WFM Metrics

This page provides you with detailed information about the various WFM (Workforce Management) metrics available in the Dashboard metric widgets. By clicking on the Learn More dropdown, you can access additional details on each metric including calculation, filters, supported channels, metric type, metric direction, and use case.

% Avg Handle Time Variance

The % Avg Handle Time Variance metric measures the percent difference between actual and forecasted average handle time for the skill group.

  • Calculation: This metric calculates the percent difference between the actual average handle time and the forecasted average handle time for the skill group.

  • Filters:

    • Employee group: Company

    • Contact group: Company

    • Attributes: Scheduling Unit, Skill Group

  • Metric type: Historical

  • Metric direction: Down, a lower metric value is best.

  • Use Case: Refining AHT Forecasts: A WFM analyst monitors % Avg Handle Time Variance over several weeks and notices that AHT is consistently 15% higher than forecasted on Mondays. The analyst identifies Monday as a high-complexity day due to weekend backlog, and adjusts the Monday AHT assumption in the forecasting model to improve schedule accuracy.

% Service Level Actual

The % Service Level Actual metric measures the actual service level agreement value for a given interval — the percentage of total interactions that agents handled within the defined service level agreement threshold, as received from the ACD.

  • Calculation: This metric calculates the percentage of contacts handled within the service level threshold out of all contacts received in the interval, using actual data from the ACD.

  • Filters:

    • Employee group: Company

    • Contact group: Company

    • Attributes: Scheduling Unit, Skill Group

  • Metric type: Historical

  • Metric direction: Up, a higher metric value is best.

  • Use Case: Tracking Service Level Attainment: A WFM manager monitors % Service Level Actual throughout the day to ensure that the contact center is meeting its committed SLA targets. When the metric drops below the target threshold in a specific interval, the manager moves agents off break early to restore coverage and bring SLA back within target.

% Service Level Forecast

The % Service Level Forecast metric shows the original forecasted service level for the skill group at each time of day, based on the staffing plan. Note: the SLA value here is defined during forecasting and may differ from % Service Level Actual, which comes directly from the ACD.

  • Calculation: This metric represents the forecasted percentage of contacts expected to be handled within the service level threshold, as defined in the staffing plan.

  • Filters:

    • Employee group: Company

    • Contact group: Company

    • Attributes: Scheduling Unit, Skill Group

  • Metric type: Historical

  • Metric direction: Up, a higher metric value is best.

  • Use Case: Evaluating SLA Planning Accuracy: A WFM planner compares % Service Level Forecast to % Service Level Actual after each week to assess whether the staffing plan correctly anticipated SLA performance. Recurring intervals where actual SLA falls short of forecast prompt a review of the staffing model’s agent availability assumptions.

% Service Level Variance

The % Service Level Variance metric measures the absolute difference between the actual service level and the forecasted service level.

  • Calculation: This metric calculates the difference between the actual service level percentage and the forecasted service level percentage for the interval.

  • Filters:

    • Employee group: Company

    • Contact group: Company

    • Attributes: Scheduling Unit, Skill Group

  • Metric type: Historical

  • Metric direction: Down, a lower metric value is best.

  • Use Case: Identifying SLA Gaps: A WFM analyst reviews % Service Level Variance at the end of each shift to identify intervals where the staffing plan significantly over- or under- delivered on SLA. Large negative variances (actual SLA well below forecast) highlight intervals that require additional staffing in future schedule builds, while large positive variances may indicate overstaffing opportunities.

% Staffing Actual Variance

The % Staffing Actual Variance metric measures the percent difference between actual open FTE (full-time equivalent) and the required number of agents from the forecast for the skill group.

  • Calculation: This metric calculates the relative difference between the actual number of open agents and the forecasted staffing requirement, expressed as a percentage.

  • Filters:

    • Employee group: Company

    • Contact group: Company

    • Attributes: Scheduling Unit, Skill Group

  • Metric type: Historical

  • Metric direction: Down, a lower metric value is best.

  • Use Case: Evaluating Staffing Efficiency: A WFM analyst uses % Staffing Actual Variance to identify skills where actual staffing consistently deviates from the forecast requirement. Skills with large positive variance (over-staffed) become candidates for headcount redeployment, while skills with large negative variance (under-staffed) receive priority in the next schedule build.

