What is Call Center Forecasting?

Call Center Forecasting is a dynamic, ever-evolving, data-driven approach. This process predicts and plans for the volume of incoming calls or other customer interactions that a call center service is likely to handle.

Accurate call center forecasting ensures that the call center is adequately staffed and equipped to handle customer inquiries and ensure customer satisfaction effectively.

Additionally, the importance of forecasting involves,

1. First-call-serve
2. Data-driven decision-making
3. Cost reduction
4. Resource optimization
5. Adaption to frequent changes
6. Give a basis to set KPIs
7. Scalability

But there is more to know about call center forecasting if you want to implement it in your call center for high adherence and low attrition rate. 

Let us move forward into the steps of call center forecasting with some statistical models such as Erlang C Formula, Simulation Models, Historical Data Analysis, etc. 

So, you can cope with seasonal fluctuations in call volume, be accurate with forecasted volume, and assign sufficient reps.

Steps In Call Center Forecasting

Call center forecasting predictions need to be very precise for future headcount. Because in call center services, there is seasonal fluctuation in call volumes, or it can be ongoing or upcoming initiatives, promotional events, etc. 

There are various measures that contact centers take to handle all of their responsibilities efficiently with adequate human resources. 

However, we have developed six practical steps to assist you, from data collection to data utilization, accurate forecasting formula application, and strategic planning. 

You will be better equipped to satisfy customer demand and business objectives by investing in long-term workforce, technology, and process enhancements.

1. Gather Data

Forecasting depends entirely on data, including cold calling or chat volumes, emailing, call duration, other consumer engagement methods, how many calls agents attend, records, etc. 

There are two types of data sources you can use for gathering data, 

1. Internal data source

2. External data source

This historical data is helpful to know about the duties that reps must perform daily or monthly.

But for this, managers and the HR team must be committed to data retention. Spreadsheets and Excel are extensively used in call centers to store all records. Some software helps work faster.

Data must be tracked and stored regularly to maintain its accuracy.

2. Evaluate Data

For forecasting effectiveness, resources need to be used efficiently by taking the right approach. That’s why you need to evaluate and filter your gathered data now.

Use the exploratory data analysis to identify patterns and trends in call volume. There can be some irregularities like peak hours or low call volumes. These trends change over time and are influenced by customers’ lifestyles, habits, or availability.

To analyze these real-time data, use visualization like graphs or charts stats for making anticipation.

3. Use Statistical Models To Forecast 

It is time to make the actual prediction using a forecasting formula. There are several statistical models with different characteristics and limitations. You can use which is suitable for your call center. 

Choosing the most appropriate forecasting method depends on factors like the available data, the complexity of call patterns, and the availability of resources or agents. These models must also be combined with ongoing refinement for optimal results.

Some standard statistical models include,

• Time Series Analysis
• Regression Analysis
• Machine Learning Models
• Extrapolation
• Queueing Theory
• Historical Averages
• Hybrid Models

We will briefly discuss these call center forecasting models in the later section. 

You can now count on one reliable equation for forecasting accuracy: “Percentage Difference.” 

Subtract the actual number of calls from the forecasted calls, divide the difference by the number of actual calls, and multiply the result by 100. Here, you will get the forecasting accuracy percentage in your hand.

4. Match Workforce Schedules And Shifts

Once you’ve predicted future call volumes, you must adjust your staff schedules and shifts accordingly. 

Determine how many agents will be required to complete the tasks. Calculate call volumes in terms of agent hours.  

Then, following the workforce, develop a staffing schedule. It is clever to make daily schedules with weekly and monthly.

Matching staff to scheduled tasks can result in high call forecast accuracy.

1. Match The Forecasted Demand

Calculating and making forecasting schedules is not enough; it is crucial that staffing levels match the forecasted demand. 

To do so, you may need to adapt to some changes like hiring additional full-time or part-time agents, hiring virtual assistants, or outsourcing in case of handling peak hours or allowing flexible timetables.

