What Is Call Center Forecasting? Methods, Benefits & Best Practices

Call center forecasting is the process of predicting fluctuating call volumes, ensuring enough agents are available to handle the estimated workload.

Generally, call centers forecast call volumes through historical data, industry trends, and marketing campaigns. This helps call centers schedule agents to handle incoming calls and maintain quality service without overwhelming their agents.

Forecasting in a call center involves accurately predicting the call volume and optimizing the resources to deliver timely customer service.

1. Optimizes Resource Allocation: Properly allocating the right number of agents on peak and off-peak days reduces idle time and extra labor cost.
2. Improve Service Levels: With a better idea of expected call volumes, you can optimize staffing to handle customers on time, increasing call center productivity.
3. Efficient Strategic Planning: Analyzing past and current market trends helps predict and prepare for changes in consumer behavior, the market, and the economic cycle.
4. Adapt to Business Needs: Easily adjust staffing needs with promotions or seasonal events, meeting fluctuating business demand.
5. Enhance Customer Experience: Efficient call handling and routing to relevant agents reduce wait times and ensure first-call resolution.

Call centers use various forecasting methods such as historical data analysis, trend and seasonality recognition, regression analysis, and time series models to predict future call volumes. Advanced approaches may also include machine learning, simulations, and expert judgment to enhance accuracy and prepare for unexpected changes in demand.

You can analyze the number of calls received, handling time, and service levels in the past during a similar time frame. Generally, analyzing historical data gives insights into why past incidents occurred and helps understand the pattern.

Historical data analysis uses descriptive statistics, trend analysis, and correlation studies to summarize the data, identify changes, and their impact.

Time series forecasting also uses past data to predict events at consistent daily, monthly, and yearly intervals. It makes predictions focusing on seasonality, trends, and cycles in time-dependent data, such as sales, stock prices, and demand.

In general, time series forecasting effectively predicts call volumes based on how past patterns will continue.

Industry professionals use expert judgment to forecast call volume. Similarly, the Delphi method gathers data from the experts through multiple rounds of anonymous questionnaires. After each round, summaries of the responses are revised based on feedback. This process continues until the group reaches a final decision.

This approach is often used when historical data is limited and requires experts’ opinions in forecasting.

Event-based forecasting predicts the call volumes that increase during specific events such as product launches, policy changes, promotions, and campaigns. Instead of relying on past data trends, it focuses on how specific planned or unexpected events might impact future results.

Machine learning and AI analyze the complex data that humans generally miss while predicting call volumes. These tools make real-time adjustments based on trends and past performance, offering more accurate predictions.

Key elements that need to be forecast in a call center include call volume, average handling time (AHT), and staffing requirements. Additional elements to forecast would be peak times and service level targets to ensure proper resource planning and maintain customer satisfaction.

Call volume is the total number of incoming calls expected in a specific time frame. This is the most essential factor in forecasting to optimize staffing and resources to meet the demand.

Service level is the rate of call answers within a particular time frame. It is generally predicted to set a target like connecting with customers within 20 to 30 seconds and addressing customers’ queries quickly.

Call handle time refers to how long it takes to resolve a customer’s issue. The average call handling time allows you to estimate how many agents are needed within the desired timeframe.

The call arrival pattern usually indicates when customers call, either at the start of the day or after executing a marketing campaign. These patterns help to schedule agents at the peak time.

Agent Availability is the number of agents available to take calls at any given time, considering breaks, training, or meetings. Forecasting agent availability ensures enough coverage, even though additional shifts or overtime are necessary.

To ensure accurate forecasting in a call center, analyze historical data, account for seasonality, and use reliable forecasting tools. Regularly review forecasts against actual performance and collaborate with other departments to adjust for upcoming events or changes in demand.

1. Leverage Historical Data: Analyze past call volumes, handling times, and trends to identify patterns and predict future demand.
2. Account for Seasonality and Events: Include known fluctuations such as holidays, promotions, and product launches that can impact call volume.
3. Use Advanced Forecasting Tools: Invest in software incorporating AI and machine learning to analyze large datasets and predict demand more accurately.
4. Monitor and Adjust Service Level Objectives: Set realistic goals, monitor whether forecasted outcomes are achievable, and adjust service level objectives to actual demand.
5. Review Forecast Accuracy: Consistently compare how accurate your forecasts have been and make necessary adjustments for future predictions.
6. Use Simulation and Scenario Planning: Run simulations of special events or high call volume periods to better prepare for the unexpected rush.
7. Incorporate Seasonal and Long-Term Trends: Calculate seasonal patterns like holidays and business cycles to improve long-term forecasting accuracy.

Call center forecasting predicts call volumes, which helps adjust staffing levels accordingly and optimize resources to improve service levels. It also forecasts service level, call handle time, call arrival time patterns, and agent availability for effective workforce management.

Historical data analysis, time series, event-based, and many other forecasting methods are used to predict call volumes accurately.
 

Call center forecasts should be updated regularly, at least quarterly or whenever significant changes occur, such as marketing campaigns or seasonal demand.

Forecasting involves predicting future call volumes and service levels, while call center scheduling involves allocating the right number of agents to meet those forecasted outcomes.

AI and machine learning are used in call center forecasting to analyze large datasets accurately, identify patterns, and make real-time adjustments.