If operational efficiency and peak performance are your top goals, then implementing a sound contact center forecasting process might be the single most important thing your organization can do. Yet, in a recent Salesforce survey, only 20% of service professionals said that their organizations excelled at forecasting demand.
If your organization is struggling with accurate forecasting and effective staff planning, this guide is for you. Brush up on the basics of contact center forecasting—or skip ahead to dive into common pitfalls, best practices, and tips sourced straight from our experts.
Table of Contents
What is contact center forecasting?
Call center forecasting is the fundamental process of predicting the volume of incoming customer interactions—calls, emails, chats, and more—over a specific period. It’s about looking ahead, using data to anticipate demand, and preparing your team to handle it efficiently. This prediction process is critical to workforce planning and a cornerstone of effective call workforce center management (WFM). Plus, as we’ll explore below, its accuracy has a direct impact on your ability to deliver exceptional customer experience.
Why is accurate call center forecasting so important?
As we discussed, call center forecasting is about anticipating demand. But why is getting that prediction right so crucial? The answer lies in its direct impact on several key aspects of your call center’s productivity and performance and, ultimately, your customer experience.
Improve service levels
SLAs are critical for measuring and maintaining service quality. Failing to meet SLAs can damage your reputation and lead to customer churn. By predicting call volumes and customer demand with precision, you can ensure that your team is prepared to handle the incoming workload, maintaining consistent service delivery.
Proactively adapt to change
Call centers operate in dynamic environments. Accurate forecasting allows you to anticipate and adapt to fluctuations in demand. Whether it’s a seasonal spike, a marketing campaign, or an unexpected event, accurate forecasting helps you stay ahead of the curve. Modern call center forecasting software also allows for quicker reactions to unpredicted changes, ensuring your team is ready for anything.
Avoid agent burnout
High workloads, stress, poor efficiency, and poor results can quickly add up to burned out agents on the frontline. With so many organizations facing issues with attrition—recently, 63% of agents expressed a high risk of burnout—implementing a sound contact center forecasting strategy can’t be overlooked. In fact, according to one survey from the Society of Workforce Planning Professionals, forecasting accuracy was actually the number-one measurement affecting overall team satisfaction.
Optimize efficiency with data-driven workforce planning
With staff costs typically making up approximately 70% of a contact center’s total costs, forecasting offers visibility and clarity to make better informed staffing and planning decisions to optimize spend and overall efficiency. Overstaffing leads to unnecessary costs, while understaffing results in long wait times and frustrated customers. By knowing when peak periods will occur, you can schedule agents effectively, ensuring you have the right people in the right place at the right time.
Enhance customer experience
Ultimately, the most significant impact of accurate forecasting is on your customer experience. Customers today expect prompt and efficient service across an array of channels. Accurate forecasting helps you minimize wait times and ensure agents are available when needed. Reduced response times and faster resolution times translate to higher customer satisfaction scores and stronger customer loyalty. When agents are properly staffed and scheduled, and better engaged, they can provide more attentive and personalized service, leading to positive customer interactions and strengthening your brand reputation.
Common forecasting challenges in the contact center
Resource forecasting in contact centers doesn’t need to be a complicated process. Then again, even with a perfect forecast and the best intentions, things don’t always go to plan. Here are the most common reasons for incorrect forecasts:
- Lack of Historical Data: Access to accurate data along (along with a recorded audit trail of previous forecasting activities) provides a solid foundation for future forecasts. Remember to archive all forecasts so that you don’t lose access to that historical data.
- Poor Validation Process: Everything changes over time including channels, agent preferences and unplanned absences. Therefore, it’s essential to stay on top of change and factor it into the forecast. That way, the contact center is always ready to react swiftly and effectively to the unexpected. Generate new forecasts by day, week or month, depending on known upcoming events.
- Working in Isolation: Make sure planning teams communicate and work with other parts of the business to improve contact center forecasting. by simplifying the communication process, it’ll be easier to keep sales and marketing in the loop, regarding planned promotions and advertising campaigns. This will also help agents be ready to tackle generated inquiries as they come.
- Missing “What-If” Analysis: Why waste time and money on forecasts that don’t work when a ‘what if’ exercise can help with forecasting models for future requirements? Choose a workforce management solution that includes “what if” modeling and know how to use it. Remember to incorporate buffers to allow for unexpected spikes in activity and unplanned absences.
- Once is Not Enough: Processes, like businesses, are not static. They are constantly moving and require frequent re-evaluation to increase efficiencies and gain a competitive edge. Reviewing processes and historical data should occur regularly. Set a consistent time each week, and make sure it’s reviewed at least once a month.
Spreadsheets as a workforce forecasting and scheduling tool should be a thing of the past
There’s one particular challenge or pitfall that’s worth delving into a bit more in-depth. While most contact centers are leveraging automation and AI-driven solutions, plenty of teams still use spreadsheets to create, manage, and maintain schedules, attendance, and staffing forecasts. This manual process is not only a time-consuming and laborious, it also inhibits the ability to quickly make and communicate changes in real time.
