What is Call Center Forecasting? A Detailed Guide for 2024
Prashanth Kancherla
Sep 15, 2024 | 9 mins read
Effective call center forecasting helps you strike a balance between understaffing and overstaffing. Too few agents can result in long wait times and frustrated customers, while too many can lead to unnecessary costs and reduced productivity. By getting it right, you’ll not only keep your customers happy but also manage your resources more effectively.
But how does it do that? Well, forecasting isn’t just about guesswork. Instead, it involves analyzing past data, considering seasonal trends, and factoring in external events that might impact call volumes. With the right approach, you can make informed decisions about staffing, training, and resource allocation.
In the following sections, we’ll explore how you can implement forecasting to improve your call center’s efficiency and service quality. Read along!
In this article, we will explore:
- 1.What Is Call Center Forecasting?
- 2. Why Do You Need Call Center Forecasting?
- 3. Key Metrics Used in Call Center Forecasting
- 4. Benefits of Call Center Forecasting
- 5.Call Center Forecasting Methods
- 6.Best Practices for Call Center Forecasting
- 7.Steps to Increase Call Center Forecast Accuracy
- 8.Optimize Contact Center Forecasting with Ozonetel
What Is Call Center Forecasting?
Call center forecasting is an approach that uses historical data, call kinds, seasonal swings, and digital communication measures (such as website chat and email usage) to predict future call volumes. Simply put, it anticipates future staffing requirements, such as how many agents are required at any particular time and the types of abilities those agents should possess.
Still confused? Wait, let me explain this with an example.
Suppose you are a retail company looking to improve its customer service by ensuring its call center is well-staffed during peak times and efficiently managed during quieter periods.
So, you use the time series forecasting to analyze past data and find that call volume spikes during the holiday season. Upon some more research, you predict a 30% increase in calls for the coming December.
Based on this forecast, you can schedule additional shifts and hire temporary staff for the busy period. Plus, you can ensure agents are trained and available during high-volume times, effectively reducing wait times and enhancing customer satisfaction.
Why Do You Need Call Center Forecasting?
Accurate call center forecasting ensures a seamless process and meets customer expectations.
When call traffic is overestimated, it can lead to understaffing, extended wait times, dissatisfied consumers, and overburdened operators. On the other hand, overestimating call volume results in overstaffing, higher expenditures, and underutilized resources.
Basically, organizations can strike the proper balance by accurately forecasting and scheduling contact centers, assuring optimal resource allocation, cost efficiency, and customer service levels.
Key Metrics Used in Call Center Forecasting
Whatever forecasting approach you use, you must guarantee that your call center software is collecting accurate data. When it comes to contact center data, there are hundreds of critical performance indicators to monitor.
Here are some metrics to consider for accurate call center forecasting:
- Call Volume: The number of incoming calls is the major statistic used to anticipate call center workload. Historical call volume data reveals patterns, trends, and seasonality, allowing for more accurate forecasts of future demand.
- Number of Agents: Workforce size has a direct impact on service levels and customer satisfaction. Forecasting the ideal number of agents based on expected call volume and other variables aids in resource allocation and workforce management.
- Average Handle Time: The average time an agent spends on each call, including talk time, wait time, and post-call activities, is critical for anticipating staffing needs. Also, past average handle time data can help in projecting the duration of future calls.
- Customer Satisfaction: When forecasting a call center, customer satisfaction is an essential factor to take into account. Customers that are satisfied with a firm are more inclined to return and suggest others to it.
- Service Level: This measures the percentage of calls answered within a specified time. This metric helps managers in determining how well the contact center is performing and optimize staffing to meet service targets.
- Shrinkage: The percentage of time agents are unavailable due to breaks, training, or meetings. It includes a shrinkage in forecasts to ensure adequate staffing during such times.
Benefits of Call Center Forecasting
Accurate forecasting keeps contact centers from spending money on overstaffing and causing unnecessary frustration for customers and employees due to understaffing. Other advantages of call center forecasting include:
- Prevent Agent Burnout: Proper forecasting balances workloads among agents, preventing overwork and burnout. If your forecast indicates a high call volume, you can adjust shifts and breaks to prevent excessive stress on your staff, leading to a more motivated and less fatigued team.
- Minimize Waiting Time: Forecasting helps ensure that enough agents are available to handle peak call volumes. For example, if forecasts predict a high call volume during certain hours, you can schedule more agents to keep wait times short and improve customer satisfaction.