% Volume Abandoned

The % Volume Abandoned metric measures the percentage of total contacts that were abandoned by callers, calculated every 15-minute interval.

  • Calculation: This metric calculates the percentage of contacts abandoned by callers out of the total number of contacts received in the interval.

  • Filters:

    • Employee group: Company

    • Contact group: Company

    • Attributes: Scheduling Unit, Skill Group

  • Metric type: Historical

  • Metric direction: Down, a lower metric value is best.

  • Use Case: Monitoring Abandonment During Peak Hours: A contact center manager notices a spike in % Volume Abandoned during lunchtime intervals. By identifying the pattern, the manager adjusts agent break schedules to ensure sufficient coverage during peak abandonment periods, reducing the percentage and improving customer experience.

% Volume Variance

The % Volume Variance metric measures the percent difference between actual and forecasted contact volume for the skill group.

  • Calculation: This metric calculates the relative difference between the actual contact volume and the forecasted contact volume for the skill group, expressed as a percentage.

  • Filters:

    • Employee group: Company

    • Contact group: Company

    • Attributes: Scheduling Unit, Skill Group

  • Metric type: Historical

  • Metric direction: Down, a lower metric value is best.

  • Use Case: Improving Forecast Accuracy: A WFM analyst reviews % Volume Variance weekly to assess how accurately the forecasting model predicted actual contact volume. Large positive variances (more contacts than forecasted) prompt over-staffing reviews, while large negative variances drive under-scheduling investigations, both feeding back into forecast model improvements.

Actual Abandon Rate

The Actual Abandon Rate metric measures the percentage of inbound contacts that were abandoned by callers prematurely before being answered by an agent.

  • Calculation: This metric calculates the percentage of inbound contacts that were abandoned by callers before being answered, out of the total number of contacts received.

  • Filters:

    • Employee group: Company

    • Contact group: Company

    • Attributes: Scheduling Unit, Skill Group

  • Metric type: Historical

  • Metric direction: Down, a lower metric value is best.

  • Use Case: Monitoring Call Abandonment Rate: A contact center manager notices a high abandonment rate during peak hours. By analyzing this metric, the manager identifies long wait times as the cause. To address this, the manager: Increases staffing during peak hours. Offers call-back options to reduce hold times. Enhances the IVR system for better self-service. These actions help reduce the abandonment rate, improve customer satisfaction, and ensure more calls are answered promptly.

AHT Actual

The AHT Actual metric measures the current average handle time as it comes in from the ACD — the average time an agent spends handling a contact, including talk time, hold time, and after call work.

  • Calculation: This metric calculates the average time agents spend handling a contact, including talk time, hold time, and after call work, based on data received from the ACD.

  • Filters:

    • Employee group: Company

    • Contact group: Company

    • Attributes: Scheduling Unit, Skill Group

  • Metric type: Historical

  • Metric direction: Down, a lower metric value is best.

  • Use Case: Monitoring Handle Time in Real Time: An intraday manager notices that AHT Actual has risen 20% above the forecast during the afternoon shift. By identifying the affected skill, the manager investigates whether a complex issue type is driving longer calls and triggers a real-time update to agent guidance documentation to help reduce handle time.

AHT Forecast

The AHT Forecast metric shows the original forecasted average handle time for the skill group at each time of day, based on the staffing plan.

  • Calculation: This metric represents the average handle time assumed in the staffing plan for the skill group, calculated from historical contact data used during the forecasting process.

  • Filters:

    • Employee group: Company

    • Contact group: Company

    • Attributes: Scheduling Unit, Skill Group

  • Metric type: Historical

  • Metric direction: Down, a lower metric value is best.

  • Use Case: Validating Handle Time Assumptions: A WFM planner uses AHT Forecast to review the assumptions baked into the staffing plan. After a product launch, the planner compares AHT Forecast to AHT Actual and finds that post-launch contacts are taking 30% longer. The planner updates AHT assumptions in the next forecast cycle to reflect the new complexity.

AHT Variance

The AHT Variance metric measures the absolute difference between actual average handle time and forecasted average handle time.

  • Calculation: This metric calculates the difference in seconds between the actual average handle time received from the ACD and the forecasted average handle time from the staffing plan.

  • Filters:

    • Employee group: Company

    • Contact group: Company

    • Attributes: Scheduling Unit, Skill Group

  • Metric type: Historical

  • Metric direction: Down, a lower metric value is best.

