7, the intent-driven customer engagement solutions, has announced that its intelligent chatbot technology, 7 Virtual Agent, leads the market with more than 500 intelligent chatbot deployments, across a range of industries including banking, healthcare, insurance, retail, telecom, travel and utilities. BC Hydro, Blue Cross and Blue Shield of Kansas City and Vodafone Qatar are among the more than 160 organizations globally using 7’s market-leading Virtual Agent to enhance the digital customer experience.
7 Virtual Agent is an intelligent enterprise chatbot that helps consumers find information they need in the moments that matter by delivering automated answers and completing transactions via website, mobile devices, Facebook Messenger and social media channels. The Virtual Agent can also act as a comprehensive, easy to use knowledge base for live agents and employees. This industry-leading technology empowers businesses to deliver a world-class self-service experience at a consumer’s crucial first point of contact, all the way through to issue resolution or task completion.
7’s self-service chatbot solutions cover the full spectrum of digital customer experiences, from informational virtual agents deflecting customer support call and email volume, to AI-driven intelligent assistants allowing customers to perform digital transactions within their chosen channel. These solutions are continually fine-tuned based on the 34 mn chat conversations that happen annually on the 7 Customer Engagement platform.
7 offers two powerful chatbot products:
7 Answers is an automated question and response system that delivers instant answers, drawing from a continually-evolving knowledge base. The solution works well for commonly asked questions and can be used to enhance website search and to replace extensive FAQs, which must be frequently updated and are often out of date. 7 Answers recognizes natural language and can provide accurate answers to questions regardless of phrasing.
7 AIVA Assistant also delivers instant answers to commonly asked questions while using AI to predict consumer intent. AIVA is conversational, human-like, delivers a personalized experience, and can perform more complex transactions. This solution also uses natural language processing and is context-aware, recognizing the consumer’s prior history, behavior, and user profile. AIVA Assistant leverages the same Natural Language model as 7 Speech, allowing enterprise customers to build one knowledge base and then expand into Interactive Voice Response (IVR).
7’s chatbots are used across a range of industries, as outlined in the company’s latest executive eBook, Everything You Need to Know About Chatbots, now available for download. This primer outlines key considerations for enterprise chatbot deployment, as well as examples of successful implementations including the following:
– Telecommunications company Vodafone’s award-winning virtual agent “Hani” is an intelligent chatbot that answers 80,000 questions per month and deflects calls away from the contact center for 75 percent of the customers it chats with. Vodafone contact center staff also use the same technology to access accurate, up-to-date information on Vodafone products and services.
-Canadian utility BC Hydro wanted to improve customer service and satisfaction for its 4 million customers and improve operational efficiency by deploying a chatbot on its website. In the first 11 months, the chatbot answered more than 720,000 questions with an accuracy rate of 94 percent.
-A major health insurance provider improved the experience for its 4 million members with an intelligent chatbot deployed as a virtual agent. With the chatbot answering 150,000 questions per month, the company is saving thousands of dollars in contact center costs by reducing calls to its staff.
“An intelligent chatbot should perform as well your top agent,” said Scott Horn, chief marketing officer at 7. “The enhancements we’ve made to our Virtual Agent products empower enterprise clients to tap into the power of AI and machine learning to provide dramatically improved experiences for consumers. Not only do the predictive capabilities of our Virtual Agent lead to improved customer satisfaction scores, but they also free up human agents for more complex, higher-value interactions.”
In addition to improving customer satisfaction and operational efficiencies, 7’s chatbots also help reduce Average Handle Time (AHT). Because the chatbot has already handled many of the qualifying questions that the live agent would have otherwise handled, AHT can be reduced by up to 10 percent. When working alongside a chatbot, agents are able to handle more chats per hour, increasing productivity.
Requirements of Enterprise Chatbots
Enterprise chatbots must be designed to drive business outcomes and improve engagement. To service large enterprises, 7’s Virtual Agents are designed to meet the following key requirements:
Natural language processing – This applies to both text and voice interactions, and maps the customer’s intent to the correct answer, regardless of how the question is phrased.
Well-conceived user interfaces – This includes graphical, voice, and multimodal interfaces. Infusing live chat with a well-conceived chatbot interface can lead to higher interaction outcomes and better business results.
Data integration – Enterprise chatbots must be able to integrate with unstructured and enterprise systems of record. 7 Virtual Agent integrates with all major CRM, Chat, and messaging tools as well as other enterprise technology.
Prediction capabilities – By leveraging big data, enterprise chatbots should have the ability to identify customer intent and predict the next best action.
Security – A critical requirement for all industries, this is especially important in banking and financial services. All chats that go through 7’s Virtual Agent are encrypted.
“I haven’t seen a vendor with more enterprise chatbot deployments than 7,” said Mitchell Kramer, senior vice president, Patricia Seybold Group. “Not all chatbots are created equal, which is why it’s important for enterprises to understand the key requirements that will make chatbots successful in that environment. These include natural language understanding, intent models that learn and improve with use, and an analytics package to gauge whether the chatbot is addressing customer needs.”