AI and machine learning can be used to augment or replace humans in customer service to reduce average handling time and improve service quality. Most people think about conversational chatbots for this, but AI-based natural language processing can be used to improve or automate customer service in other ways.
Two examples are Kylie and TrueAI, which both analyse past customer service interactions and learn how to respond to questions that have occured in the past.
They propose suitable answers automatically and offer them as a help to service agents. This may be particularly helpful while training new service agents. Since these are not unattended bot solutions, the service agent is still in control and receives AI assistance in crafting suitable answers and improving their efficiency with routine tasks.
Scanning incoming email can be a time-consuming task in many companies. Some vendors, for example Digital Genius, offer solutions that can parse incoming email and route them to the correct department automatically, dependent on the detected topic.
This additional layer of automation reduces workload and improves efficiency.
For customer service over phone, companies like Cogito and TalkIq can analyze and augment ongoing phone calls, thus working to improve conversations with behavioral science.
The customer’s voice can be analyzed for attributes like energy, interruption, empathy, participation, pace, and more. This data is used to help gain further insights, for example guiding agents to speak with more empathy or confidence, as early signs of customer frustration is detected.
A company called Chorus uses a system that records and analyses voices too, but they focus on creating summaries of each sales-meeting (physical, phone calls, or in the form of webinars) in real-time, automatically identifying important topics being discussed and providing meeting performance metrics.
Effectively, they take a data-driven and AI-based approach to improving meeting efficiency.