AI Driven Call Transcript Highlight Extraction Workflow Guide

Call Transcript Highlight Extraction

1. Data Collection

1.1 Call Recording Retrieval

Gather recorded customer service calls from various channels (phone, chat, etc.). Ensure compliance with data privacy regulations.

1.2 Transcript Generation

Utilize AI-driven transcription tools such as Otter.ai or Rev.com to convert audio recordings into text transcripts.

2. Pre-Processing

2.1 Text Cleaning

Implement natural language processing (NLP) techniques to remove filler words, non-verbal sounds, and irrelevant information from transcripts.

2.2 Segmentation

Divide the transcripts into manageable sections based on conversation turns or topics using tools like spaCy or NLTK.

3. Highlight Extraction

3.1 Keyword Identification

Employ AI algorithms to identify key phrases and terms that are frequently mentioned in customer interactions. Tools such as Google Cloud Natural Language API can be utilized for this purpose.

3.2 Sentiment Analysis

Analyze the sentiment of each segment using AI models to determine customer satisfaction levels. Tools like IBM Watson Tone Analyzer can provide insights into emotional tone.

3.3 Highlight Generation

Extract relevant highlights from the transcripts based on keyword frequency and sentiment scores. AI summarization tools such as GPT-4 or SummarizeBot can assist in condensing information effectively.

4. Review and Validation

4.1 Human Oversight

Incorporate a review process where customer service agents validate the extracted highlights for accuracy and relevance.

4.2 Feedback Loop

Establish a feedback mechanism to continuously improve the AI models based on human reviews and evolving customer service strategies.

5. Integration and Reporting

5.1 Highlight Integration

Integrate the extracted highlights into customer relationship management (CRM) systems for easy access by customer service teams.

5.2 Reporting

Generate reports summarizing key insights from the call transcripts, highlighting trends and areas for improvement in customer service. Utilize tools like Tableau or Power BI for data visualization.

6. Continuous Improvement

6.1 Model Training

Regularly update AI models with new data to enhance accuracy and effectiveness in highlight extraction.

6.2 Performance Monitoring

Monitor the performance of the highlight extraction process and make necessary adjustments based on user feedback and changing customer needs.