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We’re planning to post articles about our learning journey at Hyper Island, as well as anything data related. In the first two posts we will talk about our first client project called ‘Qualitative & Quantitative Data’. If you are interested in knowing more about the Hyper way, we are here to give you some of our insights.
Our first brief came from Hedvig (a Swedish insurance company). We were randomly divided into teams of five and were given five weeks to complete the task. The brief was to gather and analyse qualitative and quantitative data, to later come up with digital solutions to the task statement. Here are some key questions regarding the project answered by Data Analyst students: Tzu-Ling Li & Beata Wotoch. The focus of this post is to explain more about the tools and methods applied throughout the project.
Tzu-Ling:
“I think that working on a group project is a perfect way to experiment with creative methodologies, such as Double Diamond, ICE score, as well as useful digital tools, for instance Miro board for collaborating and visualising the work in progress.
One of the most important methods we applied in our project was the ‘lean startup’. The concept was introduced during the lecture with Seif Fendukly, Product Manager at Storytel. ‘Lean startup’ is a data-driven process which consists of four steps — ideate, test, analyze and implement.
After we were done with collecting and analyzing the data, we started to brainstorm and put our creative solutions with digital post-its on the Miro board. Once we had good amounts of ideas we used the ‘Impact Effort Matrix’ on Miro Board to converge them. We categorized the ideas into four groups to help us prioritize the most feasible solutions. In the “test step”, we built two versions of prototypes with Figma and ran our own A/B testing to actually see how the users would react to the different versions. In “analyze and implement steps”, my team gathered again and made adjustments in accordance with the user feedback. ‘Lean startup’ process helped us to improve and create our final proposal in a more data-driven way.”
Beata:
“During this module we were extremely fortunate to have been offered lectures from 14 different speakers, who introduced us to a range of different workframes and methodologies that we then could directly apply in our work.
One of the methodologies that helped my team to come up with solid solutions was I.A.R. formula: Insight, Action, Result. The formula was introduced just at the right time, when we had already gathered and analysed a fair amount of quantitative and qualitative data to derive insights from.
The way we applied the formula and organised our findings was very straight-forward. We created a shared Google Sheets document with connected insights, actions and results. Once all the team members had contributed we then collectively decided on the best solutions that we wanted to apply in the deliverable. This was a great way to make predictions based on data insights.”
Tzu-Ling:
“We were introduced to the definition of Quantitative data and the tools that go along with it, such as Google Analytics (GA), Mixpanel, Google Trends, and got a taste of what real-world applications are used everyday.
In the lectures, I learned that quantitative data is a good way to find out ‘what is happening’, providing us with facts. In this project, we were given access to the client’s GA account which I really appreciated, as this gave us the opportunity to gain useful insights with real data. I already had a basic understanding of GA gained at my previous workplace, but this time around I was able to dig deeper. I decided to take some of the free courses on GA offered by Google, and our project became much more intriguing and interesting as anything learnt could be directly applied onto reality.
At first all team members were just exploring individually the existing reports in GA. After a while, we realized that a more efficient way to interact with the tool is to ask the questions first.It could be something specific like which device brings more traffic to the website, or what’s the average age of the users. Once we knew what we wanted to ask, we went back to GA to try and find the answers.
Whilst trying to figure out more about the customer data, we came to realize that the information wasn’t complete. I decided to take this opportunity and create custom reports, this way I could showcase otherwise “hidden” data.”
Beata:
“In this project I learned how to collect quantitative data from surveys, though they can be used for gathering qualitative data as well, depending on the format. It’s been a while since I conducted any type of survey, the last time being during my bachelor thesis, and that survey was qualitative with open-end questions and space for reflection.
Even though we had access to our client’s Google Analytics, we decided to create our own survey in Google Forms. The main value we saw in collecting independent data was to complement our insights from GA, which only show part of the picture. We picked the most relevant questions and sent out the survey to friends and family. Ultimately, we managed to gather over 150 responses, which was an incredible result!
Google Forms is a very simple yet powerful tool for gathering insights that might otherwise be tricky to measure. When analysing the data we could immediately see clear patterns that shaped the rest of our project.”
Tzu-Ling:
“Regarding qualitative data, we were introduced to different methods and tools, such as interviews, surveys or Hotjar.
Before we had the lecture on how to execute the user research, we already finished our first usability tests with 14 individuals. It was a super interesting process to learn by doing. We figured out a practical way to do the user research, for example defining the scope and the purpose of the interview, dealing with technical limitations when it comes to interviewing, and listing guiding questions to make sure the interviews would cover the most important insights. After conducting the first interview, we adjusted some parts of the process to make it more structured, for example providing an introduction and interview consent notification before the interview started.
Later on, when we had our lecture about the user research, I found it really useful as it proved very similar to what we have done in our team, validating our reasoning. I think it was also an excellent learning opportunity to first experiment ourselves, and then receive the lecture to reflect on our own process. This way, we were able to have a more concrete understanding and a clear overview of the good parts and those that could be improved.”
Beata:
“Towards the end of our project, we had two prototypes created in Figma, that we believed held many solutions to the brief. During this module, we learned that A/B testing was a very common method for validating new ideas, thus we decided to put our two prototypes to the test. The process was a little bit similar to the user research that Tzu-Ling already talked about. Each team member asked two people to test out one of the prototypes. On Google Meet, the user would share their screen and think out-loud while interacting with the prototype. After each session, we asked follow-up questions that would help us assess how effective the solutions implemented in the given prototype were. It was a real fun experience to hear what “outsiders” had to say about our two labours of love! It truly worked magic for us, and by the end of our A/B testing research it was clear to us which way to go. A few more adjustments and our final prototype would be ready to shine! :)”
Tzu-Ling:
“I really appreciate all the lectures we were given and the chance to learn from industry leaders in product development, user experience and marketing fields. Among all, I found the user research course given by Simon Karlsson, Product Manager at HighCohesion, really useful and inspiring!
Before I joined Hyper Island, my definition of data analysis was quite narrow, merely focusing on ‘quantitative data’. However, after this lecture I realized that qualitative data is also really important when it comes to answering the ‘why’ behind the facts. It’s through the user research that we’re able to know more about the possible, important yet hard-to-detect factors that determine the user behaviors.
During this lecture, we were also introduced to the tips on how to design the user research process, what are the possible ways to do this, what kinds of questions to ask/ or not to ask and how to structure the framework of the interviews.”
Beata:
“I truly enjoyed and felt inspired by all our lectures in this module. If I really have to choose just one of them, then I think I will need to go with the lecture about the designer’s role in shaping solutions with impact. The lecture was given to us by a product designer at Spotify, Helena Stening. Among many other things, we learned about different levels of fidelity when presenting and visualising ideas. Depending on what stage in the project you’re at, you can choose to present your ideas in a form of wireframes, mockups or prototypes.
During a big chunk of the lecture, we got our hands dirty making prototypes in Figma. It was a really good introduction on how to use and apply prototyping tools in projects. I never considered myself to be aesthetically inclined, but I learned that you don’t need to be an artist to design prototypes! That was a real revelation to me, and it also inspired me to spend a fair amount of time during the project producing one of our prototypes. After receiving much support from my more experienced teammates, I now feel fairly confident in designing basic prototypes. I’m sure this will prove useful in my future endeavours.”
