Call sentiment analysis detects frustration, confusion, satisfaction, and buying intent automatically โ on 100% of calls. No survey. No guesswork. Just signal from the conversation itself.
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Sentiment analysis on customer calls goes far beyond "positive" and "negative". A well-trained model identifies distinct emotional states throughout the conversation, mapped to specific moments in the transcript. Here is what it surfaces:
Detected through raised voice, repeated questions, phrases like "main pehle bhi bata chuka hoon" or "aap sun nahi rahe". Flags calls that need immediate follow-up.
Long pauses, clarifying questions, hedging language like "lekin mujhe pata nahi" indicate a customer who has not understood the product. Conversion is unlikely without more information.
Affirmative responses, questions about next steps, and forward-looking language ("toh kab se start hoga?") signal a customer moving toward a decision.
Short responses, monosyllabic answers, and phrases like "theek hai, okay, haan haan" without genuine engagement may indicate a coerced sale โ a compliance risk in regulated industries.
| Factor | CSAT Surveys | Call Sentiment Analysis |
|---|---|---|
| Response rate | 5 to 15% typical | 100% coverage |
| When data is available | Hours or days later | Immediately after upload |
| Granularity | Single overall score | Per-segment emotion tracking |
| Actionability | Tells you what, not why | Points to the exact moment things went wrong |
| Bias | Response bias (unhappy customers over-respond) | Based on actual conversation signals |
| Cost | Survey platform + incentives | Free with Bolo Aur Likho |
Sentiment analysis on Indian sales calls is more complex than English-only tools can handle. Indian agents and customers frequently switch between Hindi, English, and regional languages mid-sentence. Emotional expression in Hindi uses different idioms, intensity markers, and indirect communication styles that a purely English-trained model misses.
Bolo Aur Likho's sentiment analysis is built on top of OpenAI Whisper and processes transcripts in their native language, preserving Hinglish expressions and their emotional context rather than forcing translation first.
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