Optimizing Predictive Staffing for Call Centers with AI Tools

Introduction

This workflow outlines a systematic approach for predictive staffing in customer service and call centers, leveraging historical call volume data to optimize staffing levels and enhance service efficiency.

A Process Workflow for Predictive Staffing Based on Historical Call Volume

In customer service and call centers, a process workflow for predictive staffing based on historical call volume typically involves the following steps:

Data Collection and Analysis

1. Gather historical call volume data across all channels (phone, email, chat, etc.).
2. Analyze patterns in call volume by time of day, day of the week, season, etc.
3. Identify any recurring spikes or dips in volume.

Forecasting

1. Utilize time series forecasting models to predict future call volumes.
2. Consider known events that may impact volume (e.g., product launches, promotions).
3. Generate hourly, daily, and weekly call volume forecasts.

Staffing Calculations

1. Determine the average handle time for different call types.
2. Calculate required staffing levels based on forecasted volume and handle times.
3. Account for shrinkage (breaks, training, etc.) to determine the total headcount needed.

Schedule Creation

1. Create shift templates to cover forecasted volume.
2. Assign available agents to shifts based on skills and availability.
3. Adjust schedules as necessary to meet service level targets.

Ongoing Monitoring and Adjustment

1. Track actual call volume versus forecast.
2. Monitor adherence to schedules.
3. Make intraday adjustments to staffing as required.

This basic workflow can be significantly enhanced by integrating AI-powered time tracking and scheduling tools:

AI-Driven Time Tracking

Tools such as Flowace utilize AI to automatically track agent activities, providing detailed insights into productivity and time allocation. This data can be used to:

• Improve handle time estimates for different call types.
• Identify opportunities for process improvements.
• Provide more accurate inputs for staffing calculations.

Intelligent Forecasting

AI forecasting tools, like those offered by Talkdesk, can analyze historical data alongside real-time information to generate more accurate predictions. These tools can:

• Identify subtle patterns that human analysts may overlook.
• Quickly adjust forecasts based on unexpected events.
• Provide omnichannel forecasts across all customer contact points.

Automated Scheduling

AI-powered scheduling tools, such as those in Eleveo’s platform, can:

• Automatically generate optimized schedules based on forecasts.
• Consider agent skills, preferences, and performance metrics.
• Quickly adjust schedules in response to changing conditions.

Real-Time Adherence Monitoring

AI tools can track agent adherence in real-time, allowing for immediate adjustments. For example, Scorebuddy’s AI-driven WFM solutions can:

• Alert managers to potential understaffing situations.
• Automatically offer overtime or time off based on real-time needs.
• Identify coaching opportunities for agents struggling with adherence.

Continuous Optimization

Machine learning algorithms can continuously analyze performance data to suggest improvements. For instance, Sprinklr’s AI-powered analytics can:

• Identify optimal staffing levels for different call types.
• Suggest schedule adjustments to improve efficiency.
• Predict potential issues before they impact service levels.

By integrating these AI-driven tools, the predictive staffing workflow becomes more dynamic and responsive:

1. AI time tracking provides accurate data on agent activities and productivity.
2. Intelligent forecasting tools generate highly accurate predictions.
3. Automated scheduling creates optimized schedules based on forecasts and agent data.
4. Real-time monitoring allows for immediate adjustments.
5. Continuous analysis drives ongoing improvements to the entire process.

This AI-enhanced workflow enables call centers to more accurately align staffing with demand, thereby improving efficiency and customer service while reducing costs. Additionally, it offers agents more flexible and equitable schedules, potentially decreasing turnover.