Forecasting

Genesys WFM Forecasting Documentation

Study Notes

Topic	Description
Master Forecast	Published forecast scenario used for scheduling
Automatic Best Method	AI analyzes 10+ algorithms to select best forecast
Forecasting Methods	ABM, WHI, HDI, Import Forecast
Main Forecast	Continuous daily calculation from latest data
Accuracy	ABM: 85-92% vs Traditional: 70-80%
Planning Groups	Organize workload by media type and route
Service Goals	Define SL, ASA, abandon rate targets
Recalculation	Main forecast recalculates nightly with new data

---

Navigation

Admin → Workforce Management → Forecasting OR Menu → Workforce Management → Forecasting → Forecasts

---

Forecasting Overview

Forecasting is the process of predicting future contact volume and average handle time to determine required staffing levels. WFM uses forecasts to create optimal schedules that balance service level goals with operational efficiency.

Forecasts form the foundation of workforce planning. Accurate forecasts drive better schedules, which drive better adherence, which drives better service levels. The forecasting process involves analyzing historical data, selecting appropriate forecasting methods, validating scenarios, and publishing the best scenario to become the Master Forecast.

Forecasting Objectives

• Volume Prediction - Predict number of interactions (offered)
• AHT Prediction - Predict average handle time per interaction
• Staffing Calculation - Determine agents needed to meet service goals
• Scenario Planning - Evaluate multiple forecasting approaches
• Continuous Improvement - Refine based on actual vs forecast variance
• Capacity Planning - Support long-range hiring decisions

Forecasting Scope

• Business Unit Level - Forecasts created for entire BU (all MUs)
• Time Granularity - Hourly and daily forecasts
• Planning Period - 26 weeks forward (can search up to 104 weeks)
• Historical Data - Requires 90+ days of data for accuracy
• Multiple Scenarios - Create and compare multiple approaches
• Master Forecast - One published forecast per BU at a time

---

Forecasting Methods

1. Automatic Best Method (ABM)

Automatic Best Method is the AI-powered forecasting approach that analyzes historical interaction data and automatically selects the most accurate forecasting algorithm from 10+ methods.

How ABM Works:

Historical Data Input
        ↓
Analyze 10+ Forecasting Algorithms:
├─ Moving Average
├─ Exponential Smoothing
├─ Trend Analysis
├─ Seasonal Decomposition
├─ Regression Models
├─ Time Series Analysis
└─ ... 4+ additional models
        ↓
Evaluate Each Against Historical Data
├─ Calculate accuracy metrics
├─ Test fit quality
├─ Assess seasonal patterns
└─ Validate trend capture
        ↓
Select Best Performing Model
├─ Lowest error rate
├─ Best seasonal fit
├─ Most stable predictions
└─ Confidence validation
        ↓
Generate Forecast
├─ Volumes (Offered)
├─ AHT (Average Handle Time)
└─ Staffing Requirements

ABM Characteristics:

• Requires minimum 90 days historical data
• Automatically evaluates 10+ algorithms
• Selects best fit without manual intervention
• Accuracy: 85-92% (vs traditional 70-80%)
• Recalculates nightly with new data
• Supports all media types
• Cloud-based processing

When to Use ABM:

• ✓ Mature contact center (90+ days data)
• ✓ Accurate historical data available
• ✓ Want AI-driven optimization
• ✓ Looking for best accuracy
• ✓ Limited forecasting expertise

ABM Limitations:

• ✗ Requires 90+ days data minimum
• ✗ Cannot account for business events manually
• ✗ May not work with highly volatile data
• ✗ Limited control over algorithm selection

2. Weighted Historical Index (WHI)

Weighted Historical Index allows forecasters to assign importance to specific historical periods, enabling forecasts that reflect anticipated trends or business changes.

