Speech Analytics System for Predictive Dialer Abandonment Rate Control
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Solution Overview
Problem
Predictive dialers in call centers face challenges in balancing agent utilization with regulatory compliance regarding telemarketing call abandonment rates, often resulting in inefficient use of resources and potential fines due to high abandonment rates.
Innovation Solution
Implementing a speech analytics system to monitor and adjust the target abandonment rate for telemarketing calls, ensuring that the actual abandonment rate remains within regulatory limits by analyzing past call recordings and adjusting the pacing algorithm accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the predictive dialer originates more calls than there are currently available agents to minimize agents' waiting times, then the agent utilization is improved, but the abandonment rate increases
Solution Approach 1:
The system continuously monitors actual abandonment rates from call recordings and uses this feedback to dynamically adjust the pacing algorithm's target abandonment rate. When the actual rate exceeds the target, the system reduces the pacing rate; when it is below the target, the system increases the pacing rate, creating a closed-loop control system that balances agent utilization with compliance.
Solution Approach 2:
The system changes the target abandonment rate parameter dynamically based on historical performance data. By adjusting this key parameter in response to monitored abandonment rates, the system optimizes the balance between calling volume and compliance, allowing the dialer to operate at different pacing rates depending on current conditions.
2Reliability
If the predictive dialer maintains a very low average abandonment rate to avoid fines, then regulatory compliance is improved, but the agent utilization decreases
Solution Approach 1:
The feedback mechanism allows the system to learn from historical performance and adjust pacing rates accordingly. By monitoring actual abandonment rates and comparing them to target rates, the system identifies optimal pacing settings that maximize agent utilization while maintaining compliance, rather than using a conservative fixed rate.
Solution Approach 2:
The system transitions from static pacing algorithms to dynamic algorithms that adapt to changing conditions. The target abandonment rate is not fixed but adjusts based on real-time performance data, allowing the system to optimize between compliance and utilization based on current call patterns, agent availability, and historical abandonment rates.
3Reliability
If the predictive dialer uses a pacing algorithm to ensure an agent is available for each call, then the abandonment rate is reduced, but the call center resources are wasted on calls encountering busy conditions or answering machines
Solution Approach 1:
The system adjusts the pacing rate parameter based on historical data about call outcomes. By analyzing which calls result in live answers versus busy conditions or answering machines, the system optimizes the pacing algorithm to dial at rates that maximize successful connections while minimizing wasted resources on unlikely calls.
Data Source
AI summary
In one embodiment, systems and methods are disclosed for processing telemarketing calls to determine an abandonment rate (“AR”). An abandoned call exists if a telemarketing call is answered by a live person and an agent is not connected to the called party within two seconds of the called party completing their greeting. In order to comply with various federal regulations, a feedback mechanism allows feedback of the measured AR to be used by the predictive dialer to manage the AR for future calls to maximize efficiency and avoid exceeding an AR limit. In one embodiment, a speech analytics system processes audio recordings of previously made telemarketing calls and provides feedback to the predictive dialer so as to adjust a target AR rate for future calls.