  • Use Case: Identifying Scheduling Impact of Handle Time Drift: A WFM manager reviews AHT Variance across skills and identifies one skill where actual handle time is consistently 45 seconds above forecast. The extra handle time is reducing effective capacity by 12%, leading to a staffing plan revision that adds one additional agent to that skill during peak hours.

ASA Actual

The ASA Actual metric measures the current average speed of answer as it comes in from the ACD — the average time it takes an agent to answer an interaction after the contact chose to speak with an agent.

  • Calculation: This metric calculates the average time in seconds it takes agents to answer contacts, based on actual data received from the ACD.

  • Filters:

    • Employee group: Company

    • Contact group: Company

    • Attributes: Scheduling Unit, Skill Group

  • Metric type: Historical

  • Metric direction: Down, a lower metric value is best.

  • Use Case: Monitoring Real-Time Service Speed: An intraday manager monitors ASA Actual throughout the day to ensure agents are answering contacts within the service level target. When ASA Actual rises above the threshold, the manager reassigns agents from off-phone activities back to handling contacts, restoring acceptable response times.

ASA Forecast

The ASA Forecast metric shows the original forecasted average speed of answer for each time of day, based on the staffing plan.

  • Calculation: This metric represents the average speed of answer expected for the skill group at each interval, as defined in the staffing plan during the forecasting process.

  • Filters:

    • Employee group: Company

    • Contact group: Company

    • Attributes: Scheduling Unit, Skill Group

  • Metric type: Historical

  • Metric direction: Down, a lower metric value is best.

  • Use Case: Benchmarking Against the Staffing Plan: A WFM analyst compares ASA Forecast to ASA Actual to validate whether the staffing plan produced the anticipated response times. When actual ASA is consistently higher than forecast, the analyst reviews whether the staffing plan assumed unrealistic handle times or agent availability levels.

ASA Variance

The ASA Variance metric measures the difference between the actual average speed of answer and the forecasted average speed of answer.

  • Calculation: This metric calculates the difference in seconds between the actual average speed of answer and the forecasted average speed of answer for the interval.

  • Filters:

    • Employee group: Company

    • Contact group: Company

    • Attributes: Scheduling Unit, Skill Group

  • Metric type: Historical

  • Metric direction: Down, a lower metric value is best.

  • Use Case: Detecting Staffing Plan Gaps: A WFM planner reviews ASA Variance at the end of each day to identify intervals where the staffing plan underestimated answer speed requirements. Large positive variances (agents slower than forecast) highlight understaffed intervals, while negative variances indicate overstaffing, both informing future schedule adjustments.

Avg Actual Activity Time

The Avg Actual Activity Time metric measures the average time an agent spent in a specific activity during the selected time frame, displayed in HH:MM:SS format.

  • Calculation: This metric calculates the average duration agents spent in a specific activity by dividing the total activity time across all selected agents by the number of distinct agents.

  • Filters:

    • Employee group: Company

    • Contact group: Company

    • Attributes: Scheduling Unit, Skill Group

  • Metric type: Historical

  • Metric direction: Up, a higher metric value is best.

  • Use Case: Benchmarking Activity Efficiency: A supervisor uses Avg Actual Activity Time to compare how long different agents spend on the same activity type. If one group of agents averages significantly longer in a particular activity, the supervisor investigates whether this reflects workload complexity, inefficiency, or a need for additional training.

In Adherence

The In Adherence metric measures the total amount of time an agent followed their assigned schedule, displayed in hours.

  • Calculation: This metric calculates the total number of hours agents were in adherence with their assigned schedule during the selected time period.

  • Filters:

    • Employee group: Company

    • Contact group: Company

    • Attributes: Scheduling Unit, Skill Group

  • Metric type: Historical

  • Metric direction: Up, a higher metric value is best.

  • Use Case: Rewarding Schedule Adherence: A WFM supervisor reviews the In Adherence metric at the end of the week to identify agents who closely followed their schedules. High-performing agents are recognized, and agents with lower adherence scores receive coaching to understand the importance of schedule compliance, reducing operational gaps and improving service levels.

Out of Adherence

The Out of Adherence metric measures the total amount of time agents were out of adherence with their assigned schedule, displayed in days, hours, minutes, and seconds.

  • Calculation: This metric calculates the cumulative time agents spent out of adherence with their assigned schedule during the selected time period.

