Individual User Data: The Why Behind Qualitative Research
It is said that knowledge is power. With the advent of the Internet of Everything and our ever increasing ability to track everything and anything to do with user engagement, perhaps it should now be said that data is power. Collecting user data with technology and analytic tools enable us to track the who, what, when, and how of individual user behavior down to the microsecond. With its promise of quantification and objectivity, the allure of user data collection through user research is undeniable. On the other hand, sure, you can track a seemingly infinite number of user data points, but this begs the question, what will you do with all that data?
Individual user data is valuable when it leads you to the important why questions to be pursued through qualitative research. The trends and anomalies in user data can help you identify key questions around user motivation and the underlying factors that drive user behavior. Linking analytics to specific users in a study allows for data visualizations and dashboards on a per user and aggregate basis. The individual user dashboards, in turn, can fuel survey questions, interview questions, and provide ease in correcting any technical issues. They can also serve as a quick visual reference to how participants are performing in the study.
Because of the large amount of data you may be able to collect from your users, it is important to define the key metrics you want to track as well as the goals you are trying to prove out. This will help in focusing efforts and ensure that metrics are meaningful. You’ll want to identify key outcomes that prove out your hypotheses and offer further insight around your product design and feature set.
So, should you go forth and start integrating the collection of individual user data into your user research?
And, if so, what do you have to gain?
- Highly regulated industries, like healthcare or automotive where regulatory bodies impose strict standards of governance.
- Highly technical user testing where users are interacting with a physical product and a digital component together.
- Habit transformation where users are being motivated by the digital product to start, stop, or change a current behavior.
- Early recognition of potential problems with an individual’s technical, user experience, or purpose – is the value proposition actually filling the user’s needs?
- Easier troubleshooting of an individual’s technical issue due to access to specific information around environmental, technical or human factors that may be causing the issue.
- Ability to understand context around data anomalies and patterns specific to individuals that can be applied to the aggregate to determine baseline behavior amongst outlier behavior.
- Participant accountability to ensure they’re properly participating in the study – by notifying participants you will be collecting data for the study, they may be more inclined to comply with the study regulations when their daily life gets in the way.
- Mix of qualitative and quantitative research to drive empathy with data driven decision making at the core.
- Talking points with participants during interviews – asking participants about their experience while reviewing the data that you have can point out that what people say they remember doing is not usually what they actually did.
- Use to make claims with regulatory agencies – back improvements in customers’ behavior with real time user data to make accurate & proven claims for the product.
- Find the delta between what people say they do vs. what they actually do in order to engage the motivational tipping point.
The most prominent area that individualized user data exposes is the tension between what people say they do vs. what they actually do. This is the key to defining what users actually need in a product. We all know that people fib, especially when it comes to things like their health or habits. These untruths aren’t meant to purposefully mislead us; they’re for personal protection or the ideal that people aspire to. However, these little white lies sure can complicate our research! At SI, we’ve found that the capture of user data allows the entire product team visibility into the actual habits of our users and enables us to make decisions based on the knowledge of what our users actually do, and not just build on what users say they want.