How WHI Works:

Select Historical Periods
        ↓
Assign Weights to Periods:
├─ Recent period: 40% weight (higher importance)
├─ Same season last year: 35% weight
├─ 2 years ago: 15% weight
└─ Older data: 10% weight
        ↓
Calculate Weighted Average
├─ Multiply volumes by weights
├─ Multiply AHT by weights
└─ Generate forecast
        ↓
Manual Adjustments
├─ Add/subtract for known events
├─ Adjust for staffing changes
└─ Account for market changes
        ↓
Forecast Output

WHI Characteristics:

• Manual weight assignment
• Incorporates business judgment
• Good for known changes
• Moderate accuracy (75-85%)
• Requires forecasting expertise
• Supports business events

When to Use WHI:

• ✓ Known business changes upcoming
• ✓ New product launch planned
• ✓ Merger/acquisition integration
• ✓ Staffing changes anticipated
• ✓ Want forecaster control

WHI Limitations:

• ✗ Requires manual configuration
• ✗ Subject to forecaster bias
• ✗ More time-consuming setup
• ✗ Less accurate than ABM typically

3. Historical Data Import (HDI)

Historical Data Import enables importing external historical data via CSV files, useful for organizations lacking internal historical data or integrating data from legacy systems.

How HDI Works:

Prepare Historical Data CSV:
├─ Date, Time, Interactions Offered
├─ Date, Time, Average Handle Time
└─ Format per Genesys specifications

Upload CSV File
        ↓
Data Validation
├─ Check date formats
├─ Validate interaction counts
├─ Verify AHT values
└─ Check for gaps

Map to Planning Groups
├─ Assign data to queues/routes
├─ Set media types
└─ Configure mapping rules

Import and Store
├─ Historical data added to system
├─ Available for forecasting
└─ Retained for 90+ days

Create Forecast
├─ ABM or WHI using imported data
├─ Generate volumes and AHT
└─ Publish to master forecast

HDI Characteristics:

• CSV-based import
• Supports external data sources
• Integrates legacy system data
• Enables quick setup for new centers
• Maintains data history
• Works with ABM/WHI methods

When to Use HDI:

• ✓ New contact center (no history)
• ✓ Migrating from legacy system
• ✓ Acquiring another company
• ✓ Need to supplement internal data
• ✓ Have external forecasting data

HDI Limitations:

• ✗ Requires proper CSV formatting
• ✗ Data validation needed
• ✗ Manual upload process
• ✗ Can't import future predictions

4. Import Forecast

Import Forecast supports uploading externally generated forecasts into Genesys Cloud, integrating with existing forecasting systems or third-party tools.

How Import Forecast Works:

External Forecast Generation:
├─ Create in Excel/third-party tool
├─ Calculate volumes and AHT
├─ Format per specifications
└─ Validate accuracy

Export as CSV/XML
        ↓
Upload to Genesys WFM
├─ Select planning group
├─ Map forecast period
└─ Validate format

System Processing:
├─ Parse forecast data
├─ Validate ranges
├─ Check for completeness
└─ Store in system

Publish to Master Forecast
├─ Available for scheduling
├─ Used for staffing calculations
└─ Drives schedule generation

Import Forecast Characteristics:

• Forecast generated externally
• Upload pre-calculated volumes/AHT
• Bypasses ABM/WHI
• Useful for specialized models
• One-time or periodic imports
• No recalculation

When to Use Import Forecast:

• ✓ Have specialized forecasting tool
• ✓ Third-party forecast provider
• ✓ Complex custom models
• ✓ Forecasting done elsewhere
• ✓ Minimal WFM forecasting expertise

Import Forecast Limitations:

• ✗ No automatic updates
• ✗ Must re-import when data changes
• ✗ No integration with real-time data
• ✗ Less adaptive to changes

---

Main Forecast

The Main Forecast is a special forecast calculated continuously (typically nightly) based on all available historical data. It provides the baseline forecast that can be used immediately or modified through scenarios.

Main Forecast Characteristics:

• Continuous Calculation - Recalculates every night
• All Historical Data - Uses complete data history
• Automatic - No manual intervention required
• Read-Only - Cannot be directly edited
• Baseline Reference - Starting point for scenarios
• Always Available - Immediately accessible
• Best Current Data - Uses latest interactions

Main Forecast Flow:

Day 1: Historical Data (30 days)
        ↓ Nightly Calculation
        ↓ Main Forecast Published
        ↓
Day 2: Historical Data (30 days) + Day 1 New Data
        ↓ Nightly Calculation
        ↓ Main Forecast Updated
        ↓
Ongoing: Continuous refinement with new daily data

---

Planning Groups

Planning Groups organize workloads by specific media types and route paths, enabling targeted forecasting and scheduling.

