Shift Trade Index Score for Contact Center Workforce Management
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Solution Overview
Problem
Current workforce management systems for contact centers lack efficiency in shift trading, as agents face uncertainty in trade request success and managers lack sufficient criteria for approval, leading to management reluctance and reduced flexibility, resulting in agent discontent and potential staffing issues.
Innovation Solution
A trade index score system that calculates and displays the likelihood of successful shift trades based on skills match, historic approvals, past no-shows, and shift lengths, providing agents with informed choices and managers with enhanced decision-making capabilities, including automatic approval thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If agents manually select target agents from a large list of eligible agents, then agents have flexibility to choose any eligible agent, but this leads to unnecessary trade requests being sent to uninterested candidates and reduces trading efficiency
Solution Approach 1:
The system calculates and displays a trade index score that provides feedback to agents about the likelihood of successful trade with each eligible agent. This feedback mechanism allows agents to make informed decisions without manually evaluating multiple factors, resolving the contradiction between flexibility and efficiency by automatically filtering and ranking options based on multiple criteria including skills match, shift timing compatibility, and historical trade success rates
Solution Approach 2:
The system automatically performs the evaluation and ranking of eligible agents based on predefined criteria, eliminating the need for agents to manually assess each candidate. The automated calculation of trade index scores and generation of ranked lists enables the system to serve itself by making intelligent matching decisions, thereby improving productivity while preserving agent flexibility in final selection
2Ease of operation
If agents send trade requests without knowing the likelihood of success, then agents can request trades freely, but this results in uncertainty and may lead to agents taking days off which affects staffing conditions
Solution Approach 1:
The system performs preliminary calculations of the trade index score before agents submit trade requests. By pre-evaluating the likelihood of success based on historical data, skills matching, and shift compatibility, the system enables agents to make informed decisions about whether to proceed with trade requests, thereby reducing unnecessary requests and improving staffing reliability while maintaining ease of operation for well-considered requests
Solution Approach 2:
The trade index score provides immediate feedback to agents about the probability of trade success before they commit to requesting a trade. This feedback loop allows agents to assess risks and make better decisions, reducing the likelihood of failed trades that would disrupt staffing conditions while preserving the ease of requesting trades when the outlook is favorable
3Adaptability or versatility
If managers manually review trade requests without sufficient criteria, then managers have discretion to approve or decline, but this leads to management reluctance and reduced flexibility due to lack of decision-making support
Solution Approach 1:
The system provides managers with automated trade index scores and detailed analysis of multiple factors including skills match, shift timing compatibility, and historical trade performance. This feedback equips managers with data-driven insights to make informed decisions, reducing management reluctance by providing clear criteria while preserving managerial discretion to approve or decline based on the comprehensive information presented
Solution Approach 2:
The system transforms multiple complex decision factors into a single standardized trade index score parameter, along with breakdowns of contributing factors. This parameter transformation simplifies the decision-making process by presenting manageable metrics while maintaining the underlying complexity of evaluation criteria, thereby reducing perceived complexity while preserving manager adaptability in final decisions
Data Source
AI summary
Shift trading systems and methods, and non-transitory computer readable media, include receiving a shift trade request from a source agent, wherein the shift trade request comprises a shift day and a shift time; matching the shift trade request with a plurality of target agents that are available on the shift day and the shift time; for each target agent from the plurality of target agents, calculating a trade index score based on a trade history success index score, a matching skill index score, a skill proficiency index score, and a trade interval index score; ranking the plurality of target agents from highest to lowest trade index score; and displaying the ranked plurality of target agents with the target agent having the highest trade index score at the top of a list.