--- name: agent-performance-analytics description: Comprehensive analytics on individual and team agent performance including tickets handled, response times, resolution rates, CSAT, quality scores, SLA compliance, productivity, and peer benchmarking with automated insights. Use when measuring agent performance, generating agent scorecards, identifying coaching opportunities, tracking team KPIs, conducting performance reviews, or optimizing agent productivity. Triggers on phrases like "agent performance", "agent analytics", "agent scorecard", "support KPIs", "agent benchmarking", "team performance", "agent metrics", "performance review". --- # Agent Performance Analytics Comprehensive analytics on individual and team agent performance to drive coaching, recognize excellence, identify skill gaps, and optimize overall support team productivity. ## Workflow ### 1. Agent KPI Collection and Scorecard Generation 1. **Core performance metrics per agent**: ``` AGENT SCORECARD — Sarah Mitchell (L2 Agent, 18 months tenure) Period: January 1-15, 2026 (15 days) ================================================ VOLUME METRICS: Tickets handled: 234 Tickets assigned: 241 Tickets resolved: 221 (94.4% of handled) Tickets pending: 13 (5.6%) Avg tickets/day: 15.6 SPEED METRICS: First response time: 8.2 min avg (team avg: 10.1 min) → ABOVE AVERAGE ✓ Resolution time: 3.4 hours avg (team avg: 4.1 hours) → ABOVE AVERAGE ✓ Reply interval: 21.3 min avg (team avg: 24.7 min) → ABOVE AVERAGE ✓ QUALITY METRICS: CSAT score: 4.7/5.0 (team avg: 4.3/5.0) → ABOVE AVERAGE ✓ Quality review score: 92/100 (team avg: 85/100) → ABOVE AVERAGE ✓ First contact resolution: 73% (team avg: 68%) → ABOVE AVERAGE ✓ Reopened tickets: 3 (1.3%, team avg: 2.1%) → ABOVE AVERAGE ✓ Negative CSAT responses: 2 (0.9%, team avg: 1.8%) → ABOVE AVERAGE ✓ COMPLIANCE METRICS: SLA compliance rate: 98.7% (team avg: 96.2%) → ABOVE AVERAGE ✓ SLA breaches: 3 (vs team avg: 6) → ABOVE AVERAGE ✓ Escalation rate: 8.1% (team avg: 11.2%) → ABOVE AVERAGE ✓ Ticket reassignment rate: 4.3% (team avg: 7.8%) → ABOVE AVERAGE ✓ PRODUCTIVITY METRICS: Tickets per productive hour: 2.1 (team avg: 1.8) → ABOVE AVERAGE ✓ Handle time per ticket: 28 min avg (team avg: 34 min) → ABOVE AVERAGE ✓ Breaks taken: 10 (within policy: 2/day) ✓ After-hours work: 4.2 hours (within threshold: <5 hrs/week) ✓ COMPOSITE PERFORMANCE SCORE: 94/100 Percentile rank: 92nd percentile (top 8% of team) Trend: ↑ 3 points from last period (improving) SKILL BREAKDOWN: Skill Category | Score | Team Avg | Rating ----------------------|-------|----------|------- Technical troubleshooting | 96 | 86 | ★★★★★ Customer empathy | 94 | 88 | ★★★★★ Billing support | 91 | 84 | ★★★★☆ Documentation | 89 | 85 | ★★★★☆ Escalation handling | 93 | 87 | ★★★★★ Communication clarity | 95 | 89 | ★★★★★ ``` 2. **Team-level performance dashboard**: - Team averages for all core metrics - Performance distribution: Top 25%, Middle 50%, Bottom 25% - Trend analysis: Week-over-week, month-over-month comparisons - Workload distribution: Tickets per agent, utilization rates - Coverage by skill area: Which agents handle which issue types 3. **Automated scorecard delivery**: - Weekly: Individual scorecard sent to agent via email - Monthly: Detailed scorecard with trends and peer comparison - Quarterly: Comprehensive review with manager (performance meeting) - Real-time: Agent self-service dashboard (login to view current metrics) ### 2. Trend Analysis and Predictive Insights 1. **Performance trend detection**: - Improving trend: Agent metrics consistently rising over 4+ weeks - Declining trend: Agent metrics consistently falling over 4+ weeks → flag for coaching - Plateau: No significant change → suggest advanced training - Volatile: Large week-to-week swings → investigate workload or external