What is call center forecasting?

Sprinklr Team

September 15, 20233 min read

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In a world where customer satisfaction reigns supreme, your call center agents are the gatekeepers of creating experiences that keep customers coming back. They play a vital role in providing customer support, handling queries and resolving issues.  

But how can you ensure consistently great experiences, even when there is a spike in call volumes? Enter the game-changer – Call center forecasting. 

Read on to find out how effective call center forecasting is crucial for maintaining optimal customer service levels and workforce management.  

Table of Contents

What is call center forecasting?

A graph showing the correlation between actual call volumes over a specific time period.

Call center forecasting is the process of predicting call volume and resource requirements over a specific period, typically done on an hourly, daily, weekly or monthly basis. The goal is to accurately anticipate the number of incoming calls, emails or other interactions to ensure adequate staffing levels and to optimize customer service.  

Benefits of call center forecasting

Call center forecasting aims to determine the required number of agents to meet future demand, leading to enhanced customer and agent experiences and positioning the team for success. Let's discuss its top benefits in detail.

  • Minimize waiting time effectively: Customers hate to wait for a service representative and are likely to not only drop the call, but abandon your business altogether, if made to wait for too long. Workforce forecasting ensures the optimal scheduling of agents with the right skill sets at the appropriate times. This means that you don't keep your customers waiting, and you can elevate the overall customer experience.
    Read more: What is call center scheduling, its benefits, strategies and best practices

A group of call center agents at their workstations, wearing headsets and engaging with customers.
  • Prevent agent burnout: Even before the COVID-19 pandemic, 74% of call center agents were susceptible to burnout. Accurate forecasts enable the fair and efficient distribution of workloads, reducing the risk of agent burnout and promoting higher levels of job satisfaction.

  • Facilitate well-informed hiring plans: Staff expenses typically account for about 70% of a contact center's total costs. Forecasting provides valuable visibility and clarity, empowering decision-makers to make informed staffing choices and optimize expenditure.

  • Boost employee morale: Forecast accuracy holds the top position as the most influential factor impacting team satisfaction. Ensuring precise workforce forecasting is crucial in maintaining high employee morale.

  • Improved customer service: Accurate call center forecasting ensures that there are enough agents available to handle incoming calls promptly, reducing wait times and enhancing customer satisfaction.

How to use data to forecast call volumes accurately

To create accurate forecasts, call centers need access to relevant data, including historical call volume data, average handling time and other relevant metrics. The process typically involves the following steps: 

  • Data collection: Gather data on call volumes, handling times and other key metrics over a defined period. The longer the historical data span, the more accurate the forecasts are likely to be.
    Digital customer service software seamlessly integrates with various data sources, consolidating vital information such as call volumes, agent performance and customer feedback. This way, call centers have access to the right information for generating accurate forecasts, identifying trends and making informed decisions.

  • Data preprocessing: Cleanse and prepare the data by removing outliers, handling missing values and transforming it into a suitable format for analysis.

  • Model selection: Choose an appropriate forecasting method based on the nature of the call center's operations, data characteristics and the level of accuracy required.

  • Model training: For machine learning algorithms, the selected model needs to be trained on historical data to identify patterns and relationships.

  • Validation and testing: Validate the forecasting model using a portion of the historical data that was not used for training. With this step, you can assess the accuracy and reliability of the model.

  • Forecasting: Once the model is validated, use it to predict future call volumes and resource requirements.

What are the different methods used for call center forecasting?

Call center forecasting can be done in a variety of ways, using varied models. Let's take a look at the prevalent ways and methods.

  1. Historical data analysis

    One of the most common methods is historical data analysis. It involves analyzing past call patterns, seasonal trends and historical call volumes to forecast future demands. By identifying patterns and trends in historical data, call centers can make informed staffing decisions.

  2. Time-series forecasting

    Time-series forecasting relies on mathematical models to project future call volumes based on historical data. Popular techniques include moving averages, exponential smoothing and autoregressive integrated moving average (ARIMA) models.

