What is a chatbot?
Definition: A chatbot is an application designed to simulate human-like interactions by using artificial intelligence (AI), natural language processing (NLP), and machine learning algorithms to interact with users through text or voice-based conversations.
The chatbot can generate a relevant and grammatically correct response by analyzing and interpreting the text input provided by the user.
Simple chatbots can only understand input they already have in their database, while more complex AI chatbots can interpret new information, learn and predict the user’s potential questions.
Businesses use chatbots for customer service and support, to handle routine queries, and assist with simple tasks like password resets, order tracking, or appointment scheduling. They can be integrated into websites, messaging apps, or social media platforms to provide instant customer support, 24/7.
Types of chatbots
There are two types of chatbots, and they differ from one another in their complexity and the technologies they use.
Basic chatbots focus on specific tasks and operate within a restricted domain and limited data. They have pre-established rules and scripts to use as responses to questions asked by users, which they already have in their database.
A chatbot designed to handle tasks can be integrated into a website to address customer inquiries, such as guiding customers through the website, providing information about products, or resolving billing-related questions.
Chatbots have become popular because they reduce response times and enhance customer satisfaction as they operate around the clock and handle multiple queries simultaneously. Also, they relieve human customer support or other staff from handling boring, routine tasks.
Still, basic chatbots have drawbacks, as they can’t understand natural language or context and cannot address complex requests.
More complex (data-oriented)
Due to the lack of NLP technology, basic chatbots need continuous supervision and updates to ensure accurate and up-to-date responses. AI chatbots and virtual agents are being developed to overcome these limitations and offer more advanced functionality.
AI chatbots use NLP, machine learning, and NLU( Natural Language Understanding) to be data-oriented and to collect patterns from interactions with humans so they can learn and implement them in future conversations.
AI chatbots can have dialogues with humans and not just answer questions. While task-oriented chatbots have limitations in their interactions with humans, AI chatbots can continue engaging with humans, predict answers, and evolve.
However, AI chatbots still have limitations when recognizing jargon and irony or dealing with personalized requests.
Common chatbot use cases with examples
In e-commerce, chatbots are used in many ways to streamline processes for customers and employees. Some of the ways chatbots are used in e-commerce are:
1. Customer service – Chatbots can be used to answer FAQs, solve technical issues like forgotten passwords and give product recommendations.
2. Order tracking – E-commerce businesses can automate the order tracking process by sending customers notifications related to their orders.
3. Sales and marketing – Chatbots can send personalized offers, reach out to customers who have abandoned their carts, or give discounts in e-commerce.
4. Surveys – E-commerce owners can integrate chatbots to ask customers to give feedback after shopping, which business owners can use to improve customer experience.
HubSpot’s GrowthBot is an example of a chatbot doing customer service, data collection, and marketing.
The healthcare industry uses chatbots to answer questions, simplify administrative tasks, and provide education and support.
Some ways chatbots are used in healthcare are:
1. Symptom checker – Some healthcare institutions use a chatbot to communicate with patients and help them decide if they need to visit a doctor or stay home.
2. Health education and support – As misinformation about healthcare proliferates online, certain institutions have taken steps to combat it by training chatbots to provide answers that medical professionals have verified.
3. Appointment scheduler – Chatbots can help make the process of setting an appointment easy and fast for both patients and employees.
A famous chatbot in the healthcare industry is Buoy’s Healthcare chatbot, which can assist patients in all areas mentioned above.
In the travel industry, chatbots can assist customers and help them have a better customer experience without the hassle. Here are some of the most common things a chatbot does in the travel industry:
1. Assistance with booking – A chatbot can help customers book hotels or flights after they answer a couple of questions.
2. Travel recommendations – More modern chatbots can go beyond assistance based on input and provide personal recommendations and tips.
3. Travel information and notifications – Automating the process of travelers getting information and notifications about their travel faster.
Skyscanner’s chatbot is used for automated travel recommendations and assistance during booking. Another popular example is Hilton’s chatbot which can answer FAQs regarding hotel amenities and availability.
Chatbots vs. AI chatbots vs. virtual agents
Rule-based chatbots are the simplest because they rely on pre-defined rules and data to respond to customer inquiries. They can’t personalize their answers or anticipate the customer's next question.
AI chatbots use machine learning to answer questions in a more human-like manner, enriching their database from interactions with humans.
Virtual agents are the most complex because they combine different technologies and can perform more complex tasks such as booking, setting reminders, and predicting questions.
Are chatbots AI or ML?
Chatbots can be powered by both AI and ML. AI is used to simulate human-like conversations and understand user inputs. NLP, an aspect of AI, interprets and processes human language, enabling chatbots to comprehend user queries and provide relevant responses.
Machine learning comes into play in training chatbot models to improve their performance over time. As chatbots are exposed to more data and user interactions, they can better understand user intent and provide more accurate and contextually relevant responses.