Contact Routing Based on Predicted Escalation Time
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Solution Overview
Problem
Contact centers face challenges in predicting and managing customer escalation, leading to inefficient resource allocation and increased costs due to unpredictable customer behavior and channel preferences.
Innovation Solution
A prediction component that analyzes contextual data, such as contact subject matter, customer traits, and channel environment, to anticipate the likelihood of contact escalation, allowing for proactive resource matching and prioritization, and dynamically updates escalation periods in real-time to mitigate escalation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If contact centers use traditional routing methods (round-robin, first-available), then the system is simple to operate, but resource allocation becomes inefficient and escalation likelihood increases
Solution Approach 1:
The system performs preliminary analysis of contact characteristics and customer history before routing to predict escalation likelihood. By evaluating factors such as contact subject matter, customer traits, and channel environment in advance, the system can proactively allocate resources to prevent escalation rather than reacting after it occurs, thereby improving resource allocation efficiency.
Solution Approach 2:
The routing system incorporates feedback loops that continuously monitor contact outcomes and escalation patterns. This feedback is used to refine prediction models and adjust routing decisions in real-time, enabling the system to learn from past performance and improve its resource allocation effectiveness over time.
2Reliability
If contact centers respond without prediction capability, then the system is easier to operate, but customer satisfaction decreases due to delayed or inappropriate responses
Solution Approach 1:
The system analyzes contact characteristics and customer history in advance to predict escalation likelihood before routing the contact. This preliminary prediction enables the system to prioritize contacts that are more likely to escalate and allocate resources accordingly, ensuring timely and appropriate responses that improve customer satisfaction.
Solution Approach 2:
The system changes the parameter of response timing and prioritization based on predicted escalation likelihood. By adjusting response strategies according to the predicted probability of escalation, the system can provide more targeted and effective customer service, improving reliability while managing system complexity through data-driven decision-making.
3Loss of energy
If contact centers allocate resources without considering escalation probability, then resource allocation is simpler, but costs increase due to unnecessary resource consumption
Solution Approach 1:
The system performs preliminary evaluation of contact characteristics and customer history to predict escalation likelihood before allocating resources. By identifying contacts with high escalation probability in advance, the system can allocate resources more efficiently to prevent escalation, reducing unnecessary resource consumption and associated costs.
Solution Approach 2:
The system adjusts resource allocation parameters based on predicted escalation probability. By varying the level of resource allocation according to the predicted likelihood of escalation, the system can optimize the balance between service quality and resource consumption, reducing costs while maintaining appropriate service levels.
Data Source
AI summary
Systems and methods that employ contact escalation periods as criterion for managing routing procedures of a contact center. A prediction component can predict when a customer is likely to escalate a contact that is forwarded to a contact center, and hence facilitate resource matching based on such prediction. Accordingly, proactive and anticipatory contact interaction is enabled, wherein routing of contacts occur in-part based on predicted likelihood of escalations.