Conversation analytics: understand every customer conversation
Spiral analyzes 100% of your calls, chats, emails and reviews to reveal sentiment, intent and the root cause behind what customers say, then flags emerging issues before they escalate.
Used by CX teams at Turo and Remitly. SOC 2 Type II, HIPAA and GDPR ready.
Definition
What is conversation analytics?
Conversation analytics is the practice of analyzing customer conversations at scale to extract structured insight. It reads calls, chats, emails, surveys and social messages, then uses natural language processing to surface sentiment, intent, topics and recurring issues. Instead of manually reviewing a small sample, teams see patterns across every interaction and act on what customers actually need. The result is faster issue resolution, better coaching and earlier warning of problems.
How conversation analytics works
A clear four-step pipeline, from raw conversation to prioritized action.
Step 1
Capture conversations
Conversations are collected from every channel: phone calls, live chat, email, surveys, app reviews and social messages. Nothing is left in an archive.
Step 2
Transcribe and structure
Audio is transcribed to text with speaker separation, so voice and digital conversations can be analyzed the same way.
Step 3
Analyze with NLP
Natural language processing scores each conversation for sentiment, intent and topic, and groups them into specific, recurring issues rather than loose keywords.
Step 4
Surface insight and act
The system ranks issues by impact, shows the root cause behind each one, and alerts teams to emerging problems so they can fix the cause, not just the symptom.
Conversation analytics is one capability inside our customer intelligence platform.
Why conversation analytics matters for CX teams
A busy contact center logs more conversations in a week than a team can review in a quarter. Conversation analytics closes that gap. It reads 100% of interactions, so quality and CX leaders stop guessing from a 2% QA sample and start seeing the whole picture.
The payoff is concrete: shorter handle times, earlier detection of product and process problems, sharper agent coaching, and a clear view of the issues driving repeat contacts. Because the analysis covers every channel, the same insight serves support, operations and product at once.
How Spiral does conversation analytics differently
Older tools only find the issues you told them to look for. You write keyword rules and boolean logic, and you catch the problems you already knew about. The emerging issue, the one a customer raises weeks before it becomes a firestorm, slips past. Spiral discovers issues autonomously, so you hear about the new problem while it is still small.
Spiral also reaches beyond the contact center. It reads product reviews, surveys, app-store feedback and social posts alongside calls and chats, so the analysis reflects the whole customer story, not just the part that reached an agent.
And it ties issues to impact. Spiral ranks what to fix first and shows the financial weight behind each issue, so the output is a prioritized action list, not another dashboard. You ask a question in plain language and get an answer with the evidence attached, no data science team required.
Conversation analytics vs speech analytics vs sales call intelligence
Three adjacent categories, three different jobs.
| Approach | What it does | Best for |
|---|---|---|
| Speech analytics | Transcribes and searches voice recordings, often by keyword spotting. | Finding known phrases in call audio. |
| Sales call intelligence | Scores sales calls and coaches reps on deals. | Revenue teams and sales enablement. |
| Conversation analytics (Spiral) | Reads 100% of conversations across every channel, finds root causes, and flags emerging issues. | CX, support and operations leaders. |
Weighing named tools? Compare conversation intelligence platforms.
Conversation analytics in practice
Remitly
Remitly used Spiral to analyze two years of support conversations, scanning 100% of interactions instead of a sample, and uncovered a gap in how it served the recipients of money transfers that manual review had missed.
Turo
Turo used Spiral to build a structured Voice-of-Customer program from support conversations, then used the insight to improve self-service and agent guidance.
Revenue lift using conversation intelligence
Increase in QA efficiency
Reduction in hold-time-violation resolution time
Outcome figures are reported results and are not attributed to the named customers above.
See conversation analytics on your own data
Start a free proof of concept and let Spiral turn your conversations into a ranked list of what to fix first.
Frequently Asked Questions
Get instant answers to common questions about our Voice AI solutions
Conversation analytics is the analysis of customer conversations at scale to extract structured insight. It reads calls, chats, emails, surveys and social messages and surfaces sentiment, intent, topics and recurring issues.
Speech analytics transcribes and searches voice recordings, often by keyword. Conversation analytics adds natural language understanding, sentiment, topic discovery and root-cause analysis across voice and digital channels.
Phone calls, live chat, email, surveys, app-store and web reviews, and social messages. Spiral analyzes all of them together.
The value comes from analyzing 100 percent of conversations. Spiral reads every interaction, so nothing is missed, unlike manual QA that samples a small fraction.
No. Because it reads reviews, surveys and social alongside support calls and chats, the same insight serves CX, operations and product teams.
With Spiral, no. You ask questions in plain language and get ranked issues with the evidence attached. No data science team is required.
Spiral connects to your existing CRM and contact-center systems and can be deployed in days when no custom integration is needed. A free proof of concept lets you see insight from your own data first.
It reveals the root causes behind repeat contacts, flags emerging issues early, and gives CX leaders a full view instead of a small sample.