Contact Complexity in Customer Service
Shu-Ting Pi Michael Yang Qun Liu
Customers who reach out for customer service support may face a range of
issues that vary in complexity. Routing high-complexity contacts to junior
agents can lead to multiple transfers or repeated contacts, while directing
low-complexity contacts to senior agents can strain their capacity to assist
customers who need professional help. To tackle this, a machine learning model
that accurately predicts the complexity of customer issues is highly desirable.
However, defining the complexity of a contact is a difficult task as it is a
highly abstract concept. While consensus-based data annotation by experienced
agents is a possible solution, it is time-consuming and costly. To overcome
these challenges, we have developed a novel machine learning approach to define
contact complexity. Instead of relying on human annotation, we trained an AI
expert model to mimic the behavior of agents and evaluate each contact's
complexity based on how the AI expert responds. If the AI expert is uncertain
or lacks the skills to comprehend the contact transcript, it is considered a
high-complexity contact. Our method has proven to be reliable, scalable, and
cost-effective based on the collected data.