Call centers are the backbone of modern customer service. But running a smooth operation requires more than answering calls—it requires predicting them.
That’s where call center forecasting comes in. Call center forecasting predicts future call volumes using historical data and seasonal trends.
The question is, why to bother? Two words: efficiency and cost. Accurate forecasting lets you staff your call center just right. Not enough agents and you risk long wait times and frustrated customers. Too many, and you’re burning money on idle hands.
This guide will show you how to master call center forecasting.
UNDERSTANDING CALL CENTER FORECASTING
Call center forecasting is necessary to predict future call volumes based on historical data and seasonal factors. When done right, it can help you:
- Improve SLAs: With precise forecasts, you can schedule the correct number of agents to handle incoming calls. This allows for shorter wait times and better service level agreements (SLAs).
- Optimize Costs: Call center forecasting helps you avoid overstaffing or understaffing, reducing labor costs and allocating resources more efficiently.
- Reduce Workforce-Related Inefficiencies: Accurate forecasts help you plan for peak and off-peak periods, minimizing idle time and overtime.
Accurate forecasting helps in-house customer service departments by improving scheduling, optimizing costs, and enhancing efficiency. It is also crucial to prepare to outsource contact center operations.
For a customer service department seeking an outsourced contact center, precise forecasting allows you to build more effective Requests for Proposals (RFPs). By clearly defining your projected call volumes and operational needs, you enable potential outsourcing vendors to propose tailored staffing models and solutions that align with your business objectives. This ensures you select the right partner to meet your service level expectations and achieve your strategic goals.
KEY STEPS TO CALCULATE PROJECTED CALL VOLUMES
Predicting future call volumes isn’t magic, but it does help you understand the past and make educated guesses about the future. Here are the key steps to get you there:
Step 1: Analyze Historical Data
The past is the key to predicting the future. Analyze historical call data to identify patterns, trends, and seasonal variations. Ideally, use at least 12 months of data to capture seasonality and annual trends. Still, two to three years of data are recommended for greater accuracy, especially for industries with irregular patterns.
One year of data captures seasonality but doesn’t account for multi-year trends or anomalies, such as significant shifts in customer behavior or external factors like economic changes.
Additional Data Sources to take into account:
- ACD (Automatic Call Distributor) Reports: For granular data on call arrival patterns.
- CRM Data: To understand customer interactions and potential impact on call volumes.
- Customer Portfolio and Behaviors: Analyze customer segments and how their behaviors influence call frequency.
Step 2: Identify Seasonality and Trends
External factors like holidays, product launches, and marketing campaigns can significantly influence call volumes. Beyond identifying these events, incorporate:
- Marketing Activities: Promotions, advertising campaigns, or events that drive customer engagement.
- Sales and Production Issues: Unplanned outages or product recalls can spike call volumes unexpectedly.
- IT or Telecom-Related Matters: Consider potential system downtimes or upgrades that impact call demand.
- External and Environmental Factors: Monitor economic, political, or environmental conditions that could disrupt customer behavior and call patterns.
Understanding these influences allows you to refine your forecasts to better align with external and internal activities.
Step 3: Collaborate Across Teams
Forecasting isn’t a solo task—engage key departments to improve accuracy:
- Marketing Teams: Share insights on upcoming campaigns or events that could increase customer inquiries.
- Sales Teams: Provide projections for new product launches or peak sales periods.
- Customer Service Teams: Offer feedback on recurring customer concerns or patterns.
- Internal Departments: Factor in cross-departmental inputs, such as IT downtimes or operational changes, that can indirectly affect call volumes.
This collaborative approach ensures that your forecasting reflects a holistic view of your organization’s activities.
COMMONLY USED FORECASTING MODELS
Now that you know the steps, let’s discuss the different models you can use to make your forecasts come alive. Here are the most common options.
Time series forecasting techniques
Time series forecasting techniques rely on historical data to predict future call volumes.
- Moving Averages: A simple but effective method that smooths out fluctuations in data. It averages historical data points to understand the call center’s overall performance.
- Appropriate Use: Best suited for data without strong seasonal patterns, as it helps understand the overall trend by mitigating random variations.
- Exponential Smoothing: This technique assigns exponentially decreasing weights to past observations, giving more importance to recent data.
- Visualize a line graph where recent call volumes influence the forecasted trend more significantly than older data points, resulting in a line that adapts quickly to changes.
- Appropriate Use: Ideal for data with no clear trend or seasonal pattern, allowing the model to adjust to data changes quickly.
- ARIMA (Autoregressive Integrated Moving Average): A sophisticated model that combines autoregression, differencing, and moving averages to handle various data patterns, including trends and seasonality.
- Consider a time series plot where ARIMA models the relationship between past values and the current value, accounting for trends and repeating patterns to forecast future call volumes.
- Appropriate Use: Suitable for short-term forecasting when data exhibits complex patterns, including trends and seasonality.
Regression analysis
Regression analysis defines relationships between call volumes and other factors, such as marketing campaigns or holidays.
For example, you can predict increased call volumes during a significant marketing campaign or a product launch. This model works best when clear variables can predict call volume.
The Erlang C model
The Erlang C model is a mathematical formula that determines the probability that a call must wait before being answered based on call arrival rates, average handling time, and the number of agents available.
Erlang C Formula:
Where:
- Pw = Probability that a call has to wait
- A = Traffic intensity in Erlangs (calculated as call arrival rate multiplied by average handling time)
- N = Number of agents
Suppose a call center receives 100 calls per hour and handles them in an average of 3 minutes (0.05 hours).