Planning Group Components:

Planning Group: Support - Inbound Voice
├─ Media Type: Voice (inbound)
├─ Queue/Route: Support_Queue_001
├─ Skill Required: Support_Skill_Level_2+
├─ Service Goal: 80% SL, 20 sec ASA, 5% abandon
├─ Staffing Group: Support_Agents
└─ Forecast Data: Volume + AHT

Planning Group: Sales - Outbound Calls
├─ Media Type: Voice (outbound)
├─ Campaign: Q1_Spring_Campaign
├─ Skill Required: Sales_Skill_Level_1+
├─ Service Goal: Contact 50% of list, 5 min calls
├─ Staffing Group: Sales_Agents
└─ Forecast Data: Dialing ratio + AHT

Planning Group: Support - Email
├─ Media Type: Email
├─ Queue: Support_Email_Queue
├─ Response Time Goal: 4 hours
├─ Staffing Group: Support_Agents
└─ Forecast Data: Volume + AHT

Planning Group: Chat Support
├─ Media Type: Chat
├─ Route: Support_Chat_Route
├─ Concurrency: 4-5 chats per agent
├─ Response Time: Immediate
└─ Forecast Data: Offered + AHT

Planning Group Creation:

1. Navigate to Admin → Workforce Management → Planning Groups
2. Click Create Planning Group
3. Configure:
   • Name - Unique identifier
   • Business Unit - Parent BU
   • Media Type - Voice, Email, Chat, Callback, Messaging, Workitems
   • Queue/Route - Associated queue or route
   • Service Goal - Target metrics
   • Staffing Model - Agents to use
4. Save and activate

Planning Group Best Practices:

• Clarity - Clear names reflecting function
• Consolidation - Group similar work together
• Skill Alignment - Agents have required skills
• Service Goals - Match business objectives
• Regular Review - Update as operations change
• Documentation - Maintain planning group mapping

---

Service Goals

Service Goals define the performance targets for a planning group: Service Level, Average Speed to Answer, and Abandonment Rate.

Service Goal Components:

Service Goal Template: Premium Support
├─ Service Level Goal: 80%
│  └─ Definition: 80% of calls answered within 20 seconds
├─ Average Speed to Answer: 20 seconds
│  └─ Definition: Average answer time across all calls
├─ Abandon Rate: 5%
│  └─ Definition: Max 5% of offered calls abandoned
├─ Media Type: Voice
├─ Time Intervals: Hourly
└─ Period: Weekly

Service Goal Template: Standard Support
├─ Service Level Goal: 75%
├─ Average Speed to Answer: 30 seconds
├─ Abandon Rate: 8%
└─ More lenient targets for lower-volume periods

Service Goal Template: Email Support
├─ Service Level Goal: 95% within 4 hours
├─ Average Speed to Answer: 2 hours (median response)
├─ Abandon Rate: 0% (not applicable)
└─ Media Type: Email

Service Goal Best Practices:

• Realistic Targets - Achieve 80-85% of time
• Business Aligned - Match customer expectations
• Media-Specific - Different for voice vs email
• Documented - Communicate to teams
• Regular Review - Adjust based on performance
• Incremental Improvement - Tighten gradually

---

Forecasting Process

Step 1: Data Preparation

1. Ensure 90+ days of historical data available
2. Validate data accuracy
3. Check for gaps or anomalies
4. Clean outliers if necessary
5. Confirm queue/route mappings

Step 2: Create Scenario

1. Navigate to Forecasts → Scenarios
2. Click New Scenario
3. Configure:
   • Name - e.g., "Q2_2026_Base_Forecast"
   • Period Start - Beginning of forecast
   • Period End - 26 weeks forward
   • Planning Groups - Select which to include
4. Create scenario

Step 3: Build Volumes

1. Open scenario
2. Click Build Volumes
3. Select forecasting method:
   • ABM - Recommended for accuracy
   • WHI - For known business changes
   • Template - Copy from similar period
4. Configure method-specific settings
5. Generate volumes

Step 4: Build AHT

1. Open scenario volumes
2. Click Build AHT
3. Select method (typically same as volumes)
4. Configure AHT-specific settings
5. Generate AHT

