I worked with two other UX researchers in this project.
We all collaborated on the survey and card sorting design as well as data analysis.
We worked closely with two product/account managers from Quicken Loans on this project.
Greta Hillburn, UX Researcher
Stephen Nisbet, UX Researcher
With recent data breaches, new regulatory changes, and user expectations related to data privacy concerns, people are becoming more protective of their data.
Quicken Loans is looking to develop strategies to educate and protect client data and enhance their overall client experience to eventually design a digital wallet for the clients.
Through a survey and card sorting, we were able to uncover people's behaviors regarding their data, their expectations about how companies should handle their data, and the sensitivity of their data.
We helped Quicken Loans on the foundational research for their UX strategy to create a digital wallet. In doing so, Quicken Loans can leverage the findings to look into the social affect of owning your own data, being in control of your data, and being compensated for your data and lead the market for data ownership.
But how did we get there?
Digital wallet solutions have hugely impacted the finance sector, and it is expected that it may soon become the default mode of payments in the near future.
One major category of the current data market is the “peer-to-peer model”. This model involves second owners of data benefiting from consuming and trading data while the first owners, or users who produced the data in the first place, receive no benefits.
The increased risks of data breaches, changes in data policy, and misuse of data by companies have shaped some attitudes and behaviors regarding online data sharing. Quicken Loans seeks to protect client data and enhance their overall client experience. In order to do this, they want to better understand the area surrounding data sensitivity, data ownership, and people’s privacy behaviors and attitudes regarding their data.
Questions we wanted to answer -
A mix-method approach gave us both depth and breadth regarding people’s opinions on data sensitivity, data ownership, and data compensation.
Our project used a mixed-methods approach where we conducted both quantitative (survey, card sorting) and qualitative (semi-structured interviews) research. Data collection was sequential, where collected quantitative data first. The data collected from the quantitative method informed some parts of the card sorting activities and interviews.
NOTE: Due to COVID-19, we combined the card sorting activity and interview together, where we interviewed the same participants that participated in our card sorting activity. This did not affect any major results in our findings.
Our objective with the survey was to quantitatively identify the spectrum of data sensitivity based on people’s reactions to being compensated and to measure which data protection strategies they employ the most.
Using a survey as our first instrument allowed us to capture a large number of people and gather a wide-range of responses to give us a broader overview of the concept of data sensitivity than a smaller-scale interview study would have.
We wanted to answer the following questions:
We conducted semi-structured interview and card sorting activities to explore people’s behaviors and motivations about data ownership and compensation more concretely.
Participants arranged cards in the level of sensitivity (high, medium, low) in three scenarios 1) data sensitivity for when you are being compensated, and 2) data sensitivity when a company is a victim of a data breach.
Afterwards, we asked them a few questions regarding why they sorted the cards in the way they did as well as questions regarding data ownership in general such as
While most of the participants indicated they would feel comfortable sharing their data for data compensation, there were also many people who indicated that they were uncomfortable.
Indeed, most advocated for transparency and control in a system where they feel taken advantage of. People want companies to be more upfront in regards to how their data would be used before they parted with it, and be able to see how it was being used after it had already been collected. People also want to be able to pick and choose what data can be collected on them as some people are more sensitive about certain pieces of information than others.
“Grant granular permission for how it is used and how it can be sold and what information i make available.” - Participant 08
People generally feel the same sensitivity of certain data points or personal information, whether that data was being accessed via a data breach or if it was being handed over voluntarily for compensation.
Ultimately, there are a lot of different areas of concern that people have with the security of their data online and data breaches are just a single piece of the puzzle.
Many participants expressed that they assumed that companies used their data but expectations essentially pointed to that they were aware that their information was being sold but wanted to know to who in specific. In addition, they believed that they have own the data that they provide to companies.
"My expectation is that a company is clear about what they do with the data, in particular if they are selling data and who they are selling that data to." - Participant 01
The data submitted by the participants suggests that most people would like to have more clear indicators on who uses or will use their data. Transparency and honesty were most noted in the survey answers given.
COVID-19 altered our original plan with having in-person card sorting and interviews. Our team had to quickly learn OptimalSort (remote card sorting software) while trying to recruit for participants online. In the end, we were able to successfully transition the study online and were able to complete the project!
Using both quantitative and qualitative methods in order to showcase people's behaviors was really powerful. We not only were able to show a map of the range of sensitivity of how people perceive their data, we were able to supplement that with actual responses from our participants. This brought depth and breadth of our data and recommendations.
The survey analysis doesn't stop here. It would be interesting to use more inferential statistics in order to help Quicken Loans with how they can approach their project roadmap to begin implementing policies, educational material, and overall user experience of their digital wallet. We leave this for future work.