinion polls to garner mood and share-ability, we measured walk activity and carried out analysis of event tracking data.
In order to test our assumptions we created a set of experiments, each one provided us with measurable qualitative and/or quantitative feedback. Metrics such as engagement, active devices, events actioned, survey responses and changes in behaviour were measured.
We managed our data analysis using three tools, Firebase, Google’s Big Query and the App Store’s own analytics. For our qualitative evidence we used the GLA’s own survey tools as well as in app opinion polls.
We added new guided photo routes to the app by mapping the Capital Ring and the Jubilee Walkway, we also integrated a new station to station walking map for London, inspired by this TfL walking map. We then incorporated a new in app opinion poll feature which asked users about how they felt before and after a walk. We then recorded how far users were managing to walk through a route.
Throughout the test period we also monitored session duration and time, walk step views, map feature use, most popular walks and general location session information. All data gathered is stored anonymised.
Through this activity and for the first time, we were able to build up a rich picture of how our app is used and why people use it. Below are 5 things we learnt through our research:
Top 5 findings:
Londoner’s love walking but don’t do it enough
Londoners love nature and walking, but almost a quarter only visit a park or green space in their local area once a month or less. For those who tested the app, using image-led routes made people feel more connected to nature and wildlife than a map alone and trees and views were the favourite aspects of people’s Go Jauntly walks.
Wayfinding & Inspo
Go Jauntly is used as a navigational aid as well as a tool for inspiration. Through monitoring which walks were actually walked versus which walks are simply ‘swiped through’ we discovered two very clear usage patterns. Walks were being viewed in large numbers by users at times when they were not outdoors on a walk. We also saw behaviours around people saving and sharing walks present during this inspiration seeking activity. The navigational use case where people are actually on-location following in-app directions was also popular with users and demonstrated exactly how one use case follows on from the other.
Popular with commuters and leisure walkers alike
We found peaks and troughs of usage in London, with different patterns emerging during weekdays and weekends. Weekday commuter times were noticeably different from weekends with a clear smooth increase from the early hours to mid morning and then more clear peaks at lunch and the during the commute home. Sustained usage was also clear until bedtime. At the weekend usage peaks a lot later in the morning inline with less defined peaks meaning usage remained constant.
Walking makes people happy
Londoners who spend time in green space are more likely to consider themselves very happy. Older people are more likely to walk for fun and consider themselves more active, however Millenials and Gen Y are the least happy group in London. This group also need the most motivation to get outside. This finding was particularly important as our biggest audience group are 25-34 year olds, meaning we are in prime position to address this need.
Partnerships on this scale are relatively new to Go Jauntly, so we wanted to test the impact such a relationship could have on our user numbers and their behaviour. Our partnerships have allowed us to understand what does and doesn’t resonate with our users, shape future partnerships and really hone our partnership proposition. In return for TfL sharing their open data with us we ensured anonymised app data was shared. We were able to let TfL know which stages of the Capital Ring and Jubilee Walkway were most frequently walked and which were generating the most views in app. We ran analysis to see which walk attributes make for a popular walk and compare how Londoners preferences differ from the rest of the UK.
As well learning lots, we loved running these experiments for three reasons:
It was with real people
The Go Jauntly app has broad appeal, so reaching a cross-section of Londoners was quite simple thanks to our partnership with Talk London. Using their panel of 40,000 Londoners we were able to aim at specific groups inline with our target audience, this meant that we did not waste time testing with unrepresentative participants.
It was remote and unmoderated
Our participants completed their assigned tasks at their own pace at a time that suited them. They were invited to choose a location of their choosing to go walking, so interruptions to their normal routine were kept to a minimum. This meant the testing results started to come back in faster than other testing methods and we hope, more reflective of real life events.
We combined multiple data sources
Combining the what and the why has helped us gain a clear picture of our user behaviours and their motivations behind them. Patterns emerged that were only discoverable through comparing data sets and customer feedback.
Furthermore runs this type of testing for our clients too! Please get in touch if you have an idea that needs testing or an hypothesis that needs validating.