By Ravi Raj
Customer support implementations also have yet to tap into the full benefits of machine learning and natural language processing to improve the customer experience at a reduced cost. Both large and small businesses can do so by implementing next-generation CX tools that leverage ML and NLP-based conversational interfaces.
Basic chatbot technology
There’s no reason corporations and customer support organizations should not implement conversational AI, as there are simple solutions available that can be deployed in as soon as two weeks and without hiring extra staff. Some of the baseline requirements for implementing automated conversational interfaces that drive superior customer experience include:
- Having the kind of depth that enables the AI to understand its users, no matter how they express themselves.
- Using long short-term memory, one of the most sophisticated deep-learning models, to bring the same sort of AI “horsepower” to an NLP interface that self-driving cars and package-delivering drones employ.
- Ensuring they can be deployed without the need to write a single line of code.
While many companies are building AI-powered chatbots on a messaging or voice platform, the challenge they are facing is making the bot intelligent enough to understand and more readily respond to natural language, which is key to its success. Not everyone can deliver on the promise of providing the fundamental building blocks for conversational AI. These building blocks include natural language understanding, intent identification, information extraction, action triggers, query understanding and transformation, sentiment analysis, natural language response generation, speech processing, personalization, and more. Only recently have groundbreaking advancements in deep learning made many of these feasible.
AI integration in customer service
Many customer service queries can easily be resolved with 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 that uses NLP to recognize user intent is configured to seek answers to a set of questions based on a decision tree. They can diagnose and instantly resolve a problem — a welcome change for consumers away from their computer or frustrated by long hold times on customer support calls.
One of the most common issues raised by customers is often resolved by the most obvious solution, such as when a customer loses internet connectivity and the solution is to simply switch off the router and power it back on. A bot can relieve the consumer of the frustration of a long wait on hold by instructing them to reboot.
With an automated conversational interface, the system can almost immediately detect an unhappy customer and automatically connect them to an agent. This system can also seamlessly hand 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 customers. Deploying NLP-based automated interfaces results in significantly lower support costs and improved customer satisfaction.
Agent assist technology
Another use case for an AI-based automated interface is “agent assist,” which has applications in the contact center business and other enterprises. Today, companies have to support an ever-increasing volume of products, documents, and information, and must adapt to the constant software updates to stay current on the various releases, features, bugs, and troubleshooting methods.
An “agent assist” automated conversational interface helps 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 learns customer and agent data so agents are better equipped to quickly resolve more issues.
The components of an effective chatbot
The key attributes of the automated conversational interface system must work seamlessly in a range of messaging and voice-based platforms, while being easy to configure without the need for computer programming. It intuitively specifies intents, attributes, and entities while easily inputting knowledge base documents. Further, it allows for webhooks to interface with various databases and systems.
The system should use the best and latest machine learning and deep learning algorithms to constantly learn, improve, and understand multiple languages. Moreover, it should seamlessly pass incoming customer calls to a human when necessary, easily picking up them back up, to remain sensitive to sentiments of users. Finally, it should provide a rich set of analytics to help understand, train, and improve the system.
The reality of creating a superior, cost-effective customer experience lies not only in AI-driven automated conversational interfaces but in the hands of those who can now easily and swiftly deploy them to exponentially improve customer support as well.
Ravi N. Raj is chief executive officer and cofounder of Passage.AI, a platform that provides the AI, NLU/P, and deep learning technology as well as the bot building tools to create and deploy a conversational interface for businesses.