AI-Driven Interaction Flow Optimization
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Solution Overview
Problem
Current methodologies for optimizing interaction flows in user interaction platforms, such as IVR and chatbot systems, are costly and time-consuming, requiring iterative experiments with human assistance and domain expert intuition, which limits the ability to efficiently evaluate and improve interaction metrics like abandonment rates and authentication rates.
Innovation Solution
The system programmatically annotates and optimizes interaction flows using artificial intelligence, allowing for the automatic evaluation of multiple scenarios without human intervention, reducing the need for manual evaluation and iterative experimentation by using AI to optimize interaction flows based on configurable target metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If iterative experiments with human assistance are used to optimize interaction flows, then optimization accuracy can be improved, but time consumption and costs increase
Solution Approach 1:
The system enables self-service optimization by automatically routing interactions to experimental flows, collecting performance data, evaluating results against target metrics, and committing optimal flows without human intervention. This automated self-optimizing loop eliminates the need for manual experimentation while maintaining optimization accuracy.
Solution Approach 2:
The system implements continuous feedback by monitoring target metrics (abandonment rate, authentication rate) of experimental interaction flows and using this feedback to automatically determine whether to commit experimental flows as baseline flows, enabling data-driven optimization without manual analysis.
2Measurement precision
If multiple experimental interaction flows are tested, then optimization results improve, but system complexity increases
Solution Approach 1:
The system manages complexity by dynamically changing the parameter of flow distribution ratios, automatically adjusting the proportion of interactions routed to experimental versus baseline flows based on performance evaluation, thereby simplifying the management of multiple experimental variants.
Solution Approach 2:
The system segments the optimization process into distinct automated stages: routing decisions based on distribution ratios, performance monitoring of target metrics, automatic evaluation against thresholds, and commitment decisions. This segmentation allows multiple experimental flows to be tested systematically without overwhelming system complexity.
3Measurement precision
If manual evaluation and iterative experimentation are performed, then interaction flow optimization can be achieved, but productivity decreases
Solution Approach 1:
The system performs self-service optimization by automatically executing the complete optimization workflow including routing interactions to experimental flows, collecting and analyzing target metric data, evaluating results, and committing optimal flows without human intervention, thereby dramatically increasing optimization productivity.
Solution Approach 2:
The system enables continuous optimization by maintaining an ongoing automated process that continuously routes interactions, monitors performance, evaluates experimental flows, and updates baseline flows without interruption or manual restart, maximizing productivity through uninterrupted optimization action.
Data Source
AI summary
Methods, apparatus, systems, computing devices, computing entities, and/or the like for optimizing interaction flows. For example, upon receiving an inbound interaction, the interaction is routed to either a baseline interaction flow or one of a plurality of experimental interaction flows. Using one or more target metrics, the experimental interaction flows can be monitored and further configured to optimize the interaction flows and parameters.