From Reactive to Intelligent: Elevating Customer Support with Conversational Analytics
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Client Overview
The Challenge
Fragmented Chatbot Performance: The bot struggled with accuracy, tone, and contextual understanding, especially for diverse user queries.
Lack of Conversational Insights: There was no visibility into what triggered fallbacks, handovers, or user dissatisfaction.
Unmeasured Sentiment & Satisfaction: The system couldn’t quantify customer sentiment, resolution quality, or friction points in conversations.
Limited Adaptability: Existing Dialogflow flows were rigid, making it difficult to evolve or learn from historical chat behavior.
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Key Constraints
GCP-Only Ecosystem: The client restricted the tech stack to native GCP services, excluding any third-party analytics, AI, or visualization tools.
No Labeled Data Available: Historical chat logs lacked labels or tags for failures, sentiment, or quality, making supervised learning unfeasible.
Mixed Workflow Types: The chatbot combined structured rule-based and dynamic (AI-driven) responses, requiring dual-mode evaluation logic.
Non-Disruptive Enhancement Mandate: Improvements had to be layered onto existing Dialogflow flows without interrupting live customer support operations.
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Our Approach
We designed a hybrid AI + analytics framework that used Gemini models and GCP-native services to analyze, score, and optimize chatbot performance in real-time—without needing manual annotations or third-party software.
AI-Powered Chat Log Evaluation:
Using Gemini AI, we analyzed unstructured chat logs to extract:
Inferred User Satisfaction Scores
Sentiment Classification (positive/negative/neutral)
Bot Response Quality and tone alignment
Response Latency tracking
Detection of fallbacks and misunderstood intents
Handover triggers and live agent escalation reasons
Dynamic prompting enabled highly structured insights from raw logs, uncovering friction points and areas of conversational confusion.
Real-Time Operational Dashboard:
We developed a Streamlit-based internal dashboard to give visibility into chatbot behavior:
Authentication Pattern Tracking: Monitored how users verified their identity and improved routing logic.
Outage-Aware Messaging: Detected service issues from user queries and auto-adjusted bot messaging accordingly.
Error Monitoring: Surfaced API and GCP-level issues that could affect bot performance.
Static Flow Miss Detection: Identified high-dropoff steps in rule-based workflows and suggested improvements.
The Outcome
Improved Bot Accuracy & Relevance with Gemini Feedback Loops
Enhanced User Satisfaction Inferred from Unstructured Logs
Fewer Escalations, Smarter Handover Detection
Real-Time Dashboarding Empowered Ops Teams
End-to-End GCP Stack Ensured Secure, Scalable Deployment
What Made This Work
Prompt Engineering: Custom Gemini prompts extracted structured performance data from noisy, unstructured chat logs.
Fallback Differentiation: Clear tracking of AI vs static flow failures helped isolate high-impact improvement areas.
Live Visibility: Dashboards made chatbot health and failure trends visible in real time to ops teams.
All-GCP Stack: Using GCP-native tools ensured tight integration, cost-efficiency, and enterprise-grade scalability.
Continuous Feedback Loop: Combined AI insights with behavioral analytics for a holistic, evolving optimization strategy.
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