Step 5: Review & Validate

1. Open Scenario Volumes view
2. Review volume trends:
   • ✓ Match business expectations
   • ✓ Seasonal patterns visible
   • ✓ Growth/decline appropriate
   • ✓ No obvious anomalies
3. Open Scenario Staffing view
4. Review staffing requirements:
   • ✓ Realistic agent counts
   • ✓ Service level achievable
   • ✓ Aligned with budget
   • ✓ Growth manageable

Step 6: Compare Scenarios

1. Create multiple scenarios if desired
2. Compare side-by-side:
   • Volume projections
   • Staffing requirements
   • Cost implications
   • Service level achievement
3. Select best scenario

Step 7: Publish to Master Forecast

1. Open best scenario
2. Click Publish to Master Forecast
3. Confirm publication
4. Master Forecast now available for scheduling

Step 8: Monitor & Adjust

1. Track actual vs forecast weekly
2. Calculate variance:
   • Volume Variance = (Actual - Forecast) / Forecast
   • AHT Variance = (Actual - Forecast) / Forecast
3. Adjust future forecasts based on variance
4. Recalculate Main Forecast

---

Forecasting Accuracy

Measuring Accuracy

Volume Accuracy:

Forecast Accuracy = 1 - |Actual - Forecast| / Actual

Example:
Forecasted Offered: 1,000 calls
Actual Offered: 980 calls
Variance: |980 - 1,000| / 1,000 = 2%
Accuracy: 98%

AHT Accuracy:

Forecasted AHT: 420 seconds
Actual AHT: 410 seconds
Variance: |410 - 420| / 420 = 2.4%
Accuracy: 97.6%

Overall Forecast Accuracy:

• Excellent - 95%+ accuracy
• Good - 90-95% accuracy
• Acceptable - 85-90% accuracy
• Poor - <85% accuracy

Factors Affecting Accuracy

Positive Factors:

• ✓ Abundant historical data (90+ days)
• ✓ Consistent seasonal patterns
• ✓ Stable agent population
• ✓ No major business changes
• ✓ Accurate data collection
• ✓ Use of ABM method

Negative Factors:

• ✗ Insufficient historical data (<30 days)
• ✗ Volatile contact volumes
• ✗ Seasonal anomalies
• ✗ Major staffing changes
• ✗ Business discontinuities
• ✗ Data quality issues

Improving Forecast Accuracy

1. Data Quality

   • Validate interaction data
   • Remove duplicate entries
   • Correct time stamps
   • Clean outliers appropriately

2. Time Frame Selection

   • Use 90+ days of data
   • Include full seasonal cycle
   • Exclude anomalous periods
   • Weight recent data higher

3. Method Selection

   • Use ABM for stable patterns
   • Use WHI for known changes
   • Test multiple methods
   • Compare results

4. Ongoing Monitoring

   • Track actual vs forecast weekly
   • Identify variance sources
   • Adjust future forecasts
   • Document lessons learned

5. Business Communication

   • Inform of known changes
   • Get campaign dates in advance
   • Understand staffing constraints
   • Align on service goals

---

Real-World Examples

Example 1: New Contact Center (Using HDI)

Scenario: New financial services contact center
Problem: No historical data in Genesys
Solution: Historical Data Import

Process:
1. Obtain 6 months of data from legacy system
2. Format as CSV (Date, Time, Volume, AHT)
3. Upload to WFM via Historical Data Import
4. Validate imported data (1,000+ calls/day confirmed)
5. Create ABM forecast using imported data
6. Generate 26-week forecast with 85% confidence
7. Publish to Master Forecast
8. Create schedules based on forecast
9. Begin tracking actual vs forecast
10. Refine with real Genesys data over time

Result: Forecast accuracy 82% week 1, improving to 90% by week 12

Example 2: Seasonal Business (Using WHI)

Scenario: Retail customer service (high holiday season)
Problem: Regular ABM doesn't account for expected surge
Solution: Weighted Historical Index