2. Continuously Track Actual Volumes Against Forecasts

Call center forecasting is an ongoing process that needs continuous monitoring and tracking of every pinch of information.

So, regularly monitor actual call volumes and compare them to your forecasts. This helps you to identify any discrepancies and adjust your staffing and scheduling strategies as needed.

Statistical Models For Call Center Forecasting

How do we measure forecasting effectively?

For this, there are several models and methods available that make a precise prediction. They have their own benefits and disadvantages. One work for a particular company may not work for yours. 

So, it needs deep analysis of the models and knowing the needs of your organization. Most importantly, remember factors like, 

1. Seasonality
2. Volatility
3. Year-on-year trends

Simulation Models For Forecasting

Simulation models create a virtual model of the call center operations. It allows managers or experts to simulate different scenarios and their outcomes. In simple terms, they mimic the functionality of the call center and then predict the forecast. 

For this, it takes data from a computer program and factors in variables like call volumes, call duration, or agent availability. It relies on hypothetical scenarios where the core is historical data. 

Multivariate Analysis For Forecasting

Another statistical approach used to produce forecasts is multivariate analysis. This method of nature is studying numerous variables simultaneously. 

This approach takes into consideration various aspects such as historical call volumes, seasonal fluctuation, agent availability, weekdays, and more. All these directly affect call center performance and demand.

Even regression analysis is used in multivariate to predict the performance key metrics. 

Since this model monitors a wide range of factors compared to other models, it influences forecasting directly. It gives more comprehensive and accurate results. You can make better decisions, leading to a high schedule adherence rate.

Historical Data Analysis For Forecasting

Historical data analysis for forecasting, or we can say historical pattern recognition, works based on history. This method depends on past call center records to make forecasts accurately. 

The main advantage is the simplicity and reliance on actual data like previous call records, call volume patterns, trends, and seasonality. A more suitable approach for short-term forecasting. 

Techniques like moving averages, exponential smoothing, or autoregressive integrated moving average (ARIMA) models are often employed in historical data analysis.

This method uses basic statistical measures like mean, median, mode, and standard deviation to calculate the result.

Time Series Data Analysis For Forecasting

Time Series Data Analysis focuses on analyzing time-dependent data to capture trends and seasonality. This makes it well-suited for long-term forecasting of call center metrics.

This is a method that mainly focuses on analyzing data points that are recorded at regular time intervals. Time intervals can be daily, weekly, or monthly. 

This is particularly useful for modeling and forecasting metrics that account for many temporal dependencies. Simply, it facilitates identifying seasonal trends and cyclic patterns.

Popular methods in time series analysis include ARIMA models, seasonal decomposition, and state space models. Autoregression, differencing, and moving average components are used to calculate the forecasting formula.

Regression Analysis For Forecasting

Regression analysis is a statistical technique in measuring forecasting used for determining correlations between a dependent variable and one more independent variable. Linear and multiple regression are prevalent methods for predicting in call centers.

The dependent variable is call volume, and the independent variable indicates seasonality or marketing spend.

This strategy is only appropriate if a clear vision of the variables and metrics can anticipate call volumes. It provides a quantitative understanding of how various factors influence call volumes.

Regression analysis, for example, could be used to determine how marketing campaigns, promotions, or other factors affect call numbers. 

By taking these characteristics into account, you may create predictive models that forecast call volumes depending on their influence.

Machine Learning Algorithms For Forecasting

These are advanced machine learning algorithms, including decision trees, random forests, support vector machines, and neural networks, that can be employed for call center forecasting. These models can capture complex relationships between multiple variables and adapt to changing patterns in call volume.

These involve training algorithms on historical data to predict future outcomes. Also, these models can handle complex relationships and non-linear patterns in call volume data, making them suitable for more accurate predictions. 

Machine learning models can evaluate enormous tons of data, identify complicated patterns, and adapt to changing dynamics. To develop more precise projections, they might combine a wide range of variables and factors. 

If you have substantial data and computational resources, ML can be your powerful tool for forecasting with great flexibility.