- Improve Customer Service: With the right number of agents scheduled based on forecasted call volumes, you can handle calls more efficiently. This leads to shorter wait times and quicker resolutions, enhancing the overall customer experience and meeting service level goals.
- Cost Efficiency: Accurate forecasting helps avoid both overstaffing and understaffing, which can lead to unnecessary costs. For example, by predicting call volumes precisely, you can align staffing levels with actual needs, reducing labor costs while maintaining service quality.
- Boost Employee Morale: Accurate forecasts contribute to better workforce management, which in turn enhances employee morale. When schedules align with predicted call volumes, agents face fewer unexpected shifts or excessive workloads, leading to higher job satisfaction.
Call Center Forecasting Methods
Now that you are clear on the various benefits of call center forecasting such as predicting call volumes and necessary resources, here are four key methods that can help you achieve accurate forecasts:
Time Series Forecasting
Time series forecasting involves looking at past data to predict future call volumes. This method works by identifying patterns, such as daily peaks or seasonal trends, to help you prepare for what’s ahead.
Techniques like moving averages and auto regressive integrated moving average (ARIMA) models are often used to spot these patterns. By understanding how call volumes fluctuate over time, you can make informed decisions about staffing levels and ensure that your team is ready to handle expected demand.
Example: You can use the ARIMA model to forecast a 20% increase in call volume during the last two weeks of December, based on the patterns observed in previous years.
Machine Learning and AI
These technologies analyze enormous amounts of data and can identify patterns that traditional methods might miss. For example, ML models can learn from historical data and adapt to changes in customer behavior, providing more accurate predictions.
Whereas, by using AI, you can respond more quickly to shifts in demand, ensuring that you have the right number of agents available when they are needed most.
Example: Let us say a new product launch is scheduled, and your ML model predicts a 30% increase in support calls based on similar past events. By using AI to anticipate this surge, you can allocate more agents to handle the expected demand, ensuring that your customers receive timely assistance.
Multiple Temporal Aggregation (MTA)
This method combines high-frequency data—hourly and daily inputs—with long-term trends. The trend is the result of averaging out contacts and special events, such as seasonality over a year. It enables teams to create forecasts based on both intraday and long-term data.
Example: Using MTA, you analyze data from the past year and notice that calls in December 2021 increased by 8% compared to December 2020. Now, by aggregating this high-frequency data with long-term trends, you can predict staffing needs more accurately and ensure your team is well-prepared for busy periods and quieter times.
Erlang C Model
The Erlang C model focuses on calculating how many agents you need to handle incoming calls without making customers wait too long. This model considers factors like the number of incoming calls, the average time spent on each call, and the number of agents on duty.
By using the Erlang C model, you can make sure you are not overstaffed or understaffed, which helps maintain a balance between service quality and operational costs.
Example: Your call center expects an average of 50 calls per hour, with each call lasting about 4 minutes. Using the Erlang C model, you can determine that you need 12 agents on duty to keep the average wait time under 30 seconds.
Regression Analysis
Regression analysis is another effective forecasting method that examines the relationship between call volumes and various influencing factors. By identifying these relationships, you can build predictive models that forecast call volumes based on variables like marketing campaigns or seasonal trends.
This method is particularly useful when external factors have a clear impact on call center demand, allowing you to make data-driven staffing decisions.
Example: Let us say a new campaign is planned for the next quarter and by applying regression analysis, you predict that this campaign will lead to a 15% rise in call volumes. With this information, you can adjust your staffing levels accordingly to ensure you are prepared for the expected rise in calls.
Neural Networks
This forecasting model attempts to model the brain by observing a series of inputs and adjusting a “hidden network” until it finds a matching output. In a call center, a neural network will analyze a series of calls and try to match the next field of data to the forecast.
These networks are adaptable because they can handle external inputs like special days, website views, and marketing activity. Furthermore, this forecasting model does not require complex algorithms because it learns and improves from existing data, automatically isolates specific days from a forecast, and models various factors.
Example: The network examines historical call data, such as call frequency, special event days, and website traffic. It then predicts future call volumes by adjusting for factors like seasonal variations or special promotions. As a result, you get a dynamic forecast that helps you optimize staffing levels and improve customer service without complex manual adjustments.
