We are in the business of creating digital experiences; and the digital assets we create need to be engaging to be valuable and exciting to the end user.
But measuring customer engagement has not been easy.
Until now. We have developed a new protocol for user testing that gives us increased data accuracy on human emotion during UX testing, and it’s helping us create better user experiences than ever before.
A Non-Intrusive Way to Measure User Engagement
With traditional UX testing, we measured primarily on the execution of a task. How long did it take? Was the user confused? Did they successfully complete the task? We would ask our user testers, “Were you engaged?” They would tell us “yes” or “no”, but we had no way of knowing if they were telling the truth. (After all, we’re paying them to do the user testing. Nobody wants to offend anybody!)
From our experience, using facial recognition to do this, we know that some people smile when they’re nervous, and others don’t even smile when they’re happy. The data can come out very biased.
By introducing a non-invasive bracelet that measures heart rate variability, we can now precisely track how our user test subjects are feeling while performing tasks, without asking them.
We didn’t do this alone. Together in a partnership with neuroscientists and researchers from Sensaura, a group of experts in multi-modal biosignal emotion recognition, we have been developing a protocol for UX testing with heart rate variability (HRV) emotion tracking. This technology gives us more scientific data on user engagement so we no longer have to rely on self-reported data to measure it.
This is a game-changer.
The KPI’s of Emotion UX Testing
There are 3 main emotion KPI’s we measure:
Engagement – the level of interest the user has in what they are doing. You can be excited and engaged. You can be happy and engaged. You can even be relaxed and engaged, - in all cases, it means you are interested and focused on what you are doing.
Boredom – this is the opposite of engaged. Boredom is a strong indicator that a user is disengaged. Was the user falling asleep?
Frustration - Frustration is a on a spectrum of stress and anger. You can be stressed, but still engaged, but if that stress turns to frustration it can be a negative. If a task makes a user become angry, it definitely needs to be assessed. Pinpointing when a user becomes frustrated or stressed is incredibly valuable for user testing, and improving on a user experience.
Emotion measurement is contextual. If you are testing a challenging game, you want to see a little bit of stress, maybe even a bit of frustration.
In user testing, 99% of the time, stress is less of an engagement, and more of a frustration. Stress can quickly turn to frustration and anger if it’s the wrong kind of stress.
We really don’t want that!
Real-time Emotion Tracking: Not a Lie Detector
The most frequent question we have about this technology, is that it sounds like a lie detector test.
It’s not a lie detector test. A lie detector measures blood pressure, pulse, respiration, and skin conductivity, and looks for changes in breathing rate, pulse, perspiration and blood pressure to indicate if a person is lying.
A lie detector test does not look at emotions.
We track emotions using a protocol that has been developed in partnership with researchers and neuroscientists focused squarely on emotion tracking – not lie detection.
During user testing, we take a subject’s heart rate, and we track the variability pattern of their heart rate as they are doing tasks in real-time.
Heart rate information helps us differentiate between positive and negative emotions of a user’s experience. Are they happy or angry? Heart rate variability (the oscillation of a heart rate) to identify the mental effort and stress level of our users helps us determine if they excited or bored.
Then, using time-frequency analysis of heart rate variability, and correlating various biometric measurements with a large database of subjects, we extract emotional cues in real time and plot them on an X / Y graph with 8 emotional quadrants.
The 8 Emotional Quadrants:
This gives us information on how excited (or engaged) a user is, and whether the excitement is positive or negative. Our tests show the data is highly accurate, and takes 100% of the bias out of user testing.
Users cannot hide their emotions when we track them in this way.
What makes your users happy?
The data we collect on emotion is extremely valuable.
We are finding this technology can be used to test a wide range of customer experiences, in order to better understand what makes customers happy, and how we can deliver better experiences.
Take the example of a vacation web site. Using traditional testing, we might just ask a user to perform a task during user testing, for example, to book a vacation. In the user testing, they book the vacation = task completed.
But how do you know the process was engaging? How about trying the competitor’s vacation booking process. How was the user’s emotion different?
We think that when you go book your vacation, you should be excited. So when we test your emotions, if you’re calm, there might be some room to improve, or maybe there’s something wrong.
We are currently creating experiences with new technologies, including experiences with VR technology. It’s invaluable for us to test emotions before putting these products on the market, so we know if these experiences are getting users excited or not.
Remember, with this technology, context is very important. Excited is not always what we are aiming for. Calm and concentrated can be good. If a user is doing an online course, or learning something, they might have a better experience if theyare calm and concentrated, so they can learn—but it’s important they are not bored or sleepy, or frustrated.
This is a real game changer. We can now test how people feel before we put a digital asset on the market. We can use it to test prototypes, and equally interesting, we can use it to test competitors.
Engagement is an incredibly important KPI when it comes to creating digital experiences, and I’m excited to say, we now have a way to put metrics on that.