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Home » Chatbots vs Conversational AI Bots: How to Build Chatbots with Conversational AI Capabilities

Chatbots vs Conversational AI Bots: How to Build Chatbots with Conversational AI Capabilities

    Chatbots vs Conversational AI Bots

    Often people mistake chatbots for conversational AI bots but they are not one and the same. Even though there may be some underlying overlaps between the two, both technologies have several differences and impact differently on your buyers’ experience and overall bottom line.

    Businesses today want to provide the best customer experience possible while cutting down costs and saving time at the same time. They have realized the only way to do so is to use AI-powered bots as they help customer service teams save 330 hours per month.

    Also, they want to make it effortless to get answers to simple questions and information about their businesses as it’s the second most frustrating thing for customers.

    Conversational Marketing report 2020
    (Image Source: Drift)

    However, the development of conversational AI confuses many as most people consider chatbots and conversational AI bots synonymous. But what exactly these technologies are and how do they differ from each other? Let’s find out:

    Chatbots vs Conversational AI Bots: What’s the Difference?

    Chatbots are typical computer programs that use NLP and AI to simulate human-like conversations to provide answers to users’ questions. They are of two types basically: rule-based or scripted chatbots and AI-powered chatbots.

    Rule-based or scripted chatbots operate on predefined conversation flows or scripts that are stored in the library. Whenever a user asks a query, the chatbots would respond based on the provided scripts, which it’ll draw from the library.

    AI-powered chatbots use natural language processing (NLP) to decipher the users’ questions and responses to answer accurately and in real-time. NLP enables chatbots to understand dialects and tones to converse like humans.

    Conversational AI is an advanced technology that recognizes the text and speech inputs to respond intelligently. It uses a set of technologies like AI, NLP, and machine learning (ML) algorithms that allow programs to understand and process human language.

    The difference between chatbot vs conversational AI

    The key difference between a chatbot and conversational AI is how they detect and respond to the text and speech inputs to offer human-like interactions. Conversational AI is a broader term that includes everything which is capable of AI-driven communication.

    The conversational AI bots use data, AI, ML, and NLP to recognize the vocals and tones of the text inputs and then facilitate the conversation flow. The interaction, here, can take place through a bot or a voice assistant like Siri or Alexa.

    Use Cases of Conversational AI Bots

    Text messages continue to be the preferred channel for customers to receive communication from brands. It gives customers a personal touch even though they can be automated ahead of time. Since they are coming from a real person and not from AI, SMS automation tools help you give far more positive results.

    Though conversational AI has several use cases, SMS or calls can come in handy when the customer issue is too technical for a chatbot to handle. That said, here are some use cases of conversational AI bots:

    • Customer Support: Conversational AI bots are replacing human agents as they can answer frequently asked questions that most users have. They can provide information such as shipping, suggest sizes, cross-selling products, and provide personalized advice.
    • Internet of Things: IoT devices such as Amazon Alexa, Google Home, and others heavily rely on conversation AI capabilities to process human speech inputs.
    • Health Care: Conversational AI bots have several use cases for the healthcare industry as it makes the services accessible and affordable for users. It can streamline administrative processes and help improve operational efficiency.
    • Sales & Marketing: Conversational AI bots can help businesses improve their sales and marketing strategies to convert leads and drive sales. They can also be used as virtual sales agents that answer questions or provide recommendations.

    How to Build Conversational AI-enabled Chatbots?

    The global chatbot market revenue is expected to reach 454.8 million USD by 2027 up from 40.9 million in 2018.

    The global chatbot market revenue
    (Image Source: Statista)

    As we discussed, businesses today have an increasing need for conversational tools to offer human-like interactions to their customers. Due to this increased need, AI-enabled chatbots have seen a significant rise in recent years thanks to their human-like responses.

    And conversational AI bots take this a notch higher. They help reduce wait time, improve customer experience, boost user engagement, and do much more. The most important thing is AI bots are available around the clock so customers can engage with them whenever they want.

    That said, here’s a process for building a conversational AI chatbot:

    1. Define the Goal & Purpose

    The first step of building a conversational AI bot is to define the purpose and goal to achieve with it. Developing a chatbot used to take months back in the day. But now the technology has advanced and the use of NLP and ML ensures that building conversational bots is efficient and takes as little as possible.

    Defining the purpose of developing the chatbot would help you move to the next step of the process. Do you want to take your customer service a notch higher, improve customer satisfaction, collect user data, or reduce the turnaround time?

    Find answers to the above questions so that you can determine the functionality of your conversational AI chatbot.

    2. Determine the Level of Coding for AI Chatbot Development

    Another consideration for the development of conversational AI chatbots is the level of coding required. There are three ways you can build an AI chatbot: writing code from scratch, using minimal coding tools, or going codeless.

    Building something from scratch has its pros and cons. For instance, it would be great if you want to customize the chatbots to fit your needs. However, it can be puzzling in terms of software terms and could become complex due to the large chunk of coding required.

    Also, the overall process can become time-consuming and complicated as you would have to find a highly skilled team of developers. Contrary to that, low-code or no-code tools enable businesses to set up their AI chatbot quickly and efficiently.

    Low-code and no-code tools have become popular dramatically these days as they help businesses reduce costs and expedite the development process. There are several tools available in marketing right now that offer to build simple and effective conversational AI bots.

    Such tools have drag-and-drop features to build a chatbot that offers personalized recommendations, provides accurate answers, processes orders and billings, etc.

    3. Select the Type of Chatbot to Develop

    As explained earlier, there are several types of chatbots script-based and AI-powered. AI-powered chatbots are considered a type of conversational AI while script-based chatbots do not make the list.

    Since they work on the keywords and other language identifiers for responding, they are not based on conversational AI. Also, these chatbots require manual training and writing responses to all possible customer queries and their variations.

    While the conversational AI chatbot is empowered enough to work on its own to understand the users’ queries even when asked multiple ways using similar phrases. They can understand the context and user intent of what customers are trying to ask.

    4. Implement Natural Language Processing

    NLP is the key component of designing and developing conversational AI chatbots. Since it’s tough for computer systems to interpret and respond to speech and vocal conversations, NLP helps bots to interpret speech and translate it into something that computers can understand.

    Computer systems only understand numbers and not words or languages. NLP enables these systems to understand user queries and provide relevant responses. Using the pre-built NLP models or designing custom ones, you can add functionalities such as,

    • Text tokenization
    • Named entity recognition
    • Sentiment analysis
    • Intent classification, etc.

    5. Testing and Deploying the Chatbot

    Testing and deploying the conversational AI chatbot is crucial for the success of the project. Create test cases that cover various user inputs and test scenarios to ensure the chatbot’s accuracy and performance.

    Analyze and evaluate the responses and make necessary improvements to boost the conversation capabilities. Moreover, conduct functional and user experience testing to detect and fix issues. 

    After you’ve done thorough testing, it’s time to deploy your conversational AI chatbot to the public. The deployment options can include, making it live on the website, integrating with business automated texting platforms, or deploying it through dedicated applications.

    Ensure the deployment process is smooth to maximize the chatbot’s reach and impact. Provide clear instructions or prompts for users to interact with the chatbot and make it easily accessible on platforms where your target audience is present.

    Conclusion

    Through this guide, we’ve tried to provide you with basic steps to develop a conversational AI chatbot. Conversational AI bots differ from traditional Chatbots greatly as they have a natural inclination to answer user questions as humanly as possible.

    These chatbots, further, have the enhanced capabilities to listen and comprehend human language and provide accurate answers. Thanks to speech recognition and NLP tools, conversational AI bots take the user experience a notch higher.

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