Call Quality

Call Quality

Aila's Call Quality system helps you maintain consistent standards across all calls through AI-powered quality checklists, automatic scoring, and risk assessment.

Overview

The Call Quality dashboard provides comprehensive monitoring of call quality and compliance across your organization. Use it to:

  • Monitor standards - Ensure all calls meet your quality criteria
  • Identify risks - Catch compliance issues and problem calls
  • Track trends - See quality improvements over time
  • Coach effectively - Find specific calls needing attention

Quality Checklists

What are Quality Checklists?

Quality checklists define the standards every call should meet. Aila automatically evaluates each call against your checklist criteria using AI.

Checklist Components

Each checklist contains:

Criteria - Individual quality standards to evaluate:

  • "Greeted customer professionally"
  • "Used customer's name at least once"
  • "Asked discovery questions"
  • "Handled objections effectively"
  • "Set clear next steps"

Scoring - How each criterion is evaluated:

  • Pass/Fail - Binary yes/no evaluation
  • Points - Weighted scoring (0-10 points per criterion)
  • Critical - Must-pass items (instant fail if missed)

Thresholds - Overall quality levels:

  • High Quality - 90%+ score
  • Acceptable - 70-89% score
  • Needs Improvement - 50-69% score
  • Poor - Below 50% score

Creating Quality Checklists

See Settings > Call Scoring for detailed instructions on:

  • Creating custom checklists
  • Defining quality criteria
  • Setting up scoring rules
  • Assigning checklists to call types

Call Quality Dashboard

Dashboard Overview

The Call Quality page shows:

Summary Metrics:

  • Total Calls Analyzed - Calls evaluated this period
  • Average Quality Score - Overall quality rating
  • High-Risk Calls - Calls failing critical criteria
  • Compliance Rate - Percentage meeting standards

Quality Distribution:

  • Chart showing score distribution
  • Trend lines for quality over time
  • Comparison to previous periods
  • Team-level breakdowns

Risk Categories:

  • Compliance violations
  • Customer satisfaction issues
  • Policy violations
  • Training needs

Filtering Quality Data

Filter the dashboard by:

  • Date range - Specific time periods
  • Team members - Individual or group performance
  • Quality score - High, medium, low quality calls
  • Risk level - High, medium, low risk
  • Call type - Inbound, outbound, specific purposes
  • Checklist template - Specific quality standards

Quality Scores

How Scoring Works

For each call, Aila:

  1. Transcribes the call
  2. Analyzes content against checklist criteria
  3. Evaluates each criterion (pass/fail or point value)
  4. Calculates overall score
  5. Flags any high-risk items
  6. Generates improvement suggestions

Score Breakdown

View detailed scoring for any call:

Overall Score - Total quality rating (0-100%)

Criterion-Level Results:

  • ✓ Passed criteria (green)
  • ✗ Failed criteria (red)
  • AI reasoning for each evaluation
  • Timestamp references to transcript

Critical Items:

  • Highlighted separately
  • Automatic flagging for review
  • Required for quality approval

Manual Score Review

Override AI scores when needed:

  1. Open call quality view
  2. Click Review Score
  3. Adjust individual criterion results
  4. Add notes explaining override
  5. Save updated score

Risk Assessment

High-Risk Call Detection

Aila automatically flags calls with:

Compliance Issues:

  • Missing required disclosures
  • Inappropriate language
  • Regulatory violations
  • Policy breaches

Quality Concerns:

  • Very low scores
  • Failed critical criteria
  • Customer dissatisfaction indicators
  • Escalation signals

Business Risks:

  • Commitments made without authority
  • Pricing errors
  • Misrepresentation
  • Legal concerns

Risk Levels

High Risk - Immediate attention needed:

  • Compliance violations
  • Customer escalations
  • Legal issues
  • Failed critical criteria

Medium Risk - Should be reviewed:

  • Below-standard quality
  • Missing best practices
  • Training opportunities
  • Process violations

Low Risk - Monitor:

  • Minor quality issues
  • Inconsistent adherence
  • Improvement areas

Risk Management Workflow

  1. Detection - AI flags potential issues
  2. Review - Manager evaluates flagged calls
  3. Action - Coaching, retraining, or escalation
  4. Track - Monitor resolution and trends
  5. Prevent - Update training and processes

Quality Monitoring Workflow

Daily Quality Review

Morning Routine:

  1. Check overnight calls
  2. Review high-risk flagged calls
  3. Address critical issues immediately
  4. Schedule coaching for low scores

End of Day:

  1. Review all calls from today
  2. Verify quality scores
  3. Leave coaching feedback
  4. Update quality trends

Weekly Quality Analysis

  1. Trends - Review quality score trends
  2. Patterns - Identify common issues
  3. Coaching - Schedule sessions for recurring problems
  4. Training - Update materials based on findings
  5. Standards - Adjust checklists if needed

Monthly Quality Reporting

Generate comprehensive reports:

  • Quality score averages by team member
  • Trend analysis (improving/declining)
  • Common failure points
  • Training needs identified
  • Compliance status
  • Recommended actions

Quality Improvement

Identifying Training Needs

Use quality data to find improvement opportunities:

Individual Level:

  • Review specific team member's low-scoring calls
  • Identify consistent failure patterns
  • Create targeted coaching plans
  • Track improvement over time

Team Level:

  • Analyze organization-wide quality trends
  • Find commonly failed criteria
  • Update training programs
  • Revise processes if needed

Coaching from Quality Data

  1. Filter for low-quality calls
  2. Review specific failures
  3. Find patterns in individual's performance
  4. Prepare coaching with call examples
  5. Track improvement after coaching

Continuous Improvement

Regular Review Cycle:

  1. Weekly - Review quality trends
  2. Monthly - Analyze patterns and update training
  3. Quarterly - Revise quality standards
  4. Annually - Comprehensive program review

Feedback Loop:

  • Quality data → Coaching → Improved performance → Updated standards

Quality Reports

Available Reports

Team Quality Report:

  • Average scores by team member
  • Quality distribution
  • Trend lines
  • Comparison rankings

Compliance Report:

  • Pass/fail rates for critical criteria
  • Risk assessment summary
  • Violations by type
  • Remediation tracking

Trend Analysis:

  • Quality over time
  • Improvement trajectories
  • Seasonal patterns
  • Before/after training impact

Exporting Quality Data

  1. Navigate to Call Quality dashboard
  2. Apply desired filters
  3. Click Export Report
  4. Choose format (PDF, CSV, Excel)
  5. Select metrics to include
  6. Download or schedule recurring reports

Best Practices

Setting Quality Standards

Start Simple:

  • Begin with 5-7 core criteria
  • Focus on most important standards
  • Add complexity gradually
  • Test with real calls before full rollout

Make Criteria Specific:

  • ✗ "Provide good service"
  • ✓ "Greet customer by name within first 30 seconds"
  • ✓ "Ask at least 3 discovery questions"
  • ✓ "Confirm next steps before ending call"

Balance Standards:

  • Mix critical must-haves with nice-to-haves
  • Include both process and outcome criteria
  • Consider different call types
  • Align with business goals

Regular Calibration

Weekly Calibration:

  • Review AI scores vs. manager assessments
  • Discuss edge cases as team
  • Refine criteria definitions
  • Update AI training if needed

Monthly Review:

  • Are criteria still relevant?
  • Do weights reflect importance?
  • Are thresholds appropriate?
  • Should any criteria be added/removed?

Using Quality Data Effectively

Don't Over-Focus on Scores:

  • Scores are indicators, not absolute truth
  • Context matters
  • Use for trends, not individual judgment
  • Combine with qualitative feedback

Positive Reinforcement:

  • Recognize high-quality calls
  • Share excellent examples
  • Celebrate improvements
  • Use library for positive examples

Coaching, Not Punishment:

  • Use data to help, not criticize
  • Focus on specific, actionable feedback
  • Provide examples and alternatives
  • Track improvement over time

Troubleshooting

Inaccurate Scores

AI Misses Context:

  • Add more specific criteria definitions
  • Include examples in criteria descriptions
  • Calibrate with manual reviews
  • Override incorrect scores with notes

Consistently Wrong on Specific Criterion:

  • Revise criterion wording
  • Add clarifying examples
  • Check if criterion is measurable
  • Consider removing if not AI-evaluable

No Quality Scores Showing

  • Verify checklist template is assigned
  • Check call processing completion
  • Ensure checklist is active
  • Review call routing rules

Scores Don't Match Expectations

  • Review actual call transcript
  • Check criterion definitions
  • Calibrate scoring thresholds
  • Discuss with team for consensus

Next Steps

ContactsOverview