The Five Forecasts of Highly Effective Contact Centers
Highly effective contact centers use five key types of forecasts—budget, hiring, scheduling, tactical, and real-time—to optimize financials, long-term planning and immediate resource allocation, increasingly leveraging AI to enhance accuracy and increase adaptability.
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The Five Forecasts of Highly Effective Contact Centers
Forecasting is a cornerstone of contact center management, guiding decisions across strategic and tactical domains. Among the various types of forecasts, five stand out for their distinct purposes, characteristics, and objectives. These come across as best practice in high performing customer care operations.
Budget Forecast
This long-term strategic forecast is used to project financial performance and lays the foundation for all contact center planning. It supports decisions on technology, seating, infrastructure, hiring, training, use of vendors, volume allocation and other high-level initiatives.
Purpose: To be the basis of financial planning and the guide for ROI-focused decisions.
Timeframe: Covers 1 to 2 years, up to 5 years in some cases.
Detail Level: Monthly to weekly data.
Key Inputs: Historical data, business projections, channel management plans, automation, and other process improvement initiatives.
Characteristics:
Lead time should allow for iterations, consensus and is determined by the financial planning calendar.
Relies on a combination of mathematical models, expert insights, and business consensus.
Typically created annually and updated quarterly, with iterations that add quarters to full years.
Results are summarized into year-over-year or quarterly views.
Hiring Forecast
The hiring forecast focuses on workforce planning, ensuring sufficient hiring, training and staffing to meet operational needs.
Purpose: Supports hiring, training, cross-training, resource allocation and workforce adjustments.
Timeframe: Spans 3 months to 1 year.
Detail Level: Weekly data with underlying daily and interval-level details.
Key Inputs: Recent historical data, updated business projections, and tangible initiatives impacting demand.
Characteristics:
Lead time is determined by hiring notice to production timeline, talent throughput, training capacity and class size,
Balances mathematical models with business input to secure alignment and support.
Typically reviewed and updated monthly, with an additional month added in each cycle.
Results are rolled up into monthly, quarterly, or annual summaries.
Scheduling Forecast
This forecast helps align staffing schedules with operational needs, ensuring resources are allocated efficiently across intervals.
Purpose: To create schedules that match workload demands and optimize resource allocation.
Timeframe: Typically spans 90 days.
Detail Level: Daily and interval-level data, sometimes aggregated into weekly summaries.
Key Inputs: Recent historical data and updated arrival patterns.
Characteristics:
Lead time should allow for schedule changes while allowing for communication, taking care of employee satisfaction.
Primarily driven by mathematical models, often enhanced by machine learning.
Updated weekly, with each iteration adding a new week.
Rarely rolled up but compared with hiring and budget forecasts for alignment.
Tactical Forecast
This short-term forecast fine-tunes interval level staffing for the next few days, focusing on immediate needs like adjusting breaks, off-line activities, managing overtime, or offering time-off opportunities.
Purpose: To address short-term variations in staffing at the interval level for upcomming days.
Timeframe: Covers 2 to 4 weeks.
Detail Level: Daily and interval-level data.
Key Inputs: The most recent historical data, updated arrival patterns, imminent or ongoing impacts.
Characteristics:
Lead time should align with the speed to implement actions needed.
Supports real-time management and schedule optimization.
Typically reviewed weekly, with adjustments made as needed.
Real Time or “Latest and Greatest”
This forecast provides real-time updates, enabling immediate staffing adjustments based on up to the last interval actual data.
Purpose: To manage interval-level staffing needs in real-time for the upcoming hours with focus on current day and tomorrow.
Timeframe: Covers from now to the next 48 to 72 hours.
Detail Level: Interval-level data updated after every interval.
Key Inputs: The most recent interval-level data and ongoing arrival trends.
Characteristics:
Reviewed every interval or hourly at a minimum, guiding real-time actions for optimal efficiency.
Results are often rolled up at the end of the day for comparisons with tactical and scheduling forecasts.
Strategic vs. Tactical Forecasting
Strategic forecasts rely on long-term historical data to guide big-picture planning, while tactical forecasts prioritize recent data to address immediate needs. The overarching principle is simple: for short-term accuracy, look at the most recent trends; for long-term planning, consider broader historical patterns.
The Impact of AI on Forecasting
AI and machine learning have transformed forecasting by enabling multiple models to run concurrently, incorporating more data points, and uncovering previously overlooked patterns. These technologies refine forecasts with every iteration, adapting to changes and improving accuracy. As AI continues to evolve, forecasting tools become even more dynamic and precise.