Predictive Routing System for Contact Center Resource Optimization
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Solution Overview
Problem
Contact centers face inefficiencies in routing customer inquiries, leading to unnecessary usage of computing resources and time, as the provided options and routing paths may not be optimal for resolving client issues, resulting in reduced capacity to service multiple clients.
Innovation Solution
A system that determines and transmits the most efficient routing path by assigning predictive and intent level scores based on client activity and queries, using machine learning models to select the highest scoring options, thereby routing clients along the most direct path to address their inquiries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional routing paths are used to route customer inquiries, then the routing process is simple and easy to implement, but the computing resources and time consumed increase unnecessarily, reducing the contact center's capacity to service multiple clients
Solution Approach 1:
The system performs preliminary analysis of customer inquiries and pre-determines the optimal routing path before the customer actually needs service. By analyzing historical data and predicting the best route in advance, the system avoids unnecessary computing resources being consumed during the actual service delivery, thereby resolving the contradiction between productivity and energy loss
Solution Approach 2:
The system changes the parameters of the routing decision by introducing predictive scoring mechanisms that evaluate multiple routing options based on various factors. This transforms the routing process from a static, fixed path to a dynamic, optimized path that adapts to individual customer needs, reducing wasted computing resources while increasing service capacity
2Loss of time
If traditional routing paths are used, then the system complexity is low, but the routing path is not optimal for resolving client issues, requiring re-routing and increasing time consumption
Solution Approach 1:
The system performs preliminary analysis of customer inquiries and pre-determines the optimal routing path before the customer actually needs service. By analyzing historical data and predicting the best route in advance, the system avoids unnecessary computing resources being consumed during the actual service delivery, thereby resolving the contradiction between productivity and energy loss
Solution Approach 2:
The system incorporates feedback mechanisms that continuously learn from customer interactions and routing outcomes. This feedback loop allows the routing system to refine its predictions and improve its accuracy over time, reducing time consumption through better routing decisions while managing complexity through iterative improvement
Data Source
AI summary
In some implementations, a device may obtain data indicating a client activity, and may determine predictive level scores corresponding to predictive level options associated with the client activity. The device may transmit, to a client device, a selected predictive level option having a highest predictive level score. The device may receive, from the client device, a client query based on a rejection of the selected predictive level option, and may determine intent level scores corresponding to intent level options associated with the client query. The device may identify a selected intent level option having a highest intent level score and may initiate client experience(s) associated with the selected intent level option. The predictive level scores and/or the intent level scores may be determined based on historical training data associated with combinations of client activities, predictive level options, client queries, intent level options, client experiences, and associated success scores.