Client
A Bangalore-based AI-backed contact center software provider, building conversation intelligence solutions that record, transcribe, and analyze customer conversations.
The Challenge
Understanding customer emotions is at the heart of conversation intelligence. Beyond words, it is tone, pace, and expression that reveal whether a customer is satisfied, frustrated, or disengaged. The client needed to build AI models capable of detecting not just broad sentiments like positive, negative, or neutral, but also sub-emotions such as anger, happiness, frustration, sadness, greetings, and more.
The challenge was compounded by:
- Long-form audio and video with multiple emotions expressed within a single conversation
- Need for precise timestamp segmentation to capture shifts in sentiment
- Ensuring native fluency in Hindi, covering variations in speech, slang, and conversational flow
- A large dataset requirement of 400 hours of labeled Hindi content
The Solution
Globik AI designed a tailored workflow for large-scale emotion tagging:
- Segmentation and Labeling
Conversations were segmented into timestamped intervals wherever a shift in emotion occurred.
- Hierarchical Emotion Tagging
Each segment was first classified into a main sentiment (Positive, Negative, Neutral). Sub-emotions such as anger, happy, frustration, sad, pissed, or greetings were tagged under the parent category.
- Native Linguistic Expertise
Hindi language experts performed manual labeling to ensure accuracy in tone-based interpretation and cultural nuances that automated systems often miss.
- Quality Framework
A multi-level review process ensured consistency in labeling across the 400 hours of audio and video content.
The Result
The client received a fully segmented, timestamped, and emotion-tagged dataset covering 400 hours of Hindi conversations.
This enabled them to:
- Train AI models to accurately detect customer emotions in real-world contact center conversations
- Build sentiment dashboards for agents and supervisors to better understand customer mood in real time
- Reduce missed emotional cues, leading to improved customer satisfaction and faster conflict resolution
- Expand their conversation intelligence platform into native-language markets, strengthening adoption in India
Real-World Use-Cases
- Customer Support Prioritization: Calls showing high frustration or anger were flagged in real time, allowing supervisors to step in before escalation.
- Agent Performance Coaching: Sentiment timelines highlighted where an agent improved a customer’s mood or missed emotional cues, helping in targeted training.
- Churn Prediction: Repeated negative sentiments across multiple customer interactions were linked to churn risk, enabling proactive retention strategies.
- Product Feedback Mining: Sub-emotion tagging such as “frustration” or “happiness” around product features gave actionable insights to product teams.
Why It Matters
Customer conversations are no longer just about words. Emotions drive outcomes such as loyalty, churn, and brand trust. By delivering a deeply segmented and labeled dataset, Globik AI enabled the client’s platform to go beyond transcription and into true emotional intelligence. This helped the client strengthen its competitive edge in the fast-growing contact center AI industry.
Key Highlights
- 400 hours of Hindi audio and video conversations labeled
- Multi-level emotion tagging: Positive, Negative, Neutral with detailed sub-emotions
- Timestamp-based segmentation for multi-emotion conversations
- Validated by native Hindi experts for cultural and tonal accuracy
- Enabled use cases: Customer prioritization, churn prediction, agent coaching, product feedback mining
