Automatic QA case study: a Nordic telecom

A leading Nordic telecom provider had relied on manual quality assurance for years, spending significant time and money evaluating customer service calls. The process was slow and labour-intensive: quality managers could review only around 10 calls per agent per month — nowhere near the volume happening daily. When the chance came to pilot automatic quality assurance, they took it.

This is the story of how they moved from reviewing under 1% of calls to scoring 100% — and what changed as a result.

The challenge: reviewing under 1% of calls

The call centre was busy. Agents handled between 50 and 80 calls a day; some were short, others complex and 15 to 20 minutes long. A full-time agent averaged around 1,430 calls a month. Reviewing 10 of them meant judging overall service quality from less than one percent of conversations.

The gaps were obvious:

• Slow feedback loops — issues were often spotted too late, missing coaching opportunities.
• Subjectivity — the same call could score differently depending on the evaluator.
• No real coverage — the vast majority of calls were never reviewed at all.

The solution: an automatic quality assurance pilot

Feelingstream developed Automatic Quality Score as a new feature within its existing conversation analysis platform. The telecom provider had already used that platform for some time and trusted the partnership, so when the pilot was offered, they did not hesitate.

Unlike the old human-reviewed process, the new approach was AI-driven and could automatically assess 100% of calls, delivering instant, objective insight with full coverage.

A shift in mindset

Adopting the technology meant changing how the team thought about quality. First, they defined clear service standards, setting out what excellent looked like. Automatic scoring made it easy to find outstanding calls to share in training, so every new agent could hear what a truly great call sounded like. Establishing a baseline score for each team and agent let them measure improvement over time.

Agent buy-in mattered just as much. Rather than feeling micromanaged, agents became more engaged once they saw every interaction assessed fairly and consistently. Immediate feedback prompted them to focus on clarity and problem-solving, and they came to treat the score as a tool for self-improvement.

The impact: full visibility, instantly

The effect was immediate. The company gained complete visibility into how service measured against expectations. Work that used to take weeks — manually assessing a small percentage of calls — now happened in an instant, with real-time insight into every interaction.

The ability to measure resolution had the biggest effect. The system showed whether a call solved the customer's problem, and when it did not, it gave a specific reason. That let the team address recurring issues at the root and improve First Call Resolution (FCR). Higher FCR meant fewer repeat contacts — and a reduction in operational cost.

The results

The gains showed up across efficiency, cost and satisfaction:

• Quality assessment time dropped by 90%, freeing managers to coach rather than score.
• Assessing 100% of calls cost just 10% of the previous manual process.
• NPS rose as customers had their issues resolved faster and more smoothly.
• Agent performance improved with clear, data-driven feedback and targeted training.

A new standard for customer service

By replacing manual reviews with AI-driven insight, the provider set a new bar for quality assurance in its market. Teams felt more empowered, and customer experience improved in real time. It raises a fair question for the rest of the industry: how many companies still rely on guesswork from a tiny sample when they could see 100% of their service quality?

Frequently asked questions

What problem did the telecom start with?

Manual QA that reviewed only about 10 calls per agent per month — under 1% of volume — with slow feedback and inconsistent, subjective scoring.

What changed with automatic quality assurance?

Every call was scored automatically and objectively, giving full, real-time visibility instead of a small manual sample.

What results did they see?

Quality assessment time fell by around 90%, assessing all calls cost roughly 10% of the old process, FCR improved and NPS rose. See the verify notes above for the source's exact framing.

Why did agents respond well?

Because scoring was consistent and fair, and feedback was immediate. Agents used the score as a tool for self-improvement rather than feeling micromanaged.

Where to go next

• The pillar guide: Automated call-centre quality assurance
• The QA product: Automatic quality assurance
• How the scoring works: Automatic quality score with generative AI
• More on our customers: Customers

If you are ready to leave manual scoring behind and measure customer experience in real time, let's talk. Book a demo.