6 Conversational AI Examples for the Modern Business

Conversational AI helps businesses anticipate customer needs, recommend the right products/services, and gain consumer trust. The more customers interact with your business AI applications, the more data you’ll collect on your customer base. This means more accurate buyer personas, target market research, and customer segmentation. Given that conversational conversational ai example AI decreases customer wait times, increases first contact resolution rates, eliminates human error, and prevents major miscommunications, it’s easy to understand why. The whole Internet has been getting more conversational with time — you have to have a more personable connection with people, machines, and businesses when navigating online.

AI-powered chatbots can collect data and understand what each user is likely to want. Setting up chatbots to suggest products or content based on those insights is a great way to engage users. For example, if a user is looking for ski goggles, the chatbot can help them decide and then try to recommend other ski equipment. Conversational AI is usually a way to offer faster and smoother support over digital channels. A chatbot on Messenger, an in-app virtual assistant, or an AI-powered bot on a website can all be common use cases for conversational AI. Alexa, Siri, Cortana and Google Home are the more advanced examples of this type of technology.

Getting Started with Conversational AI

Machine Learning (ML) is a sub-field of artificial intelligence, made up of a set of algorithms, features, and data sets that continuously improve themselves with experience. As the input grows, the AI platform machine gets better at recognizing patterns and uses it to make predictions. After the conversational AI assistant is deployed, the development team monitors its performance and provides technical support to stakeholders. Some companies overlook the importance of AI trainers and developers being present for the long haul, but the truth is that a conversational AI doesn’t learn new data on its own.

It can also be programmed with varying levels of complexity, offering a more sophisticated model to accommodate a wider array of use cases. The bot or IVR would ask the user a series of questions in a deductive Menu Tree to find the action or transfer needed. While precise, this model can frustrate the customer and often does not provide the correct question for their specific need. In this course, learn how to design customer conversational solutions using Contact Center Artificial Intelligence (CCAI).

What Is Machine Learning?

There’s nothing more frustrating than getting redirected several times while on the phone with customer service or feverishly yelling “representative! Conversational AI helps alleviate this frustration, learning the user’s problem or question they need answered then directing them to the proper contact for support. From there, the customer gets a direct answer, and the business resolves the case more quickly. In this course, learn how to develop more customized customer conversational solutions using Contact Center Artificial Intelligence (CCAI).

• You may have had bad user experiences with chatbots through social media channels like Facebook Messenger, WhatsApp, and Google Assistant.
• If you wish to develop your AI solution internally, you’re doubtless already aware that this represents a significant cost.
• However, this type of interaction does not typically require a human resource to be engaged.
• Second, all data gets outdated over time—and FAQ sections aren’t an exception.
• They aid in customer service conversations and can improve the overall customer experience.