Equipping your chatbot for the front lines of customer service is no easy task, and the work starts with defining its vocabulary and technical knowledge. The good news is that OEMs already possess much of the information needed to ready a chatbot for customer service—but assembling it requires data orchestration and analytics capabilities.

By Andy Chinmulgund

Chatbots are transforming the lives of customers and contact center agents

As businesses embrace a wider array digital channels to interact with their customers, customer experiences are being transformed by digital assistants. Powered by conversational AI, intelligent chatbots can instantly assist customers when and where they prefer (at home, at work, or while commuting), anticipate their specific needs (inquiries for new purchases and upgrades, or for customer support and service) and deliver contextually relevant, personalized responses.

The Gartner Market Guide for Conversational Platforms predicts that, by 2021, 15% of all customer service interactions will be handled completely by AI. The new wave of digitization and automation will impact not only complex tasks, systems and processes on the factory floor, but also transform contact center and service support practices. Chatbots can already handle ‘routine queries’ so that contact center agents can focus on more complex and strategic customer requests. This same intelligent automation will also empower customers themselves to get accurate and efficient self-help to resolve many matters themselves.

Our conversations with top manufacturers and service providers mirror these predictions. One OEM that analyzed call patterns of incoming calls at a contact center noted that 80% of these in-bound calls involve non-critical and often repeat issues. Examples are calls to schedule or reschedule a service call, service pricing inquiries, technician or parts arrival status, and other routine and mundane tasks. These are the sorts of interactions that don’t necessarily require the expertise of a contact center agent; they can be handled by a competent chatbot.

Chatbots need specialized training data — lots of it

Your customers expect a chatbot solution to understand their requests, anticipate their needs, save then time and—most importantly—be available at any time of the day (or night). For a chatbot to live up to these expectations and be useful on the front lines of customer interaction, rigorous training is critical.

The better the training data, the better the chatbot will perform once deployed. Assembling that data from around the organization is no easy task, but advanced natural-language processing techniques can significantly shorten the time to market for the chatbot. That’s where Bruviti’s expert data analytics team can help.

We work closely with service groups at top OEMs to understand their unique technical terms, model numbers, technical faults, service histories, parts and other pertinent data that comprise the knowledge that a chatbot needs to perform. Often, this data is unstructured and scattered across databases and knowledgebases across the organization. Here, Bruviti’s data orchestration techniques, combined with our industry-specific models, deliver all the information needed to develop a well-informed chatbot.

After deployment, the language training continues. Here, a comprehensive knowledge-management database plays an important role. It consistently feeds the chatbot with information captured during caller interaction, agent notes, updated technical information, and more. With an efficient knowledge framework, chatbots can respond to today’s user queries with the right responses and also learn from every interaction so they can anticipate tomorrow’s questions.

As you embark on research for your chatbot, Bruviti can assist every step of the way to ensure your shiny new virtual assistant is equipped to meet and exceed your customers’ needs.