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Chatbots vs. Conversational AI: Which is Right for Your Business? - VEP Test Site
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Chatbots vs. Conversational AI: Which is Right for Your Business?

Embark on a journey to explore the dynamic landscape of Chatbots and Conversational AI. As businesses increasingly adopt chatbots to engage customers and drive growth, the global chatbot market is expected to reach $994 million by 2024. With 36% of companies using chatbots to generate more leads and achieving a 67% improvement in sales, it’s evident that chatbots are reshaping customer interactions as one of the primary customer support channels, according to Gartner.

Another technology revolutionizing customer engagement is Conversational AI which is projected to hit $32.62 billion by 2030. Nearly 80% of CEOs are already adapting their strategies to incorporate Conversational AI technologies. Moreover, 67% of businesses believe that without Conversational AI implementation they will lose their clients.

In this article, we will analyze the differences between Chatbots vs Conversational AI. Explore the distinctions, benefits, and examples to determine which solution suits your business needs best.

Chatbot vs Conversational AI: Definition and Types

What are Rule-Based Chatbots?

Rule-based chatbots are the simplest form of chatbots for customer support. They operate based on a predefined set of rules and decision trees. Each rule corresponds to specific keywords or patterns in user input, and the chatbot responds accordingly. Rule-based chatbots lack the ability to learn or adapt beyond these predetermined responses. While they are suitable for handling basic and straightforward interactions, they often struggle to understand ambiguous queries or respond contextually.

Mechanics of Rule-Based Chatbots

Types of Rule-Based Chatbots

  • Button-based chatbots offer a limited set of options to the user in the form of buttons. They are simple to build and can be used to complete simple tasks.
  • Keyword-based chatbots identify keywords in the user’s query and match them with the best available response. They are limited by the fact that they cannot correctly recognize other ways of asking the same question, typos, or slang.
  • Data collection chatbots are focused on collecting data from users. They can be used to create more sophisticated chatbots that can combine predesigned journeys with user inputs, make decisions, and complete common business interactions.
  • Decision-tree chatbots work on a “decision-flow” basis, where every answer leads to fewer and fewer potential conclusions. They can be used to narrow down the options available to the user or to perform some task based on the data gathered.
  • Quiz chatbots present a set of questions to the user and then rate their answers. They can be used to spike customer attention, assess customer needs, or collect information from users.
  • Questionnaire chatbots are similar to quiz chatbots, but they do not score the user’s answers. They are used to collect aggregated information from users, such as customer satisfaction surveys.

What is Conversational AI?

Conversational AI is a sophisticated form of artificial intelligence (AI) that simulates human-like conversations through automated messaging and voice-enabled applications. Powered by natural language processing (NLP) and machine learning (ML), Conversational AI enables computers to understand and process human language, generating appropriate and personalized responses.

This technology encompasses various methods, from basic NLP to advanced ML models, allowing for a wide range of applications, including chatbots, virtual assistants, customer service interactions, and voice assistants. By delivering near-human interactions, Conversational AI boosts customer experiences, increases satisfaction, and drives loyalty, making it a powerful tool for businesses seeking intelligent automation to meet and exceed customer expectations across various communication channels.

Conversational AI Components

Components of Conversational AI

Conversational AI leverages advanced algorithms, including NLP, to facilitate context-rich dialogues with users. With exposure to a diverse range of user inputs, the AI enhances its pattern recognition and predictive capabilities. The interaction of Conversational AI with users involves five essential steps:

Step 1: Input Generation: Users submit queries or requests via different channels like websites, mobile apps, or voice assistants, using written text or spoken language.

Step 2: Input Analysis: User input undergoes analysis to derive meaning and intent. Text-based inputs are interpreted using NLP techniques like natural language understanding (NLU), while voice inputs are transcribed using automatic speech recognition (ASR) before NLU analysis.

Step 3: Dialogue Management: After analyzing the input and identifying the user’s intent, the system manages the ongoing conversation by determining appropriate responses and maintaining context and dialogue diversity.

Step 4: Output Generation: The Conversational AI system generates responses to user queries or requests using natural language generation (NLG) techniques, creating human-readable text or speech.

Step 5: Reinforcement Learning: Conversational AI systems improve over time through reinforcement learning, refining responses based on user feedback and updating ML models continually.

Types of Conversational AI

Conversational AI encompasses a variety of advanced technologies designed to facilitate interactive and human-like conversations with users. One of the most prominent types is the Conversational AI chatbot, which employs NLP and AI to engage users, respond to queries, and execute tasks seamlessly.

Voice and Mobile Assistants, on the other hand, interpret voice commands and provide hands-free interaction, automatic sorting of information, and multilingual support.

Interactive Voice Assistants (IVA) serve as automated phone systems, intelligently routing calls and assisting during peak hours, while Virtual Assistants like Siri, Alexa, or Cortana leverage machine learning to continuously improve and adapt autonomously, enabling omnichannel deployment and lower development costs.

