Are artificial intelligence (AI) and machine learning (ML) buzzwords or a practical reality for your contact center? It’s one thing to grasp how powerful these technologies can be. It’s another to know where or how to start putting them to use. Here are three ways we’ve seen organizations realizing real results with an AI-powered contact center.
1. Capture customer sentiment and learn from it
Contact centers are an organization’s window into customer feedback, trends, and sentiment. Is a caller content, confused, or upset? Is there a recurring issue with a specific product? Gathering this data and making it actionable for CX leaders and contact center associates can be a challenge. Asking an agent to assess a customer’s mood across hundreds of calls and report those consolidated impressions makes their job even harder than it already is.
But when an AI/ML-powered contact center can automatically flag phrases like “not happy” or “cancel my subscription”—or even pick up on tone of voice—an organization can gather a rich vein of data while agents stay focused on the matter at hand. AI/ML can transcribe calls, track customer sentiment, detect common issues and customer trends, or even pinpoint discrepancies—such as a price promotion in an email that doesn’t match the promotion on the website. The result is an up-to-the-minute picture not just of what people are calling about, but also of how they feel about your company and its service. These insights reports can also chart agents’ performance to uncover coaching and training opportunities.
Ring is a smart home security company that provides support to their customers, who they call “neighbors.” Using the ML features of Amazon Connect, Ring’s customer support team can understand neighbor sentiment, needs, and safety issues in real time. When a neighbor calls in with an issue that requires additional support, agents have context about their account so they can help resolve the customer issue quickly. This helps Ring deliver faster, more personalized service right from the start of a conversation.
2. Eliminate friction with personalized self-service
AI/ML can also streamline customer self-service so they get answers quickly without having to spend lengthy time searching out of date websites, navigating frustrating menus of “Press 1 for sales or 2 for support,” or waiting for a human agent. Today, customers can naturally explain in their own voice why they are calling and where they need help. AI/ML understands customer intent, makes sense of the request, and formulates a response, providing specific information to the customer that is timely, accurate, and personalized.
For example, early in the COVID-19 pandemic, MetroPlus Health, part of the largest municipal health system in the United States, needed to reach thousands of members quickly. This scale was impossible to accomplish without automation, but they didn’t want to lose the personal touch. MetroPlus Health used an Amazon Connect interactive chatbot powered by ML to quickly understand people’s health needs, then reached out to as many as 10,000 people per day with proactive, personalized messages that connected people with care, resources, and support.
3. Streamline the agent experience to better serve customers
People reach out to contact centers to get answers to complex and sometimes urgent problems. They want accurate answers as fast as possible. They don’t want to repeat information a company should know about them, like their name or what product they purchased, or wait as an agent digs through overwhelming amounts of information to find the answer to what they are calling about.
Today, AI/ML can augment agent work to simplify a large range of tasks like automating real-time caller authentication to making voice interactions faster and more secure. AI/ML can automatically assemble relevant customer information from multiple applications for the agent as soon as the support call or text interaction begins. It can provide agents relevant recommendations and answers across knowledge repositories, applications, internal wikis, and FAQs. And when a call ends, AI/ML can automatically create post call summaries to save agents time and deliver more insights to agents and supervisors the next time the customer may call. All these and more help agents better know who they are speaking with, assist them in better understanding the customer’s issue, and expediting answers and satisfactory resolution.
One organization realizing benefits today is Traeger Grills, the outdoor cooking choice of food enthusiasts. As Lizzy Mitchell, Head of Customer Experience Analytics at Traeger Grill, shares, “At Traeger, our mission is to help create a more flavorful world. Our customers are passionate about their grills. We handle tens of thousands of contact center contacts every month where people have questions on everything from grill care to WiFi connectivity. Our Traeger Techs used to spend a ton of time navigating multiple systems to find customer data. Now, when agents are connected to our customers, Amazon Connect automatically surfaces a unified customer profile that shows who is calling, their contact history, purchase history and grill type. Our agents no longer have to toggle between applications to find information and that helps provide a world-class customer experience. Since implementing Amazon Connect Customer Profiles, we’ve seen a ~25% reduction in handle time and a ~10% increase in CSAT.”
AI/ML: Buzzwords or a practical reality?
These are just three areas which we’ve seen organizations benefit from AI/ML-powered contact centers. If you are interested in exploring how businesses across industries are benefiting from cloud and AI technologies in their contact centers, check out our featured customer stories for Amazon Connect!
Author: Cory Glover, Senior Manager Product Marketing, Amazon
Guest blog post written by AWS. To learn more about this topic and others, visit the events page to check out all of our upcoming events.
