Speech Analytics for Call Center Optimization

Project Overview:

Objective

The “Speech Analytics for Call Center Optimization” project aims to create a dataset for training machine learning models to analyze and extract valuable insights from recorded customer service calls. This dataset will enable call centers to enhance their operations, improve customer service quality, and streamline processes.

Scope

This project involves collecting audio recordings of customer service calls from various call centers, transcribing them into text, and annotating them with relevant information, such as sentiment, topic, and call outcome.

Sources

Call Centers: Collaborate with call centers and customer service providers to obtain recorded customer service calls.
Transcription Services: Use automatic speech recognition (ASR) technology to transcribe audio recordings into text.

Data Collection Metrics

Total Call Center Call Recordings: 20,000 recordings
Transcribed Calls: 15,000

Annotation Process

Stages

Sentiment Analysis: Annotate each call transcript with sentiment labels, such as “Positive,” “Negative,” or “Neutral,” to assess customer sentiment during the call.
Topic Classification: Label each call transcript with topics or categories related to customer inquiries or issues, such as billing, technical support, or product inquiries.
Call Outcome: Annotate the call outcome, indicating whether the issue was resolved, escalated, or required follow-up.
Metadata Logging: Log metadata, including call date and time, customer ID (anonymized), agent ID (anonymized), and call duration.

Annotation Metrics

Call Transcripts with Sentiment Labels: 15,000
Topic Classifications: 15,000
Call Outcomes: 15,000
Metadata Logging: 15,000

Quality Assurance

Annotation Verification: Implement a validation process involving call center experts to review and verify the accuracy of sentiment labels, topic classifications, and call outcomes.
Data Quality Control: Ensure the removal of low-quality or irrelevant call transcripts from the dataset.
Data Security: Protect sensitive customer information and adhere to data privacy regulations.

QA Metrics:

Annotation Validation Cases: 1,500 (10% of total)
Data Cleansing: Remove low-quality or irrelevant call transcripts

Conclusion

The “Speech Analytics for Call Center Optimization” dataset is a valuable resource for improving call center operations and customer service quality. With accurately annotated call transcripts and comprehensive metadata, this dataset empowers call centers to analyze customer sentiment, identify common topics and issues, and assess call outcomes. It provides a foundation for developing advanced speech analytics models and tools that can enhance call center efficiency, customer satisfaction, and overall business performance.