Call Center Forecasting: Methods & Best Practices

Managing call center operations is a balancing act between providing stellar customer service, keeping costs down, and adequately supporting agents.

Understaffing leads to stressed employees, long customer wait times, and–because 44% of consumers find even a 5-15 minute hold time unacceptable–lost revenue.[*]

On the other hand, overstaffing call centers lowers productivity levels, drives up operating costs, and negatively impacts employee engagement. The key to having the right number of agents on call at any given time lies in the precision of contact center forecasting.

• Overview
• Objectives
• Types of Methods
• Choosing a Method
• Metrics
• Identifying Data Sources
• How to Create a Forecast
• How to Measure Accuracy
• Best Practices
• FAQs

What is Call Center Forecasting?

Call center forecasting is a strategy that analyzes historical data, call types, seasonal fluctuations, and digital communication metrics (like website chat and email volume) to forecast future call volumes. Call center forecasting predicts future staffing needs, including how many agents are needed at any given time and what types of skills those agents should have.

While voice is still the preferred communication channel for 59% of consumers, call center forecasting takes a holistic view of the contact center to ensure increased efficiency across the board.[*]

The Objectives of Call Center Forecasting

When call center forecasting is working well, workloads are evenly distributed among team members which leads to reduced wait times, better overall CX, and many other benefits.

Call center forecasting has 5 key objectives:

• Workforce management and optimization: Call volume and call type predictions let admins know how many agents are needed at any given time and what skill sets those agents should possess
• Agent performance management: Balanced workloads mean that every agent receives fair treatment and can be more accurately evaluated
• Determining hiring needs: Staffing predictions let admins and supervisors know how many agents they will need to hire in order to run an effective call center without waste
• Meeting service level targets: Accurate forecasting enables call centers to adequately prepare for peak calling times so that service levels don't drop and wait times remain within acceptable limits
• Agent support: Properly staffed call centers can better ensure adequate PTO and sick leave. This not only helps agents to feel appreciated and experience more job satisfaction, but it reduces agent turnover

Workforce Management and Optimization

Workforce optimization (WFO) and forecasting go hand in hand. Workforce management tools track the number of available agents, their skill sets and performance levels, and feed that data into the forecasting methods used to predict staffing requirements.

Similarly, forecasting predicts future call volumes that workforce optimization tools use in resource allocation and scheduling.

Agent Performance Management

Accurately tracking agent performance is challenging when some team members consistently take more calls than others. Call center forecasting balances workloads and takes each agent’s strengths and weaknesses into account when creating schedules. This results in more accurate performance metrics.

Additionally, forecasting anticipates the need for time off and breaks, which ensures that every agent is able to perform at their best.

Determining Hiring Needs

Scaling can be costly and time consuming, and ending up with too many or too few new employees only adds to that expense. Call center forecasting accurately predicts how many agents will be needed to offer consistent customer service, while planning for unavoidable disruptions such as accidents or illness.

Call center forecasting also provides guidance on new agent training by predicting specific contact center needs, such as faster live chat response or increased product knowledge.

Service Level Compliance

A call center’s service level refers to the percentage of incoming calls that are answered within a time frame. Most companies set service level agreements and goals ahead of time that must be met.

Using call center forecasting methods, such as the Erlang C method, can provide an estimated headcount of agents needed based on service level goals and anticipated call volume.

Agent Support

Call center forecasting provides businesses with the customized data that they need to best support agents by balancing the workload, making sick time and PTO available, and working in tandem with WFM tools such as scheduling and shift swapping. When call center agents are not overwhelmed, productivity increases and turnover decreases. Call center agent turnover is an issue that 33% of companies cite as a major concern[*]

Call Center Forecasting Methods

Call center forecasting is complex and ever-evolving. There are several forecasting methods to choose from, each using different types of data and algorithms to support various business goals.

Let’s take a look at the most common forecasting methods:

Long-Term vs. Short-Term Forecasting

Call center forecasting operates on two distinct timelines, and each one serves a different purpose.

Long-term forecasting covers monthly, quarterly, and annual projections. It focuses on staffing plans, budget allocation, and hiring cycles. You're looking at year-over-year trends, seasonal patterns, and growth projections to determine how many agents you'll need six months from now. Long-term forecasts rely heavily on historical data and broader business indicators like planned marketing campaigns or product launches that will drive call volume up.

Short-term forecasting covers daily and intraday intervals, sometimes in 15- or 30-minute increments. This is where you're deciding how many agents need to be on the phones at 2 p.m. on a Tuesday versus 10 a.m. on a Saturday. Short-term forecasts account for real-time variables like weather events, service outages, or a sudden spike from a social media post.

Most call centers need both, and the two feed into each other.

• Long-term forecasts set the baseline for headcount and scheduling capacity
• Short-term forecasts adjust that baseline for day-to-day reality
• Intraday forecasts let supervisors reassign agents or activate overflow queues mid-shift
• Monthly reviews compare actual volume against long-term projections to improve future accuracy

A strong long-term forecast with poor intraday planning still results in missed service levels during peak hours. The reverse is also true. Getting granular with daily forecasts won't help if you don't have enough agents hired in the first place.