  • Filters:

    • Employee group: Company

    • Contact group: Company

    • Attributes: Scheduling Unit, Skill Group

  • Metric type: Historical

  • Metric direction: Down, a lower metric value is best.

  • Use Case: Identifying Adherence Issues: A WFM manager notices that a team’s Out of Adherence value has increased over the past two weeks. By drilling down by agent, the manager identifies that several agents are consistently taking extended breaks. The manager schedules a team meeting to clarify break policies and adjusts shift timings to reduce out-of-adherence occurrences.

Over Scheduled

The Over Scheduled metric measures the number of times more agents were scheduled than required, for the selected time period.

  • Calculation: This metric counts the number of intervals in which the number of scheduled agents exceeded the number of agents required by the forecast.

  • Filters:

    • Employee group: Company

    • Contact group: Company

    • Attributes: Scheduling Unit, Skill Group

  • Metric type: Historical

  • Metric direction: Down, a lower metric value is best.

  • Use Case: Reducing Over-Scheduling Costs: A WFM planner uses Over Scheduled to identify days and skills where surplus agents are scheduled. By reducing over-scheduled intervals, the planner reallocates agent hours to understaffed periods or reduces total scheduled hours, lowering labor costs while maintaining service levels.

Scheduled Time

The Scheduled Time metric measures the total amount of time an activity was scheduled for agents, displayed in days, hours, minutes, and seconds.

  • Calculation: This metric calculates the total duration of all scheduled activities for agents during the selected time period, excluding any unscheduled time.

  • Filters:

    • Employee group: Company

    • Contact group: Company

    • Attributes: Scheduling Unit, Skill Group

  • Metric type: Historical

  • Metric direction: Up, a higher metric value is best.

  • Use Case: Validating Schedule Coverage: A WFM planner compares Scheduled Time against actual handle time to validate that enough hours were scheduled to meet forecasted demand. If Scheduled Time is consistently lower than required, the planner adjusts shift templates to add coverage during high-volume periods.

Staffing Actual

The Staffing Actual metric measures the FTE (full-time equivalent) of agents that were considered open by the ACD within each time interval. The value is reported as FTE rather than individual agent count.

  • Calculation: This metric calculates the total FTE of agents reported as open and available by the ACD within each interval.

  • Filters:

    • Employee group: Company

    • Contact group: Company

    • Attributes: Scheduling Unit, Skill Group

  • Metric type: Historical

  • Metric direction: Up, a higher metric value is best.

  • Use Case: Intraday Staffing Validation: An intraday manager compares Staffing Actual to Staffing Required throughout the day. When Staffing Actual drops significantly below Staffing Required due to agent absences, the manager activates the contingency plan — calling in off-duty agents or adjusting break schedules — to restore adequate coverage.

Staffing Actual Variance

The Staffing Actual Variance metric measures the difference between the actual number of open agents and the required number of agents from the forecast.

  • Calculation: This metric calculates the difference between the actual number of agents open in the ACD and the number of agents required by the forecast for each interval.

  • Filters:

    • Employee group: Company

    • Contact group: Company

    • Attributes: Scheduling Unit, Skill Group

  • Metric type: Historical

  • Metric direction: Down, a lower metric value is best.

  • Use Case: Detecting Real-Time Staffing Gaps: A WFM manager reviews Staffing Actual Variance at the end of each day to identify intervals with the largest staffing gaps. Intervals with large negative variance (fewer agents than needed) become the focus of the next intraday planning session, where the manager works to reduce gaps through improved schedule adherence or targeted overtime planning.

Staffing Forecast

The Staffing Forecast metric shows the originally forecasted number of agents required per interval, as generated by the staffing plan.

  • Calculation: This metric represents the number of agents the staffing plan originally forecasted as required to meet demand for each interval.

  • Filters:

    • Employee group: Company

    • Contact group: Company

    • Attributes: Scheduling Unit, Skill Group

  • Metric type: Historical

  • Metric direction: Up, a higher metric value is best.

  • Use Case: Reviewing the Staffing Plan Baseline: A WFM planner uses Staffing Forecast as the baseline reference when evaluating actual staffing performance. By comparing it to Staffing Actual and Staffing Scheduled, the planner can identify whether staffing gaps originated from forecast inaccuracies, scheduling shortfalls, or real-time absences.

