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can also streamline customer self-service so they get answers quickly without having to spend lengthy time searching out of date websites, navigating frustrating menus of “Press 1 for sales or 2 for support,” or waiting for a human agent.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.

The post 3 Ways AI and ML Can Take Your Customer Experience to the Next Level  appeared first on Execs In The Know.

Click here to catch up on Part 1 – Customer Expectations, Part 2 – Channels, and Part 3 – Operations.

The fourth installment of this series focuses on thoughts on technology.

• AI will begin to automate tasks for the agent and the customer – however AI will take longer to mature than previously thought, due to data infrastructure not mature enough to fully leverage.
• Companies will begin to shift from handling transaction to personalizing service.
• Digital will continue to experience strong growth, powered by mobile usage.
• Messaging channels will continue to grow social volumes.
• More conversations around the “social ethics” of AI deployment.
• Continued push on technology – moving infrastructure to the cloud.
• Deep/machine learning will play a role in 2018 for brands to meet customers where they are. I think brands are starting to get on board with the concept of machine learning and using data to predict behavior. But deep learning can extend beyond things like chatbots. If a brand can predict what a customer might be looking for, it can tailor content to the customer that allows the customer to self-service, send push notifications, etc.
• Thinking about AI secondarily to the types of use cases and outcomes desired that require it.
• Companies will have to address AI and ML in their strategies for customer acquisition and engagement. 2017 may be seen as a year of hype as lots of new vendors emerge on the scene. Expect consolidation and actual AI/ML deployments to increase in 2018.
• Interest in – and deployment of – customer journey analytics tools will increase, as companies try to identify and solve problems upstream before they occur.
• Strategic enterprise automation (front to back office) will help businesses begin to improve the customer experience, while optimizing back office operations, leading to stronger sales and a better bottom line. Examples include automated machine learning/AI, self help, chatbots/avatars, to RPA and beyond.
• People continue to overestimate innovation in the short term and underestimate it in the long term.
• We will see the first practical examples of AI in the areas of contact center routing, compliance and quality, with practical use cases.
• Some brands are testing AI for knowledge management (ie. agent support tool powered by AI vs. a traditional knowledge management tool). A lower risk approach to testing AI vs. directly with clients.
• Over the next 3 to 5 years, I believe that the majority of inquiries that come into the contact center will no longer be voice or email, but some form of messaging. SMS is only one form of messaging: it encompasses a broad swath of methods including iMessage, Facebook Messenger, Kik, WhatsApp, and more. Many people are following WeChat, which is now one of the largest standalone messaging apps with over 963 million monthly active users. Part of the reason it has been so successful is that you can do more than just send messages. Apple is moving down the path with the announcement of its Business Chat messaging solution which is an extension of iMessage and will include some ApplePay capabilities. The convergence of these technologies will be what ultimately tips the scale to shift consumers and businesses away from phone and email to messaging.
• Maturity issue with emerging virtual assistance technologies (chatbots, AI capabilities, etc.), continues to lag – requirements for specific user case studies at an all-time high.
• Buzz and hype are high, but adoption is low. Brands realize they need more data in order to leverage these technologies to be accurate.
• I’m not convinced that brands have the capacity/talent/full understanding of what precisely goes into machine learning and true AI (beyond chatbots). I think we’re going to see a wave of failed adoption on that front.
• Technology – Proliferate of technology out there – from measuring CX, to ‘omnichannel’, to speech analytics.

Stay tuned next week for the final installment in the series Part 5 – Use Case Studies/Data and Security/Risk. 

The post 2018 Predictions for the Service Leader: Part 4 – Technology appeared first on Execs In The Know.

The following is a guest blog from Coveo. 

The overwhelming majority of support leaders have recognized that their organizations’ self-service channels are their customers’ most preferred method for getting support (and that assisted service has become something they want to avoid). Customers want to find the answers to their questions independently and conveniently. As such, 97% of companies are investing in improving their customers’ self-service experience in 2017.

Self-service is dramatically more cost effective than other service channels, both in cost per resolution, and because it reduces the overall case load on contact centers. According to TSIA, phone and email support are each well over 100x more expensive per incident than web self-service, while chat costs over 30x more. Read more about that here.

New technologies are making it possible to create an intelligent self-service experience – one that is easy, relevant, and intuitive – that can generate results quickly.

For example, WatchGuard Technologies created an intelligent self-service experience, that improved its case deflection rate from three to 11 percent within only four months.