The best approach pairs annual capacity planning with rolling 30-day forecasts that get refined weekly, then daily.

Traditional Methods

• Historical Trend Analysis: Looks at historical data to find when peaks and valleys in call volume occur
• Workload Distribution: Categorizes calls by type (service, IT, etc.) in order to ensure that enough agents with the necessary skills to handle each call type are available
• Multiple Temporal Aggregation (MTA): Evaluates weekly, daily, and hourly historical data and identifies long-term trends
• Erlang C Model: A queueing formula that calculates the number of agents required to hit a target service level, based on call volume, average handle time, and acceptable wait time

Statistical Methods

• Time Series Analysis: Includes any forecasting method that leverages historical data, including call volume and other call center KPIs like abandonment rate, call type, etc.
• Autoregressive integrated moving average (ARIMA): A form of time series analysis that compares past patterns and observations with current data and takes a moving average
• Triple Exponential Smoothing (Holt-Winters Technique): Contact center data is distributed into three categories- levels, trends, and seasonality- and an average is calculated in each category for the data period
• Multiple Linear Regression: Multiple independent variables (day of the week, promotions, spend, etc.) are analyzed to determine a dependent variable (call volume, etc.)
• Logistic Regression: Estimates the probability of an event occurring, such as a service level achievement, based on a given set of independent variables

AI-Powered Methods

• Neural Networks: Leverages artificial intelligence and machine learning tools to uncover many different patterns and trends that might otherwise be missed. Requires very large data sets

Here's an overview comparison table of the forecasting methods mentioned above:

Which Forecasting Method Should You Choose?

The best forecasting method for your business depends on contact center size, available data, and desired accuracy levels. A good rule of thumb is to start with a simple method like historical trends or time series, and then move on to more complex methods like ML analysis, if necessary.

Here are some general guidelines:

• For startups and small businesses: The historical trend or time series analysis are straightforward methods that don’t require a large amount of data
• For SMBs and teams looking to expand and hire: The workload distribution method will provide clarity on agents and skill sets that are needed
• For teams focused on hitting service level targets: Erlang C provides straightforward agent headcount calculations based on volume and acceptable wait times
• For enterprises with large amounts of data available: AI and machine learning algorithms can provide deep insights and superior accuracy
• For seasonal businesses that need long term forecasting: Triple exponential smoothing or MTA will be best, because they calculate averages from data collected over long time periods
• For short term forecasting: Multiple linear regression and MTA will work for short term forecasting and don’t require multiple years of historical data

Key Metrics Used For Forecasting in Call Centers

Whatever forecasting method you are using, you will need to ensure that your call center software is collecting the correct data. When it comes to contact center data, there are hundreds of possible key performance indicators to track.

Here are some metrics to focus on for accurate call center forecasting:

• Call Volume: Refers to the number of incoming and outgoing calls in a specified time period such as hourly, daily, weekly, annually, etc.
• Call Abandonment Rate: Percentage of calls that are abandoned by the customer before an agent answers. The average is 5.91%.[*]
• Average Handle Time (AHT): The average amount of time that an agent spends on a call and completing pre and post call work
• Average On-Hold Time: The average amount of time that callers are placed on hold waiting to speak to an agent
• Call Type: Organizes and analyzes categories of calls such as sales calls, IT questions, service calls, appointment scheduling, etc.
• Communication Channel: Organizes and analyzes interactions by communication channel such as email, text, Facebook messenger, phone call, etc.
• Most Common IVR Selection: Displays the most common call flow paths for customers
• Customer Satisfaction (CSAT) scores: Uses after call surveys to determine how satisfied customers are with their experience

Identifying Relevant Data Sources

Accurate call center forecasting depends on the quality of the data feeding your models. Before running any analysis, you need to know where your data lives and whether it's clean enough to trust.

Three systems supply the bulk of forecasting data:

• Automatic Call Distributor (ACD): Your ACD logs call volumes, wait times, abandonment rates, and handle times at the interval level. This is your primary source for volume-based forecasting.
• Computer Telephony Integration (CTI): CTI platforms capture interaction-level detail like call transfers, IVR paths, and screen pops. They fill in context the ACD misses, especially for routing complexity.
• CRM: Your CRM ties customer records to interaction history. It shows repeat contact rates, case types, and resolution outcomes, all of which help segment demand by reason for contact rather than raw volume alone.
• Workforce Management (WFM) platform: WFM tools store historical schedules, adherence data, and shrinkage rates. Without this, you're forecasting demand but not capacity.

Pulling data from these systems means nothing if it's inconsistent. Common problems include duplicate records from CRM syncs, mismatched time zones between ACD and WFM exports, and missing intervals caused by system outages.

