December 01, 2022

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What is a Conversational Agent?

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A conversational agent is a program designed to converse with humans using natural language.

A conversational agent is any dialogue system that conducts natural language processing (NLP) and responds automatically using human language. Conversational agents represent the practical implementation of computational linguistics, and are usually deployed as chatbots and virtual or AI assistants.

A conversational agent is a virtual agent you can use to communicate with a human in natural language. It simulates human-to-human interaction and understands context and meaning just as humans do.

It can talk to people on phones, computers, and other devices, allowing them to order food or do other functions through voice, text, or chat. It can achieve these using technologies like natural language processing (NLP), machine learning (ML), speech recognition, text-to-speech synthesis, and dialog management to interact with people through various mediums.

In this article, we'll discuss how they differ from chatbots, how they work, and how they can be applied in different industries.

Difference between chatbots and conversational agents

A chatbot is a software program designed to simulate human conversation. You can use it to provide information, answer questions, perform tasks, and make purchases. They can be rule-based, too, which means they can only respond based on specific text or button inputs. In addition, these bots are more narrowly focused on their objectives. For example, they're either employed to resolve particular customer queries such as looking up an insurance policy or help with the e-commerce checkout process.

On the other hand, conversational agents are programs that use NLP and natural language understanding (NLU) technology to converse with humans. The program can understand human emotions, answer basic questions, respond to commands, and interact through natural language conversations. These agents are often used to automate customer support and marketing campaigns.

Basically, chatbots are conversational agents, but conversational agents are not necessarily chatbots.

How does a conversational agent work?

The programs used by conversational agents use technologies like NLU, semantic analysis, text generation, dialogue management, and dialog state tracking. Due to this, they’re able to understand what you say and respond appropriately. They do this by converting spoken words into machine code. This process is called automatic speech recognition (ASR). Then, NLU identifies the parts, names, and semantics (meaning) of words. Finally, text generation creates sentences based on the information gathered.

While the NLU algorithms are doing their job, a dialog manager is also running simultaneously to ensure that the conversation starts from where it was left earlier. This process prevents the conversation from going off track, and allows conversational agents to be used in multiple ways.

Example Types of Conversational Agents

Use cases of a conversational agent

Conversational agents are often deployed via mobile apps, desktop applications, web pages, or other Interfaces. You can use them to automate customer support, sales, marketing, education, entertainment, and other tasks. Some of them include the following:

Customer service

Businesses can use conversational agents to answer questions quickly and efficiently without hiring additional staff or paying agency fees to an outsourced call center. Additionally, chatbots can handle routine tasks such as resetting passwords or booking flights which is quite common in enterprise chatbots.

It can also answer basic product questions that don't require human judgment—making them ideal for low-cost services like online banking, where customers may not want to wait for a real person to get back to them.

Information retrieval

You can offer information about products or services through a chat interface instead of having the user search through articles on your website or in an app store search box.

The user can ask about the price of an item, for example, and you can provide that information in real-time. It can be anything from pointing a customer towards an item they're looking to purchase or providing details on how to use your product. It can also offer answers to more nuanced queries like how many days it'll take to receive a product, etc.

Revenue optimization

Conversational agents can optimize sales by suggesting products to customers who have not yet purchased them. They can collect first party data and be connected to customer relationship management (CRM) or email marketing software to send cart abandonment emails. It can also prompt users within the app/browser window to encourage a purchase.

It can help increase conversion rates by providing additional information about products that would go unnoticed. A conversational agent can replace pop up windows and act as a modern day concierge service for your site visitors.

Examples of a conversational agent

Here are a few examples of conversational agents that are currently being used in the market:

  1. Iris: Conversational agent for data science tasks

While most conversational agents focus on simple tasks like offering customer support and booking appointments, Iris is coded differently. This agent can help users accomplish complex data science tasks like plotting a histogram from a dataset or conducting statistical analysis for those datasets.

The bot maps user inputs with pre-existing commands to decide what responses to offer. It does that by transforming those commands into automata that the bot can compose, sequence, and execute, providing the desired output. These commands require additional input, which the agent gets by either speaking to the user to resolve arguments or relying on previous conversations to understand the intended task.

Iris’s underlying model

Using this agent, data scientists can complete predictive modeling tasks 2.6 times faster, decreasing the analysis time dramatically.

Example of an executed Iris command
  1. Woebot: Mental Health App

Woebot is a mental health conversational agent that can help you monitor your mood and manage your mental health. It uses NLP, psychological expertise, and excellent copywriting to form a human-like conversation—making it easier for individuals to interact with it.

It works on the principles of Cognitive Behavior Therapy (CBT), a therapeutic approach to challenge recurring problematic thoughts. It can help anyone, irrespective of age, and a recent study confirmed its ability to reduce anxiety and depression in those who use it.

Woebot’s mental health app
  1. Roof.ai: Real estate conversational AI chatbot

Roof.ai is a conversational agent that helps real estate marketers automate interaction with leads and lead score assignment. Using Facebook as its prime channel, the bot interacts with potential leads and prompts them with questions that can help them qualify the lead. Once it assigns the score, it passes the conversation to a real estate agent who can take it forward.

Roof.ai’s real estate lead scoring app

Leverage conversational agents for business productivity

With the current rise of conversational agents, businesses have better customer access, and operation costs have been significantly reduced. They have several use cases in different departments like logistics, marketing, customer support, etc.

Although we still need human involvement, the agent can handle more challenging tasks and take care of more straightforward questions without involving a human operator.

It also helps build trust with customers and increases conversion rates. They also act as a shield between the user and human operator, reducing costs and allowing people more time to focus on other things while waiting for assistance.

As the technology progresses, conversational agents will become more accurate with time—increasing the pool of potential applications.

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