Ōura Ring Case Study
*This was written in early 2022, before I had any Product or Design experience.
Introduction
Oura describes itself as “an award-winning and fast-growing startup that helps people track all stages of sleep and activity using the Oura Ring and connected app. By providing daily feedback and practical steps to inspire healthy lifestyles, we've helped hundreds of thousands of people improve their sleep, understand their bodies, and transform their health.”
In this case study, I analyze Ōura as a company and explore the Oura Ring app (which I’ve used intermittently over the past two years). I identify usability and behavioral insight gaps, outline how I’d validate these hypotheses with users, and propose solutions to make the app more actionable and habit-forming.
2. Understanding Ōura
To understand how Oura frames its value proposition, I reviewed its site and product messaging. A few points stood out:
Mission: “We’re on a mission to empower every person to own their inner potential…”
Taglines: “Accuracy above all,” “Sleep, deciphered,” and “Perfect your sleep.”
Core Value Propositions: Sleep, activity, and readiness metrics.
Differentiators: Heart rate and temperature sensors, cycle tracking for women, and guided mindfulness content.
From this, we can infer the intended customer journey:
Users follow their usual daily routines.
They wear the ring passively.
Oura collects physiological data in the background.
The app delivers insights meant to help users build better habits.
Health improves over time, ideally generating user satisfaction and word-of-mouth growth.
This loop - passive data collection leading to active behavior change - is at the core of Oura’s product promise.
3. Core Features
The app offers several high-value features:
Sleep Score: Based on total sleep, latency, wakefulness, REM, and deep sleep.
Activity Score: Measures daily movement, steps, and activity trends.
Readiness Score: Synthesizes recovery, sleep, and activity to indicate how prepared users are for the day.
Tags: Users can manually log events (e.g., workouts, meals, emotions) to track variables that may influence health.
Guided Sessions: Meditation, NSDR, and sleep-focused audio content.
4. Identifying Opportunities
While I am mostly impressed with the accuracy and sheer volume of information that is produced from wearing the Oura ring, the app falls short in helping users translate data into action.
Oura’s mission statement highlights that their goal is to empower individuals to improve their health, but there is very little direction provided by the Oura ring app on how an individual can alter their behavior to improve their health. Here’s a typical user scenario I’ve experienced:
My sleep scores have been steady, but one morning I feel unusually groggy. I open the app, see a low sleep score,
… now what? I’m left guessing whether it was due to stress, caffeine, poor timing, or something else. Who’s to say?
Ōura provides the “what” but not the “why” or “how”. The app doesn’t surface clear, personalized suggestions based on data patterns, and the burden of interpretation falls on the user.
You might say, “Well, that’s what tags are for. Just look at what you did yesterday and take steps to avoid those things.” But there are three problems with this:
High friction: Tagging is buried under menus and poorly organized.
Manual correlation: Users must draw their own conclusions about what affected their scores.
Missing context: The app ignores subjective well-being factors like mood and energy levels.
This gap between insight and action is where I see the greatest product opportunity.
5. Validating the Problem
Obviously, these problems I’ve identified are exactly one data point of feedback (from myself), and ideally I would try to obtain feedback from a larger number customers. I would validate these issues through:
Qualitative interviews with a diverse group of users (recruited via social or in-app prompts, incentivized with discounts).
App store review mining to spot patterns in complaints, possibly with the help of AI summarization tools.
In-app surveys asking users if they’ve been able to change their habits over time.
Key questions I’d seek to answer:
How frequently do users tag their data? Do they find it valuable?
Which features are most and least useful?
What frustrates them about tracking sleep with Ōura?
Do users feel the app reflects how they actually feel—mentally and physically?
Additionally, I would align my questions and conduct with the following in mind:
These types of ‘validation’ interviews are particularly sensitive to bias, so I would try to not bring up my specific hypothesis until the end or ask leading questions.
I would ask open ended questions, ask about their day and the context in which they use the Oura app.
I would let them do most of the talking and see if they talk about the problems I identified on their own.
6. Brainstorming Solutions
A. Inputting Tags
Tags are a powerful but underutilized feature. Today, they’re buried under several clicks, poorly categorized, and inconsistently labeled.
This is the current process (clicks in magenta):
Problems with this design:
Tags are hidden under multiple button clicks
Huge, disorganized, alphabetized tag list.
Separate flows for logging workouts and meditation vs. tagging.
Confusing mix of mood, environment, food, and medical tags (no sense of categorization)
Irrelevant options (e.g. “giving birth” as a regular tag?).
Personally, the thought process that I would have while entering tags would be: “Ok what did I eat today, let’s put that in. What about exercise? Supplements? Hydration? Work? What was my mood and energy level at today?”
Potential Solution: A new “Tag” tab organized by context (e.g., Diet, Activity, Environment, Mood). Here’s how that might look:
Diet → Carbs → Bread, Rice, Potatoes
Activity → Cardio, Lifting, Yoga
Self → Energy, Mood, Stress
Additional enhancements:
Recently used tags section.
One-tap tagging at key app touchpoints (e.g., right after waking).
While the exact design I have shown still needs some improvement, an interface like this would encourage users to input a significant amount more information about their health and habits.
An additional feature to this could include the most frequently used tags by users to save time going through the map.
B. Tracking Mood & Alertness
Another feature that I think is lacking is that Oura doesn’t do a good job of tracking how you are actually feeling on a given day; it can take its best guess based on your readiness score and sleep score, but your mood and energy levels could be quite different from what Oura thinks it is. And since how you actually feel day-to-day is more important than a calculated score, it is important to measure.
Proposed Feature: Prompt users once per day to rate their mood and energy level, with a simple scale or emoji slider. This can be:
Integrated into the tagging flow.
Triggered by a daily push notification.
Used to refine the accuracy of readiness predictions.
There may also be an opportunity for this feature to be included in the selection map that I described above.
C. Insights with Advanced Analytics
This is the most impactful feature opportunity: automated pattern detection between tags and health outcomes.
Imagine a new “Insights” tab that shows:
“Cardio workouts correlate with improved sleep.”
“Dairy intake often precedes lower readiness scores.”
“Evening meditation is linked to better recovery.”
These can start as simple correlations and evolve into machine learning-driven pattern recognition, offering users personalized experiments to test (“Try removing caffeine for 3 days and track your sleep”).
This tab provides specific, actionable insights into people’s habits. E.g. “Ok, let me try to take a week off of eating dairy since it seems to be affecting my sleep. Maybe I’m allergic.”
The above mockups display simple correlation analyses, but could hypothetically involve a machine learning model that finds trends between your inputs (food, exercise, etc.) and outputs (sleep, energy, mood).
7. Final Thoughts
Ōura has built an impressive foundation. Hardware that works, metrics users trust, and a growing base of health-conscious users. But the true magic of wearables is not in data collection, it’s in behavior change.
By reducing friction in self-reporting, layering in subjective inputs like mood, and delivering targeted insights, Ōura can fully deliver on its mission to help people own their potential.