To prepare your data before forecasting:

1. Standardize timestamps across all sources to a single time zone
2. Remove or flag duplicate interaction records
3. Fill gaps from outages using adjacent-period averages rather than leaving blank intervals
4. Validate that contact reason codes map consistently between your CRM and ACD

Spending a few hours on data cleansing before each forecasting cycle prevents compounding errors that distort staffing decisions weeks down the line.

How to Create a Call Center Forecast: Step-by-Step

Building a call center forecast doesn't require a data science degree, but it does require a structured process. Follow these steps to move from raw data to a reliable staffing plan.

1. Gather historical contact data: pull at least 12 months of records across all channels: calls, chats, emails, and social messages. Include timestamps, handle times, abandon rates, and any notes on unusual spikes. The more granular the data, the more accurate your output.
2. Clean and normalize the data: remove outliers caused by one-time events like system outages or product recalls. Adjust for known anomalies so they don't skew your baseline. Flag holidays, marketing campaigns, and seasonal patterns separately.
3. Identify patterns and drivers: look for recurring trends: day-of-week fluctuations, monthly cycles, and seasonal peaks. Cross-reference contact volume with external factors like billing cycles, product launches, or weather events that historically affect demand.
4. Select a forecasting method: match your method to your data maturity. Small teams with limited history can start with historical trend analysis. Larger operations with 2+ years of clean data benefit from time series models like ARIMA or AI-powered forecasting tools.
5. Generate and validate the forecast: run your model, then compare its output against a holdout set of actual data you didn't use in building it. Measure forecast accuracy using mean absolute percentage error (MAPE), aiming for a variance under 5%.
6. Monitor, adjust, repeat: no forecast stays accurate forever. Review actual vs. predicted volume weekly. Update your model as new data comes in, and recalibrate quarterly to account for shifting customer behavior or business changes.

How To Measure Forecast Accuracy?

Knowing how wrong your forecast is matters just as much as making the forecast itself. Two metrics give you the clearest picture: forecast accuracy percentage and forecast bias.

Forecast accuracy is typically calculated as:

Forecast Accuracy (%) = (1 - |Forecasted Contacts - Actual Contacts| / Actual Contacts) × 100

If you forecasted 1,000 calls on a Monday and 920 came in, your accuracy is 92%. Most well-run contact centers target 90-95% accuracy at the daily level and 95%+ at the weekly or monthly level. Hitting those numbers consistently means your staffing models are built on solid ground.

Accuracy alone doesn't tell you which direction you're missing, though. That's where forecast bias comes in.

Forecast bias measures whether you're consistently over- or under-predicting contact volume:

• Positive bias means you regularly forecast more contacts than actually arrive, leading to overstaffing and wasted budget
• Negative bias means you consistently underestimate volume, causing understaffing, longer wait times, and agent burnout
• Near-zero bias is the goal, indicating your errors balance out over time rather than skewing in one direction

To track bias, subtract actual contacts from forecasted contacts across multiple periods and look for patterns. A single off day isn't a problem. A string of 10 days where you overestimated by 8%+ signals a systemic issue with your data inputs or assumptions.

Keep in mind that high accuracy and low bias aren't the same thing. You could average 93% accuracy while consistently under-forecasting Mondays and over-forecasting Fridays. Review accuracy and bias by day of week and time interval to catch hidden patterns before they compound into staffing problems.

Best Practices For Call Center Forecasting

Call center forecasting offers huge benefits, but it is a complex and sometimes cumbersome process. Here are some best practices and tips to ensure your call center forecasting efforts are productive and fruitful:

• Take agent skills and performance into account: Some agents may have a higher AHT or may be specifically talented at taking service calls. These factors should be considered for scheduling and to inform training. For example, research shows that companies who invest in active listening skills for agents can see a boost in revenue up to 120% [*]
• Be flexible: Situations that impact forecasted numbers (IVR updates, natural disasters, etc) can happen quickly and render predictions useless. Call center managers should build in flexibility to accommodate unanticipated changes
• Utilize workforce management software: WFM platforms streamline scheduling by analyzing historical call data, automatically generating forecasts and agent schedules, enabling shift trades, processing PTO requests, and more. On average, contact centers using WFM solutions spend 25% less time on scheduling and service level management tasks than those that use traditional/manual methods [*]
• Look out for aberrations that impact averages: Occasionally, there may be a surge in call volume due to an unanticipated, one-time event such as a product recall. Any such spikes should be removed from future forecasting calculations.
• Don’t forget to account for employee vacations, sick leave, etc.: When scheduling agents, be prepared for some absences, requests to leave early, late arrivals, PTO, etc. Research shows that nearly 50% of U.S. workers don't use all of their allotted PTO leave which can lead to increased stress and decreased productivity [*]
• Decide whether to err on the side of being over or under staffed ahead of time: Although a perfect number of agents is the goal, it’s not always feasible. Companies that prioritize customer support should err on being overstaffed, while businesses that need to keep costs down may err on the side of being understaffed.

FAQs