Traffic intensity A = 100 × 0.05 = 5 Erlangs (5 call hours of traffic per hour)
This means that the minimum number of agents in the call center would be 5.
The Erlang C model is precise. However, it assumes that calls are distributed equally during the hour.
Appropriate Use: The Erlang C model is beneficial for determining staffing requirements to meet specific service levels, especially in environments with random call arrivals and when aiming to minimize customer wait times.
INTEGRATING FORECASTING INTO WORKFORCE PLANNING
Effective workforce management (WFM) in a call center extends beyond accurate call forecasting. For accurate workforce planning, you must consider other factors, such as agent break distribution over the day and the absenteeism rate.
To ensure optimal performance and service quality, it’s essential to integrate several key elements into your WFM strategy:
1.Real-Time Adherence Monitoring
Monitoring agents’ adherence to their schedules in real-time allows for immediate adjustments, ensuring that staffing levels meet actual demand. This proactive approach helps maintain service levels and reduces customer wait times.
2. Continuous Training and Development
Regular training programs keep agents updated on product knowledge and customer service techniques, enabling them to handle a variety of call scenarios effectively. Investing in development also aids in employee retention and performance.
3. Technology Integration
Advanced WFM software can automate scheduling, forecasting, and reporting processes, increasing efficiency and accuracy. Integration with other systems, such as Customer Relationship Management (CRM) tools, provides a holistic view of operations.
By incorporating these elements alongside accurate call forecasting, call centers can develop a comprehensive WFM strategy that enhances operational efficiency, employee satisfaction, and customer experience.
FORECASTING INSIGHTS FOR BUILDING A WINNING RFP
A well-crafted RFP is your key to finding the perfect outsourcing partner. Accurate call volume forecasts can help you create a compelling RFP that attracts top-tier vendors and guarantees a successful partnership.
Key Metrics to Include
To paint a clear picture for potential vendors, don’t forget to include these key metrics in your RFP:
- Average Handle Time (AHT): AHT reveals how long an agent spends on average on each call. A lower AHT indicates efficient handling of customer inquiries. Historical AHT data and future projections will help your vendors understand the complexity of your call volume.
- Call Volume Projections: You’ll also need to share your forecasted call volumes for the upcoming period, focusing on peak and off-peak hours. These details help your vendors plan their staffing accordingly and avoid service disruptions.
- Peak Hours: What are the specific times of day and days of the week when call volumes in your call center are highest? This KPI will help your vendors allocate resources effectively and ensure enough coverage during peak periods.
- Required Service Levels: Most importantly, you’ll need to clearly define your expected service levels, such as average wait times and abandonment rates. These metrics help vendors understand your quality standards and tailor their proposals as needed.
Defining Expectations
Clear expectations set the stage for a successful partnership. Luckily, you have all the forecasts to articulate your specific needs to a potential vendor. Here are the three factors you shouldn’t forget to outline:
- SLAs: Clearly state your desired SLAs, such as average wait times, abandonment rates, and first-call resolution rates. For example, you might aim for an average wait time of less than 30 seconds and an abandonment rate below 5%.
- Agent Expertise: Specify the required skills and experience levels for your agents. You’ll also need to mention your expected language proficiency, technical knowledge, and customer service skills from the agents.
- Scalability: Outline your anticipated growth or seasonal fluctuations and the need for flexible staffing solutions. As we mentioned, you may expect a massive increase in call volume during specific marketing campaigns or holiday seasons.
Budget Alignment
Accurate forecasts also help you set more realistic budget expectations. The more detailed your projections are, the more likely your vendor will tailor their proposals to fit your budget constraints.
You can ensure budget alignment by sharing historical call volume data to provide context for your vendor. For instance, you can highlight any peak seasons you’ve had in the past and how much it has cost to keep up with them.
This is also the time to outline your anticipated call volume growth and the corresponding budget implications. You want your vendors to understand the long-term financial commitment of working for you.
Finally, it never hurts to try to spend less. You can offer strategies that may have worked for you in the past, such as implementing self-service options or optimizing agent schedules. These measures can help you maximize your budget without compromising your desired outcomes.
Scenario Planning
Your RFP is incomplete without thoughtful scenario planning. To accommodate sudden shifts in demand, prepare for the best, worst, and average-case scenarios.
First and foremost, make sure the outsourcer has contingency plans in place. What will you do during natural disasters or system outages, and how will your vendors mitigate their impact?
Beyond disruptive cases, you need to outline potential growth scenarios and the need for scalable solutions. For example, you may anticipate a product launch that could significantly increase call volume. Lastly, don’t forget to ask your vendors how they’ll adjust their staffing and pricing models to any potential economic downturns.
TOOLS AND TECHNOLOGIES TO ENHANCE CALL CENTER FORECASTING
Forecasting doesn’t have to be a hit or miss. Today, you can access many powerful tools and technologies to make your predictions more accurate and reliable.
For instance, forecasting software like Oracle and IBM Cognos Planning can automate the analysis of historical data, identify trends, and generate future forecasts. AI and machine learning algorithms like Google Cloud AI Platform and Amazon SageMaker take it further. They learn from past data to make even smarter predictions.
CONCLUSION: TURNING FORECASTING INTO ACTION
Remember, the future of your call center lies in the power of data and intelligent forecasting. So, why wait? You can start harnessing the power of forecasting now. Connect with industry-leading call center outsourcing vendors through NAOS Solutions today.