factors 2. **Predictive analytics**: - **Burnout risk**: Predict based on workload, after-hours work, CSAT decline - **Churn risk**: Agent likely to leave based on engagement metrics - **Promotion readiness**: Agent performance exceeds L2 threshold → recommend L3 - **Skill development trajectory**: Predict time to mastery for new skills 3. **Correlation analysis**: - Training completion → performance improvement correlation - Ticket complexity → resolution time correlation - Work schedule → productivity correlation - Tenure → performance improvement curve ### 3. Coaching Priority and Action Planning 1. **Automated coaching recommendations**: ``` COACHING RECOMMENDATIONS — Weekly Auto-Generated ================================================== PRIORITY 1 — Urgent (declining performance): Agent: James T. Issue: CSAT dropped from 4.4 to 3.8 over 3 weeks Root cause hypothesis: Handling more billing tickets (complex, lower CSAT) Recommendation: Billing support training + shadow top performer (Sarah M.) Deadline: Complete training by Jan 25 PRIORITY 2 — Improvement opportunity: Agent: Maria L. Issue: Resolution time 6.2 hours (45% above team average) Root cause hypothesis: Thorough but inefficient troubleshooting process Recommendation: Troubleshooting efficiency workshop + decision tree training Deadline: Review progress in 2 weeks PRIORITY 3 — Recognition and retention: Agent: Sarah M. Issue: Top 8% performer but after-hours work increasing (potential burnout risk) Recommendation: Acknowledge excellence + reduce ticket allocation by 10% Action: Manager conversation scheduled Jan 20 PRIORITY 4 — Skill gap filling: Agent: David K. (new hire, 6 weeks) Issue: Escalation rate 22% (vs 11% team avg, but expected for new hire) Recommendation: Continue standard onboarding, pair with mentor for 4 more weeks Progress: ↓ from 35% escalation rate at week 2 → on track ``` 2. **Skill gap identification**: - Compare agent performance across issue categories - Identify categories where agent performs below average - Map gaps to available training resources - Create personalized development plan - Track improvement after training completion ## Templates & Frameworks ### Agent Performance Dashboard ``` TEAM PERFORMANCE DASHBOARD — January 2026 ========================================== TEAM OVERVIEW: Active agents: 42 (L1: 28, L2: 12) On PTO this week: 3 New hires (this month): 4 Average tenure: 14.2 months Team composite score: 82/100 (↑ 2 from December) Performance distribution: Top 25% (score >88): 11 agents (26.2%) Middle 50% (score 72-88): 23 agents (54.8%) Bottom 25% (score <72): 8 agents (19.0%) TEAM KPIs (Last 30 Days): Metric | Team Avg | Target | Status | Trend ---------------------------|----------|-----------|--------|------ Tickets handled/agent | 178 | 180 | ⚠ -1.1%| ↗ ↑ First response time | 10.1 min | <15 min | ✓ | ↘ ↓ Resolution time | 4.1 hrs | <5 hrs | ✓ | ↘ ↓ CSAT score | 4.3/5.0 | >4.0 | ✓ | → = First contact resolution | 68% | >65% | ✓ | ↗ ↑ SLA compliance | 96.2% | >95% | ✓ | ↗ ↑ Quality review score | 85/100 | >80 | ✓ | → = Reopened tickets | 2.1% | <3% | ✓ | ↘ ↓ Escalation rate | 11.2% | <15% | ✓ | ↘ ↓ TOP PERFORMERS (This Month): Rank | Agent | Score | Tickets | CSAT | FCR | Recognition -----|--------------|-------|---------|--------|--------|------------ 1 | Sarah M. | 94 | 234 | 4.7/5.0| 73% | 🏆 Agent of Month 2 | Tom R. | 92 | 228 | 4.6/5.0| 71% | ★★★★★ 3 | Lisa K. | 91 | 215 | 4.6/5.0| 70% | ★★★★★ 4 | Alex P. | 89 | 219 | 4.5/5.0| 69% | ★★★★☆ 5 | Maria L. | 88 | 201 | 4.5/5.0| 68% | ★★★★☆ NEEDS ATTENTION (This Month): Rank | Agent | Score | Primary Issue | Action Taken | Deadline -----|--------------|-------|----------------------------|---------------------|---------- 1 | James T. | 68 | CSAT declining (4.4→3.8) | Training + mentoring | Jan 25 2 | Kevin W. | 66 | High escalation rate (28%) | Skill assessment | Jan 22 3 | Rachel H. | 65 | Slow resolution (8.2 hrs) | Efficiency coaching | Jan 28 4 | Mark D. | 64 | High reopen rate (5.4%) | Quality review | Jan 24 5 | Amy S. | 63 | Low productivity (9.2/d) | Workload assessment | Jan 21 WORKLOAD DISTRIBUTION: Average tickets/agent: 178 Highest load: Sarah M. (234 tickets, 31% above avg) ⚠ Lowest load: David K. (112 tickets, new hire — expected) Workload balance score: 78/100 (acceptable, but top-heavy) Recommended rebalancing: Transfer 15 tickets from Sarah M. to team pool Add capacity: Consider hiring 2 more L1 agents (current utilization: 89%) TRAINING IMPACT: Training completed this month: 34 courses across 22 agents Performance improvement post-training (avg): +4.2 points Training completion rate: 67% (target: >70%) ⚠ Most effective training: "Billing Support Mastery" → +6.1 points avg improvement "Advanced Troubleshooting" → +5.4 points avg improvement "Customer Empathy" → +4.8 points avg improvement COACHING SESSIONS: Scheduled this month: 12 Completed: 9 (75%) Improvement after coaching (avg): +5.7 points (measured at 30 days) Coaching priority queue: 8 agents needing attention this month PREDICTIVE INSIGHTS: Burnout risk (high workload + declining metrics): 3 agents flagged Promotion readiness (L1 → L2): 4 agents meet criteria New hire ramp tracking: 4 new hires, avg 62% of team avg (on track for 80% by week 8) Attrition risk (engagement decline): 2 agents flagged for manager check-in ``` ### Performance Review Template ``` QUARTERLY PERFORMANCE REVIEW — Q4 2025 Agent: Sarah Mitchell | Period: October 1 - December 31, 2025 =============================================================== PERFORMANCE SUMMARY: Composite score: Q3 91 → Q4 94 (↑ 3 points) Percentile rank: 92nd (top 8% of team) Overall rating: EXCEEDS EXPECTATIONS KEY ACHIEVEMENTS: 1. Highest CSAT on team (4.7/5.0) for all 3 months 2. Reduced resolution time by 12% while maintaining quality 3. Mentored 2 new hires (both on track for ramp completion) 4. Created 5 internal KB articles (most on team) 5. Zero SLA breaches in November (perfect month) STRENGTHS: • Technical troubleshooting (96/100 — top skill) • Customer empathy (94/100) • Communication clarity (95/100) • Consistency (minimal week-to-week variance) AREAS FOR GROWTH: • After-hours work trending up (3.2 hrs → 4.2 hrs/week) → burnout risk • Workload management (handling 31% above team avg) → sustainability concern • Delegation opportunities (could mentor more systematically) GOALS FOR Q1 2026: 1. Maintain CSAT >4.6 while reducing ticket load by 10% 2. Complete "Leadership Fundamentals" training (promotion path) 3. Mentor 1 additional new hire 4. Create workflow automation template (share best practice) 5. Manage after-hours work <3.5 hrs/week COMPENSATION AND RECOGNITION: Q4 performance bonus: $2,400 (top 10% tier) Recognition: Agent of Month (November, December) Promotion readiness: On track for L3 consideration Q2 2026 MANAGER COMMENTS: "Sarah continues to be our benchmark agent. Her technical depth and customer empathy are exceptional. Key focus for Q1: sustainable workload management and leadership development to prepare for L3 transition." ``` ## Integration Points - **Help desk platforms** (Zendesk, Intercom, Freshdesk): Ticket data, resolution times, CSAT collection - **Quality management** (MaestroQA, QualityHub): Quality review scores, evaluation frameworks - **CRM** (Salesforce, HubSpot): Agent activity logging, customer context quality - **LMS** (Docebo, Lessonly): Training completion, certification tracking, course effectiveness - **HR systems** (BambooHR, Workday): Performance records, promotion tracking, compensation - **BI tools** (Tableau, Power BI, Looker): Dashboard