What Is a Conversational Agent? How It Differs From a Chatbot

Conversational agents have blurred the lines between talking to a real person or a bot on digital channels. Modern conversational agents can perfectly mimic human dialogue and assist customers like a support person would. 

Many businesses put intelligent virtual assistants on their digital channels to engage their audience at scale. These assistants are conversational agents equipped with technology like natural language processing (NLP) and machine learning. 

Normally, these businesses tell customers they’re talking to a virtual agent. They also provide an option to connect with live support professionals in case the issues are urgent or critical. 

Conversational agents have supported and empowered businesses in multiple ways. This article will cover these aspects and dive deeper into its concept. 

The tasks these bots perform are tailored to the industries they’re in. Primarily, they are used by customer service teams to receive and route service requests. Other departments use conversational agents to gather information, check symptoms, and even perform online transactions.

These tools are equipped with natural language processing (NLP), speech recognition, machine learning (ML), deep learning, and various other technologies that help mimic a human’s dialogue pattern. 

Types of conversational agents

Organizations use multiple types of conversational agents based on their use case. While some use basic programmed agents, others rely on conversational artificial intelligence (AI) for better customer experience.

Rule-based chatbot

Rule-based chatbots address specific problems or goals. Governed by a set of predefined rules, they map conversations like a flowchart. These bots are also relatively easier to develop. These chatbots may be relevant when users are expected to ask a limited set of questions. However, they become unpredictable when faced with situations outside their set of rules. They also become tricky to manage when several rules govern the bot's response. 

These chatbots may be relevant when users are expected to ask a limited set of questions. But, when several rules govern the bot’s response, it might get tricky to manage it. 

AI chatbots

AI chatbots, supported by machine learning and NLP,  autonomously generate responses after analyzing the intent and goal of the input made by a customer. They excel in handling queries that are complex or in different languages. Since these chatbots are trained in multiple languages, they let the organization address queries in a language their customers prefer.

Internal teams generally use them to perform tasks quickly for added productivity. 

Voice bots

Voice bots perform tasks based on voice commands. They convert vocal instructions into machine-readable text, allowing them to understand the context and deliver output as programmed. This interaction is relatively natural, requiring minimum labor from the user. 

These bots can perform versatile tasks ranging from searching the internet to simple commands like switching on a smart light. Amazon’s Alexa is a good example of such bots.

Hybrid chat

Hybrid chatbots combine the capabilities of rule-based and AI chatbots. They’re well-equipped to understand context from user input but can also generate responses based on predefined rules. 

In practice, there’s a fine line between hybrid and AI chatbots. AI chatbots serve best to address open-ended questions that come from customers. Hybrid chatbots address such questions, and at the same time, they can be adjusted to fit your business needs.

Interactive voice assistants (IVA)

IVAs automate phone systems and let callers interact with computerized phones using voice and keypad inputs. They’re common in banking, logistics, and travel businesses. 

An IVA understands the caller’s input and performs tasks or gives information accordingly. They can also route the call to specific departments for complex requests or if a caller asks them. 

When times get busy, IVAs enhance employee productivity by handling calls where the callers need general information like order status. This allows employees to focus on the more critical calls. 

How does a conversational agent work?

A conversational agent follows a series of steps to produce the desired output. 

• Receives user input: When a user interacts with a conversational agent, it accepts the input. The input can be text, voice, gesture, image, or graphics. 

• Converts input into machine-readable format: The conversational agent uses an automatic speech recognizer (ASR)  to recognize speech and textual input and convert them into a machine-readable form. 

• Understands the context: The conversational agent then uses natural language processing to comprehend the user's desires. It analyzes the context and determines the intent of the prompt. 

• Generates response: Based on the prompt, the agent responds to the user. If the prompt is clear, precise, and detailed, the user generally gets a response that aligns with the prompt’s context. 

• Collects feedback: After delivering the response, the conversational agent encourages the user for feedback to improve future interactions. 