Best Practices for Call Center Forecasting
If you actually want to maximize the productivity of your call center, there are numerous further steps you can take to optimize your forecasting and scheduling procedures. This includes:
Prepare for the Unexpected
Forecasting and scheduling use previous data to produce forecasts, but they may not fully account for current or future events. When forecasting, managers must account for unforeseen events such as attrition, breaks, meetings, sick days, and national holidays. Building flexibility into the calendar helps the contact center to respond to unforeseen events while maintaining service levels.
Create an Agent Schedule
Once you’ve determined how many agents you’ll need, you can construct timetables to handle your expected call volume:
- If your projection suggests a generally regular call volume over the timeframe, you could stick to a daily or weekly timetable.
- If the prediction shows a lot of peaks and valleys, you can allocate more agents during busy periods and fewer during slow times.
Also, when assigning shifts, consider your agents’ qualifications, proficiency, and scheduling preferences in mind.
Schedule the Correct Agents
Having the correct agents scheduled is critical for satisfying service level agreements (SLAs), response times, and other support objectives. Each agent has unique abilities, proficiencies, skills, production levels, and scheduling preferences.
Taking these individual aspects into account during the forecasting process ensures that agents are assigned jobs that are appropriate for their skills, resulting in enhanced performance and customer satisfaction.
Gain Automated Insights
The more you use your WFM tools, the more insight you will receive. In addition to attendance and timetable information, look for solutions that may automatically provide productivity and work-life balance reports. Using this, you can identify possibly overworked agents and meet their schedule requirements.
Steps to Increase Call Center Forecast Accuracy
High forecast accuracy is critical for successful call center operations and workforce management (WFM). Here are some techniques for improving call center forecast accuracy:
- Refine Historical Data Analysis: Look for seasonal changes, promotional effects, and other events that influence customer behavior. This insight helps in creating more accurate forecasts by understanding past trends.
- Use Advanced Forecasting Techniques: These methods analyze complex data, uncover detailed patterns, and adjust to dynamic call center environments, significantly enhancing forecast accuracy.
- Leverage Real-Time Data: Monitoring current call volumes and customer behavior allows for immediate adjustments, making forecasts more precise and responsive to sudden changes.
- Implement WFM Strategies: Schedule breaks, vacations, and shifts according to predicted call volumes to avoid overstaffing or understaffing. This helps in optimizing resource allocation based on accurate forecasts.
- Continuous Monitoring and Adjustment: Adjust your predictions and resource plans as needed to address any discrepancies. This ongoing review ensures your forecasts stay accurate and in tune with current operations.
Optimize Contact Center Forecasting with Ozonetel
Call center forecasting is an essential component of running a successful contact center that surpasses customer expectations while also supporting employees. Although technology is constantly improving to provide more accurate and specific predictions, it is still critical to analyze forecasting accuracy and continuously improve the forecasting process.
And tools like Ozonetel make it easy to do this by providing features like:
- Smart Routing: Assigns calls to the most appropriate agents, optimizing distribution and increasing customer satisfaction.
- Automation and Streamlining: Uses AI-powered insights to automate workforce processes such as forecasting, scheduling, adherence, and optimization.
- Real-time Monitoring: Uses a dashboard to track agent activities, productivity, and quality, as well as screen recordings for coaching purposes.
- Data Sync: Automatically synchronizes call data and activities to improve efficiency and streamline workflows.
- CRM Integration: Seamlessly integrates with popular CRM platforms such as Salesforce and Zendesk, allowing you to access workforce management tools from your CRM dashboard.
Final thoughts
By choosing the right AI-powered solution, companies can significantly improve customer satisfaction, reduce operational costs, and gain valuable insights into their service performance. And as you decide on the AI call center software to deploy in your organization, consider Ozonetel’s comprehensive suite of AI-powered tools.
Ozonetel offers a robust platform that integrates seamlessly with your existing systems, providing advanced features like intelligent routing, sentiment analysis, and real-time agent assistance. With its user-friendly interface and quick
Want to see what Ozonetel can do for your company? Sign up today for a free 7-day trial.
Frequently Asked Questions
Call center forecasting involves predicting future communication volumes, whether through calls, emails, or chats, to ensure adequate staffing and resources. It helps call centers prepare for fluctuating demand and maintain efficient operations.
The purpose of a forecast call is to align the entire revenue team, including executives, sales, marketing, finance, and customer success, on key business opportunities. By doing so, the team can focus their efforts on areas that will have the most significant impact, such as targeting specific accounts for marketing campaigns.
