Automated AI Driven Post Call Summary Generation Workflow

Automated Post-Call Summary Generation

1. Call Initiation

1.1 Customer Service Call

Customer initiates a call to the customer service department.

1.2 Call Routing

The call is routed to the appropriate customer service representative (CSR) based on the nature of the inquiry.

2. Call Handling

2.1 Real-Time Speech Recognition

AI-driven speech recognition tools, such as Google Cloud Speech-to-Text or Amazon Transcribe, convert spoken language into text in real-time during the call.

2.2 Context Understanding

Natural Language Processing (NLP) algorithms analyze the conversation context, identifying key topics, customer sentiment, and intent.

3. Data Capture

3.1 Information Extraction

AI tools, such as IBM Watson or Microsoft Azure Text Analytics, extract relevant data points, including customer details, issues raised, and resolutions provided.

3.2 Call Metadata Logging

Call metadata (duration, time of day, CSR ID) is logged automatically for future reference.

4. Summary Generation

4.1 Automated Summary Creation

Using AI summarization tools, such as OpenAI’s GPT-3 or Hugging Face Transformers, a concise post-call summary is generated that encapsulates the main points discussed.

4.2 Review and Edit

The generated summary is reviewed by the CSR for accuracy and completeness. The CSR can make necessary edits before finalizing.

5. Summary Distribution

5.1 Integration with CRM Systems

The finalized summary is automatically integrated into the Customer Relationship Management (CRM) system, such as Salesforce or Zendesk, for easy access and tracking.

5.2 Customer Follow-Up

If required, follow-up emails or messages are sent to the customer with the summary and any additional information or next steps.

6. Performance Analysis

6.1 Reporting and Insights

AI analytics tools aggregate data from the summaries to provide insights into common issues, customer satisfaction, and CSR performance.

6.2 Continuous Improvement

Feedback loops are established to refine AI models and improve the accuracy of summaries and customer interactions over time.