To ensure the right number of staff are available at any given time to deliver exceptional customer service, call centers must fully understand how much “traffic” they expect to handle.
Enter call center traffic forecasting, an art form that helps operations stay on top of dynamic demand levels, so they can best plan for traffic spikes and dips.
Most workforce optimization (WFO) and workforce management (WFM) systems come with built-in forecasting capabilities to predict how traffic volumes could change in a given period.
Yet, many contact centers – typically with fewer than 30 seats – still rely on manual methods.
How do WFO/WFM Tools Forecast Call Centre Traffic?
WFO/WFM tools leverage built-in algorithms to arrive at highly accurate forecasts. These include:
- Averaging Algorithms – These analyze call center traffic over several years to predict the possible volume for a specific day of the week, month, or date.
- Point Estimate Models – These assess call volumes for special events – including holidays, Black Friday, and marketing campaigns – to better predict traffic when these events reoccur.
- Time Series Models – These evaluate changes in traffic volume over various quarters/fiscal years to predict contact volumes for a day, factoring in time-based change.
Typically, WFO/WFM tools use a combination of all of these models to process the massive volumes of call records they store and generate predictions.
These solutions will also contain many forecasting models and algorithms – possibly hundreds – which contact centers can test to find the most accurate for their unique environment.
With these tools, planners can automate much of the forecasting process. Yet, they can still make tweaks based on their planning experience.
Moreover, WFM systems reforecast at regular intervals to ensure maximum accuracy.
How Can Contact Centers Forecast Manually?
Manual forecasting for a contact center of over 30 agents is arduous, as operations typically use spreadsheets that grow and spiral out of control.
Moreover, it is tricky to achieve high forecasting accuracy, balancing contact volume data to spot trend and seasonality – with special considerations for “what if” scenarios.
Yet, it is possible. First, businesses must consider contact volumes from the past three to five years to estimate an annual demand for the year ahead – giving the most recent years more precedence.
Then, they split this demand into months, accounting for traditional seasonality.
From there, such demand is broken down into weeks, days, and hours, closely considering trends such as: which day do we typically get the most contacts? And what does our call arrival pattern across the typical day look like?
Yet, this is only the bare bones of a manual forecasting technique known as “triple exponential smoothing,” which contact centers have used for decades.
Why You Need to Forecast Call Centre Traffic
Traffic volumes for inbound call centers vary hugely based on market forces.
For example, during the holidays, retailers could face a sudden spike in customer queries, return requests, etc.
Moreover, unexpected events may also wreak havoc. Floods, strikes, and future pandemics may all lead to a sharp rise in traffic, creating new problems for customers.
Without adequate preparation, understaffing becomes a severe issue, leading to long call queues, customer frustration, high abandon rates, and often customer churn.
Conversely, forecasts must anticipate traffic dips, as agent idle time often results in overheads.
Fortunately, WFO and WFM tools use AI to assess historical call logs and interaction patterns to predict how many agents are necessary to tackle traffic volatility.
How Can Companies Improve Call Center Traffic Forecasting?
Ultimately, creating accurate call center traffic forecasts is utilizing the right tools.
Most companies today will use a combination of analytical and reporting tools alongside WFM software to assist with their forecasting process.
Some solutions can make predicting call traffic based on historical data much more straightforward, using automated data processing and even AI insights.
Meanwhile, the same tools can also allow business leaders to assign shifts to each team member based on their predictions for better resource management.
In addition, WFM solutions are particularly useful for call center traffic forecasting because they can consider various variables, including availability, workloads, planned absences, and seasonality.
Yet, whether tech-driven or manual, forecasts are most accurate when businesses maintain regular forecast updates based on the latest data.
Looking for more advice to improve contact center planning? Try reading our article: Putting WFM at the Heart of the Contact Center