Customer satisfaction is of utmost importance to WatchGuard. The company provides several customer support options, including a 24/7 call center, and a wide range of online technical resources such as a knowledge base, technical documentation, video tutorials, product datasheets and user forums. However, before implementing an intelligent self-service solution – and despite all their available resources – the self-service capabilities were still falling short. Results from a TSIA Benchmark Review helped them realize that their self-service site was not intuitive to their customers and there was no easy way to search and filter through all the information.

Watch Joanne Miller, Managing Director of Product Training and Publications at WatchGuard Technologies, explain her journey to intelligent self-service in this video.

Making self-service easy.

Your customers expect to be able to find the answers they need with minimal effort. Unifying your content and making it searchable allows them to do so. A unified index consolidates all of your organization’s information from across your entire ecosystem and creates a single hub that puts relevant information at your customer’s’ fingertips. Intelligent search taps into that index to find exactly what is being searched and delivers the answers your customers need, when they need them.

Making self-service relevant.

Offering access to case-resolving content is an essential first step, but self-service is even more effective when the content is relevant and proactively recommended to each customer based upon their unique situation.

By analyzing each customer’s real-time site activity, product and service history, and other profile characteristics, your intelligent self-service site can automatically deliver contextually relevant information that has proved helpful to other similar customers in the past. The relevance of results self-tunes through techniques that weigh results based upon implicit and explicit contextual factors.

Making self-service intuitive.

With the use of machine learning, intelligent self-service solutions offer predictive insights that are characterized by the most relevant information and content being anticipated, suggested and recommended. Intuitive self-service continuously adapts and evolves based on the interactions of other users with the self-service site in real-time and provide valuable insights. These insights allow your organization to automatically make contextually relevant recommendations, that promote products and offers for add-on, upsells and cross-sells.

If your executive team has considered or committed to improving your organization’s customer service this year, you’ll want to make sure the company’s dollars are put where they can make the biggest impact. Intelligent self-service unquestionably offers the highest and most compelling return on investment. By enabling your people, processes and technology to work together, your intelligent self-service site can be rolled out and show improvements in as little as three months.

To learn more about intelligent self-service and how your executive team can build it into their 2017 plans, download our eBook, Case Deflection and Self-Service Success.

For more on this topic and others like it, join us at Customer Response Summit Las Vegas – February 6-8, 2017 at the ARIA Resort & Casino in Las Vegas, NV.

The post Intelligent Self-Service: Balancing Support Costs with CSAT appeared first on Execs In The Know.

The following is a guest blog from Leslie Joseph, Senior Director, Product Marketing at [24]7. For more information about [24]7, visit http://www.247-inc.com/.

Decades ago, the idea of putting a man on the moon seemed like an impossible dream. Yet, in 1969 Apollo 11 became the first manned mission to land on the moon. Fast forward to today, scientists at NASA have explored the surface of Mars and it won’t stop there with planned Mars landings in the 2030s. Technology has a tendency to evolve at neck-breaking speeds, which can make it challenging for businesses to keep up.

The future of enterprise chat is much the same. The technology has been around for years but its capabilities, both for the customer and for the agents, are evolving rapidly.

Forrester Research has stated that chat is quickly rising as the channel of choice among consumers to connect with companies, both in usage terms as well as overall satisfaction. However, consumers now know what qualifies as a great experience on the web, having been exposed to “best in class” experiences from tech giants like Apple and Google, and they expect the same level of service from every business. Consumers expect companies to be smart about addressing their needs, using data to provide intuitive experiences that are out of this world.

There are four key ways in which the chat channel can adapt to the continued advancements in technology and customer experience. Businesses should start preparing by examining four pillars that form the blueprint for the future.

1. Portability: Chat will be a part of the organic journey. It’s going to be pervasive and intelligently built into other channels – not just embedded somewhere in a corporate website.

2. Data: Big data will be used to make real-time predictions at every stage of the chat funnel. AI-driven machine learning will also take a larger role in making chat more effective across the customer journey or funnel.

3. Design: The design and interaction of the chat experience is going to get a lot richer, more visual, and more data driven.

4. Digital Agents: Live chat agents will be able to increase their productivity and workflow effectiveness, by utilizing improved chat technologies.

You can learn more about the Future of Chat – and it’s benefits to you and your customers – by attending our webinar on Wednesday, April 20, 2016 at 1:00 P.M. ET. You can register by visiting: https://attendee.gotowebinar.com/register/7449194949962566660.

The post The Future of Chat appeared first on Execs In The Know.