Callback Timing Using Call-Wrap Pattern Prediction
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Solution Overview
Problem
Existing callback systems are limited to either prioritizing agent or customer experience, leading to inefficiencies in addressing both agent satisfaction and efficiency, as they may be used to address the challenges of both agent experience, as they may be used to improve the customer experience, but not both, and cannot effectively integrate them into a seamless system, with existing systems failing to provide a balance between agent and customer satisfaction.
Innovation Solution
Implementing a machine-learning based callback system that analyzes call wrap patterns to predict agent availability, using indicators such as speech patterns and volume changes to initiate callbacks at optimal times, ensuring agent suitability for pending calls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If agent-first callback strategy is used, then customer experience is improved, but agent efficiency deteriorates
Solution Approach 1:
The system dynamically switches between agent-first and customer-first callback strategies based on real-time conditions. The callback manager can select different dialing strategies for different customers or time periods, making the system flexible rather than static. This allows optimization of both customer experience and agent efficiency under different operational contexts.
Solution Approach 2:
The system changes the parameter of callback strategy selection based on agent availability, customer priority, and call wrap predictions. By adjusting which strategy is applied (agent-first vs. customer-first) based on changing parameters, the system resolves the contradiction between customer experience and agent efficiency.
2Productivity
If customer-first callback strategy is used, then agent efficiency is improved, but customer experience deteriorates
Solution Approach 1:
The system dynamically adjusts between customer-first and agent-first strategies based on real-time conditions. When agent availability is uncertain or customer priority is high, the system can switch to agent-first strategy, preventing customer experience deterioration while maintaining overall agent efficiency through intelligent routing.
Solution Approach 2:
The system uses call wrap pattern analysis to predict when agents will become available, providing feedback that informs callback timing decisions. This feedback mechanism allows the system to optimize customer experience by initiating callbacks at predicted agent availability times, avoiding both premature and delayed connections.
3Loss of time
If average call handling time is used to predict agent availability, then callback timing is improved, but reliability deteriorates
Solution Approach 1:
The system replaces the simple mechanical averaging method with machine learning-based call wrap pattern analysis. By using ML models trained on historical call data to identify patterns indicating imminent call endings, the system achieves more reliable predictions of agent availability while maintaining efficient callback timing.
Solution Approach 2:
The system introduces call wrap pattern analysis as an intermediary layer between raw call data and callback timing decisions. This intermediary uses ML to translate complex call dynamics into reliable availability predictions, bridging the gap between timing efficiency and prediction reliability.
Data Source
AI summary
A computerized method is provided for managing automated customer callbacks in a call center. Systems and methods can include machine-learning analysis of call-wrap patterns to identify speech patterns and/or words or phrases indicative that a call is about to end. Methods can use this information to more precisely que a next customer callback in order to minimize customer and agent wait times while increasing satisfaction and efficiency.