Cove Relaxation Index
Improving the Cove app through data visualization and providing users with meaningful session feedback along their journey.
Cove Relaxation Index
Improving the Cove app through data visualization and providing users with meaningful session feedback along their journey.
Cove Relaxation Index
Improving the Cove app through data visualization and providing users with meaningful session feedback along their journey.
Some Context: Cove is a physical device that uses patented vibrations to mimic affective touch therapy for 20 minute sessions. This helps people to fall asleep faster, feel more rested, and become mentally resilient to the stresses of life.
TLDR
Cove helps people relax and sleep better, but to notice these effects, it took on average 10 days. This was too long and people returned their devices. By showing users their progress over time, we saw a 300% increase in people who viewed their session biometrics, which contributed to a 10% decreased in returns.
Some Context: Cove is a physical device that uses patented vibrations to mimic affective touch therapy for 20 minute sessions. This helps people to fall asleep faster, feel more rested, and become mentally resilient to the stresses of life.
TLDR
Cove helps people relax and sleep better, but to notice these effects, it took on average 10 days. This was too long and people returned their devices. By showing users their progress over time, we saw a 300% increase in people who viewed their session biometrics, which contributed to a 10% decreased in returns.
Some Context: Cove is a physical device that uses patented vibrations to mimic affective touch therapy for 20 minute sessions. This helps people to fall asleep faster, feel more rested, and become mentally resilient to the stresses of life.
TLDR
Cove helps people relax and sleep better, but to notice these effects, it took on average 10 days. This was too long and people returned their devices. By showing users their progress over time, we saw a 300% increase in people who viewed their session biometrics, which contributed to a 10% decreased in returns.
Why were people returning their Cove?
We spoke to our customers, diving into their needs, wants, and expectations to find out where we missed the mark.
We accomplished this by sending out 250 surveys and sorting through 175 responses.
Cove session being activated.
Why were people returning their Cove?
We spoke to our customers, diving into their needs, wants, and expectations to find out where we missed the mark.
We accomplished this by sending out 250 surveys and sorting through 175 responses.
Cove session being activated.
Why were people returning their Cove?
We spoke to our customers, diving into their needs, wants, and expectations to find out where we missed the mark.
We accomplished this by sending out 250 surveys and sorting through 175 responses.
Cove session being activated.
Trends we spotted
1.
Users wanted to track their progress to be reassured of the product's effectiveness.
2.
Users were seeking guidance to ensure they were using the product the right way.
3.
Users mistakenly believed that the device should be used while sleeping.
Trends we spotted
1.
Users wanted to track their progress to be reassured of the product's effectiveness.
2.
Users were seeking guidance to ensure they were using the product the right way.
3.
Users mistakenly believed that the device should be used while sleeping.
Trends we spotted
1.
Users wanted to track their progress to be reassured of the product's effectiveness.
2.
Users were seeking guidance to ensure they were using the product the right way.
3.
Users mistakenly believed that the device should be used while sleeping.
Prioritizing the problems
I knew we could reduce returns by addressing the first two issues and worked with marketing to refine the third. We decided to focus on creating data-viz for users to show them how they were making progress.
I partnered with engineering and data science to understand what technical constraints and data we had, then shortly after, kicked off visual explorations.
Prioritizing the problems
I knew we could reduce returns by addressing the first two issues and worked with marketing to refine the third. We decided to focus on creating data-viz for users to show them how they were making progress.
I partnered with engineering and data science to understand what technical constraints and data we had, then shortly after, kicked off visual explorations.
Prioritizing the problems
I knew we could reduce returns by addressing the first two issues and worked with marketing to refine the third. We decided to focus on creating data-viz for users to show them how they were making progress.
I partnered with engineering and data science to understand what technical constraints & data we had, then shortly after, kicked off visual explorations.
We had our starting point
Cove already had a feature called "Session Details," which utilized raw data available from the backend to present it to consumers. It displayed session duration, as well as the users' average heart rate and movement during the session.
These two details were all we needed to figure out our solution.
We had our starting point
Cove already had a feature called "Session Details," which utilized raw data available from the backend to present it to consumers. It displayed session duration, as well as the users' average heart rate and movement during the session.
These two details were all we needed to figure out our solution.
A data-driven peace of mind; visualizing user progress, session by session
Building off of Cove’s session details, our solution was clear: create visualizations based on the biometric data that users were already familiar with.
We created an algorithm that recorded users’ recorded heart rate and micro-movements over the course of their 20 minute sessions to calculate stress and relaxation levels. We then plotted these calculations onto a graph, the Cove Relaxation Index, and pinpointed when users were most relaxed during their sessions.
A data-driven peace of mind; visualizing user progress, session by session
Building off of Cove’s session details, our solution was clear: create visualizations based on the biometric data that users were already familiar with.
We created an algorithm that recorded users’ recorded heart rate and micro-movements over the course of their 20 minute sessions to calculate stress and relaxation levels. We then plotted these calculations onto a graph, the Cove Relaxation Index, and pinpointed when users were most relaxed during their sessions.
A data-driven peace of mind; visualizing user progress, session by session
Building off of Cove’s session details, our solution was clear: create visualizations based on the biometric data that users were already familiar with.
We created an algorithm that recorded users’ recorded heart rate and micro-movements over the course of their 20 minute sessions to calculate stress and relaxation levels. We then plotted these calculations onto a graph, the Cove Relaxation Index, and pinpointed when users were most relaxed during their sessions.
Figuring out how this might look
Figuring out how this might look
Figuring out how this might look
All that glitters isn’t gold, after all
After some explorations and stakeholder alignment, we took our top options to our users for testing.
The result? Users wanted more detailed information from our design.
All that glitters isn’t gold, after all
After some explorations and stakeholder alignment, we took our top options to our users for testing.
The result? Users wanted more detailed information from our design.
All that glitters isn’t gold, after all
After some explorations and stakeholder alignment, we took our top options to our users for testing.
The result? Users wanted more detailed information from our design.
We made some vital changes
Users wanted more granularity in the data. This inspired the creation of the “Relaxation Score,” which averaged the users’ relaxation index across their entire session.
We added a few more final touches, improved our information hierarchy, and cleaned up the visuals before launching the final product.
We made some vital changes
Users wanted more granularity in the data. This inspired the creation of the “Relaxation Score,” which averaged the users’ relaxation index across their entire session.
We added a few more final touches, improved our information hierarchy, and cleaned up the visuals before launching the final product.
We made some vital changes
Users wanted more granularity in the data. This inspired the creation of the “Relaxation Score,” which averaged the users’ relaxation index across their entire session.
We added a few more final touches, improved our information hierarchy, and cleaned up the visuals before launching the final product.
Results
We launched and found success. We leaned heavily on the invaluable research and cooperation of our users throughout this process, and it paid off. They provided the missing pieces that helped us find success in this initiative.
200%
increase in users who visited the details page at least once.
300%
increase in sessions viewed
10%
fewer returns
Results
We launched and found success. We leaned heavily on the invaluable research and cooperation of our users throughout this process, and it paid off. They provided the missing pieces that helped us find success in this initiative.
200%
increase in users who visited the details page at least once.
300%
increase in sessions viewed
10%
fewer returns
Results
We launched and found success. We leaned heavily on the invaluable research and cooperation of our users throughout this process, and it paid off. They provided the missing pieces that helped us find success in this initiative.
200%
increase in users who visited the details page at least once.
300%
increase in sessions viewed
10%
fewer returns