The Ultimate Guide to Call Center Forecasting:
Benefits & Methods

US businesses lose $1.6 trillion to poor customer service, with long wait times being one of the biggest stressors. However, call center forecasting can do more than just prevent customer churn.

Effective forecasting and scheduling are the cornerstones of any successful call center. In this article, we’ll cover everything you need to know about these processes, including how to carry them out and what metrics to use to be as effective as possible.

Key Takeaways:

• Call center forecasting lets you predict future business requirements and maintain a healthy team size and daily staffing based on historical data.
• Call center forecasting is crucial for maintaining productivity, boosting customer and agent satisfaction, fueling client retention, and improving your bottom line.
• To forecast and schedule accurately, you need to choose the right metrics to track, prepare them for modeling, and leverage an appropriate call center forecasting method.

What Is Call Center Forecasting?

Call center forecasting refers to the process of estimating future call volume based on historical data in order to identify necessary long-term agent team size and short-term staffing requirements. Forecasting is dynamic and needs to be done on a rolling basis. 

Call center forecasting and scheduling are carried out by dedicated workforce managers. They often use workforce management (WFM) tools to track metrics, evaluate data, prepare forecasts, and develop accurate plans to meet a business’ contact needs.

Why Is Call Center Forecasting Important?

Efficient call center forecasting is crucial for effectively managing all incoming calls on time, which drives increased customer satisfaction, client retention, company reputation, and overall profitability.

Meanwhile, mishandling your staffing has the inverse effect. If you have too few agents on duty on any given day, customers will likely grow frustrated with long wait times, contributing to a higher abandonment rate. And 30% of those callers never return.

However, having too many representatives on duty can be equally damaging. While all calls may get handled on time, and customers will walk away happy, you’ll still end up overpaying for the high capacity, while most of your agents can only fiddle their thumbs.

What Are the Benefits of Call Center Forecasting?

Call center forecasting affects far more than one might think. Efficient scheduling and staffing can provide your business numerous benefits in terms of productivity, costs, and customer satisfaction. Examples include:

Better Customer Service

Forecasting allows managers to accurately predict peak call times, ensuring that sufficient agents are available to handle the influx of calls without long wait times. This leads to improved CSAT as callers receive prompt and effective responses.

Reduced Operating Costs

By accurately predicting call volumes, managers can schedule just the right amount of staff, avoiding the costs associated with overstaffing during low activity periods and the overtime expenses incurred during unexpectedly high volume.

Increased Agent Satisfaction & Productivity

Proper staffing prevents agent overload and burnout, as agents are neither idle nor overwhelmed. This balance improves their efficiency and job satisfaction, which contributes to lower turnover rates and higher employee engagement.

Optimized Training & Development

Forecasting provides insights into expected call volumes and types of customer interactions, which can help managers plan targeted training sessions during anticipated downtime, ensuring that all agents are well-prepared for peak times.

Improved Response Rates

With advanced forecasting, managers can not only schedule agents more effectively but also improve the handling of different types of calls, which can vary by time or season. This allows for better preparation and customization of customer service strategies.

Data-Driven Decision-Making

The use of historical data and predictive analytics in forecasting equips managers with data to justify decisions regarding staffing, budget allocations, and other operational aspects, which minimizes the guesswork and enhances the strategic planning process.

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How Does Call Center Forecasting Work?

Call center forecasting can be divided into eight specific steps. Below, you’ll find a quick overview of all the necessary activities to achieve optimal performance and scheduling.

1. Collect Data: Start by gathering historical data on your call volumes, durations, and other relevant patterns from either your call center software or the WFM tool.
   
2. Analyze Trends & Patterns: Abstract the data and try to identify trends and seasonal fluctuations based on previous years to plan scheduling accordingly.
   
3. Account for External Factors: Don’t look at the data in isolation. Consider external factors like marketing campaigns that might skew insights.
   
4. Choose a Forecasting Model: Choose an appropriate forecasting model based on your priorities like Time Series or Erlang C. We’ll discuss these shortly.
   
5. Simulate & Test the Model: Fill your chosen model with your historical data to test its accuracy and evaluate its feasibility for your specific use case.
   
6. Implement Forecasting: Assuming your model works well, start implementing it into your daily activities to help oversee future staff and resource management.
   
7. Monitor & Adjust: To ensure the long-term accuracy and effectiveness of your forecasting, you’ll need to regularly feed the model new data and track activity.
   
8. Create a Feedback Loop: Use real-world performance feedback to improve forecasting accuracy.

How To Efficiently Use Data in Call Center Forecasting

It should go without saying that your call center forecasting can only be as good as the data you base it on. And the process for acquiring relevant information is two-fold. First, you need to choose the right metrics to analyze and then prep them for forecasting.

Key Metrics to Use in Call Center Forecasting

• Call Volume: This metric tracks the number of incoming calls to a call center. Historical data on call volume helps identify patterns, trends, and seasonal fluctuations, aiding in accurately predicting future demand.
  
• Average Handle Time (AHT): This measures the average duration of a call handled by an agent, including talk time, hold time, and after-call work. Analyzing historical AHT data is crucial for forecasting how long future calls will take and determining staffing needs.
  
• Number of Agents: The total number of agents available directly impacts service levels and customer satisfaction. Forecasting the right number of agents based on expected call volume and other metrics is vital for efficient resource allocation and workforce management.
  
• Customer Satisfaction: This metric assesses how satisfied customers are with their call center experience. High customer satisfaction leads to repeat business and positive referrals, which are important for long-term business success and should be considered when forecasting call center needs.

