AI Support Prioritization Using Latent Emotion Prediction
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Solution Overview
Problem
Existing customer support systems fail to preemptively address and deflect support request escalations, leading to increased customer effort and potential loss of customers, as sentiment analysis is often performed after frustration has already set in, making it difficult to maintain high customer retention and avoid significant financial losses.
Innovation Solution
A system utilizing a data analytics environment with AI capabilities maps customer touchpoints to emotions and translates them into predictive analytics, employing machine learning models to flag potential escalations and provide proactive escalation management, enabling smart prioritization and workflow triggers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sentiment analysis is used to assess customer satisfaction, then customer satisfaction can be measured, but customer frustration is detected too late (after escalation occurs)
Solution Approach 1:
The system performs preliminary sentiment analysis on customer interactions before frustration escalates to formal complaints or support tickets. By analyzing social media posts, forum discussions, and other customer feedback channels proactively, the system detects early signs of dissatisfaction and triggers preventive actions, thereby measuring sentiment accurately while detecting frustration in advance rather than after escalation.
2Ease of operation
If manual customer support handling is used, then customer issues can be addressed, but customer effort increases and retention decreases
Solution Approach 1:
The system enables self-service by automatically detecting customer frustration through sentiment analysis and routing issues to appropriate resolution channels without requiring extensive manual customer support intervention. The system can automatically generate responses, escalate to relevant departments, or provide self-help resources, thereby reducing customer effort while maintaining reliable customer retention through proactive issue resolution.
3Reliability
If proactive escalation management is implemented, then customer retention improves, but system complexity increases
Solution Approach 1:
The system segments customer feedback channels (social media, forums, surveys, support tickets) and sentiment types (positive, negative, neutral, frustrated) into distinct categories. This segmentation allows the complex proactive escalation management system to handle different channels and sentiment types through specialized processing rules, thereby improving customer retention while managing system complexity through modular, organized architecture.
Data Source
AI summary
Embodiments described herein are generally related to data analytics environments, and are particularly directed to systems and methods for use with a data analytics environment to enable use of AI in providing customer support. Machine learning AI models are trained based on one or more previous service request lifecycles of service requests of a customer to determine latent emotions of the customer based on determined customer problem data. A customer service prioritization signal related to a current service request of the customer is generated by a predictive analytics application that includes the models. The customer service prioritization signal is indicative of a need to prioritize a current service request of the customer based on the determined latent emotions of the customer and is generated during and prior to the end of the lifecycle of the current service request whereby escalation of the current service request may be deferred or prevented.