Describe you experience of creating personas from different users’ perspectives gathered in the interview data. Was there enough commonality between the 4 people interviewed to form a coherent persona? Or did it make more sense to create a second different persona?
Commonalities. I found it very fascinating uncovering similarities we have as a group as we answered questions about a specific topic. Coming from different nations, specially, makes it a lot more eye opening and entertaining. This activity has yet again shattered a few mindsets I had, uncovered surprising information, brought down walls, too, and has yet again recalibrated my mind further to see things from a different and fresher perspective.
The topic we chose is about our experience in catching and using public transportation. 3 of us often use trains and buses, while 1 mostly walks or uses her bicycle, and only catches the bus on certain occasions. Reviewing our data, we found out that while we have a lot in common, we still have varying factors that makes it difficult to actually combine everything into a single, coherent persona. We had 2 distinct groupings in both the demographic and behavioural variables, but crosschecking these groupings, we only came up with 2 people who actually share very similar variables, so we combined these two to form one persona. For the remaining 2 persons in the group, we didn’t find enough data to actually combine them or create separate personas out of them. We can probably create two more but then to make a persona out of a single person’s data just doesn’t seem too reliable.
Do you think your final persona(s) was successful in generating empathy with users?
However, I find that the final persona we come up with strongly represents the commonalities we found in our group. It empathises well with the main characteristics of the group, and I think that despite the differences of the other 2 persons who were not included in the persona, they are still closely tied to the common variables identified that disregarding the differences seems justified. But of course, if more data could be gathered, and we find strong reasons to actually highlight these differences, then it can become completely logical to create separate personas for the differences cited.
What would you change to make it better?
To improve the results of this activity, I think it would help to have more participants in order to have more data to work with, and stronger basis for the results. It’s nice that we don’t have a specific goal or topic we’re working on as it keeps us from having a bias and working out the data towards results that would favour our needs. But it would still definitely be of great importance to ensure that the variables we are measuring are actually relevant to the topic.
Week 2. This particular tutorial session reminds me of how it was to actually interview users and catch their insights on existing processes, their frustrations, their needs, and their ideal or suggested solutions to solve issues they’re currently facing. And I often, if not always, find it interesting to see people’s thoughts unfold before me. Same goes with reading interview transcripts, apparently. I wasn’t able to finish the whole material though. If only I were a faster reader. So it’s good that completing it isn’t necessarily the goal of this activity.
1. How did this exercise help you build empathy with prospective users?
Taught me to listen despite having preconceived notions.
Reading through the material, I found myself engrossed in the subject at hand, and the thought processes of the interviewee. It allowed me to see all the trouble she had to go through just to make things work. I felt her frustrations, learned from the strategies she had to use to solve her/their problems, agreed with some of the solutions she envisioned, and had better ideas for others. In some sense, it allowed me to be her in this particular scenario. It showed me how it is to experience interacting with the system given her specific circumstances. We often have certain mindsets when dealing with issues we also encounter ourselves so this is a great exercise to help us break those mindsets, go beyond ourselves, and add to our knowledge as we see how reactions and experiences can change depending on a person’s background, needs, and motivations, among other factors.
Reinforces ideas and information gathered.
Highlighting the interviewee’s interests, needs, frustrations and motivations, on the other hand, helped reinforce in me the information I just gathered. Being easily engrossed in stories and mostly focusing my energy in understanding and connecting with the storyteller, I tend to lose track of the details and only retain the emotions that flowed during the conversation. So while the interview process taught me to feel the situation with the interviewee, highlighting helped keep my mind there as well.
Helped me focus on the user, more than anything else.
Highlighting also allowed me to track the important details, in raw format. That is, as mentioned by the interviewee, and not as how I perceived it. I think that it’s important to keep a record of these things and to go back to them every now and then, to ensure that our focus is still on the actual users, and not on ourselves as designers. I, in particular, tend to inject my thoughts into what I’m working on such that it becomes more about me and my design, instead of primarily being about the user’s actual needs, so this is something I should always remind myself of to keep me focused on the prospective users.
2. How did the clustering of information help you understand user needs?
Clustering of information helped me see how the data gathered can be used to highlight key needs by eliminating redundancies, breaking preconceived notions, and creating unforeseen connections. It helped us identify which data made more sense when put together, and see how certain information can seem to belong to one category but is actually part of another.
Studying the data and their connections also helped us understand the user’s actual needs versus their perceived needs. This is very important as people can sometimes say one thing but actually mean another thing, and the truth only surfaces when we observe more how often they say something and the context in which they use them.
With the number of times we moved post-its around, clustering also made me realise that things aren’t always as simple as how we see them, or sometimes also not as complicated as we think them to be. Perspectives, ideas, and strategies can change when we actually see things in front of us, instead of just processing them all in our head. It helped us assess our data well and ask questions we wouldn’t have asked, allowing us to better create more defined and specific groupings. And I think that the quality of these groupings, that is, the quality of the needs we’ve identified, will greatly affect the efficiency and effectivity of our proposed solutions later on.
3. What was difficult or challenging with the technique? How would you do it better next time?
I found the highlighting part particularly difficult for 2 reasons: first, I tend to get too involved in the discussion that I forget my primary goal, which is to capture accurate information that I can later work on. This is good in the sense that I get to really empathise with the user, but also bad as my thoughts get drowned in the process, losing track of the details and even potentially forming a bias. I think it would be ideal to have a good grasp of what the user is saying, and not saying, while still maintaining distance and his/her role as interviewer and designer. Secondly, I also found it challenging determining which points should be highlighted. It’s just that I ended up highlighting a lot of things, and while I thought they were valid, it made me question if I was doing it right. I think this can be developed by practicing more though.
For the clustering part, the process of grouping itself was difficult due to the nature of the information we gathered. We thought that they could actually be grouped in a lot of different ways, so it was a challenge deciding which grouping to go for. We realised, however, that even the information we gathered can actually be broken down further in order to make groupings that made more sense, and that were more specific. And this can be solved by improving the data gathered from step 1, ensuring that they are simple and contain only 1 point. It was also difficult deciding how to name the clusters we made as we tend to jump directly to solutions we envision, eg. “I want a good GPS application,” when in fact, what the user needs is a good way to get around, and this is not necessarily addressable only by a GPS application, and it’s possible that it’s also not the best solution. This phenomenon in particular limits one’s creativity and opportunities for innovation. So I think it is best to avoid focusing on already existing technologies in this stage.
Also, as we obviously didn’t finish our diagram, it was certainly a challenge for us working fast and coming up with groupings we can all agree with, and on deciding on the level of specificity we should go for. But we did learn that we really should go for specific groupings, and I think the groupings we came up with were pretty good, so with that realisation plus more practices, I think we’ll eventually get the hang of it.