Until recently, half of call centers were still manually creating forecasts for digital channels, lowering planning efficiency and effectiveness. Workforce managers and support leaders need to easily visualize forecasted schedules across multiple channels to easily understand where the team is understaffed, overstaffed, and covered, and instantly make adjustments to meet demand.
A more comprehensive workforce management solution that automates schedule creation and forecasts based on historical demand, seasonality, and performance data to meet target SLAs, response times, and productivity levels is vital for success.
Understanding call center forecasting methods and models
Effective and efficient scheduling relies on good forecasting. WFM forecasting predicts future headcount needs for ongoing or upcoming initiatives such as new product or service launch, marketing promotions, seasonal events, and more.
Workload projections guide workforce forecasting, and uses historical trends, performance or seasonal data to predict personnel needs. To forecast accurately, team leaders need to adopt an effective technique.
Here are five leading call center forecasting methods:
1. Triple Exponential Smoothing
Known as the Holt Winters technique, this forecasting method splits forecast data into three components—level, trend, and seasonality—and averages the inputs from one period to the next.
For example, in a monthly forecast, the three components translate to:
- Level. The last month’s forecast.
- Trend. The increase or decrease in anticipated contacts from the previous month.
- Seasonal. The effect of seasons on data. This component measures the difference between the general average and a specific month.
This forecast model is easy, but teams need to be mindful not to “overfit” data to avoid historical volume anomalies such as demand peaks or outages, as this can lead to an odd-looking forecast.
2. Autoregressive Integrated Moving Average (ARIMA)
A slightly advanced call center forecast method, ARIMA encompasses three main areas:
- Auto-Regression. This involves comparing data against past patterns, such as results from 52 weeks ago.
- Integrated. The difference between past and present observations.
- Moving average. The focus here is to smooth out data over given periods.
ARIMA leverages historical data to present data sets based on past inputs. Data from past periods smooth out existing inputs and make forecasts more accurate.
3. Neural Networks
Leading organizations have adopted neural networks for artificial intelligence in areas such as search algorithms and speech analytics. This forecasting model tries to model the brain by observing a series of inputs and then attempting to adjust a “hidden network” until it discovers a matching output. In a call center, a neural network will examine a series of calls and attempt to match the next field of data to the forecast.
Neural networks are flexible as they can accommodate external inputs such as special days, website views, and marketing activity. Also, this forecasting model does not require complex algorithms as it learns and improves from existing data, automatically isolate specific days from a forecast, and model different factors.
Neural networks are input heavy, time-consuming, and may not be best for teams that rely heavily on trends.
4. Multiple Temporal Aggregation (MTA)
This forecasting method combines high-frequency data — hourly and daily inputs — with trends that span an extended period. An example of MTA in practice will be comparing the number of contacts acquired in 2025 with 2024 and getting an 8% increase.
The result, which is the trend, averages out the contacts and special events, such as seasonality over the year. MTA allows teams to focus on intraday and longer-term data in generating forecasts.
5. Erlang C and A Formulas (industry standard headcount prediction)
At their core, Erlang forecasting models—which come in the “C” and “A” varieties—allow you you to model the relationship between staffing, support volume, and response time. Conceived by Danish mathematician Agner Erlang in 1917, Erlang C and A are capacity planning tool that allows workforce managers to identify their staffing needs by inputting in the number of agents they have at their disposal, their support volume, and the average response time of their operation.
Erlang-based analysis removes some of the guesswork by clarifying how staffing decisions impact bottom-line customer-facing outcomes such as response time and service level, and ensures the ideal number of agents are available to meet demand every time.
At Calabrio, we apply both Erlang A and Erlang C principles to enhance and develop a comprehensive range of new forecasting technologies that drive efficiencies and performance in today’s contact centers. Calabrio ONE users can simply choose the option that suits your set up best to give you the most benefit.
5 contact center forecasting and scheduling best practices
Now that you have a solid understanding of the importance of forecasting and the potential challenges, let’s explore some actionable best practices. By implementing these strategies, you can significantly enhance the accuracy of your forecasts—and drive benefits felt across your entire organization.
Start with the Right Forecasting Measures
When improving your workforce and demand forecasting process, it’s first important to decide what constitutes success. Without proper context, it’s impossible to develop an accurate forecast.
Typically, +/- 5 percent accuracy is the industry standard, but the math isn’t always that simple. For example, if the target is set at 100 contacts, 106 contacts will show as failure, meaning contact centers can’t always rely on industry standards when setting goals.