  3. Erlang C model

    The Erlang C model is a widely used formula to forecast call center traffic. It takes into account call arrival rate, average handling time and the number of agents available to predict the probability of callers having to wait before an agent becomes available.

  4. Workload distribution

    This method involves categorizing call types into different groups and allowing call centers to allocate resources more efficiently. By considering the complexity and handling time of different call types, call centers can ensure that the right number of agents with the appropriate skills are available.

  5. Machine learning algorithms

    With advances in artificial intelligence and machine learning, call centers are increasingly adopting predictive models to forecast call volumes. Machine learning algorithms can analyze a vast amount of data and identify patterns that may not be evident through traditional methods. 

Which method should you use for your company? 

The most appropriate method for call center forecasting depends on various factors such as

  • Call center size

  • Data availability

  • Historical data quality

  • Budget

  • Desired level of accuracy

Smaller call centers with limited data might find simple time-series forecasting methods or Erlang C model sufficient, whereas larger call centers with access to extensive historical data may benefit from machine learning algorithms.

Consider your call center's unique requirements and resources when choosing a forecasting method. It's often a good idea to start with a simple approach and then explore more advanced methods as data and technology capabilities grow.

Read more: A comprehensive guide on call center technology & tools

Best practices for call center forecasting and scheduling

To achieve effective workforce forecasting and scheduling in call centers, it is crucial to adopt the right approach and incorporate these best practices into your WFM process for optimal results. 

  • Schedule the right agents: Ensuring the right agents are scheduled is paramount in meeting service level agreements (SLAs), response times and other support goals. Each agent possesses unique abilities, proficiencies, skills, productivity levels and schedule preferences. Taking these individual factors into consideration during the forecasting process ensures that agents are assigned tasks that align with their strengths, leading to improved performance and customer satisfaction.

  • Plan for the unexpected: While forecasting and scheduling rely on historical data to make predictions, it may not fully account for present or future events. Managers must be prepared for unexpected factors such as attrition, breaks, meetings, sick days and national holidays when creating forecasts. Building flexibility into the schedule allows the contact center to adapt to unforeseen events without compromising the service levels.

  • Tailor the scheduling system to your needs (short/long term): A successful forecasting and scheduling system should align with the current business objectives and the specific requirements of the call center. Team leaders must consider the number and length of calls, available employees, full-time equivalents (FTEs), individual agent performance and other unique factors when creating forecasts and schedules. This customized approach ensures that resources are optimally allocated to meet the fluctuating demand.

  • Leverage WFM software: Utilizing workforce management can significantly streamline the call center's forecasting and scheduling processes. These tools harness historical scheduling, performance and seasonal data to generate accurate forecasts rapidly and automatically. WFM solutions empower call centers to meet demand at scale efficiently, while also enabling seamless management of employee scheduling.

A graph showing the correlation between actual call volumes over a specific time period.

With AI-powered analytics and reporting tools, your call center can gain a comprehensive view of your operations. By monitoring your call center metrics, agent performance and customer feedback in real-time, you can quickly adapt to the changing demands and optimize resource allocation on the fly.

By carefully considering these factors, you can make an informed decision when selecting the most suitable WFM tool for your call center, empowering your team to optimize operations and deliver exceptional customer service.

Ready to elevate your call center operations?  

Call center forecasting and scheduling are essential for ensuring efficient operations, maintaining customer satisfaction and optimizing resource allocation.  

To stay ahead of the competition and meet customer demands effectively, call centers must embrace data-driven forecasting approaches and leverage the power of technology to deliver exceptional customer experiences. 

A customer service solution offers a holistic platform for managing customer service operations effectively. By leveraging advanced forecasting capabilities, seamless data integration and real-time insights, call centers can thrive in the fast-paced world of customer support.

Experience the power of Sprinklr Service and embrace the future of call center forecasting and customer service excellence. Register for a demo today! 

Frequently Asked Questions

Call center forecasting should typically be performed regularly, depending on the call center's operational needs. Daily, weekly or monthly forecasts are common practices.

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Outbound Call Center Software

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