Voice analytics is the process of capturing, analysing, and interpreting voice data from users to gain insights into behaviour, intent, sentiment, and usability.
Why It Matters
- Improves user experience by understanding how people interact with voice-enabled features.
- Helps detect frustration, confusion, or satisfaction in spoken feedback or commands.
- Enables smarter product decisions through real-time customer sentiment tracking.
- Supports accessibility by identifying speech patterns and optimising voice interfaces.
- Powers innovation in voice-based apps, especially in healthcare, finance, and support.
Use This Term When...
- You're building a voice-enabled app or feature (e.g., voice search, commands).
- You’re analysing how users interact through speech to improve performance.
- Planning to integrate NLP or sentiment tracking into customer service solutions.
- Reviewing conversational UX or voice assistant behaviour.
- Optimising app accessibility for voice-interaction users.
Real-World Example
In one of our projects, we implemented voice analytics to analyse user interactions with the app’s voice commands. This helped us optimise the voice UI, improve recognition accuracy, and uncover new opportunities for voice-driven features.
Founder Insight
Many founders add voice features without tracking how users actually speak. Voice analytics helps you align voice UX with real user language — not just assumptions.
Key Metrics / Concepts
- Speech-to-Text Accuracy – Measures how well spoken input is transcribed.
- Sentiment Score – Analyses tone and emotion in the user’s voice.
- Voice Command Success Rate – Percentage of commands that trigger the correct response.
- Latency – Time it takes for the system to process and respond to a voice input.
- Intent Recognition – How accurately the system understands the user’s goal.
Tools & Technologies
- Google Cloud Speech-to-Text – Converts voice into text with high accuracy.
- Amazon Transcribe – Scalable speech recognition used in analytics pipelines.
- VoiceBase / Observe.AI – Used to analyse voice calls for sentiment, keywords, and more.
What’s Next / Future Trends
Voice analytics is moving toward real-time emotion detection, multilingual context understanding, and seamless integration with conversational AI. As privacy laws evolve, expect a rise in on-device processing and user consent controls.
Related Terms
Natural Language Processing (NLP) – Interpreting user speech and language.
Utterance – A unit of speech analysed in voice systems.
Sentiment Analysis – Identifying emotions in speech or text.
Accessibility – Making apps usable for all, including voice interaction.
AI – Powers the core intelligence behind voice analytics.
Helpful Videos / Articles / Pages
Blog: Understanding App Analytics Using Google Analytics and Other Tools
Blog: 7 Reasons to Pay Attention to Web Analytics
Blog: Mobile App Monitoring, Analytics, and Continuous Improvement
Call to Action
Thinking of building voice-powered features in your app? Book a session with our product team to see how voice analytics can uncover what your users are really saying.