Case Study: experience research in Central Park
In this article the author discusses her insights on using mobile ethnography for a diary study as a design researcher. The project revolves around understanding how people navigate around Central Park. However, for this article she is be focusing on how she used this tool for a diary study as opposed to the complete project that includes exploratory, generative and evaluative research.
ExperienceFellow* as a product works for both ends. The participants can record and assess their experiences with a free mobile app while researchers use the web-based software for data analysis.
As a researcher for my project, I set up my goals and expected outcomes from this exercise and recruited my participants. The nature of my research was Exploratory which is generally conducted for a problem that has not been studied more clearly, intended to establish priorities, develop operational definitions and improve the final research design. It helps determine the best research design, data-collection method and selection of subjects.
I conducted this research because I wanted to learn more about navigation patterns and identify pain points (if any) of New Yorkers in a park as big in scale as Central Park.
I started with an observation protocol using the “Outside-In” methodology where I observed : Territory (space, nature, buildings), Stuff (technologies, products), People (behavior, norms, feelings), Talk (conversation, vocabularies).
After these primary observations, I designed the Diary Study on ExperienceFellow*.
Designing Research on ExperienceFellow*
I learnt about ExperienceFellow* during class and decided to explore it further. Ideally for a diary study, ...
... data is self-reported by participants longitudinally — that is, over an extended period of time that can range from a few days to even a month or longer. During the defined reporting period, study participants are asked to keep a diary and log specific information about activities being studied.
Flaherty, Nielson Norman Group
However, since I was time-capped and needed a different kind of data from my Diary Study, I designed an experience based study on ExperienceFellow*.
How does ExperienceFellow* work?
As a researcher, I create the project and configures the mobile app according to the project’s focus. I also recruit/invite participants to the project in order for them to report their experiences.
The participants report their experiences with the exp.fellow* mobile app and report their touchpoints.
Touchpoints consist of a descriptive text, emotional value, images, videos and location. These can be analyzed by the researcher on a ExperienceFellow’s* browser based app. The reported experiences can be visualized, analyzed, tagged, filtered, and exported.
After making an account, I started setting up my project online, it started by asking to set the Project title and description and then went further into granular details like customizing legal terms (which I didn’t use).
Then I went on to customize options for what the participants can see when they use the app from their study. I started with –
The welcome screen – defining what the participants can see on their mobile screen when entering the ExperienceFellow* project. I used it to describe my project and gave clear instructions on what they should document.
Location – GPS data can be tracked automatically for every touchpoint. This makes it possible to see where the participant was located when reporting a specific touchpoint and to follow their movements on a geographical map. I made tracking GPS data mandatory because I wanted to learn about their navigation journey (especially because my tasks revolved around navigation).
Media types – I enabled sharing pictures and videos to see what the participants saw and hear what they heard.
Scale setup – from 1–5 (5 being the highest and 1 being the lowest) to mark their experiences.
Participants profile – I didn’t need a lot of information from my participants so I didn’t collect their name or contact information. However I did end up asking for their ago group and for how long they’ve been in New York (which are the basic screeners for my research).
Once everything was setup, ExperienceFellow* generated this PDF to share with my participants —
My tasks were to start from Point A and end at Point B. I kept the points same for each participant because I wanted to record whether they’ll go off-track and what way they’ll choose to get to point B.
Once my participants were done syncing their data with ExperienceFellow*, I analyzed and came up with findings. ExperienceFellow* offers numerous ways for analysis.
I started by looking at customer journey of each participant — which is automatically generated by the tool and I found it to be super helpful for data collection.
In the context of this tool, Customer Journey is a sequence of touchpoints the participant experiences in the course of completing the assigned task of walking in Central Park. Each touchpoint results in an experience — either positive, negative, or neutral.
This tools enabled me to see the GPS, pictures, and videos are shown with an icon, as well as the satisfaction rating (the colored triangle on the bottom) of the participant. I was also able to see the average satisfaction score, age, gender and the participant’s name along with emotion curve.
I was also able to cumulatively analyze their touchpoints
Finally, since I had GPS tracking enabled, I was able to see exactly where they had their touchpoints and where all did they de-tour and drift away from the assigned path.
I was able to determine what the partipicant added to each touchpoint and at the same time see one or more journeys at the same time to compare their movement. Each touchpoint is displayed in clusters of positive or negative touchpoints give a hint of where can we dig deeper and answer questions like,
“What happens at this place?”
“ What is going wrong and how can you improve it?”
Clicking on a tracking point shows touchpoint details and enables us to observe as to what happened at that touchpoint.
What else I could’ve done?
ExperienceFellow* offers many other ways to understand participant data. The researcher can collapse touchpoints, update perspectives, export data in excel, .csv formats, filter experiences etc.
However, during my research and analyses, I didn’t have to use other features in order to determine my findings. And hence, I will not be discussing them in detail.
This diary study was followed by a series on conversational interviews with the participants. This along with the observations conducted before, I came up with multiple findings, like —
- People generally had problems around navigation when it came to determining their current location and where to go next
- People preferred google maps because of poor signage and there are too many random directions and streets to consider
- Maps in Central Park are cluttered and there are too many people around them
- The landmarks and areas are barely labelled giving the visitors a lot of uncertainty
- The Central Park experience is about ‘getting lost and finding new paths’
ExperienceFellow* is an amazing mobile ethnography tool. I didn’t use it the traditional way but I got amazing results for my research. My favorite features included the effective way to analyze data through cluster based maps and journey maps. I also really liked how thorough the project setup system was. I just wish it could be a little more personalized.
( * ) This case study refers to our mobile ethnography tool called ExperienceFellow which is not available anymore. For further information drop us a line at firstname.lastname@example.org