AWS’s contact center service adds capabilities that make it easier for businesses to identify customer issues and trends, search call and chat transcripts, and improve agent performance
Intuit, John Hancock, News Corp, and Accolade are among customers using Contact Lens for Amazon Connect
SEATTLE–(BUSINESS WIRE)–#AWS–Today at AWS re:Invent, Amazon Web Services Inc. (AWS), an Amazon.com company (NASDAQ: AMZN), announced AWS Contact Lens, a set of capabilities for Amazon Connect enabled by machine learning, that gives contact centers the ability to understand the sentiment, trends, and compliance of customer conversations to improve customer experience and identify crucial customer feedback. Amazon Connect is a fully managed cloud contact center service, based on the same technology that powers Amazon’s award-winning customer service. Companies like Intuit, GE Appliances, and Dow Jones use Amazon Connect to run their contact centers at lower cost, while easily scaling to thousands of agents. With AWS Contact Lens, customer service supervisors can discover emerging themes and trends from customer conversations, conduct fast, full-text search on call and chat transcripts to troubleshoot customer issues, and improve customer service agents’ performance with call and chat-specific analytics – all from within the Amazon Connect console. Coming mid-2020, Contact Lens will also provide the ability for supervisors to be alerted to issues during in-progress calls, giving them the ability to intervene earlier when a customer is having a poor experience. Contact Lens requires no technical expertise, and getting started takes just a few clicks in Amazon Connect. To learn more about Contact Lens for Amazon Connect, visit https://aws.amazon.com/connect/contact-lens.
Contact centers are often the only personal connection that a customer has with a company, and the experiences these customers have interacting with agents can have a profound impact on customer trust and loyalty. Contact centers field large volumes of customer calls every day, resulting in millions of hours of recorded calls. These conversations contain valuable customer feedback but, given the volume, companies struggle to extract and analyze this information in a timely fashion, if at all. Most companies that try to get value from this data use existing contact center analytics offerings, but these technologies are expensive, slow at providing call transcripts, and lack the required level of transcription accuracy – all of which makes it difficult to quickly detect customer experiences and provide precise feedback to customer service agents and supervisors. Existing contact center solutions also lack the ability to provide real-time analytics on in-progress calls, which prevents supervisors from identifying and helping frustrated customers before they hang up. As a result of these challenges, many organizations face high levels of customer churn, long hold times, agent turnover, and regulatory fines.
Contact Lens helps contact center users address these problems by providing new, fully managed machine learning-powered analytics capabilities that are available within Amazon Connect, and do not require any coding or ML experience to use. Contact Lens uses highly accurate speech transcription technology to transcribe calls, and automatically indexes call and chat transcripts so they can be searched from the Amazon Connect console. This makes it easy for supervisors to search voice and chat interactions based on content (e.g. customers asking to cancel a subscription or return an item) and customer sentiment (e.g. calls that ended with a negative customer sentiment score). By clicking on the search results, supervisors can view a contact detail page to see the call transcript, customer and agent sentiment, and the cause of unusually long pauses, and use this information to share feedback with their agents through the same page. Contact Lens also helps supervisors find new issues without supervisors needing to actively search for them (e.g. an unknown product shortcoming) by automatically surfacing themes across multiple conversations on the Amazon Connect dashboard. Contact Lens presents these themes in an easy to understand visual format that helps supervisors quickly respond to customer feedback. Contact Lens gives supervisors the ability to automatically monitor all of their agents’ interactions for customer experience, regulatory compliance, and adherence to script guidelines by defining custom categories on a new page in Amazon Connect that allow them to organize customer contacts based on words or phrases said by the customer or agent (e.g. a customer mentioning a competitor).
Coming mid-2020, Contact Lens will introduce new features providing supervisors real-time assistance by offering a dashboard that shows the sentiment progression of live calls in a contact center. This dashboard continuously updates as the interactions evolve and allows supervisors to look across live calls to spot opportunities to help their customers. Real-time alerting gives supervisors the ability to engage and de-escalate the situation earlier. Contact Lens draws on Amazon’s vast experience delighting its customers and puts this same technology in the hands of AWS customers.
