5: Reporting & Analytics

1. Tutorial: Why Reporting & Analytics Matter

Reporting and analytics are the feedback loop of Workforce Management. They show whether forecasts, schedules, and real-time adjustments achieved the desired outcomes.

Key WFM Metrics

  • Service Level (SL): % of calls answered within target time (e.g., 80% in 20 seconds).
  • Average Handle Time (AHT): Average duration of calls, including talk + after-call work.
  • Occupancy: % of time agents spend handling calls vs. idle.
  • Shrinkage: % of paid time agents are unavailable (breaks, training, absenteeism).
  • Forecast Accuracy: How close actual demand was to forecasted demand.

Best Practices

  • Use dashboards for real-time visibility.
  • Compare forecast vs. actuals to refine future models.
  • Track trends over time, not just daily snapshots.
  • Share insights with leadership to align WFM with business goals.

2. Scenario: Investigating a Service Level Drop

Last week, your call center’s service level fell from 85% to 75%. Leadership wants answers.

You analyze the data:

  • Forecasted calls: 9,000
  • Actual calls: 9,800 (forecast error = +9%)
  • AHT increased from 6.0 to 6.5 minutes
  • Shrinkage rose from 28% to 32%

👉 What does this tell you?

  • Forecast underestimated demand.
  • Agents took longer per call (AHT increase).
  • Higher shrinkage reduced available staffing.

Conclusion: The service level drop was caused by a combination of forecast error, rising AHT, and shrinkage.

3. Test: Quick Knowledge Check

  1. Which metric measures how close actual demand was to forecasted demand?
    1. Service Level
    1. Forecast Accuracy
    1. Occupancy
    1. Shrinkage
  2. True or False: AHT includes both talk time and after-call work.
  3. If forecasted calls = 10,000 and actual calls = 10,500, what is the forecast error?
  1. 5%
  1. 10%
  1. 15%
  1. 20%

✅ Answers

  1. B) Forecast Accuracy → It measures the gap between forecasted and actual demand.
  2. True → AHT includes both talk time and after-call work.
  3. A) 5% → (10,500 – 10,000) ÷ 10,000 = 0.05 = 5%.

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