These diverse types of Conversational AI contribute to enhancing user experiences, streamlining processes, and providing valuable assistance in various industries.

Chatbots vs Conversational AI: Advantages and Disadvantages

Pros and Cons Conversational AI vs Chatbots

The use of Conversational AI presents a range of advantages and drawbacks when compared to rule-based chatbots. Rule-based chatbots are quicker to train and more cost-effective, relying on predefined rules and clear guidelines for predictable conversational flow and high certainty in performing specific tasks. However, they lack the adaptability to handle complex user inputs, cannot learn from interactions, and have limited knowledge beyond their programmed rules.

On the other hand, Conversational AI employs sophisticated algorithms and NLP to engage in context-rich dialogues, offering benefits like 24/7 availability, personalization, and data-driven decision-making. AI-driven chatbots can handle various tasks, provide immediate responses, and scale customer support efficiently. While they offer a more human-like experience and continuous learning, they require more time for training, may lack context in certain interactions, and demand ongoing updates and testing.

The choice between rule-based and Conversational AI chatbots depends on specific use cases, considering factors like speed, cost, flexibility, and the desired level of user experience.

Main Differences Between Chatbot vs Conversational AI

Technological Differences

Rule-based chatbots are built on predefined rules and simple algorithms, making them less sophisticated than Conversational AI. They rely on basic keyword recognition for language understanding, limiting their ability to comprehend nuanced user inputs. In contrast, Conversational AI harnesses advanced NLU powered by machine learning algorithms. This empowers Conversational AI to understand context, intent, and user behavior, resulting in more intelligent and contextually relevant responses.

Channel Inclusion

Rule-based chatbots are often limited to handling interactions in a single channel, typically text-based messaging platforms. They may not be equipped to process voice inputs effectively, limiting their accessibility and versatility. In contrast, Conversational AI is designed to be omnichannel with multimodal capacities, seamlessly integrating with various platforms, including websites, mobile apps, social media, and voice-enabled assistants. This broadens the reach of Conversational AI and ensures consistent user experiences across different channels.

Input Types and Training

Rule-based chatbots rely on predefined patterns and rules, making them effective for handling specific input formats and predictable interactions. However, they may struggle to understand complex or unstructured inputs. Conversational AI, powered by ML and advanced NLU, can process various input types, such as text, voice, images, and even user actions. Moreover, Conversational AI has the ability to continuously learn and improve from user interactions, enabling it to adapt and provide more accurate responses over time.

Tasks Range

Rule-based chatbots excel in handling specific tasks or frequently asked questions with predefined answers. They are suitable for simple, straightforward interactions, such as providing basic information or performing routine tasks like order tracking. Conversely, Conversational AI goes beyond task-oriented responses and engages users in more sophisticated conversations. It can understand intent, context, and user preferences, offering personalized interactions and tailored experiences to users.

Naturalness and User Engagement

Rule-based chatbots often produce static and scripted responses, lacking the natural flow of human-like conversations. Users may find the interactions predictable and less engaging due to their limited ability to adapt and learn from user feedback. In contrast, Conversational AI’s use of ML and advanced NLU enables it to mimic human-like conversation patterns and provide more fluid and natural responses. This naturalness fosters better user engagement and satisfaction.

Cost-Effectiveness

Rule-based chatbots are relatively easier and less expensive to develop and deploy due to their simplicity and predefined nature. However, as the scope of interactions expands or updates are needed, maintenance can become cumbersome and costly.

Conversational AI, while requiring more initial investment, offers higher long-term cost-effectiveness. Its ability to learn and adapt reduces the need for constant manual updates, and its scalability ensures it can handle a growing volume of interactions without a proportional increase in resources.

Application of Conversational AI vs Chatbot

Chatbots vs Rule-Based Chatbots Use Cases

Rule-based chatbots offer a structured and deterministic approach to conversational interactions, making them ideal for specific chatbot use cases where the conversation flow can be well-defined. Let’s explore several applications where rule-based chatbots excel in delivering efficient and effective solutions:

