Contact Center Workflow Selection via Self-Learning Insights
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Solution Overview
Problem
Current contact center workflows are complex and costly to maintain, requiring extensive professional services, and struggle to effectively address emerging issues with known solutions, as they are not dynamically adaptable to changing customer needs.
Innovation Solution
The system learns multiple paths through workflows by logging execution paths and performance metrics, correlating decision points with successful outcomes, and dynamically selects the most effective workflow based on customer intent, providing insights to guide both customers and agents through the most successful paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If custom analytics solutions are used to recommend next best actions, then the ability to address emerging issues is improved, but the system complexity and maintenance cost increase significantly
Solution Approach 1:
The system performs self-learning by automatically executing workflows, logging performance metrics, and correlating decision points with successful outcomes without requiring external analytics systems. The workflow system serves itself to generate insights, eliminating the need for complex custom analytics solutions while maintaining adaptability to emerging issues
Solution Approach 2:
The system implements feedback loops where workflow execution results are logged and fed back into the system to learn from actual performance. This continuous feedback mechanism enables the system to adapt to emerging issues using simple, built-in learning rather than complex external analytics
2Reliability
If extensive professional services are deployed to define and maintain workflows, then workflow effectiveness is improved, but the cost and time required for maintenance increase
Solution Approach 1:
The system automatically learns from workflow executions and generates insights without requiring professional services for analysis. The automated learning process continuously improves workflow effectiveness while eliminating the time-consuming manual maintenance and analysis that would otherwise be required
Solution Approach 2:
The system performs preliminary learning during normal workflow executions by logging performance metrics and correlating outcomes. This preliminary action accumulates insights over time, so when workflow optimization is needed, the system already has the knowledge required, eliminating the need for time-consuming professional analysis services
3Ease of operation
If predefined workflows are used for known problems, then operational simplicity is maintained, but the ability to handle emerging issues is reduced
Solution Approach 1:
The system dynamically adapts predefined workflows by learning from actual execution outcomes. The workflow system remains simple to operate while automatically evolving to handle emerging issues through continuous learning from logged performance data, combining operational simplicity with adaptability
Data Source
AI summary
Embodiments of the invention provide systems and methods for managing workflows in a contact center. More specifically, embodiments of the present invention are directed to dynamically influencing workflows based on learned insights into those workflows. With end-to-end Customer Relationship Management (CRM) suites, which manage the entire customer service journey from consumer website to knowledge base to escalation to a live agent via a communication channel to incident creation and incident resolution, it is possible to provide an out-of-the-box, simple-to-use solution to the above problems that offers unique advantages over the expensive, custom, bolt-on solutions.