Telecom: Omnichannel Customer Service AI
1. Problem
1a. Statement
Telecom customer service centers handle 80 million+ annual contacts, with 65% of calls for routine issues like billing inquiries, service troubleshooting, and appointment scheduling. A major cable and internet provider faced $12+ average cost per call, 15-minute average handle times, and customer satisfaction scores declining as hold times exceeded 20 minutes during peak hours. The company needed an AI-powered omnichannel platform spanning voice IVR, web chat, and SMS to automate routine interactions while seamlessly escalating complex issues to human agents.
1b. Client Profile
1c. Motivation
2. Analysis
2a. Requirements
The platform required unified AI capabilities across voice IVR, web chat, and SMS channels with consistent conversation context. Natural language understanding handled intent recognition across billing, technical support, and service scheduling domains. Voice integration with existing telephony infrastructure enabled AI-powered IVR with natural speech recognition and synthesis. Diagnostic workflows guided customers through troubleshooting steps for common issues including internet connectivity, TV signal, and equipment problems. Appointment scheduling connected to field service dispatch for technician visits. Agent escalation preserved full conversation history and customer context for seamless handoffs.
The platform required unified AI capabilities across voice IVR, web chat, and SMS channels with consistent conversation context. Natural language understanding handled intent recognition across billing, technical support, and service scheduling domains. Voice integration with existing telephony infrastructure enabled AI-powered IVR with natural speech recognition and synthesis. Diagnostic workflows guided customers through troubleshooting steps for common issues including internet connectivity, TV signal, and equipment problems. Appointment scheduling connected to field service dispatch for technician visits. Agent escalation preserved full conversation history and customer context for seamless handoffs.
2b. Constraints
3. Solution
3a. Architecture
3b. Implementation
4. Result
4a. DUBEScore™
4b. Outcomes
4c. Learnings
Voice latency was critical for natural conversation. Optimize speech recognition pipeline before NLU.
Technical troubleshooting flows needed dynamic branching. Static decision trees frustrated customers.
Cross-channel context preservation was the most valued feature. Invest in unified conversation memory.
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