What is conversation analytics? A practical guide

For most organisations, it is surprisingly difficult to know what customers really think. Contact centres handle thousands of calls, chats and emails every week, yet leaders typically see only a tiny fraction of them. Decisions get made in the dark, or on gut feeling.

Conversation analytics closes that gap. It turns every customer interaction into structured, searchable data, so you can understand what customers ask for, why they get in touch, and how well you respond — across 100% of conversations, not a 1–2% sample.

This guide explains what conversation analytics is, how it works, and where it delivers measurable value.

What is conversation analytics?

Conversation analytics is the process of extracting usable insight from human conversation. It combines automatic speech recognition (ASR) to turn speech into text with natural language processing (NLP) — a blend of linguistics, computer science and artificial intelligence — to analyse what was said.

Applied to customer interactions, it lets you evaluate voice calls, live chat and email in one place: what topics come up, how sentiment shifts, which issues repeat, and how consistently agents follow process.

The two ideas that matter most:

• 100% visibility — analyse every conversation, not a hand-picked sample.
• Actionable insight — move from raw facts to decisions you can act on.

How does conversation analytics work?

At Feelingstream, turning a raw conversation into insight happens in a few stages.

1. Speech-to-text (for calls)

Voice recordings are transcribed to text using our own speech-to-text models. Ideally, agent and customer are recorded on separate channels so each speaker can be analysed independently.

Our models are trained on audio and transcriptions for specific languages, with a focus on spontaneous speech in real phone calls — which is very different from scripted or studio audio. This is why domain-tuned models outperform generic ones, particularly for Nordic and Baltic languages such as Finnish, Norwegian and Estonian.

2. Text analytics across every channel

Once calls are transcribed, they join your chats and emails as text. With hundreds or thousands of conversations available, you can review interactions over any timeline, filter by channel or language, and aggregate the data to suit the question you are asking.

3. Semantic search and similarity

Simple keyword search is powerful, but language is messy — customers describe the same problem in many ways. Our semantic similarity models (customer- and language-specific) expand a search to related phrasings, so you find the whole topic, not just the exact words you typed.

4. Classification, summaries and scoring

On top of search, conversation analytics adds structured outputs:

• Topic classification — automatically label what each conversation is about.
• Automatic summaries — a concise summary of every interaction, no manual note-taking.
• Automatic quality scoring — score every conversation against your criteria, not a sample.

What can you do with conversation analytics?

The value shows up in day-to-day decisions across the contact centre and beyond:

• Understand demand — see the real reasons customers contact you, ranked and trended over time.
• Reduce avoidable contact — spot repetitive and avoidable calls and fix their root cause.
• Raise quality — coach agents on evidence from every interaction, not a handful of reviews.
• Protect revenue — detect churn-risk signals and act early.
• Grow sales — learn which arguments win deals with sales monitoring.
• Inform the business — feed product, marketing and operations with the voice of the customer.

From facts to actionable insight

A count of call topics is a fact. Knowing that a specific process change would remove 8% of your inbound calls is an actionable insight. The difference is what makes conversation analytics worth the investment.

Getting there follows a repeatable pattern: start with a business question, segment the relevant conversations, look for the pattern behind the numbers, then decide and act. Analytics that stops at dashboards rarely changes anything; the goal is a decision.

Which channels and languages are supported?

Conversation analytics works across calls, live chat and email in one platform, so you can compare how customers behave in different channels. Feelingstream's ASR is built for multilingual environments with particular depth in Nordic and Baltic languages. Text analysis works across the languages your customers actually use.

Is conversation analytics secure and GDPR-compliant?

Yes — and for regulated industries this is non-negotiable. Feelingstream is ISO 27001 certified, supports on-premises and closed-cloud deployment, keeps EU data in the EU, and can anonymise (mask) personally identifiable information in text and audio. See data security in conversation analysis for the full picture.

Frequently asked questions

What is the difference between conversation analytics and speech analytics?

Speech analytics focuses on voice calls. Conversation analytics is broader — it analyses calls, chat and email together, giving you one view of the customer across every channel.

Do I need call recordings to get started?

For voice analysis, yes — ideally with agent and customer on separate channels. Chat and email are already text, so they can be analysed directly.

How accurate is the speech-to-text?

Accuracy depends on language and audio quality. Our models are trained on spontaneous phone-call speech per language, which is why domain-tuned ASR outperforms generic engines — especially for Finnish, Norwegian and Estonian. See multilingual speech-to-text.

How is customer data protected?

Through ISO 27001-certified processes, EU data residency, on-premises or closed-cloud options, and data masking for PII. Read more on data security.

Where to go next

• Explore the platform: Product overview
• Efficiency: The guide to efficiency with AI
• Quality: Automated call-centre quality assurance
• See it on your data: Request a demo

Ready to see 100% of your customer conversations instead of a sample? Book a demo and we will show you conversation analytics on your own data.