After defining success, it’s important to determine metrics so you can determine where your contact center currently is—and if you’re making progress towards your goal of a more-accurate forecast. Here are the top three metrics contact center managers should use to ensure an accurate forecast, every time:
- Contact (or Call) Volume
How many calls your contact center receives is a critical piece of the forecasting puzzle. But it’s not the only piece. That data should not be used to simply look ahead; it should also be used to see how closely forecasted interactions match the actual number of contacts. With this complete view, you’ll be able to make adjustments to achieve greater accuracy.
- Handle Time
To better understand your agent’s availability to answer new requests, you need to calculate the average handle time. Average handle time is how long it takes your agents to resolve an inquiry. Now, it’s important to note that call handle time shouldn’t be based on just the first transaction. Instead, it should be calculated using the entire series of subsequent calls that are related to the same case.
- Daily Contact Arrival Pattern
Also known as Forecast Accuracy by Interval, daily contact arrival patterns show the busiest and slowest times of day. This allows you to determine the number of agents you need to manage daily peaks and valleys. From there, you’ll be able to account for schedule inflexibility. This ensures the scheduling plan matches the number of agents required to handle the work to the required staffing based on the forecast workload and the assumptions.
While these numbers are important, how you gather them should be equally considered. Be sure to use real-time and historical data across specific time periods and apply prior year trending data to compensate for the increases or decreases in call volume. In addition, don’t forget to include non-phone interactions such as chat and email, because those still require time and resources from your team.
Inspect Your Historical Data…
One of the most commonly overlooked aspects of creating accurate forecasting models is paying attention to historical data. Historical volume data should be properly aligned with expectations and scrubbed for anomalies. What does this mean?
- Compare the number of interactions (calls) received against the expected number (forecast). If the actual significantly deviates from the expectation (+/- 15%), the anomaly should be investigated.
- Maybe there was a promotion that ran that wasn’t expected, or maybe there was an outage that caused heavy call volumes.
- These unexpected anomalies should be scrubbed from the forecast, as they can pull the averages in the wrong direction, affecting future historical comparisons.
- Expected anomalies should be set aside in their own special forecast. These are repeatable, expected events such as Black Friday, holiday closings, or the day of the month that customers receive their bills.
Building a forecast without removing or smoothing anomalies can have a dramatic effect on your accuracy, often to the tune of 20-40% swings in the wrong direction. Taking the time to accurately identify historical data anomalies is a big step towards improving the accuracy of a forecast.
…And Choose the Right Historical Data
The historical data you put into your forecast is the most crucial piece to ensure its accuracy. There is no “Industry Standard” for how much data to use. It could be 1 week, 1 month, 1 year, or 10 years. It all depends on your organization. If you have recently overhauled your routing, older data might no longer be as useful. This is where knowing your business will help you determine what data is right to use.
An important step in creating an accurate forecast is choosing the proper data that informs the forecast. If your data is clean from anomalies, the next step is to choose the historical time period(s) that most accurately represent the future time period. Aside from accurate historical data, this might be the most important skill of a good forecaster. Ask questions that help understand the time period and then hunt down the data.
Increase Communication
The other critical step in creating accurate forecasting models is to be informed. This involves meeting with other departments of the organization to understand what their decisions will do to the contact center’s volume. Communication is key with marketing, sales, operations, shipping, and any other department. Some ways to increase communication between departments include:
- Set up recurring meetings with other departments to discuss the calendar and what is expected
- Look at historical data for the last time the event occurred to understand how it might affect the forecasting model
- Help the other departments understand what an unexpected swing in volume can do to the health of the contact center
By improving communication channels throughout the contact center, your organization will be better prepared to share information. All of this is essential for the forecasting team to improve forecasting techniques and accuracy.
Leverage the Right Forecasting and Scheduling Software
The art of forecasting is very important, but the science part shouldn’t be ignored either. Investing in the right software is a huge factor in getting greater forecasting accuracy. To find the best contact center forecasting solution for your team, be sure to look for the following features and capabilities:
- Enables users to easily clean the historical data
- Allows for multiple date ranges to be selected for accurate forecasting
- Provides information in a flexible and easy-to-use interface to quickly create, modify, and publish schedules
- Supports forecasting for multiple channels, activities, and time zones
- It provides accurate staffing estimates based on the forecasted data (this is the whole point, right?)
- Provides tools to manage attendance and approval processes, and integrates with your existing payroll systems to streamline operations
- Offers robust reporting and analytics to understand schedule efficacy and adherence at channel and activity level
Choose Calabrio ONE as your complete workforce management solution
Luckily, Calabrio’s leading contact center workforce management solutions deliver these capabilities and more—as part of a comprehensive suite for workforce optimization that brings together automated forecasting and scheduling, AI-driven quality management, powerful interaction analytics and more to meet the needs of the modern contact center.
If you’re looking to improve your forecasting and scheduling process, engage agents, and elevate the customer experience, book a demo today to learn more about how the Calabrio ONE workforce performance suite can help.
