Automated Scheduling Perk Selection for Contact Center Service Levels
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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 real-time data analysis to select and offer scheduling perks to contact center agents, by predicting the impact of these perks on service level targets through a predictive model, ensuring that incentives are offered without compromising service level adherence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If scheduling perks are offered to agents to improve satisfaction and retention, then agent satisfaction and retention are improved, but service level targets may be compromised due to reduced agent availability
Solution Approach 1:
The system dynamically adjusts scheduling perk offerings based on real-time workload forecasts and service level predictions. The predictive model continuously evaluates the impact of potential scheduling changes on service level targets, enabling the system to adapt to changing conditions and offer perks only when service level requirements are maintained.
Solution Approach 2:
The system implements a feedback loop where the predictive model evaluates the impact of proposed scheduling perks on future service level targets. This feedback mechanism ensures that scheduling decisions are made with knowledge of their potential impact on service levels, allowing the system to balance agent satisfaction with operational requirements.
2Loss of information
If real-time predictive modeling is implemented to evaluate scheduling perk impact, then informed scheduling decisions are enabled, but system complexity increases
Solution Approach 1:
The predictive model serves multiple functions: it forecasts workload, predicts service level outcomes, evaluates scheduling perk impacts, and guides incentive offerings. This multi-functionality reduces the need for separate systems for each task, thereby managing complexity while providing comprehensive decision-making information.
Solution Approach 2:
The system automatically performs predictive analysis and generates scheduling recommendations without requiring manual intervention. The predictive model self-evaluates potential scheduling scenarios and provides actionable insights, reducing the complexity burden on operators while delivering rich decision-making information.
3Productivity
If automated selection and offering of scheduling perks is implemented, then operational efficiency is improved, but system complexity increases
Solution Approach 1:
The system automatically selects appropriate scheduling perks and generates incentive offers without manual intervention. The automated selection process evaluates agent performance, predicts future service level impacts, and generates personalized offers, thereby improving operational efficiency while managing complexity through automation.
Solution Approach 2:
The manual process of evaluating and offering scheduling perks is replaced with an automated computational system. The predictive model and automated selection algorithm substitute for manual analysis and decision-making, improving efficiency while consolidating complexity into a unified automated system rather than multiple manual processes.
Data Source
AI summary
A method for incentivizing contact center agents with scheduling perks that includes: receiving a workload forecast and staffing plan for a current shift; receiving a service level target; receiving types of scheduling perks; performing a selection routine for determining a select scheduling perk for offering to a select agent; and sending a scheduling perk offer via electronic communication to the select agent. The selection routine may include: selecting a proposed scheduling perks and a proposed agent; modifying the staffing plan to create a proposed staffing plan that reflects the proposed scheduling perk; predicting a proposed forecasted target adherence using the modified staffing plan; determining whether the proposed forecasted target adherence satisfies a threshold defined by an acceptable forecasted target adherence and, if so, deeming the proposed scheduling perk as select scheduling perk and proposed agent as select agent.