Learnings and tools for customer experience design
ExperienceFellow

The researcher’s perspective on ExperienceFellow

7. April 2017

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The researcher’s perspective on ExperienceFellow

Here comes an introduction of the possibilities a researcher has when using Experience Fellow as a tool for mobile ethnography.

This short video digs into the details of what you as a researcher can do with ExperienceFellow. It shows how to analyze the data once you get it with tags, perspectives, filters and how to share the results with others.

Also check out a video on the participants’ perspective.

Transcript

Hi there, I’m Marc, the co-founder of EF. Today we would like to show you in a brief video the researcher’s perspective of EF. If you’re interested how participants of EF go through the process, there are other videos on that, we put a link down there. But in this video we only focus on the researcher perspective. 

So, in general EF is a mobile ethnography app where you can set up a project and you can invite real participants, real users, real customers to document their experience step by step using their own smartphone. It’s a simple app, it works more or less like a diary study on their phone. 

You get the data in real time, visualized as customer journey maps. And then you have different tools to work with the data, to analyze and to export it. And this is what we’re going to show you right now. 

So what you see now is the overview of a project in EF. A project in EF goes through different stages. See that right here, you have a little walkthrough. Currently were in the stage of collecting data. Before that you need to set up a project, there are many ways how you can customize a project. At some point you can stop collecting data and you can archive a project. 

You invite participants simply through a QR-Code. So you can include this QR code into an e-mail, into a website into a paper based invitation. They scan this QR code with the app and can start documenting the data right away. And this is how it looks like as soon as data comes in. So what you see here is the sequence of steps your participant really went through. It’s their experience, kind of a diary study step by step. You see immediately what is positive, and what is negative. 

And now you can zoom into each of these steps. Take a look what they actually wrote, their evaluations have date and time is associated with each step. You can take a look at their photos and videos. So this example project we anonymized some their photos, that’s why we put smileys on it. But it really is a qualitative approach to understand their experience step by step from their perspective. 

What you can do as well, is you can create different perspectives. So different researchers can work with different perspectives of a data. It is a complete copy of your data set. And within this perspective you can actually work with the data, so you can change the data. What you can do then to compare i.e. two experiences from different people is you can add gaps to it. Or you can actually change the sequence. It works like digital post-it notes. 

What it can do as well is you can tag them, so you can assign certain code words to it. So you can filter it later on. You can also add comments to it, so you can have a conversation with different researchers to it. The idea behind that is to allow you when you work in a project researcher triangulation. So to reduce subjectivity and to get a more holistic perspective on it. 

You see these little blue icons here? The tag flag and the comment flag. And then you see what kind of data is associated with the step. So is it a photo, is it text, is there GPS data with it? So you actually see a little map where you know exactly where they documented it. So this icon is for videos, so you can just watch the videos within the app. Just click on play, you can enlarge it if you like, to have a look what somebody actually recorded. 

So here somebody was playing the piano, obviously within the main station of Amsterdam. 

So what can you do with the data once you tagged it? Well you can filter. You can for example filter for certain emotions like show me everything negative in this project. Now you see on the left hand side only those touchpoints or steps within the journey are highlighted, which are negative. 

Now we can combine this filter as well. For example show me everything negative related to the tag ticketing. Apply filter. Now we see those steps who have a negative experience related to ticketing on public transport. You see some statistics up here, 5 out of 118 touchpoints in how many journeys. Through that you get a really good feeling for your data. It allows you to identify patterns in your data set. 

We are currently working on a new overview once you tag data. We call this overview. Where it actually splits up your data. We’re getting into a mix now of qualitative and quantitative research. For example you can filter by different tags and split it by emotion. So immediately you see that certain experiences like the tag walking is rather negatively commented, while others are rather positive lie being on the tram. 

Through that you identify patterns really really easily. But it is actually a qualitative approach so you can always go into the detail and understand why this actually was a negative experience. Take a look at the photos, videos and text they uploaded. Since we also have the GPS position we can also take a look on the data on the map. So this is Amsterdam, if I show all the data now layered on top of each other you can actually identify positive and negative clusters as well. So if we zoom in, to where we started our workshop, we see a rather diverse starting point. We have some positive experiences, where somebody actually discovered busses. Juhe 😀 

And you find a few negative ones. Some are quite often negative when it comes to orientation. On which side does the tram leave. 

If you dig in to the data a little bit more you can find clusters of positive and negative experiences so if you zoom into the central station you probably find some positive experiences related to a certain area and some negative experiences related to a certain area. So again, this helps you to identify patterns within your research data. 

When it comes to exporting your data we have different options. So on the one hand you can create a PDF, a long format. So this is something you plot out, you use in your workshop, you hang up on the wall. It really changes also how you talk about research if you bring real journey maps on real customers. Print it out and hang them on the wall. So I really recommend to try that compared to like a summary and a powerpoint presentation. 

If you don’t have a large format printer, plotter, we also offer an A4 page export, you can export an Excel file if you like to progress the data in a different software. Or you can also create a complete ZIP file of your data that includes all the photos, videos and the Excel file so you can use the data as input for any talks you do or any further reports you create. So, this was EF in a really brief nutshell.

Marc is co-founder of More than Metrics, and editor and co-author of the award-winning books This is Service Design Thinking and This is Service Design Doing. He regularly gives talks and workshops on service design and innovation, and teaches at various business and design schools.
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