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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.

 

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 means 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 which forecasting methods use 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[*]

 

Why is Accurate Call Center Forecasting Important?

Accurate forecasting prevents contact centers from wasting money on overstaffing and causing undue frustration to customers and employees due to understaffing.

Accurate forecasting offers call centers numerous benefits, including:

 

Increased Call Center Efficiency

Overstaffing is inefficient because agents end up with too much downtime. Similarly, understaffing also leads to inefficiency, because issues take longer to resolve–if they are resolved at all.

Call center forecasting ensures that there are enough available agents, balances workloads, abd gives agents enough time to complete pre and post-call work. Most importantly, effective forecasting provides customers with prompt assistance across channels.

 

Improved Customer Service 

48% of consumers will switch brands for a better customer experience–highlighting the importance of providing fast, accurate, and superior support.[*]

Call center forecasting ensures there are enough staff to reduce or even eliminate on-hold times. Balanced workloads that take each agent’s strengths into account empower team members to provide better customer support instead of just putting out fires.

 

Reduced Costs

Call center forecasting predicts staffing levels so that money is not wasted on unnecessary employees. Additionally, forecasting predicts what types of calls will come in, allowing for the contact center to create more efficient IVR and self help tools.

Forecasting can also reduce agent training costs by eliminating unnecessary training and reducing agent turnover.

 

Improved Agent Performance

Call center forecasting takes into account agent skill sets so that agents not only have a more manageable workload, but also handling more of the types of customer interactions they are most comfortable with. This results in higher employee confidence and better performance. Additionally, with lower wait times and less anger from customers, agents will have a higher chance of excelling.

 

Decrease Agent Turnover

Call centers have one of the highest industry turnover rates at 41%.[*] Reasons attributed to the high attrition rate include non-challenging work, inflexible work environment, excessive stress, and abusive calls. Forecasting addresses all of these concerns by fairly distributing agent workloads to reduce stress, scheduling consistent breaks and time off to improve work environment, and alleviating some of the customer pain points that result in abusive calls.

 

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:

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
  • Logistic Regression: Estimates the probability of an event occurring, such as a service level achievement, based on a given set of independent variables
  • Erlang C Model: Multiplies the daily number of calls by the average handle time to determine how many agents are needed

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.)

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

 

Forecasting Method Type Pros & Cons Best For
Historical Trend Analysis Traditional Pro: Quick and easy

Con: Difficult to account for anomalies such as power outages, large sales, etc.

 SMBs that have limited data to work with
Workload Distribution Traditional Pro: Accounts for different skillsets in agents

Con: Not as accurate as other methods

 Companies with multiple departments
Multiple Temporal Aggregation Traditional Pro: Gives both big picture and hourly predictions

Con: Works best with lots of data

 Larger companies that have been in operation at least one year
Logistic Regression Traditional Pro: Allows company to focus on meeting specific goals

Con: May take some trial and error to select the right variables to track

 Companies that need to meet specific service level goals
Erlang C Traditional Pro: Straightforward, works with limited data

Con: Only gives daily volume prediction, not hourly

 SMBs with limited hours
Time Series Statistical Pro: Quick and easy

Con: Requires a lot of data to be accurate

 Startups and small businesses that have been tracking KPIs
ARIMA Statistical Pro: Takes both historical and current trends into account

Con: For companies that have undergone lots of changes or growth, it won't be as accurate because it takes an average

 Companies that have been around for a long time
Holt-Winters Technique Statistical Pro: Gives predictions for seasonal changes as well as daily levels

Con: Have to watch out for anomalies that will impact the averages taken

 Businesses that experience large seasonal shifts
Multiple Linear Regression Statistical Pro: Takes more variables into account than other methods such as promotions, spend, etc.

Con: Have to know which variables to track which may take some trial and error

 Companies in the sales or marketing industries
Neural Networks AI-Powered Pro: More accurate than other methods with deep insights

Con: Requires large amounts of data

 Large companies and enterprises

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 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

 

How To Increase Forecast Accuracy?

Whether you are a new call center working out staffing needs for the first time, or a veteran company trying to increase efficiency, there are several ways to increase forecast accuracy. Accurate forecasting is key to achieving a balanced workload for agents and providing optimal customer service.

The general forecast accuracy formula is the difference between the number of forecasted calls and the number of actual calls in a given time period, divided by the number of actual calls. Multiply by 100 to get a percentage. This percentage is the difference between what was forecasted and what actually occurred.

Here are some ways to improve forecast accuracy:

  • Review Historical Data for accuracy: Forecasting accuracy depends on the inputted data to make predictions, and it is therefore important to manually review data and remove abnormalities, fill in missing information when possible, and convert to a usable format
  • Leverage Artificial Intelligence and Machine Learning tools: AI and ML tools are currently able to offer the most accuracy of any forecasting method. The drawback is they require large data sets
  • Track Call Center Data in Real Time: Real-time reporting and analytics software constantly collects new data, combines it with historical data to enlarge your overall data set, meaning more accurate forecasts
  • Utilize WFO and WFM Tools and Strategies: WFO and WFM tools automate workload distribution and scheduling, reducing human error and providing more data for forecasting

 

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.

 

Call Center Forecasting Challenges

To get the most accurate predictions possible, ensure your data set is robust, complete, and includes all business departments. Here are some call center forecasting pitfalls to be aware of:

  • Missing a variable: There are many factors that impact call volume, such as the day of the week, day of the month, promotions, holidays, etc. Missing one of these variables can result in inaccurate forecasting.
  • Not accounting for variations based on call type: Different types of calls will have different call volumes and AHTs. These variations need to be taken into account to appropriately staff the call center with enough agents that can handle each type of call.
  • Not taking a holistic view of the contact center: When forecasting and staffing, cross department communication is essential to ensure that other related considerations, such as employee meetings and trainings, don’t interfere with peak call times
  • Not having data from a long enough period of time: Using one year's worth of call volume data and dividing by 12 is known as a “naïve forecast” because it results in an inaccurate monthly forecast. Data from previous years is necessary to see monthly and seasonal trends.

 

How Companies Can Improve Call Center Forecasting

Call center forecasting is a vital part of running an efficient contact center that meets and exceeds customer expectations while supporting agents. The methods and technology are continually improving to provide more accurate and specific predictions, but it’s still important to analyze forecasting accuracy and consistently improve the forecasting process

Additionally, call center forecasting is a dynamic system. For example, if 10% of a contact center’s calls come from customers who have first tried live chat, there will be a decrease in call volume once live chat agent staffing is sufficient.

Companies can improve call center forecasting by constantly monitoring KPIs and making adjustments, regularly checking forecasting accuracy, and thoroughly training AI-powered forecasting tools.

 

Quick Tips For Optimal Call Center Forecasting

Here are some key takeaways to ensure that forecasting numbers remain accurate and your call center workforce remains optimized:

  • Properly Collect and Prepare Data: Make sure the data you’re collecting is relevant to achieving the specific goals of your business. Review the data to remove outliers, fill in any missing values, and ensure the format is correct for evaluation purposes.
  • Experiment with Different Forecasting Techniques: Select one of the forecasting models mentioned above based on your data, company size, and business goals–but don’t be afraid to test out different methods if one isn’t working for your business needs.
  • Train and Test AI tools: If using an AI-powered forecasting tool, train the AI to understand what data should be used and how it should be interpreted. Then, use different data to run tests and ensure that forecasting predictions are accurate
  • Constant Monitoring: Monitor KPIs in real time to continually increase your database and improve accuracy. Calculate your forecasting accuracy regularly using the forecasting formula and make adjustments as needed.