creation, trend analysis, executive reporting - **Communication** (Slack, Teams): Scorecard delivery, coaching notifications, recognition ## Edge Cases - **New agent with insufficient data for scorecard**: Agent in first 2 weeks has only 30 tickets (vs 180 monthly avg): - Minimum threshold: 50 tickets or 14 days before full scorecard generation - Ramp benchmark: Compare to other new hires at same tenure point - Weekly check-ins: Manager reviews qualitative performance (not just metrics) - Progressive scorecard: Metrics gradually unlock as data accumulates - Mentor feedback: Primary assessment method during first 30 days - **Agent handles only one ticket type**: Specialist agent handles only billing tickets (narrow scope): - Category-specific benchmarks: Compare against other billing specialists - Depth over breadth: Higher weight on category-specific quality metrics - Skill development: Encourage cross-training to broaden scope - Fair comparison: Exclude irrelevant categories from composite score - Recognition: Highlight specialist excellence despite narrow scope - **Performance dip due to product outage**: 3-day outage causes all agents' metrics to decline (unfair to penalize): - Event flagging: Mark period as "exceptional" in trend analysis - Peer context: Show that all agents were affected (not individual issue) - Exclusion: Option to exclude exceptional periods from quarterly averages - Recovery tracking: Measure performance post-outage (return to baseline) - Manager guidance: "Performance dip was team-wide, driven by outage" - **High volume but low quality vs low volume but high quality**: Two agents, opposite profiles: - Balanced scorecard: Weight quality and volume equally (or per company preference) - Contextual analysis: Volume agent handles simpler tickets? Quality agent handles complex? - Ticket complexity adjustment: Normalize for difficulty of tickets assigned - Coaching paths: Volume agent → quality training; Quality agent → efficiency training - Both can be "top performers" in different categories - **Agent metrics manipulated (gaming the system)**: Agent closes tickets quickly with "resolved" but issues recur: - Reopen rate monitoring: High reopen rate flags potential gaming - Quality review sampling: Random tickets reviewed for resolution completeness - Customer follow-up: CSAT surveys 48 hours after resolution detect unsatisfied customers - Escalation pattern: Customer re-contacting for same issue → investigation triggered - Penalty: If confirmed, reset metrics, retraining, coaching on resolution quality ## Output ### Monthly Performance Summary ``` TEAM PERFORMANCE MONTHLY SUMMARY — January 2026 ================================================ TEAM HEALTH SCORE: 82/100 ✓ (↑ 2 from December) KEY WINS: 1. CSAT improved to 4.3/5.0 (↑ 0.1 — team record) 2. First contact resolution reached 68% (↑ 3% — target achieved) 3. SLA compliance at 96.2% (↑ 1.2% — sustained improvement) 4. 4 agents achieved "Top Performer" status for 3+ consecutive months 5. Training completion rate improved to 67% (↑ 8%) AREAS FOR IMPROVEMENT: 1. Bottom 25% agents still 15 points below team average 2. Workload imbalance: Top 5 agents handling 20% of all tickets 3. Training completion not yet at 70% target 4. 3 agents flagged for burnout risk COACHING IMPACT: Agents coached last month: 12 Average improvement after coaching: +5.7 points Coaching ROI: $18,000/month estimated (from improved resolution and CSAT) NEXT MONTH PRIORITIES: 1. Complete burnout risk interventions (3 agents) 2. Hire 2 additional L1 agents (reduce workload pressure) 3. Launch "Leadership Fundamentals" program (4 promotion-ready agents) 4. Implement automated performance alerting (real-time metric drop detection) 5. Q1 performance review preparation (schedule with all 42 agents) ```