By MuckAI Girish
Customer service, a key function of any successful business, can make or break a company. Customer dissatisfaction leads to defections and a tarnished brand. On the other hand, effective customer service helps companies differentiate themselves with a superior value proposition while increasing customer retention and improving metrics, such as their NPS (net promoter score), CSAT (customer satisfaction) and CES (customer effort score) scores.
Consumers are increasingly using voice (such as Amazon Echo and Google Home) and text-based (such as Facebook Messenger and Slack) communications as well as a variety of devices and applications to engage with companies and each other. Yet corporations by and large have not leveraged the full capabilities of modern conversational tools, like messaging platforms and voice assistants, to make it easier for their customers to interact with their customer support teams. Nor have customer support implementations tapped into the full benefits unleashed by machine learning (ML) and natural language processing (NLP) to improve customer experience at a reduced cost. Both large and small businesses can improve their customer experience and reduce costs by implementing next-generation of the customer support, which optimizes these ML and NLP-based conversational interfaces.
Many customer service queries typically include the same questions or concerns voiced repeatedly, which can easily be resolved by an automated interface powered by AI, eliminating the need for a phone or chat-based discussion with a person. In many cases, an AI system uses NLP to recognize user intent and is configured to seek answers to a set of questions based on a decision tree. These can often diagnose a problem and instantly provide a resolution — a welcome change for customers not in a position to use their phone or who are frustrated by long hold times that are so common on such a call.
For example, when Internet access goes down, often the solution is simply to switch off the modem and power it back on. However, many customers call their broadband provider, endure long wait times until they get connected, and then discover that all they had had to do was this simple step. With an automated conversational interface, this could have been accomplished immediately. When the system detects an unhappy customer, it automatically connects them to an agent. This system can also seamlessly hand off calls back to the automated interface, and vice versa, as needed. This reduces the load on call center staff, leading to lower wait times for all customers. Deploying NLP-based automated interfaces results in significantly lower support costs and improved customer satisfaction.
Another use case of AI-based automated interface is agent assist, which has applications in the contact center business in addition to enterprises. Businesses must not only cope with an ever-increasing volume of products, documents and information, but also adapt to the constant software updates, forcing support staff to stay current on the various releases, features, bugs and troubleshooting methods. An automated conversational interface can help the support staff answer questions accurately when a customer calls with a problem. With machine learning and integration with CRM and help desk systems, the system can be trained with customer and agent data, leaving agents being better equipped to more quickly resolve more issues. The company can hire higher quality personnel, train them on aspects that are harder to automate or program, leading to a more effective customer service staff and improving employee retention.
Let us examine the key attributes of such a system. It should work seamlessly in a range of messaging and voice-based platforms — something customers are used to — while being easy to use and configure, without the need for computer programming. It should have an intuitive way to specify intents, attributes and entities, it should be easy to input knowledge base documents, and it should allow web hooks to interface with various databases and systems. It should use the best and latest machine learning and deep learning algorithms to constantly learn, improve, and understand multiple languages. Moreover, it should seamlessly hand calls off to a human, as easily as it should pick up them back up, as it remains sensitive to sentiments of users. Finally, it should provide a rich set of analytics to help understand, train and improve the system.
At Passage AI, we are committed to helping you take customer service to the next level working across the myriad of text and voice-enabled platforms. The Passage AI solution offers an easy-to-use and intuitive UI, intent, entity and attribute-specification methods, API-based architecture — all while leveraging state-of-the-art deep learning algorithms to enable superior customer service. If you would like to learn more about Passage AI, please visit us at www.passage.ai.