  • Appointment Scheduling: Rule-based chatbots can efficiently manage appointment bookings for businesses such as medical clinics, salons, or service providers. They can check availability, confirm appointments, and send reminders, reducing administrative tasks and enhancing appointment management.
  • Order Tracking and Updates: E-commerce businesses can benefit from rule-based chatbots to provide customers with real-time updates on their orders, delivery status, and tracking codes. The chatbot can instantly respond to inquiries about the order’s location and estimated delivery time.
  • Information Retrieval: In scenarios where users need accfess to specific information, such as event details, business hours, or location directions, a rule-based chatbot can swiftly provide the relevant details without the need for human intervention.
  • FAQ and Knowledge Base Interaction: Rule-based chatbots can navigate through an organization’s FAQ or knowledge base, extracting relevant information and presenting it to users in a user-friendly manner. This saves users time in finding answers to their questions.
  • Feedback Collection: Businesses can utilize rule-based chatbots to collect feedback from customers after completing a purchase or service. The chatbot can ask specific questions and record responses, enabling businesses to gather valuable insights for improvement.
  • Survey and Polling: Rule-based chatbots can conduct simple surveys and polls, collecting responses from users and generating valuable data for analysis and decision-making.
  • Onboarding and User Guidance: Rule-based chatbots can guide new users through the onboarding process of a website or application, providing step-by-step instructions and ensuring a smooth user experience.
  • Event Registration: For events and webinars, rule-based chatbots can handle registration processes, provide confirmations, and send reminders to participants, streamlining event management.

Conversational AI Use Cases

Conversational AI’s ability to learn from data enables them to handle complex language patterns and offer more human-like interactions. Here are several compelling applications where Conversational AI solutions, including AI-based chatbots, demonstrate their strengths:

  • Virtual Customer Service Representatives: Conversational AI-powered chatbots can serve as virtual customer service representatives, providing personalized assistance to customers across various channels. They can handle complex queries, recommend products or services, and offer support 24/7, enhancing the overall customer experience.
  • Voice and Mobile Assistants: Conversational AI can power voice and mobile assistants that enable users to interact with devices or applications through voice commands. These assistants can perform tasks such as setting reminders, searching for information, and controlling smart home devices.
  • Personalized Shopping Assistance: E-commerce businesses can leverage Conversational AI to offer personalized shopping experiences. The chatbot can understand user preferences, recommend products based on past interactions, and provide real-time product information, helping users make informed purchasing decisions.
  • Virtual Health Assistants: Conversational AI can be employed in the healthcare industry to act as virtual health assistants. These assistants can provide health-related information, schedule appointments, and remind patients about medication intake, contributing to better patient engagement and care.
  • Interactive Educational Tools: In the education sector, Conversational AI can be integrated into learning platforms to deliver interactive and personalized learning experiences. The chatbot can answer students’ questions, provide study materials, and offer feedback on assignments.
  • Travel Planning and Booking: Conversational AI can streamline the travel planning process by assisting users with flight and hotel bookings, suggesting travel itineraries, and providing destination information, making travel arrangements more convenient.
  • Financial Management: Conversational AI can be utilized in finance to help users manage their finances better. The chatbot can offer budgeting advice, track expenses, and provide insights into investment opportunities, promoting financial literacy.
  • Technical Support and Troubleshooting: In the tech industry, Conversational AI can serve as a technical support assistant, guiding users through troubleshooting processes for software or hardware issues and resolving problems efficiently.

Chatbots vs Conversational AI: Examples

Rule-based and Hybrid Chatbot Examples

Lufthansa’s Elisa Chatbot

Elisa is an airport chatbot developed by Lufthansa that is trained on a large dataset of text and code, which allows it to understand and respond to a wide range of customer queries. Elisa can be used to answer questions about flights, refunds, or cancellations, check-in for flights, and make changes to reservations. Elisa serves as a reliable travel companion, delivering valuable information to passengers and enhancing their flying experience with Lufthansa.

Lufthansa’s Elisa Chatbot

Major Tom’s Skylar FAQ Chatbot

Another chatbot example is Skylar, Major Tom’s versatile FAQ chatbot designed to streamline customer interactions and enhance user experiences. Skylar serves as the go-to digital assistant, promptly addressing frequently asked questions and guiding visitors to the information they seek. With Skylar at the helm, Major Tom offers seamless customer support, delivering top-notch marketing solutions with every interaction.

Major Tom’s Skylar FAQ Chatbot

GOL Airlines’ Virtual Attendant Gal

Gal, GOL Airlines’ trusty FAQ Chatbot is designed to efficiently assist passengers with essential flight information. Gal is a bot that taps into the company’s help center to promptly answer questions related to COVID-19 regulations, flight status, and check-in details, among other important topics. By capturing information from the help center, Gal ensures passengers receive accurate and timely responses, saving valuable time for GOL’s customer support team.

GOL Airlines’ Virtual Attendant Gal

Conversational AI Examples

Telecom Virtual Assistant

Exemplifying the power of Conversational AI in the telecom industry is the Telecom Virtual Assistant developed by Master of Code Global for America’s Un-carrier. Over the course of 24 months, this cutting-edge Virtual Assistant has been at the forefront of customer engagement, participating in more than 1.1 million conversations and offering a diverse range of over 40 use cases, spanning from bill payments to mobile service management.

Customers now benefit from expedited and streamlined self-service options, resulting in an impressive 45% containment rate for one-time payments and AutoPay, and a remarkable 73% containment rate for the Netflix experience, among other achievements. With an extensive repertoire of over 70+ intents, the Virtual Assistant swiftly addresses customer inquiries with precision and efficiency, driving a notable enhancement in overall customer satisfaction.

