Oura Replaces CSAT Surveys with AI
We built a score that evaluates every conversation with a simple formula. If it’s 100 percent, the experience was good, and anything less signals something went wrong. This gives us something that more than replaces CSAT.
Skylar deWitte
CX AI Architect, Oura
About Oura
Oura is a health technology company behind the Oura Ring, a wearable device that helps millions of members understand their sleep, readiness, and overall health.
Health Tech
~1000 Employees
CHALLENGE
CSAT was unreliable and QA was stuck in low-value work
“Our CSAT response rates are down. Our NPS scores…who knows what they mean? We hate CSAT as an org. At best, it’s a vibe check from a tiny slice of people.”
Skylar deWitte
CX AI Architect, Oura
Oura’s CSAT and NPS response rates had dropped into single digits, making them increasingly unreliable. Yet these survey metrics continued to be reported up to the C-suite, leaving leaders to make decisions based on a narrow, biased subset of members.
QA was stuck in low-value contractual work, spending hours on random BPO audits that resulted in uniformly high scores, offering little diagnostic value and consuming time that could be spent on real analysis.
The rollout of Decagon chatbot, now handling 60% of contacts, added another gap: bot performance could only be assessed through the vendor’s opaque reporting, with no independent way to validate accuracy or risk.
solution
Replacing CSAT with a custom, AI-powered Experience Score
With Rippit, Oura built a custom Experience Score to replace CSAT and give the business a trustworthy, full-coverage view of true Voice of the Customer.
Instead of relying on single-digit survey responses or opaque chatbot reporting, Oura now measures every interaction—bot and human—using transparent logic tailored to their business.
The four signals that power the Experience Score
Using Rippit’s AI Metric Builder, Oura created four classifiers that evaluate the core components of experience quality and return clear Yes / No / Not Enough Info outputs:
AI Classifier 1
Did the customer express frustration about the quality of service they are receiving?
AI Classifier 2
Was the customer’s issue fully and correctly understood?
AI Classifier 3
Was the customer’s issue fully resolved or properly escalated?
AI Classifier 4
Was the customer sentiment after the solution positive?
“We can explain exactly how these AI metrics work. They’re not blackbox, out-of-the-box scores where you don’t even know what the prompt is. The value comes from building something transparent and tailored to our business.”
Skylar deWitte
CX AI Architect, Oura
How the score is calculated
Rippit combines the four classifier outputs into a single Experience Score using a simple, transparent formula:
Good ÷ (Good + Bad) = Experience Score
A score of 100% is considered a good experience and anything less is a signal that something went wrong.
Example Calculation
AI
AI Output
Did the customer express frustration?
No
GOOD
Was the customer’s issue fully and correctly understood?
Yes
GOOD
Was the customer’s issue resolved or properly escalated?
No
BAD
Was the customer sentiment after the solution positive?
Yes
GOOD
3 Good, 1 Bad → 75% Experience Score
BAD
The result is a metric that’s custom-built, transparent, defensible at the C-suite level, and runs across 100% of conversations.
Impact
Enterprise-level clarity into every customer experience
100%
Predictive CSAT coverage vs. 8% CSAT coverage
“Our CSAT response rates are down. Our NPS scores…who knows what they mean? We hate CSAT as an org. At best, it’s a vibe check from a tiny slice of people.”
Skylar deWitte
CX AI Architect, Oura
Reliable, AI-powered experience measurement
Oura replaced an 8 percent survey sample with 100 percent experience visibility, creating a trusted signal they’re already linking to referrals, renewals, and repeat purchases.
Independent chatbot performance validation
The Experience Score gives Oura a clear, vendor-independent read on how Decagon performs—revealing accuracy, escalation quality, and risks that were previously invisible.
QA time redirected to high-value analysis
AI coverage removed the need for low-value contractual sampling, allowing QA to focus on deep diagnostic work and uncover systemic issues in product, policy, and process.
C-Suite Visibility
Oura’s leaders now have a clear and defensible experience metric they can rely on to guide strategy and understand revenue impact.
Oura is now exploring additional product-specific AI classifiers to surface product issues earlier and guide engineering and product decisions across the organization.
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