Voice of the Customer (VoC) Analysis
Analyze thousands of transcribed customer conversations using AI to identify trends, understand sentiment, discover pain points, and extract valuable feedback for product and service improvement.
Key Advantages
Discover how our AI voice agents transform lead qualification
Analyze 100% of Interactions
Gain insights from every transcribed call, not just survey responses or sampled calls.
Identify Emerging Trends
Detect recurring topics, issues, or feature requests mentioned across many conversations.
Understand Customer Sentiment
Track overall sentiment trends and identify drivers of customer satisfaction or dissatisfaction.
Discover Pain Points
Pinpoint specific frustrations, product issues, or service problems mentioned by customers.
Extract Product Feedback
Capture unsolicited feedback, feature requests, and usability issues mentioned during support calls.
Competitive Intelligence
Identify mentions of competitors and understand why customers might be choosing them or switching.
How AI Analyzes Voice of the Customer
Leverage transcription and NLP techniques to extract topics, sentiment, and key phrases from call data.
Transcribe Customer Calls
All relevant customer interactions (support, sales, etc.) are transcribed.
AI Performs NLP Analysis
The platform applies sentiment analysis, topic modeling, keyword extraction, and potentially intent recognition to transcripts.
Explore Insights via Dashboard
Analysts use dashboards to visualize trends, filter by topic/sentiment, and drill down into specific call examples.
Share Actionable Findings
Summarize key VoC insights and share them with relevant teams (Product, Marketing, Support) to drive action.
Connect VoC Insights Across the Business
Feed insights into product management tools, CRMs, BI platforms, and customer feedback repositories.
BI Tools (Tableau, Power BI, Looker)
CRM Systems (Salesforce, HubSpot)
Product Management Tools (Jira, Productboard)
Customer Feedback Platforms (e.g., Qualtrics, Medallia)
Data Warehouses
Collaboration Tools (Slack, Teams)
Frequently Asked Questions
Voice analysis captures unsolicited, conversational feedback in the customer's own words, often revealing nuances, emotions, and issues not covered by structured survey questions. It analyzes actual interactions rather than solicited responses.
AI can identify recurring themes or topics like mentions of specific products, features, bugs, pricing, competitors, website issues, support experiences, policy confusion, etc., based on patterns in the language used across calls.
Sentiment analysis provides a good directional indicator (positive, negative, neutral) at scale but can struggle with sarcasm, complex sentences, or mixed emotions. It's best used for identifying trends rather than relying on the score for every single call without context.
Yes, platforms often allow you to define specific keywords, product names, or categories of interest to track their frequency and associated sentiment within conversations.
While the AI automates the large-scale processing, human analysis is still needed to interpret the trends, drill into examples, synthesize findings, and determine appropriate actions based on the insights.
Effective VoC analysis requires accurate transcription and NLP models for each language spoken by your customers. Ensure the platform supports the languages relevant to your customer base.
Ready to transform your operations?
Schedule a demo to see how our solutions work for your industry.