Delphi Method

This method is more likely to work in qualitative techniques for forecasting that depend on expert opinions that come to an agreement for making the prediction. 

There is a panel of experts to answer questionnaires in two or more rounds. They provide their estimates for future call volumes and other metrics. Those opinions were also shared with the group after each round. With experts reviewing the group’s anonymized statements in each round. The process is repeated iteratively. 

With this technique, call centers aim to get a consensus forecast to take advantage of collective expertise but try to minimize biases.

Erlang C Formula

Erlang C formula is commonly used in call center adherence to predict the number of agents required to meet the service level. 

This calculates a wide range of call center operations. The result ranges from 80% to 90% but can never be 100%. Because reps need to take breaks for several reasons during their shift, and it is not possible to work continuously for hours.

However, to maximize the rate, companies keenly notice that their employees do not spend more time on personal stuff like Facebooking or texting. But there should be enough entertainment options so reps do not get bored.

It takes data about arrival call rates, service timing, the target, and others. So, the agents can perform operations whenever needed, in peak or non-peak periods. 

This reduces the abandonment rate and customer waiting rate. Ensure agents are sticking to and maintaining the given responsibility properly. Gradually lead towards more punctuality and customer satisfaction.

Benefits Of Call Center Forecasting

Nothing can replace using forecasting in the call center to ensure that the entire call center runs properly. Here are the top five advantages of having a well-structured forecasting plan.

1. Minimize Waiting Time Effectively

Shorter wait times undoubtedly lead to improved customer satisfaction. Here, forecasting helps call centers anticipate call volumes accurately. This lets the managers allocate resources efficiently to minimize customer waiting times. 

2. Reduce Attrition Rate

The high attrition rate in call centers is like a nightmare. However, forecasting can reduce stress and job dissatisfaction since it provides a more predictable and manageable workload. This, in turn, helps lower the attrition rate as agents are less likely to leave their positions.

3. Prevent Agent Burnout

Call center forecasting ensures that agents are not consistently devastated with high call volumes. They can be pre-prepared for the peak hours. Thus, agent burnout can be prevented with a healthier and more sustainable work environment.

4. Facilitate Well-Informed Hiring Plans

Accurate forecasting enables call centers to plan their hiring needs effectively. Hiring additional agents in time of need won’t be a hassle, with the facility of avoiding over or understaffing.

5. Boost Employee Morale

When agents can meet performance goals more easily, their job satisfaction and morale improve. Happy and motivated employees are more likely to work dedicatedly.

6. Improved Customer Service

There is less customer waiting time. Managers can assign agents according to their expertise and skill. So they can better clear customers’ pain points and, know precisely what the consumers need and then assess them. A better customer service quality indicates a better overall customer experience.

Goal Of Call Center Forecasting

For any call center, the main focus is customer satisfaction, which is only possible by managing the workforce cleverly. Here, forecasting plays a role.

The goal is to predict the possibilities so the whole organization, including managers and agents, can better visualize their roles and fully fulfill their responsibilities.

Statistics show that 32.3% of customers do not like to wait. If there is no proper plan on how to handle high call volumes, then the customer is sure to go on to the competitor’s hand. 

Or, if the wrong agent attends the call where they do not know about the specific product, users will surely be irritated and may never want to contact them.

Only forecasting where the approach reads the history counts the number of workers, and studies the workload can give an optimal outcome. Based on this, the company takes the next step.

The whole process is not confined to only customer satisfaction. But try to reduce cost, properly use resources, have more flexibility and efficiency in work, better employee-employer relationships, and ultimately, a successful business.

Verdict

The nature of call center forecasting is to predict the responsibility of the agents by studying the history. If done correctly, then the business is bound to see the most growth.

By call center forecasting, agents get a clear vision about handling any situation. The company can better make plans and reduce the burnout rate.

To do this, methods and techniques need to be utilized effectively with high expertise. But some factors should be considered from the very beginning, like,

• Collecting and recording all the data.
• Using the right statistical model.
• Regular monitoring of the performances.