17 Real-Life Chatbot Examples for Your Conversational Strategy

Conversational AI tools can also record all the calls and provide detailed analytics, helping you capture customer information and spot patterns in their behavior. With these insights, you’ll be able to optimize your customer service and your overall business strategy. Conversational AI can also help you enhance human communication and increase the first call resolution rate by personalized call routing. A conversational AI bot will ask your customer questions to determine their needs and then transfer a call to a human agent with the right expertise.

A familiar use case is virtual call center agents for customer support, which is what Normandin’s company Waterfield Tech handles. This creates continuity within the customer experience, and it allows valuable human resources to be available for more complex queries. Moreover, conversational intelligence can be trained to recognize and respond to individual customers’ preferences and habits, thereby providing personalized recommendations and enhancing customer engagement. By reducing the need for large teams of human customer support agents, implementing conversational AI can save money while improving response times and accuracy.

Examples of Conversational AI

It relies on natural language processing (NLP), automatic speech recognition (ASR), advanced dialog management and machine learning (ML), and can have what can be viewed as actual conversations. In the event of canceled flights or missed connections, passengers can simply chat with Lufthansa Group airlines’ chatbots for help. Their AI-powered chatbots are available for round-the-clock support and can do more than just answer frequently asked questions. In case the chatbots are unable to offer help, passengers can request to speak with a human agent, all without having to wait in line. With the chatbot implementation, their customer service centers have reduced time spent on general questions while concentrating more on the inquiries that the bots are unable to answer. Conversational AI is a form of artificial intelligence that enables people to engage in a dialogue with their computers.

Keep in mind that conversational AI technology doesn’t come in just one form. Some of the conversational AI categories include customer support, voice assistance, and the Internet of Things. Conversational AI can go beyond helping resolve customer issues by selling, or upselling. Customers can search and shop for specific products, or general keywords, to receive personalized recommendations.

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A simple chat like this is able to save the sales and support department a whole lot of phone calls since the services offered are very case-specific. Another proof that effective and intelligent chatbots don’t necessarily need to rely on AI. And so, they created Mindhope, a bot that puts people in touch with mental health professionals from the safety of their homes (for free). First, the bot collects personal data including information about person’s mental state.

Selecting the right enterprise conversational AI platform is crucial for success. Consider factors such as NLP capabilities, integration options, scalability, and customization. “A giant source of frustration for consumers is repeating information they’ve already shared, like re-confirming a phone number or having to re-explain a problem to multiple agents. According to Radanovic, conversational AI can be an effective way of eliminating pain points in the customer journey. Twenty-six percent of those polled said bots are better at providing unbiased information and 34% said they were better at maintaining work schedules.

A conversational AI platform can personalise customer conversations if it integrates with other tools and the tech stack of a company. During the implementation stage, this becomes one of the biggest challenges – the platform is not compatible with other software. Integrations are important for seamless syncing and personalising the customer experience. Customer support division can be expensive, particularly if you respond to customer queries 24×7 and in multiple languages.

• They use the final seconds of the call to thank you for calling, ask you if there is anything else they can help you with and sometimes request you for a user satisfaction score.
• This engine understands and responds to human language, learns from its experiences, and provides better answers in subsequent interactions.
• Thanks to the adoption of a chatbot in its customer service, the user will be able to find products faster and more efficiently.
• Mimicking this kind of interaction with artificial intelligence requires a combination of both machine learning and natural language processing.

After understanding what you said, the conversational AI thinks fast and decides how to respond. It may ask you additional questions to get more details or provide you with helpful information. Content generation tools use keywords provided to sift through the best-performing blogs and content on that topic. Based on that information, outlines, keywords, headings and subheadings, and more can be created.

In the past, mental health services haven’t been the most accessible, and there’s no guarantee that patients could receive the help they need at the moment they need help the most. Bixby is a digital assistant that takes advantage of the benefits of IoT-connected devices, enabling users to access smart devices quickly and do things like dim the lights, turn on the AC and change the channel. For even more convenience, Bixby offers a Quick Commands feature that allows users to tie a single phrase to a predetermined set of actions that Bixby performs upon hearing the phrase. Replicating human communication with AI is an immensely complicated thing to do. After all, a simple conversation between two people involves much more than the logical processing of words. It’s an intricate balancing act involving the context of the conversation, the people’s understanding of each other and their backgrounds, as well as their verbal and physical cues.

That customer engagement alone is a great way to start building leads and conversions, since it keeps the customer actively involved during their visit and has them engaging with the website. Since they’re asking the chatbot questions, it means they’re learning about the things they’re interested in, rather than searching the site and digging through pages that might not matter to them. One great feature of conversational AI is just its ability to engage with people.

Applications of Conversational AI

Here, the chatbot helped register new drivers and help with the onboarding of new delivery staff. Yellow Class is an online education platform in India that offers live and engaging hobby classes for children. Their popular classes attract many new customers who want to learn more about the service. Because of the high number of queries, Yellow Class started to look for an automated solution to handle these questions. Doctors and nurses don’t have time to follow up personally with every patient experience that gets discharged from the hospital.

They gave out coupons via the Facebook Messenger channel and managed to achieve high online to offline conversions to their physical counters. Who wouldn’t admire the awesome science and ingenuity that went into Conversational AI? But the most powerful motivator of progress has been the pragmatic, bread-and-butter benefits of technology. Alphanumerical characters present a challenge, as they can “sound” similar and make spelling out email addresses or even phone calls or numbers difficult, with a high rate of misunderstanding. Venturing into the nuts and bolts of conversational AI involves deciphering a number of acronyms that define the structure and underpinnings of the technology. Brands can also maximize scalability by automating their booking appointments and increasing capacity for more client bookings and demos.

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