Contact Center Load Forecasting with Dynamic Time Zone Adjustments
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Solution Overview
Problem
Conventional workforce management (WFM) forecasting systems for contact centers face limitations in accounting for Daylight Saving Time (DST) events, leading to underperformance in contact load prediction, which can result in inefficient workforce scheduling, increased wait times, and resource mismanagement.
Innovation Solution
A method to generate contact totals based on customer behavior in different time zones by obtaining geographic location information, analyzing time zone behaviors, and adjusting accumulated numbers to account for DST transitions, allowing for improved load prediction and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional WFM forecasting systems are used, then the system structure is simple, but the contact load prediction accuracy deteriorates due to inability to account for DST events
Solution Approach 1:
The system dynamically adjusts forecasting parameters and time zone mappings based on DST event information. The workload management system receives DST event data and automatically updates its time zone behavior models, allowing the system to adapt to changing time zone rules without manual reconfiguration. This dynamic adaptation enables accurate contact load predictions across multiple time zones while managing system complexity through automated adjustments.
Solution Approach 2:
The system changes temporal parameters by adjusting time zone offsets and DST event timing parameters in the forecasting model. By incorporating DST event information as input parameters and modifying time zone behavior parameters dynamically, the system achieves more accurate contact load predictions. The parameter changes allow the system to account for seasonal time zone variations while maintaining a manageable system architecture through standardized parameter adjustments.
2Adaptability or versatility
If the contact center serves extended geographical areas with multiple time zones, then the customer service coverage is improved, but the workforce scheduling efficiency deteriorates due to DST transitions
Solution Approach 1:
The system segments the customer base by time zone and DST behavior patterns. By dividing customers into distinct time zone groups with specific DST characteristics, the system can generate separate contact load forecasts for each segment. This segmentation allows the workforce management system to schedule agents more efficiently by matching skills and availability to specific time zone requirements, thereby maintaining high productivity while serving extended geographical areas.
Solution Approach 2:
The system performs preliminary actions by pre-calculating contact load forecasts for different time zones and DST scenarios before actual scheduling occurs. The system generates forecasted contact totals in advance, allowing workforce managers to prepare schedules that account for upcoming DST transitions. This preliminary forecasting and planning enables efficient workforce scheduling while maintaining broad geographical coverage, as the system proactively adjusts to future time zone changes rather than reacting to them.
Data Source
AI summary
An example method of generating contact totals based on customer communication patterns in different time zones may include obtaining information indicative of geographic locations for customers of a contact center. The obtained information may be analyzed to determine locations for the customers based on the information indicative of geographic locations. The time zone behaviors for each of the determined locations of the customers may be determined. The method may include accumulating numbers of the customers contacting the contact center for a sampled time period. Contact totals may be generated based on the accumulated numbers. The accumulated numbers may be stored along with the corresponding time zone behaviors for the determined locations of the customers.