There are few things more important to businesses than keeping their customers satisfied and loyal. It costs more to bring in new customers than it does to retain them, and happy customers can bring positive reviews and word-of-mouth to a business. This requires good customer engagement and communications, as proper service can make all the difference between retaining and losing a customer.
With the rapid growth of artificial intelligence (AI), particularly generative AI, many companies are wondering how they can use AI to improve their customer service operations. Amazon Web Services (AWS) has been adding AI and machine learning-powered features to its AWS Communication Developer Services, which help companies build communications channels into their apps and platforms.
So, how can companies use AI and machine learning (ML) to provide a superior service experience? Let’s explore.
AI-Powered Call Analytics
One major use of AI and ML in customer service is in call analytics. Organizations can add voice analytics with machine learning to their contact center applications, gaining new insights into overall customer sentiment, potential issues, and customer conversations.
Machine learning and AI-powered analytics can take many forms and present an array of features, but they all rely on understanding what customers are saying in order to gain a better understanding of the customer experience.
Tone and Sentiment Analysis
Voice tone analysis, which analyzes linguistic information (that is, what customers say) alongside tonal information (how they say it) to understand customer sentiment, is gaining in popularity.
If a customer is frustrated, growing more irritated, or becoming calmer over the course of the call, AI analytics can detect it in their voice and word choice. This information can help contact center agents provide better service while they’re on calls with customers. It can also provide the organization with valuable data and insights around customer attitudes about the company’s products and services.
Identifying Trends
AI can also be used to listen in on calls to understand what common topics or questions customers are calling about. This helps identify recurring or growing issues so that businesses can proactively address them before they become larger problems.
For instance, if there’s an uptick in customers calling about a problem they’re having with a new software release or product update, AI can spot this, enabling the business to quickly issue guidance to contact center teams and work on fixing the problem.
Transcriptions
AI with natural language processing (NLP) can be used to accurately transcribe conversations, often in real-time. This is important for a wide range of teams, including sales, support, and operations, to help employees remain productive, ensure compliance, and improve the customer experience.
Agents can reference the transcriptions and live captions both during and after the conversation, so if they need to go back through the conversation to check an ID number a caller gave them, or confirm an address, it’s already written down for them. And for certain industries—such as legal, financial, and healthcare—call transcriptions are essential tools for record-keeping and safety regulation compliance.
This, combined with insights and analytics, can help businesses identify sticking points and areas where agents may need further training. In fact, businesses can use voice tone analysis alongside transcripts to improve their call records by including notes on the sentiment around different products or services.