Staffing Required

The Staffing Required metric measures the number of agents required to handle the interactions within each time interval. The number comes directly from the forecast or staffing plan used to generate the schedule.

  • Calculation: This metric represents the number of agents required to handle forecasted contact volume within each interval, as determined by the forecast or staffing plan.

  • Filters:

    • Employee group: Company

    • Contact group: Company

    • Attributes: Scheduling Unit, Skill Group

  • Metric type: Historical

  • Metric direction: Up, a higher metric value is best.

  • Use Case: Capacity Planning Validation: A WFM planner uses Staffing Required alongside Staffing Scheduled and Staffing Actual to run a three-way comparison. When Staffing Required consistently exceeds both Staffing Scheduled and Staffing Actual, the planner identifies a structural under-scheduling issue and proposes adding permanent headcount or adjusted shift patterns.

Staffing Scheduled

The Staffing Scheduled metric measures the number of agents that are scheduled to be open to handle interactions within each time interval.

  • Calculation: This metric calculates the total number of agents scheduled to be available and open to handle contacts within each interval.

  • Filters:

    • Employee group: Company

    • Contact group: Company

    • Attributes: Scheduling Unit, Skill Group

  • Metric type: Historical

  • Metric direction: Up, a higher metric value is best.

  • Use Case: Reviewing Schedule Coverage: A WFM planner compares Staffing Scheduled to Staffing Required before publishing schedules to ensure adequate coverage in every interval. Intervals where Staffing Scheduled falls below Staffing Required trigger a schedule revision before the schedule is released to agents.

Staffing Scheduled Variance

The Staffing Scheduled Variance metric measures the difference between the original number of agents scheduled to be open and the required number of agents from the forecast.

  • Calculation: This metric calculates the difference between the number of agents scheduled to be open and the number of agents the forecast identified as required for each interval.

  • Filters:

    • Employee group: Company

    • Contact group: Company

    • Attributes: Scheduling Unit, Skill Group

  • Metric type: Historical

  • Metric direction: Down, a lower metric value is best.

  • Use Case: Improving Schedule Build Quality: A WFM analyst reviews Staffing Scheduled Variance after each schedule build to identify intervals where scheduling consistently falls short of requirements. Persistent negative variance in specific intervals prompts a review of scheduling constraints such as agent availability, shift rules, or skill coverage minimums, leading to more balanced future schedules.

Total Actual Activity Time

The Total Actual Activity Time metric measures the cumulative time all selected agents spent in a specific activity during the selected time frame, displayed in HH:MM:SS format.

  • Calculation: This metric calculates the total time all selected agents spent in a specific activity during the selected time period, expressed in HH:MM:SS format.

  • Filters:

    • Employee group: Company

    • Contact group: Company

    • Attributes: Scheduling Unit, Skill Group

  • Metric type: Historical

  • Metric direction: Up, a higher metric value is best.

  • Use Case: Workforce Planning Validation: A WFM manager uses Total Actual Activity Time to validate whether the total time agents spent in productive activities matches the forecasted demand. If total time in customer-facing activities falls short of the forecast, the manager adjusts schedules or reallocates agents to close the gap.

Volume Abandoned

The Volume Abandoned metric measures the total number of inbound contacts that were abandoned by callers before being answered.

  • Calculation: This metric calculates the total number of inbound contacts that callers abandoned before being connected to an agent.

  • Filters:

    • Employee group: Company

    • Contact group: Company

    • Attributes: Scheduling Unit, Skill Group

  • Metric type: Historical

  • Metric direction: Down, a lower metric value is best.

  • Use Case: Tracking Abandonment Trends: A supervisor monitors Volume Abandoned across skills to identify which queues have the highest abandonment counts. High numbers trigger an investigation into wait times and staffing levels, leading to targeted schedule adjustments to reduce abandonment.

Volume Active

The Volume Active metric measures the number of interactions that are ongoing from the previous interval — contacts that started in an earlier interval and are still being handled.

  • Calculation: This metric counts the number of contacts that carried over from the prior interval and are still being handled by agents at the start of the current interval.

  • Filters:

    • Employee group: Company

    • Contact group: Company

    • Attributes: Scheduling Unit, Skill Group

  • Metric type: Historical

  • Metric direction: Up, a higher metric value is best.

