Dynamic Point Valuation for Contact Center Scheduling Perks
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Solution Overview
Problem
Contact centers face challenges in efficiently incentivizing agents with scheduling perks while ensuring service level targets are met, due to dynamic workload variations and lack of real-time data for informed decisions.
Innovation Solution
A computer-implemented method that uses a predictive model to determine the value of points earned by agents, allowing them to trade points for scheduling perks during their shift, while ensuring that service level targets are maintained.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional static incentive systems are used, then implementation is simple, but they cannot adapt to dynamic workload variations and lack real-time responsiveness
Solution Approach 1:
The patent implements dynamic incentive valuation by continuously updating point values based on real-time service level metrics and workload forecasts. The system transitions from static predetermined rewards to dynamically adjusted point values that reflect current operational conditions, enabling the incentive system to adapt to changing workloads while maintaining operational simplicity through automated calculations
Solution Approach 2:
The system incorporates real-time feedback loops where service level performance data and workload forecasts continuously inform point valuation adjustments. This feedback mechanism enables the system to respond to dynamic conditions by modifying incentive values based on actual performance outcomes and predicted workload trends, resolving the contradiction between adaptability and complexity
2Productivity
If real-time dynamic point valuation is implemented, then incentive effectiveness and service level optimization are improved, but computational complexity and data processing requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing historical service level metrics and workload patterns. This preparation enables rapid real-time valuation computations without excessive computational complexity, as the system leverages pre-processed data and established algorithms to quickly determine point values based on current conditions
Solution Approach 2:
The patent changes key parameters from fixed values to dynamic variables that adjust in real-time. Service level thresholds, workload forecasts, and point valuation formulas are transformed into flexible parameters that automatically adapt to current operational conditions, improving incentive effectiveness while maintaining computational efficiency through parameter-based adjustments rather than complex recalculations
3Reliability
If points are valued based on service level target adherence, then alignment with performance goals is improved, but measurement and forecasting accuracy requirements increase
Solution Approach 1:
The system introduces intermediary elements such as standardized service level metrics and aggregated performance indicators that bridge the gap between raw data and point valuation. These intermediaries simplify measurement requirements by translating complex performance data into standardized metrics that can be reliably used for point calculations without demanding extreme measurement precision
Solution Approach 2:
The patent uses historical performance data and workload patterns as copies or proxies for predicting future service level adherence. By leveraging validated historical patterns rather than requiring perfect real-time forecasting, the system achieves reliable performance alignment while reducing the stringent accuracy requirements for real-time measurements
Data Source
AI summary
A method for incentivizing agents with scheduling perks that includes: receiving a workload forecast; receiving a staffing plan; receiving data describing a service level target; receiving data describing the scheduling perks; performing a valuation routine for determining a value of points in relation to trading for the scheduling perks during; and performing an offer routine offering the scheduling perks to the current agents per the determined value of the points. The valuation routine includes: measuring current shift performance data; providing the staffing plan, the workload forecast, and the current shift performance data as inputs to a predictive model and calculating therewith an actual forecasted target adherence for the service level target; comparing the actual forecasted target adherence against an acceptable threshold to determine a valuation parameter; and valuing the points according to a direct relationship with the valuation parameter.