“At Amazon, customer obsession drives everything we do. Over the years, we’ve developed unique expertise in using machine learning to better understand our own large volumes of customer contacts and take appropriate action,” said Larry Augustin, Vice President Productivity Applications, AWS. “Contact Lens brings together the technology and expertise Amazon has developed to support its call center operations and delivers it to Amazon Connect customers – without requiring any machine learning or programming expertise to use it. We are excited to see how our customers benefit from our experiences to improve their customers’ trust and loyalty.”
Contact Lens capabilities are built right into the Amazon Connect experience. Contact Lens metadata (such as transcriptions, sentiment, and categorization tags) is available in customers’ S3 buckets in a well-defined schema. Businesses can easily export this information and use additional tools like Quicksight or Tableau to do further analysis and combine it with data from other sources.
Intuit Inc. is a financial software company that develops and sells financial, accounting, and tax preparation software and related services for small businesses, accountants, and individuals. “As Intuit embarks on our next transformation into an AI-driven expert platform, using machine learning is critical to helping our customers make data-driven decisions and providing them the right support they need,” said Ashok Srivastava, Chief Data Officer, Intuit. “With Contact Lens for Amazon Connect, we’re able to quickly understand our customers’ needs, and use those insights to create new machine learning models and solutions that best serve our customers.”
John Hancock provides financial advice, insurance, and wealth and asset management solutions for individuals, groups, and institutions. One of the largest life insurers in the United States, John Hancock supports approximately 10 million Americans with a broad range of financial products. “Our business relies on providing relevant financial services through the best customer experience,” said Tracy Kelly, AVP Shared Services, John Hancock. “With Contact Lens for Amazon Connect, we can now evaluate all our customer interactions and quickly find out what is working well and how we can get better. Being able to automatically understand the underlying reasons for why customers are calling us and their severity is going to be very helpful for us.”
News Corp is a global diversified media and information services company focused on creating and distributing authoritative and engaging content to consumers and businesses throughout the world. “For the News Corp Service Desk, we saw immediate time to value after using Amazon Connect,” said Simon Clark, SVP End User and Infrastructure Services, News Corp. “We are now looking forward to using Contact Lens for Amazon Connect because its powerful features for both voice and chat interactions will make it possible for our contact center staff to provide a better experience for our employees by seamlessly leveraging the power of machine learning.”
Accolade is a personalized health and benefits solution that serves employers across the country. “The new AWS offering of Contact Lens for Amazon Connect will help us to continue delivering the best experience to our customers,” said Stephen Murphy, Senior Director Cloud Engineering, Accolade. “The seamless integration of AI into Amazon Connect accomplishes what the other contact center platforms and niche speech analytics/AI vendors have struggled to achieve. Contact Lens for Amazon Connect exceeds what we could have built by making it easy to programmatically select which calls to automatically transcribe and by offering an enhanced contact search and analysis page to interact with the results. We estimate that, with Contact Lens for Amazon Connect, we have saved several months of development time that would have been needed to mirror the same interactions and results.”
Founded in 2010, amaysim is a provider of energy and SIM-only mobile plans. In nine years, the online-led business has grown to be the fourth largest independent mobile services provider and the largest Mobile Virtual Network Operator (MVNO) in Australia, with over 1 million mobile subscribers. “At amaysim, Amazon Connect has been a big win for us because it is designed to be easily deployed by contact centre staff and IT with little knowledge of AWS. The ability to quickly innovate and change has been a big advantage to us. At amaysim, we make build vs. buy decisions very consciously and our strategy is to build pieces that are specific for our business,” said Peter James, IT Operations Director, Amaysim. “Contact Lens for Amazon Connect not only provides us with an end-to-end application experience for AI-powered contact centre analytics but also gives us the flexibility to do custom BI analytics and the agility to build proprietary data science models using its rich metadata.”
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