Saving ~2,800 Man Hours for Inside Sales
How we analyzed over 36,000 live chat transcripts to build a qualification chatbot framework, filtering out low-quality queries.
Problem
The Simplilearn landing page receives roughly 35,000+ chats every month from users worldwide. While 40% convert into viable leads, the remaining 60% are non-qualified chats and drop-offs. Over 21,400 non-lead conversations consumed more than 2,800 consultant hours monthly, pulling advisors away from high-intent learners.
"More than 56% of total chat initiations resulted in non-leads. Over 21,400 non-lead conversations consumed more than 2,800 man hours monthly."
My Role
- Conversation Designer - led transcript audit, competitor benchmarking, and chatbot flow design in Figma.
- Manually classified a 10% sample (3,716 transcripts) after automated NLP parsing proved insufficient.
- Defined rollout phases with inside sales and regional ops teams.
Constraints
- Live chat platform: Flow changes had to work within the existing sales chat vendor - no custom backend.
- Regional rollout: Phase 1 limited to specific Indian regions before nationwide expansion.
- Data quality: Colloquial, messy chat logs resisted automated NLP - manual audit was required for reliable insight.
Process
I aggregated 36,000+ raw chat logs from September and manually audited a representative 10% sample (3,716 transcripts), classifying each as High Quality or Low Quality:
- High Quality: Users willing to share contact info and upskilling goals to receive program details.
- Low Quality: Random inquiries, spam bots, or mid-greeting drop-offs.
Key findings from the audit:
- Only 2.8% of chat initiations converted into paying, registered program users.
- 56.79% of chat volume comprised non-leads.
- Learners frequently requested curriculum PDFs, schedules, and pricing before speaking to an advisor.
I audited 13 ed-tech competitors, mapped 4 qualification chatbot patterns, and synthesized learnings into a custom flow:
| Competitor Chatbot Feature | Learnings for Our Flow |
|---|---|
| Direct transfer to human | Creates instant queues - pre-qualify first. |
| Multi-choice course selector | Helps users identify interest before transfer. |
| Automated PDF downloads | Fulfills core user needs in-chat, reducing human load. |
Key Design Decisions
- Pre-qualification nodes: Ask domain of interest and experience level before notifying a human consultant.
- Instant multimedia fulfillment: Syllabus brochures and pricing available inside the chat interface.
- CSAT rating trigger: Automated feedback prompt on chat completion for continuous flow tuning.
Measured Outcome
What Changed Because of This Work
Inside sales consultants spend less time on syllabus requests, pricing FAQs, and spam chats - the bot handles self-serve fulfillment and qualification first. Phase 1 launched in targeted Indian regions with metric tracking and copy iteration; Phase 2 extended nationwide with multi-language support. Hand-auditing 10% of transcripts (when NLP failed) became the team's standard for validating conversational changes before rollout.