Process:
1. Run ABM to get baseline forecast
2. Weight recent 4 weeks: 50%
3. Weight same season last year: 30%
4. Weight 2 years ago: 20%
5. Manual adjustment: +25% for new catalog
6. Manual adjustment: +10% for holiday promotions
7. Result: 15,000 calls/day (vs ABM baseline 12,000)
8. Schedule accordingly with additional flex agents
9. Publish to Master Forecast
10. Monitor first week for variance

Result: Forecast accuracy 88% during peak season
Alternative: Would have been 65% with unmodified ABM

Example 3: Business Event (Using Import Forecast)

Scenario: Product launch with external forecast
Problem: Marketing has already forecasted demand impact
Solution: Import Forecast from external source

Process:
1. Marketing provides forecast:
   - Week 1: +30% volume increase
   - Week 2: +50% volume increase
   - Week 3: +40% volume increase
   - Week 4-8: Declining to normal
2. Obtain volumes from marketing
3. Format as CSV per Genesys spec
4. Upload via Import Forecast
5. Map to Support planning group
6. Validate import completeness
7. Publish to Master Forecast
8. Create high-staffing schedule for weeks 1-3
9. Monitor actual vs marketing forecast
10. Adjust staffing based on real results

Result: Forecast accuracy 92% (marketing expertise applied)
Cost: Hired 50 temporary agents, all utilized during surge

---

Best Practices

Forecasting Process

• Establish Baseline - Start with ABM for consistency
• Regular Reviews - Monthly variance analysis
• Scenario Planning - Test multiple approaches
• Documentation - Record assumptions and decisions
• Communication - Share forecasts with operations
• Validation - Compare to business expectations

Data Management

• Data Quality - Daily validation and cleanup
• Retention - Keep 90+ days for pattern recognition
• Integrity - Prevent manual edits without documentation
• Backup - Maintain forecast history
• Audit Trail - Track who changed what when

Continuous Improvement

• Weekly Tracking - Actual vs forecast variance
• Root Cause Analysis - Understand variances
• Method Adjustment - Change weights/methods as needed
• Feedback Loop - Share insights with business
• Seasonal Adjustment - Update for known patterns

---

Interview Cheat Sheet

Question	Answer
What's ABM?	Automatic Best Method - AI selects best algorithm from 10+
ABM accuracy?	85-92% (vs traditional 70-80%)
ABM data requirement?	Minimum 90 days historical data
When use ABM?	Mature center, good data, want best accuracy
When use WHI?	Known business changes, want forecaster control
When use HDI?	New center, migrating systems, external data
When use Import?	External forecast available, specialized models
What's Main Forecast?	Automatically calculated nightly using all data
What's planning group?	Organizes work by media type and route
What's service goal?	Targets for SL, ASA, abandon rate
Where forecasts created?	Business Unit level
How far forward?	26 weeks (can search up to 104)
How often recalculate?	Main forecast nightly, scenarios on demand
Forecast impacts?	Drives scheduling, staffing, service level
How measure accuracy?	Compare actual vs forecast volume and AHT

---

Key Takeaways

• AI-Powered - ABM automatically selects best method from 10+ algorithms
• Accuracy - ABM achieves 85-92% vs traditional 70-80%
• Flexibility - Four methods (ABM, WHI, HDI, Import) for different scenarios
• Continuous - Main Forecast recalculates nightly with new data
• Planning Groups - Organize by media type and route for precise forecasting
• Service Goals - Define targets for SL, ASA, abandon rate
• Data-Driven - Requires 90+ days of data for accuracy
• Validation - Regular monitoring of actual vs forecast variance
• Business Events - WHI and Import methods support known changes
• Foundation - Forecast quality directly impacts schedule quality

---

Additional Resources

Official Documentation

• Forecasting Overview: help.genesys.cloud/articles/forecasting-overview/
• Automatic Best Method: help.genesys.cloud/articles/automatic-best-method/
• Planning Groups: help.genesys.cloud/articles/planning-groups-overview/
• Service Goals: help.genesys.cloud/articles/service-goals-overview/

Support & Training

• Genesys University: genesys.com/training
• Community Forums: https://community.genesys.com
• Technical Support: https://support.genesys.com

---

Document Version Info

Last Updated: March 2026
Source: Genesys WFM Official Documentation
Validated: Current with January-March 2026 releases
Version: 1.0