Telecom Virtual Assistant

Payment Refund Chatbot

Another fantastic example of Conversational AI in action is the Payment Refund Chatbot developed for a popular fast-casual Mexican dining chain in North America. Facing challenges in efficiently processing a high volume of refund requests, the brand implemented the Chatbot developed by Master of Code Global to reduce operational costs, minimize human errors, and alleviate customer frustrations caused by long wait times.

By extending the existing Conversational AI solution, the Chatbot intelligently gathers information about the purchase method, issue details, and initial payment, making precise refund decisions. The results have been outstanding, with agent escalation dropping between 42% and 66%, leading to $10.2 million in refund cost savings. The Chatbot’s success is attributed to its sophisticated business logic, which provides consistent and clear refund rules, improving customer satisfaction and operational efficiency.

Payment Refund Chatbot

Esso Entertainment Chatbot

Fueling the love of hockey for Canadians, the Esso Entertainment Chatbot emerged as a game-changing application of Conversational AI. As the official fuel sponsor of the NHL, Esso aimed to engage hockey fans and promote their brand uniquely. Collaborating with BBDO Canada, Master of Code Global created the bilingual Messenger Chatbot, introducing the innovative ‘Pass the Puck’ game. The objective was to entice as many Canadians as possible to participate, passing the puck from coast to coast.

Through enticing social ad marketing, over 84,000 Canadians engaged with the Chatbot, with an impressive 83% sign-up conversion rate and 94% player retention rate. The puck traveled over 1.2 billion kilometers, reaching all three Canadian coastlines and more than 2,500 towns. A resounding success in fostering connections and delight among hockey enthusiasts, the Esso Entertainment Chatbot is a testament to the power of Conversational AI in elevating brand engagement and delighting users nationwide.

Esso Entertainment Chatbot

Luxury Escapes Travel Chatbot

Unveiling the Luxury Escapes Travel Chatbot — an incredible application of Conversational AI that is redefining the luxury travel experience. Luxury Escapes, a leader in providing top-notch travel deals, partnered with Master of Code Global to create this travel chatbot, offering personalized and engaging experiences to travelers. Launched in February 2019, the Chatbot revolutionized how users search and book luxurious trips, leading to an astonishing 3x higher conversion rate than their website.

Users engaged enthusiastically, with over 7400 retargeting interactions and more than 16,800 plays of the fun ‘Roll the Dice’ vacation selector game. The Chatbot’s success story includes generating over $300,000 in sales revenue within just 3 months of its launch. As mobile and conversational commerce thrive, the Luxury Escapes Travel Chatbot stands as a testament to the power of Conversational AI in driving user engagement and expanding brand authority on a global scale.

Luxury Escapes Travel Chatbot

Chatbots vs Conversational AI: How to Choose the Right Solution for Your Business?

Conversational AI and chatbots are both valuable tools for improving customer service, but they excel in different areas. Chatbots, based on predefined rules, are ideal for simple, repetitive tasks, providing a cost-effective solution for basic customer queries. However, they may struggle with complex or personalized interactions.

On the other hand, Conversational AI, powered by AI, offers more advanced capabilities. It can learn and adapt over time, providing natural and personalized conversations. Conversational AI excels at handling complex questions and tasks, making it suitable for sophisticated customer interactions.

To make an informed decision and select the most suitable solution for your business, it’s essential to consider various factors. Firstly, take into account the complexity of customer requests. If your clientele often presents intricate and diverse inquiries, a Conversational AI might better serve your needs, as it can understand context and intent, and provide personalized responses and seamless customer support experience.

Budget is another crucial factor. Chatbots, being rule-based and simpler, are generally more cost-effective to set up and maintain. On the other hand, Conversational AI, with its advanced capabilities and machine learning algorithms, might involve a higher initial investment but can offer long-term cost-effectiveness through continuous learning and reduced manual updates.

Assess your technical resources as well. If your business has limited technical expertise or resources, a chatbot’s ease of deployment and maintenance could be advantageous. However, if you have the capacity for more complex integration and development, Conversational AI may be worth considering for its dynamic, non-linear interactions and ability to integrate with existing databases and text corpora.

Finally, keep your long-term goals in mind. If scalability and expansion are part of your business strategy, Conversational AI’s adaptability and potential to grow with your company make it an attractive option. Master of Code Global has provided a checklist of key differences in the table below to aid your decision-making process.

Chatbots vs Conversational AI A Checklist of Key Differences for Decision-Makers

By carefully assessing your specific needs and requirements, you can determine whether a chatbot or Conversational AI is the better fit for your business.


Chatbots vs. Conversational AI: Which is Right for Your Business? was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.


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