Conversational agent vs. chatbot

All chatbots are conversational agents, but the reverse isn’t true. A chatbot is a specific type of conversational agent.

Conversational agents are great at mimicking human interactions. They use natural language processing and various other technologies to understand the context of a user’s search query. 

Conversational agents are put on the customer support side to assist live agents in responding to general customer questions. This helps reduce wait time and alleviates the agents’ responsibility to answer repetitive questions. With time, AI conversational agents learn to better respond to customer queries. 

Professionals use chatbots to gather information, get answers, perform tasks, etc. They can be rule-based, responding to specific text or button inputs, and may not necessarily possess NLP or machine learning capabilities. 

They can be basic bots that provide set answers for specific questions. When not equipped with AI, these bots are navigation-focused, following a particular flow of dialogue. Modification in the dialogue flow in such chatbots requires reconfiguration. 

Conversational agents use cases

Conversational agents can be used differently based on the industry. 

Businesses prefer adding them on their customer support front as it helps reduce expenses. 

Correctly deployed conversational agents reduce back-and-forth with customers across channels, enhancing efficiency and customer satisfaction. Some companies use these agents to generate inbound leads. Cigniti, a software company, observed a close to 40% conversion rate for its conversational agent. 

Professionals primarily view conversational agents as their assistants. They ask open-ended questions and make requests in natural language to perform a task. 

Here are a few conversational agents' use cases: 

Data collection

Conversational agents aid businesses in making better decisions by collecting and analyzing data. The agents can analyze customer calls to produce data on customer sentiments. This opens up bottlenecks in processes, allowing businesses to resolve them. 

Companies can integrate conversational agents into customer relationship management (CRM) software, and score leads accordingly. 

Retail and e-commerce

Conversational agents offer customer data by analyzing their interactions with brands. Brands can use this data to upsell or cross-sell with the right timing and provide product recommendations. 

These agents also engage customers across multiple channels, offering a remarkable customer experience. It frees up live agents from hopping between different channels to assist customers, giving them more time to prioritize critical conversations.

Healthcare

Conversational agents help gamify online diagnoses for patients. Users can answer a few specific questions to get personalized recommendations for healthcare products. Medical institutions can use these agents to collect patients’ symptoms and schedule appointments with the appropriate physician.

These agents provide a safe space for patients to convey their illness and for doctors to write notes, prescriptions, and treatments while maintaining information security. 

These agents can also assist medical assistants and patients in understanding complicated medical topics, which in turn facilitates the offering and receiving of better care. 

Internet of Things (IoT) devices

Internet of Things devices enable conversational agents to control various smart devices like lights, speakers, and thermostats at homes and offices. These devices also incorporate voice assistants like Apple’s Siri and Amazon’s Alexa. 

Some IoT devices use conversational agents to monitor data and send alerts or notifications when a specific threshold crosses. 

Others

Use cases of conversational agents don’t have an upper limit. As technology evolves, more applications will come into the limelight. 

• Banking and finance: Conversational AI helps convey transactional details and account balances, and raise requests. The agents enhance customer experience by supplying the details faster. 

• Travel: Conversational agents supply details on travel itineraries and tickets while assisting the customer to check the present status of their flight, train, or bus. The agent can also assist customers in booking a hotel reservation. Some travel brands leverage them to collect customer feedback. 
• Real estate: Conversational agents generate leads by offering details and information about locality, rent, deals, and more offers. They handle initial customer conversations and qualify them before passing them to a real estate relationship manager. These agents can take multiple conversations at a time, empowering businesses to never miss an opportunity to sell. 

Conversations that convert

Conversational agents effectively engage audiences by providing information, suggestions, and support at scale. These interactions influence the audience’s interest in a brand and encourage them to take desired actions. Notably, conversational agents are better at converting new visitors and increasing satisfaction rates for present customers. 

Overall, conversational agents help businesses convert and retain paying customers at scale with a comparatively lesser resource investment. 

Learn more about interactive virtual assistants and the top five intelligent virtual assistants (IVA) software in 2024.