Key Steps for Preparing Forecasting Data

• Data Cleaning: Remove duplicates, correct errors, and handle missing values to ensure the dataset is accurate and complete.
  
• Normalization: Standardize the data to ensure consistency across different time periods and types of data.
  
• Feature Selection: Identify and select the most relevant features that significantly impact call volumes. This might include time of day, day of the week, and external events.
  
• Aggregation: Summarize the data into useful formats, such as hourly, daily, or weekly averages, to facilitate analysis and modeling.

What Are The Main Methods of Call Center Forecasting?

Because the process of analyzing and cross-examining such large quantities of data would be too difficult and time-consuming for a human, businesses use call center forecasting models, which are specialized types of software, designed to automate the process.

These can vary wildly depending on their intended use case and the specific technology they use. As such, it’s important to be intentional with your choice of call center forecasting methods, and it may be good to use different types for different use cases.

The main types of call center forecasting models include:

Time Series Analysis

What it is: Time Series Analysis (TSA) is a statistical method for analyzing time-ordered data points to identify patterns and trends over time.

How it works: TSA utilizes historical data to forecast future values by recognizing temporal dependencies and seasonality. Common models include ARIMA (AutoRegressive Integrated Moving Average) and SARIMA (Seasonal ARIMA).

When to use it:

• When data shows clear trends, seasonality, or cyclical patterns over time.
• For short and medium-term forecasting where trends are likely to continue.

Benefits:

• Can accurately capture and model temporal dependencies.
• Effective for detecting and leveraging seasonality in call volumes.
• Uses models that highlight how past data impacts future predictions.

Regression Analysis

What it is: A statistical approach to model the relationship between a dependent variable and one or more independent variables.

How it works: Estimates the coefficients of the independent variables to predict the dependent variable. Can be linear (e.g., Linear Regression) or non-linear (e.g., Polynomial Regression).

When to use it:

• When there is a clear relationship between call volumes and external/internal factors.
• To understand the impact of specific variables on call volume.

Benefits:

• Simple to implement and interpret.
• Effective for identifying and quantifying relationships between variables.
• Can be extended with regularization techniques to improve model performance.

Machine Learning Algorithms

What they are: A set of algorithms that enable computers to learn from and make predictions based on data.

How they work: Involves training models on historical data to identify patterns and make predictions. Common algorithms include Random Forest, Gradient Boosting, and Support Vector Machines.

When to use them:

• When dealing with complex, non-linear relationships in the data.
• Suitable for large datasets with multiple features influencing call volumes.

Benefits:

• Can handle a high degree of complexity and interaction between variables.
• Typically achieve higher accuracy compared to traditional statistical models.
• Flexible and can be adapted to different types of data and forecasting needs.

Neural Networks & Deep Learning

What they are: Advanced machine learning techniques modeled after the human brain, capable of learning from large amounts of data.

How they work: They consist of interconnected layers of nodes (neurons) that process input data and adjust weights to minimize prediction error. Deep learning involves multiple hidden layers that can capture intricate patterns and relationships.

When to use them:

• When forecasting requires modeling highly complex and non-linear interactions in large datasets.
• Ideal for long-term forecasting and scenarios where feature engineering is challenging.

Benefits:

• Can model extremely complex patterns and relationships.
• Automatically extract features from raw data, reducing the need for manual feature engineering.
• Continually improve with more data, providing better accuracy over time.

Best Practices for Call Center Forecasting & Scheduling

If you truly want to make your call center as productive as it can be, there are several more things you can do to further improve your forecasting and scheduling processes. These include:

• Implementing Real-Time Monitoring: Establish systems for real-time monitoring of call volumes and key performance indicators (KPIs). This allows for immediate adjustments to staffing levels and other resources in response to unexpected changes in call patterns.
  
• Scenario Planning and Stress Testing: Develop multiple forecast scenarios (e.g., best-case, worst-case, and most likely) to prepare for various situations. Conduct stress tests to see how well your call center can handle unexpected spikes or drops in call volume.

• Utilizing Advanced Analytics: Use analytics features offered by call center software like CloudTalk to get comprehensive insights into your agents’ performance to estimate average call duration, plan staffing, and create tailored training plans.

• Optimizing Workforce Management (WFM) Integration: Integrate forecasting with workforce management systems to streamline scheduling and staffing processes. This ensures that forecasted call volumes directly inform staffing decisions, improving efficiency and service levels.
  
• Focusing on Continuous Improvement: Establish a culture of continuous improvement by regularly reviewing and refining forecasting processes. Encourage feedback from staff and use performance data to make iterative enhancements to your forecasting models and methodologies.

Optimize Call Center Forecasting with CloudTalk

As we alluded to earlier in the article, call center software is another important part of any productivity-oriented operation, along with WFMs. Apart from powering your inbound and outbound conversations, tools like CloudTalk also offer many advanced tools for further automating and streamlining your forecasting and scheduling processes. 

Here are just a few examples:

• Agent Reporting: Access accurate performance KPIs for individual agents directly through CloudTalk’s app, see how they play into your operation as a whole, and optimize their productivity on a case-by-case basis.
  
• Advanced Analytics: Centralize all your team’s data. Access everything you need to know regarding your agent’s performance and call statistics, and use it to further improve your forecasting and scheduling.
  
• WFM Integration: Use one of CloudTalk’s 35+ native integrations or set up custom ones via our Open API to leverage seamless two-way synchronization and automatically feed information into your forecasting models. 

Call center forecasting and scheduling are vital aspects of any good operation, and with CloudTalk, yours can be so much better. So, what are you waiting for?

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