Outbound Agent Scheduling with Historical Connect Rate Weights
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Solution Overview
Problem
Scheduling employees in outbound call centers is complex due to various variables such as agent skills, call types, and contact media, making it difficult to minimize costs while maximizing service quality, especially with the added complexity of varying agent skill sets and contact methods like telephone, email, and IP communications.
Innovation Solution
A system and method for scheduling outbound agents using a user interface and scheduling software that generates potential schedules based on historical call connect rates and desired service goals, weighing higher connect rate intervals, and selecting schedules to adjust staffing according to workload, potentially combining outbound and inbound workloads and evaluating them in a common currency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional scheduling methods are used for outbound agents, then scheduling simplicity is maintained, but productivity and service level optimization are insufficient
Solution Approach 1:
The scheduling system dynamically adjusts agent schedules based on real-time workload fluctuations, historical connect rates, and service level requirements. The system continuously optimizes scheduling decisions rather than using static schedules, allowing it to adapt to changing conditions and maximize productivity while maintaining manageable complexity through automated decision-making.
Solution Approach 2:
The system changes key scheduling parameters such as connect rate weights, service level targets, and workload thresholds to optimize agent productivity. By adjusting these parameters based on historical data and current conditions, the system improves scheduling effectiveness without requiring complex manual intervention for each change.
2Reliability
If staffing levels are increased to meet service goals, then service level is improved, but cost increases
Solution Approach 1:
The system applies partial staffing during periods when historical connect rates indicate lower workload demand, while maintaining adequate service levels. By using historical connect rate data to predict actual needed capacity, the system avoids overstaffing during low-demand periods while ensuring sufficient coverage during peak times, thus reducing overall staffing costs while maintaining service reliability.
Solution Approach 2:
The system uses historical connect rate data to predict future workload patterns and pre-optimizes schedules accordingly. By analyzing historical data beforehand, the system can anticipate periods of high and low demand, allowing it to adjust staffing levels proactively rather than reactively, thereby maintaining service levels while minimizing unnecessary staffing costs.
3Productivity
If schedules are adjusted frequently to match workload, then service quality improves, but scheduling complexity increases
Solution Approach 1:
The scheduling system automatically adjusts schedules based on workload conditions without requiring manual intervention. It uses historical connect rate data and current service level requirements to self-optimize scheduling decisions, thereby improving service quality while keeping the operational complexity manageable through automation rather than manual schedule adjustments.
Solution Approach 2:
The system continuously monitors actual connect rates, service levels, and workload patterns, using this feedback to refine and adjust schedules. By incorporating real-time and historical feedback loops, the system adapts to changing conditions and improves service quality while maintaining scheduling complexity through data-driven automated adjustments rather than manual processes.
4Measurement precision
If historical connect rates are weighted heavily in scheduling, then scheduling accuracy improves, but adaptability to new patterns decreases
Solution Approach 1:
The system dynamically balances the weight of historical connect rates with more recent data patterns. Rather than statically relying heavily on historical data, the system adjusts its data weighting based on changing conditions, allowing it to maintain scheduling accuracy from historical patterns while remaining adaptable to new workload patterns and emerging trends through continuous data reevaluation.
Data Source
AI summary
Systems, methods and computer-readable media for scheduling outbound agents are provided. A representative system incorporates a user interface and scheduling software that receives scheduling data, at least some of which is provided via the user interface, and generates scheduling constraints. The scheduling data includes historical call connect rates related to time intervals for which a schedule is to be generated and information corresponding to a desired service goal for the schedule. The desired service goal indicates a level of service to be provided by the schedule. The search engine uses the scheduling constraints to generate potential schedules for outbound agents. The push forward discrete modeler analyzes the potential schedules to compute at least one of overstaffing and understaffing with respect to the potential schedules.