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.
Customized Recommendations
AI can also help understand customer intent and can provide customized recommendations. This is often done through a kind of machine learning model called a “recommender model,” which predicts what certain users and customers would like from a set of products, and then uses that information to create personalized campaigns.
While people may often joke that their computers are reading their minds when ads pop up for something they were just thinking about, it’s not mind-reading at all—it’s machine learning that recognizes attributes, behavior, and patterns to predict the user’s needs. This helps them find what they’re looking for faster and with less fuss.
AI in AWS Communication Developer Services
While understanding the potential benefits of AI is important, organizations also need to know how to add AI and machine learning to their contact centers and get the most out of these tools.
Amazon Web Services has been making it easier to add AI-powered analytics to contact centers through AWS Communication Developer Services. These cloud-based APIs and SDKs help businesses integrate communication capabilities into their websites, including voice, video, SMS, and chat, with minimal coding required.
The Amazon Chime SDK, for instance, helps businesses already lets developers add messaging, audio, video, and screen-sharing capabilities to their web and mobile applications, and new updates added the ability to integrate machine learning-based voice analytics.
Developers using the AWS Management Console can integrate call analytics capabilities—including voice and tone analytics—with the Amazon Chime SDK, without needing any coding or expertise in AI.
When developers are managing their analytics through the AWS console, they can choose the AI service they want to use: voice analytics, Amazon Transcribe, or Amazon Transcribe Call Analytics. As the names suggest, these offer various AI-powered analytics and transcription capabilities designed to gain new customer service insights.
Contact center managers can also choose where to send the analytics data, and use services like Amazon QuickSight or Tableau to create dashboards visualizing the data. AWS also offers prebuilt dashboards to save time and simplify the process.
Additionally, Amazon Pinpoint can connect to “recommender” machine learning models to help organizations create personalized campaigns for their customers. A business can send a templated message to users, and Amazon Pinpoint will use the “recommender” model to identify what products or services they should suggest to the customer and fill in the template accordingly.
These are just a few examples of how artificial intelligence and machine learning can help businesses improve their customer service operations. As the technology continues to develop, new features and capabilities will emerge, enabling companies to truly delight their customers.
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