Oura Replaces CSAT With a 100% Coverage, AI-Powered Experience Score

How Oura built a custom Experience Score powered by LLMs with MaestroQA—one that works across both human and bot interactions.

Industry
Health Tech
Use Case
Company Size
~1000 Employees

Increase in CSAT

30%

Decrease in agent ramp time

120%

Increase in monthly coaching sessions

300%

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, Customer Experience Tooling, 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 MaestroQA, 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 Maestro’s LLM Metric Builder, Oura created four classifiers that evaluate the core components of experience quality and return clear Yes / No / Not Enough Info outputs:

LLM Classifier 1

Did the customer express frustration about the quality of service they are receiving?

LLM Classifier 2

Was the customer’s issue fully and correctly understood?

LLM Classifier 3

Was the customer’s issue fully resolved or properly escalated?

LLM Classifier 4

Was the customer sentiment after the solution positive?

“We can explain exactly how these LLM 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, Customer Experience Tooling, Oura

How the Score Is Calculated

MaestroQA 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.

The result is a metric that’s custom-built, transparent, defensible at the C-suite level, and runs across 100% of conversations.

See how MaestroQA can help you unlock the power of conversation data

Get in touch to learn how MaestroQA helps you:

  • Uncover business-critical issues hidden in support conversations
  • Replace disconnected metrics with insights that drive action
  • Equip teams across product, ops, and support with data that matters
  • Connect the dots between customer experience and company outcomes

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Oura Replaces CSAT With a 100% Coverage, AI-Powered Experience Score

Watch the Webinar

Industry
Health Tech
Use Case
Company Size
~1000 Employees

Increase in CSAT

30%

Decrease in agent ramp time

120%

Increase in monthly coaching sessions

300%

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, Customer Experience Tooling, 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 MaestroQA, 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 Maestro’s LLM Metric Builder, Oura created four classifiers that evaluate the core components of experience quality and return clear Yes / No / Not Enough Info outputs:

LLM Classifier 1

Did the customer express frustration about the quality of service they are receiving?

LLM Classifier 2

Was the customer’s issue fully and correctly understood?

LLM Classifier 3

Was the customer’s issue fully resolved or properly escalated?

LLM Classifier 4

Was the customer sentiment after the solution positive?

“We can explain exactly how these LLM 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, Customer Experience Tooling, Oura

How the Score Is Calculated

MaestroQA 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.

The result is a metric that’s custom-built, transparent, defensible at the C-suite level, and runs across 100% of conversations.

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Oura Replaces CSAT With a 100% Coverage, AI-Powered Experience Score

100%

Conversations analyzed for sentiment, up from <8%

50%+

of analyst capacity freed for strategic analysis

“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 replace CSAT.”

Skylar deWitte, Customer Experience Tooling, 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, Customer Experience Tooling, 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 MaestroQA, 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 Maestro’s LLM Metric Builder, Oura created four classifiers that evaluate the core components of experience quality and return clear Yes / No / Not Enough Info outputs:

LLM Classifier 1

Did the customer express frustration about the quality of service they are receiving?

LLM Classifier 2

Was the customer’s issue fully and correctly understood?

LLM Classifier 3

Was the customer’s issue fully resolved or properly escalated?

LLM Classifier 4

Was the customer sentiment after the solution positive?

“We can explain exactly how these LLM 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, Customer Experience Tooling, Oura

How the Score Is Calculated

MaestroQA 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.

The result is a metric that’s custom-built, transparent, defensible at the C-suite level, and runs across 100% of conversations.

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Impact

Enterprise-Level Clarity Into Every Customer Experience

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 LLM classifiers to surface product issues earlier and guide engineering and product decisions across the organization.

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