Call Analysis for Scoring and Compliance
Introduction
A US-based call center managing customer interactions for multinational corporations was struggling with a highly manual and resource-intensive process for call review. The need to meet compliance standards and identify sales opportunities made this process even more critical, but the lack of automation led to delays, inconsistencies, and missed opportunities.
Incoming Calls
Manual Transcription & Review
Agents spend time analyzing calls
Delays & Potential Errors
Hard to scale and ensure compliance
Inconsistent Final Reports
Late and non-systematic analysis
The Problem
Manual Call Review
Thousands of daily calls were being manually reviewed for compliance and quality, which was slow, inconsistent, and labor-intensive.
Regulatory Compliance
Call centers in the US must adhere to strict standards (e.g., proper greetings, customer consent documentation, and disclosures).
Missed Sales Insights
Opportunities for upselling and cross-selling products were often overlooked due to delays in analyzing call data.
Scalability Issues
The company's manual process struggled to keep up with the demand for calls.
Implementation Process
Phase 1: Data Collection and Preparation
Client-Specific Data Integration
Historical call recordings, agent logs, and scoring guidelines were collected from each client.
Regulatory Mapping
Compliance rules, such as industry-specific privacy laws and disclosure requirements, were documented for AI training.
Data Annotation
Key compliance events and sales triggers were labeled in the data to serve as training inputs for the machine learning model.
| Data Type | Format | Volume | Purpose |
|---|---|---|---|
| Call Audio | WAV | 10,000 calls (~500 hours) | Train & fine-tune the Speech-to-Text model |
| Agent Interaction Logs | JSON | 2GB of logs | Identify conversation patterns & keywords |
| Compliance Guidelines | PDF/Docs | 200 pages | Define compliance triggers for ML model |
Phase 2: AI Model Training
Speech-to-Text Model
Fine-tuned using client-provided audio data to improve transcription accuracy for domain-specific terminology and accents.
Scoring Model
A supervised learning approach was used to train the compliance and sales scoring algorithms. Features included:
Tone and sentiment analysis to detect empathy and professionalism.
Keyword spotting to identify regulatory phrases or sales opportunities.
Sequence analysis to evaluate conversation flow and resolution quality.
Audio Input
We start with raw call recordings. They can vary in length, quality, and accent. First, we ensure the audio meets basic standards (e.g., consistent sample rate) so later steps run smoothly.
Speech-to-Text
The audio goes to a speech recognition model that’s been fine-tuned for the client’s specific domain. This produces an accurate transcript aligned with the audio timeline.
Feature Extraction
From the transcript, we derive key metrics—sentiment scores, keywords (like mandatory disclosures), and conversation flow indicators. These features are numerical inputs to the scoring model.
Scoring
A supervised model uses these features to assign two primary metrics: - Compliance Score: Reflects adherence to legal and procedural standards. - Sales Potential Score: Highlights upselling or cross-selling opportunities.
Output
The system produces final compliance and sales scores along with relevant metadata. These results can be integrated into dashboards or reports, helping supervisors identify areas for improvement and potential revenue opportunities.
Example Score Display
Here’s how scores might appear in a dashboard. Each call entry shows compliance and sales potential scores. Supervisors can sort, filter, or review details to spot issues or opportunities.
| Call ID | Compliance Score (%) | Sales Potential (%) | Key Alerts |
|---|---|---|---|
| C-20231001-001 | 94 | 60 | Regulatory script followed; Potential upsell on warranty. |
| C-20231001-002 | 78 | 20 | Incomplete disclosure; Limited sales interest. |
Phase 3: Dashboard Development
Loading compliance data...
Technology Stack
Built using NextJS for the front end and Python-based APIs for back-end data processing.
Real-Time Data Pipeline
Integrated a stream-processing AWS Kinesis, to handle live call data and ensure real-time updates on the dashboard.
Visualization Features
Designed interactive graphs, heatmaps, and filter options to provide actionable insights.
Phase 4: Testing and Iteration
Model Validation
Tested AI models using live call data to ensure accuracy in transcription and scoring.
Dashboard Usability Testing
Conducted user feedback sessions to refine the dashboard interface and features.
Pilot Deployment
Rolled out the solution to a small set of agents before scaling to the entire organization.
The Solution
To solve these issues, we designed a three-part solution:
1. AI-Driven Call Transcription
Using a cutting-edge speech-to-text model, we automated the transcription of all incoming calls. The system:
Delivered over 95% accuracy, even with accents and variations in speech.
Included keyword spotting to highlight compliance-related terms or sales triggers.
2. Compliance and Call/ Sales Scoring
We built a machine learning model trained on historical call data to analyze conversations and assign scores based on:
Compliance adherence, such as customer greetings, consent confirmation, and required disclosures.
Quality metrics like tone, empathy, and resolution effectiveness.
Recognize patterns in conversations that indicate upselling or cross-selling potential, such as customer interest in additional services.
3. Dashboard
A custom-built dashboard brought all this data to life. It provided supervisors and analysts with:
Visualized Insights
Call scores, compliance metrics, and quality trends displayed via graphs and heatmaps.
Searchable Transcripts
Quick access to call recordings and transcripts filtered by agent, keyword, or score.
Compliance Alerts
Instant notifications for calls flagged as non-compliant.
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