Call Analysis for Scoring and Compliance
Introduction
A US-based call center managing customer interactions for multinational corporations faced a highly manual, time-consuming process for reviewing calls. Meeting strict compliance standards while identifying sales opportunities was critical, but lack of automation caused delays, inconsistencies, and missed revenue potential.
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
The manual call review process:
Thousands of daily calls were being manually reviewed for compliance and quality, slowing down QA and creating inconsistencies.
US call centers must adhere to strict legal and industry standards (greetings, consent, mandatory disclosures).
Delayed analysis led to missed opportunities for upselling and cross-selling.
The manual QA process could not scale with growing call volumes.
Phase 1: Data Collection and Preparation
Historical calls, agent logs, and compliance guidelines were integrated into the AI pipeline.
Mapped compliance rules and disclosures for accurate AI training.
Labeled key compliance and sales events to train the ML models.
Data Collection Overview
| 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
Fine-tuned on client audio data to improve transcription accuracy for domain-specific language and accents.
Supervised model trained to generate compliance and sales scores using:
Sentiment and empathy analysis.
Keyword detection for regulatory phrases and sales triggers.
Conversational flow analysis to assess call quality.
Audio Input
Raw call recordings are first pre-processed to standardize audio quality and format.
Speech-to-Text
Audio is transcribed using a fine-tuned ASR model adapted to the client’s domain and terminology.
Feature Extraction
Key features — sentiment, empathy markers, compliance phrases, conversational flow — are extracted from transcripts.
Scoring
Supervised ML models assign two key scores: Compliance Score (adherence to legal and procedural standards), Sales Potential Score (upselling or cross-selling opportunities).
Output
Scores and metadata are delivered to the dashboard, enabling supervisors to quickly identify risks and opportunities.
Example Score Display
Dashboard view with per-call compliance and sales scores, searchable and filterable by key dimensions.
| 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: Real-Time Data Pipeline & Dashboard
Next.js frontend with Python-based backend APIs for processing.
AWS Kinesis used for live call processing and dashboard updates.
Interactive graphs, heatmaps, and search/filter capabilities for QA teams.
Phase 4: Testing and Iteration
Tested models with live call data to ensure transcription and scoring accuracy.
User feedback sessions improved dashboard UX.
Pilot launched with select agents before full rollout.
Review recent customer calls assessed for compliance. Click "View Details" to see the reasons behind the compliance score and listen to the call recording.
Recent Calls
The Solution
To solve these challenges, we designed an AI-powered solution with three main components:
1. AI-Driven Call Transcription
Using a fine-tuned speech-to-text model, we automated transcription of all call recordings. The system:
Delivered 95%+ transcription accuracy, handling accents and speech variations.
Keyword spotting highlighted compliance violations and sales triggers.
2. Compliance and Call Scoring
We developed a supervised machine learning model to analyze conversations and generate scores based on:
Compliance adherence: greetings, consent, mandatory disclosures.
Quality factors: tone, empathy, resolution.
Sales potential: conversational patterns indicating upsell or cross-sell.
3. Interactive Dashboard
A custom-built dashboard visualized key metrics, providing supervisors and analysts with:
Compliance and sales scores visualized with trends and heatmaps.
Indexed transcripts searchable by agent, keyword, or score.
Real-time alerts for non-compliant calls.
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