10 Conversational AI Examples Across Industries

Conversational AI in eCommerce: 9 of the Most Successful Chatbot Examples Medium

example of conversational ai

Mitsuku is the most popular online chatbot and it won the Loebner Prize Turing Test four times. If you are an online store or any other business that handles many customers, you should know one thing. See how HR Chatbots are transforming HR operations, from improving employee experience to streamlining HR processes, HR Chatbots will help reduce busywork.

example of conversational ai

This can lead to bad user experience and reduced performance of the AI and negate the positive effects. Voice assistants offer that human language type of interaction without the need of an actual person on the other end of the device. Customers can easily order more products and get product support, leaving your customer support agents to take care of more urgent requests and needs.

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We might be biased, but Heyday by Hootsuite is an exceptional conversational AI chatbot for ecommerce platforms. With Heyday, you can even set your chatbot up to include “Add to cart” calls to action and seamlessly direct your customers to checkout. It can increase your team’s efficiency and allow more customers to receive the help they need faster. While you can create custom AI applications for your business, choosing a pre-built AI platform is easier, faster, and ideal for beginners. Including the option to connect to a live agent when creating IVR system menus and programming chatbots solves these issues. Another less catastrophic–but still frustrating–Conversational AI challenge is the technology’s frequent failure to properly understand what users are saying and what they want.

example of conversational ai

Such conversational AI platforms can assist customers with a wide range of requests—from changing their pin code and checking account balance to handling lost card reports or processing a payment. Meanwhile, conversational assistants keep track of every interaction, enabling more accurate customer behavior analysis. As a result, the company is more informed about the needs of every segment of its target audience and can personalize its client interactions.

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NLP is capable of detecting and categorizing phrases, words, and even the sentiments of the user’s message. For one, conversational AI still doesn’t understand everything, with language input being one of the bigger pain points. With voice inputs, dialects, accents and background noise can all affect an AI’s understanding and output.

example of conversational ai

Instead of having service reps manning phones and email all the time, companies can move to a conversational AI platform and see drastic benefits in customer and employee experience. Before generating the output, the AI interacts with integrated systems (the businesses’ customer databases) to go through the user’s profile and previous conversations. This helps in narrowing down the answer based on customer data and adds a layer of personalisation to the response.

These conversational AI are more advanced and capable than your regular chatbots and provide a better and more interactive user experience for your customers. As your customer base grows, it can get more difficult for your customer service team to reply and respond to every message. Eventually, you may easily run out of people to keep up with customer service demands.

  • Conversational AI refers to all the tools that can be used within AI chatbots to make them more…well, conversational.
  • Last, but not least, is the component responsible for learning and improving the application over time.
  • In that case, conversational AI can also help connect the caller to the agent best equipped to answer it.
  • AI-pushed conversational AI is becoming more and more popular as a manner to enhance client engagement, automate lead generation, and force conversions.

In these cases, customers should be given the opportunity to connect with a human representative of the company. Alternatively, they can also analyze transcript data from web chat conversations and call centers. If your analytical teams aren’t set up for this type of analysis, then your support teams can also provide valuable insight into common ways that customers phrases their questions. Conversational AI combines natural language processing (NLP) with machine learning. These NLP processes flow into a constant feedback loop with machine learning processes to continuously improve the AI algorithms.

How can Conversational AI enable your teams?

Input analysis is the process of breaking down the input from a user into chunks that can be used to generate responses. Accessibility limits can strain any customer’s relationship with a company. Customer service that’s only available in certain languages, at certain times, or via certain channels can shut entire sections of your customer base out. Running a contact center of human agents to meet this standard would be unrealistically costly and most likely impossible. NLU is built to overcome obstacles such as mispronunciation, sub-optimal word order, slang, and other natural parts of human speech.


HubSpot’s content assistant is a great example of a tool that uses generative AI to help marketers create written content. Conversational AI and chatbots are often discussed together, so knowing how they relate is important. Learn how to join the discussion and drive sales with conversational commerce. Alanna loves helping social media marketers and content creators navigate the fast-paced world of digital marketing.

Read more about https://www.metadialog.com/ here.

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