  • Use Case: Understanding Carry-Over Load: A WFM intraday analyst uses Volume Active to understand how many contacts from the prior interval are still active. A high Volume Active count indicates that agents are handling complex or long interactions, which may affect staffing needs in the current interval.

Volume Actual

The Volume Actual metric measures the total number of interactions that were both handled and abandoned during the selected time period.

  • Calculation: This metric calculates the total number of contacts received during the selected period, including both contacts that were handled by agents and contacts that were abandoned by callers.

  • Filters:

    • Employee group: Company

    • Contact group: Company

    • Attributes: Scheduling Unit, Skill Group

  • Metric type: Historical

  • Metric direction: Up, a higher metric value is best.

  • Use Case: Comparing Actual vs Forecast Volume: A WFM manager compares Volume Actual against Volume Forecast to gauge how well the staffing plan matched real demand. Significant differences prompt adjustments to scheduling assumptions for the following week.

Volume Answered

The Volume Answered metric measures the total number of interactions that were successfully handled by agents.

  • Calculation: This metric calculates the total number of contacts that were successfully answered and handled by agents during the selected time period.

  • Filters:

    • Employee group: Company

    • Contact group: Company

    • Attributes: Scheduling Unit, Skill Group

  • Metric type: Historical

  • Metric direction: Up, a higher metric value is best.

  • Use Case: Measuring Productivity: A supervisor uses Volume Answered to gauge team productivity across intervals. Low Volume Answered during scheduled peak hours may indicate staffing shortfalls or elevated handle times, both of which are actionable for workforce planning.

Volume Backlog

The Volume Backlog metric measures the number of interactions that have not yet been answered — contacts waiting or unresolved.

  • Calculation: This metric counts the number of contacts that have been received but not yet answered by agents, representing contacts currently waiting in queue or unresolved at the end of an interval.

  • Filters:

    • Employee group: Company

    • Contact group: Company

    • Attributes: Scheduling Unit, Skill Group

  • Metric type: Historical

  • Metric direction: Down, a lower metric value is best.

  • Use Case: Reducing Queue Backlog: An intraday manager monitors Volume Backlog in real time to detect growing queues. When the backlog spikes, the manager moves available agents from lower-priority skills to the impacted skill, reducing wait times and preventing abandonment.

Volume Forecast

The Volume Forecast metric shows the original forecasted contact volume for the skill group at each time of day, based on the staffing plan.

  • Calculation: This metric represents the number of contacts the staffing plan originally forecasted for the skill group at each interval, based on historical contact patterns used during the forecasting process.

  • Filters:

    • Employee group: Company

    • Contact group: Company

    • Attributes: Scheduling Unit, Skill Group

  • Metric type: Historical

  • Metric direction: Up, a higher metric value is best.

  • Use Case: Validating the Staffing Plan: A WFM planner uses Volume Forecast alongside Volume Actual to evaluate whether the forecasting model accurately reflected customer demand. Recurring gaps between forecast and actual volumes are used to recalibrate the forecasting algorithm or adjust historical data inputs.

Volume Received

The Volume Received metric measures the total number of interactions received during a given interval, regardless of whether they were answered.

  • Calculation: This metric counts all inbound contacts received during the interval, including contacts that were answered by agents and contacts that were not answered, such as those that overflowed or were abandoned.

  • Filters:

    • Employee group: Company

    • Contact group: Company

    • Attributes: Scheduling Unit, Skill Group

  • Metric type: Historical

  • Metric direction: Up, a higher metric value is best.

  • Use Case: Understanding Total Inbound Demand: A WFM analyst uses Volume Received to understand the true inbound demand per interval, including contacts that overflowed or were abandoned before being answered. This gives a more complete picture of customer demand than Volume Answered alone.

Volume Variance

The Volume Variance metric measures the absolute difference between actual contact volume and forecasted contact volume.

  • Calculation: This metric calculates the difference between the total number of contacts actually received and the number of contacts the staffing plan forecasted for the same period.

  • Filters:

    • Employee group: Company

    • Contact group: Company

    • Attributes: Scheduling Unit, Skill Group

  • Metric type: Historical

  • Metric direction: Down, a lower metric value is best.

  • Use Case: Assessing Forecast Precision: A WFM manager reviews Volume Variance by interval to identify periods where actual demand significantly exceeded or fell short of the forecast. Persistent positive variance (more contacts than expected) in morning intervals leads to a shift in how historical morning patterns are weighted in future forecasts.