A chatbot is a computer program that simulates human conversation with an end user. Though not all chatbots are equipped with artificial intelligence (AI), modern chatbots increasingly use conversational AI techniques like natural language processing (NLP) to understand the user’s questions and automate responses to them.
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Chatbots can make it easy for users to find information by instantaneously responding to questions and requests—through text input, audio input, or both—without the need for human intervention or manual research.
Chatbot technology is now commonplace, found everywhere from smart speakers at home to consumer-facing instances of SMS, WhatsApp and Facebook Messenger to workplace messaging applications like Slack. The latest evolution of AI chatbots, often referred to as “intelligent virtual assistants” or “virtual agents,” can not only understand free-flowing conversation through use of sophisticated language models, but even automate relevant tasks. Alongside well-known consumer-facing intelligent virtual assistants like Apple's Siri and Amazon Alexa, virtual agents are also increasingly used to in an enterprise context to assist customers and employees.
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The earliest chatbots were essentially interactive FAQ programs, programmed to reply to a limited set of common questions with pre-written answers. Unable to interpret natural language, they generally required users to select from simple keywords and phrases to move the conversation forward. Such rudimentary traditional chatbots are unable to process complex questions, nor answer simple questions that haven’t predicted by developers.
Over time, chatbot algorithms became capable of more complex rules-based programming and even natural language processing, allowing customer queries to be expressed in a conversational way. This gave rise to a new type of chatbot, contextually aware and armed with machine learning to continuously optimize its ability to correctly process and predict queries through exposure to more and more human language.
Modern AI chatbots now use natural language understanding (NLU) to discern the meaning of open-ended user input, overcoming anything from typos to translation issues. Advanced AI tools then map that meaning to the specific “intent” the user wants the chatbot to act upon, and use conversational AI to formulate an appropriate response. These AI technologies leverage both machine learning and deep learning—different elements of AI, with some nuanced differences—to develop an increasingly granular knowledge base of questions and responses informed by user interactions. This sophistication, drawing upon recent advancements in large language models (LLMs), has led to increased customer satisfaction and more versatile chatbot applications.
How to build a chatbot
The terms chatbot, AI chatbot and virtual agent are often used interchangeably, which can cause confusion. While the technologies these terms refer to are closely related, subtle distinctions yield important differences in their respective capabilities.
Chatbot is the most inclusive, catch-all term. Any software simulating human conversation, whether powered by traditional, rigid decision tree-style menu navigation or cutting edge conversational AI, is a chatbot. Chatbots can be found across any nearly any communication channel, from phone trees to social media to specific apps and websites.
AI chatbots are chatbots that employ a variety of AI technologies, from machine learning that optimize responses over time to natural language processing (NLP) and natural language understanding (NLU) that accurately interprets user questions and matches them to specific intents. Deep learning capabilities allow AI chatbots to become more accurate over time, which in turns allows humans to interact with AI chatbots in a more natural, free-flowing way without being misunderstood.
Virtual agents are a further evolution of AI chatbot software that not only use conversational AI to conduct dialogue and deep learning to self-improve over time, but often pair those AI technologies with robotic process automation (RPA) in one interface to act directly upon the user’s intent without further human intervention.
To help illustrate the distinctions, imagine that a user is curious about tomorrow's weather. With a traditional chatbot, the user can use the specific phrase “tell me the weather forecast.” The chatbot says it will rain. With an AI chatbot the user can ask, “what’s tomorrow’s weather lookin’ like?”—the chatbot, correctly interpreting the question, says it will rain. With a virtual agent, the user can ask, “what’s tomorrow’s weather lookin’ like?”—the virtual agent can not only predict tomorrow’s rain, but also offer to set an earlier alarm to account for rain delays in the morning commute.
Consumers use AI chatbots for many kinds of tasks, from engaging with mobile apps to using purpose-built devices such as intelligent thermostats and smart kitchen appliances. Business use is equally varied: Marketers use AI-powered chatbots to personalize customer experiences and streamline e-commerce operations; IT and HR teams use them to enable employee self-service; contact centers rely on chatbots to streamline incoming communications and direct customers to resources.
Conversational interfaces can vary, too. AI chatbots are commonly used in social media messaging apps, standalone messaging platforms, proprietary websites and apps, and even on phone calls (where they are also known as integrated voice response, or IVR).
Typical use cases include:
The ability of AI chatbots to accurately process natural human language and automate personalized service in return creates clear benefits for businesses and customers alike.
Improve customer engagement and brand loyalty
Before the advent of chatbots, any customer questions, concerns or complaints—big or small—required a human response. Naturally, timely or even urgent customer issues sometimes arise off-hours, over the weekend or during a holiday. But staffing customer service departments to meet unpredictable demand, day or night, is a costly and difficult endeavor.
Today, chatbots can consistently manage customer interactions 24x7 while continuously improving the quality of the responses and keeping costs down. Chatbots automate workflows and free up employees from repetitive tasks. A chatbot can also eliminate long wait times for phone-based customer support, or even longer wait times for email, chat and web-based support, because they are available immediately to any number of users at once. That’s a great user experience—and satisfied customers are more likely to exhibit brand loyalty.
Reduce costs and boost operational efficiency
Staffing a customer support center day and night is expensive. Likewise, time spent answering repetitive queries (and the training that is required to make those answers uniformly consistent) is also costly. Many overseas enterprises offer the outsourcing of these functions, but doing so carries its own significant cost and reduces control over a brand’s interaction with its customers.
A chatbot, however, can answer questions 24 hours a day, seven days a week. It can provide a new first line of support, supplement support during peak periods, or offload tedious repetitive questions so human agents can focus on more complex issues. Chatbots can help reduce the number of users requiring human assistance, helping businesses more efficient scale up staff to meet increased demand or off-hours requests.
Generate leads and satisfy customers
Chatbots can help with sales lead generation and improve conversion rates. For example, a customer browsing a website for a product or service may need have questions about different features, attributes or plans. A chatbot can provide these answers in situ, helping to progress the customer toward purchase. For more complex purchases with a multistep sales funnel, a chatbot can ask lead qualification questions and even connect the customer directly with a trained sales agent.
Selecting the right chatbot platform can have a significant payoff for both businesses and users. Users benefit from immediate, always-on support while businesses can better meet expectations without costly staff overhauls.
For example, an e-commerce company could deploy a chatbot to provide browsing customers with more detailed information about the products they’re viewing. The HR department of an enterprise organization may ask a developer to find a chatbot that can give employees integrated access to all of their self-service benefits. Software engineers might want to integrate an AI chatbot directly into their complex product.
Whatever the case or project, here are five best practices and tips for selecting a chatbot platform.
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