Time-Off Approval Probability System Using Machine Learning
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Solution Overview
Problem
Contact center agents lack visibility into the approval probability of their time-off requests, leading to inefficient planning and a high rate of declined requests, as managers manually assess staffing needs for each request.
Innovation Solution
A system that calculates and displays the approval probability for time-off requests using a trained machine learning model, considering net staffing, agent skills, past requests, and manager approvals, and suggests alternate dates with higher approval probabilities, allowing for automated approval processes when thresholds are met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If managers manually check net staffing data for each time-off request, then they can make informed decisions about staffing requirements, but the process becomes time-consuming and inefficient
Solution Approach 1:
The patent introduces an automated system acting as an intermediary between managers and time-off requests. This system calculates approval probabilities by analyzing net staffing data, agent skills, and historical patterns, providing managers with pre-evaluated recommendations. This intermediary processing maintains decision accuracy while dramatically reducing the time managers spend on manual assessments.
Solution Approach 2:
The system enables self-service by automatically evaluating time-off requests against staffing requirements and providing approval probability scores. The automated analysis of net staffing data, agent replacement availability, and historical approval patterns allows the system to serve the assessment function independently, freeing managers from manual checking while maintaining informed decision-making.
2Adaptability or versatility
If agents submit time-off requests without visibility into approval probability, then they can request any date, but the approval rate drops and planning efficiency decreases
Solution Approach 1:
The patent implements feedback by providing agents with approval probability information for their requested dates and alternative date suggestions. This feedback loop allows agents to see the likelihood of approval before submitting requests, enabling them to adjust their timing or select alternative dates with higher approval probabilities, thereby improving overall approval rates and planning efficiency.
Solution Approach 2:
The system performs preliminary analysis of time-off requests by calculating approval probabilities and identifying alternative dates before the actual submission and review process. This preliminary action provides agents with informed guidance on which dates are most likely to be approved, allowing them to make better scheduling decisions upfront and reducing the number of declined requests.
3Ease of operation
If the system provides detailed probability calculations and alternate date suggestions, then agents can plan more effectively, but the system complexity increases
Solution Approach 1:
The patent applies universality by creating a multi-functional system that simultaneously performs net staffing analysis, agent skill matching, historical pattern recognition, approval probability calculation, and alternative date suggestion. This consolidated system handles multiple functions through integrated machine learning models and data processing, providing comprehensive time-off planning support without requiring separate complex systems for each function.
Data Source
AI summary
Workforce management systems and methods, and non-transitory computer readable media, including receiving a time-off request from a first agent, wherein the time-off request comprises an agent ID of the first agent and a first requested date; providing, to a trained machine learning model, staffing data on the first requested date, skills of the first agent, pending time-off requests from other agents on the first requested date, and time-off taken by the first agent in the past; calculating, by the trained machine learning model, an approval probability of the time-off request; and displaying, on a graphical user interface, the approval probability of the time-off request to the first agent and to a manager.