AI chatbots can be a powerful tool to qualify leads, answer frequently asked questions, and even provide customer support. Learn how to create your own chatbot and make it an effective part of your business strategy.
Fin is Intercom’s conversational AI that uses the GPT models. It also has built-in safeguards and flags when a question is beyond its knowledge base, prompting a customer service rep to take over the call.
What is a chatbot?
Using Artificial Intelligence, chatbots can interpret context and respond to user requests. They use Natural Language Processing (NLP) and Machine Learning to determine the right answer. The more advanced bots use predictive analytics to predict what the user is asking for. Check out more at GPT Girlfriend.
Chatbots can also be used to handle basic customer service questions. For example, an AI can ask the user if they want to change their account information or arrange a warranty claim. The bot can then take them through the process and hand over to a human agent if needed.
A growing number of businesses are using chatbots to help their customers self-solve problems or find relevant information. For example, a health care company might ask the user to list their symptoms and then recommend self-care options or refer them to a physician. They can even upload their product catalog and detailed descriptions to help the customer make a purchase decision. This helps to reduce costs and improve customer service.
How do I create a chatbot?
A chatbot is software that converts audio signals into text. It’s able to do this thanks to artificial intelligence and NLP (Natural Language Processing) algorithms that mimic human behavior. AI chatbots can understand more nuanced questions and even learn from each interaction to provide better answers in the future.
To create a chatbot, log in to our AI chatbot builder and start by choosing a trigger that will make the bot appear (for example, when someone opens your specific landing page). Then add an “Visitor says” node with predefined data sets. These will help the chatbot recognize keywords and phrases to find the right answer.
Next, add decision nodes with quick replies to guide the user through your website and products. Finally, add a few “Button options” where users can select a button rather than providing a full text response. Once you’re happy with your bot, click Publish. You’ll now have a draft version of the chatbot, which will let you continue tweaking without updating your published version.
How do I train a chatbot?
The key to building an effective chatbot is ensuring that the chatbot is trained on the right data. This data should be organized and representative of the types of queries that your customers are likely to make.
It’s also important to prioritize the queries that are actually being solved by the chatbot. This will help you keep your training focused on the most important tasks.
For example, if your visitors are asking questions about your opening hours, you’ll want to ensure that those questions are being correctly recognized by the AI chatbot. This is where NLP (natural language processing) comes in handy.
At Tidio, we use a NLP engine that recognizes queries using predefined words and phrases. You can add these to the Visitor says node, which will “feed” the NLP engine and help it correctly recognize similar queries in the future. It’s easy to do and is a great way to increase the quality of your chatbot.
How do I test a chatbot?
Chatbots can interpret users’ inputs and provide a range of options tailored to their requirements. This allows them to be genuinely helpful and provides users with a seamless experience. To ensure that this functionality is up to standard, it is important to test a chatbot’s response accuracy, understanding of user input, adaptability, personality alignment, navigation ease and intelligence.
This testing should be comprehensive, spanning a wide variety of user scenarios. For example, a robust input validation process should be in place that checks for entities (the keywords or data within user utterances). Testing entity extraction against a large set of valid and invalid values is crucial to ensure the chatbot can handle situations where it is presented with unexpected or out of context data.
Finally, it is essential to test a chatbot’s ability to keep the conversation flowing. Failure to do so can negatively impact a chatbot’s correct answer ratio and overall performance. Using automated tests, developers can verify that these capabilities are working properly.
