Chatbots And Conversational AI: Parsing The Unique Characteristics

 

The boom of AI is not new for anyone, and we all know how the same is infiltrating almost every aspect of life. Artificial Intelligence development companies are making sure that AI is almost everywhere. The use of AI is in creating artistic imagery (Midjourney),  for revolutionizing research (ChatGPT), and is trying to improve and make everything seamless. One such thing that is seriously affected by AI is chatbots, whether it is to provide machine learning solutions or conversational AI solutions.  Talking about chatbots is nothing but a set of computer programs that are meant to indulge in human-like conversation and work on customer queries. 

 

Some of the most prominent examples of Chatbots are the basic complaint registration elements the brands follow like OLA, UBER, Zomato, and a lot more to mention. Moving further in this article, we will try to understand what exactly chatbots are, how they work, their benefits for business, and the role of conversational AI in the revolution of chatbots.

 

Chatbots – Understanding what they are

 

Let’s understand it with an example: a lead or a customer tries to approach a brand for a query via a channel, and as soon as it happens, chatbots come into play. These chatbots can help the customer find a solution for their problem if it is not that complex. In case the problem is complex, the chatbot can simply transfer the same to a real-time executive available for the resolution of the issue. Not only this, but the chatbots can also help the customers register their complaints via mail, service request, and more so that the higher authorities can get to know the exact problem. 

 

If we date back to the roots of chatbots, you will be easily able to identify using the primitive ones for decades. Yes, here we are talking about the phone trees. Remember how we were supposed to press different numbers on a customer care call to get different solutions? That concept was pretty similar to chatbots. 

 

The main reason behind the development of chatbots was the rapid adoption of technology all around the globe. The machine learning solutions were effective and easy to replicate. They play a major role in the automation revolution and reduce a lot of workloads for organizations looking for customer satisfaction. Moreover, they tend to offer a personalized experience to the customers, which was missing in the frequently asked Questions (FAQs) database offered by different brands. Conversational AI companies are trying to take this up to an even better and more intimate level.

 

Working of chatbots

 

You’ll see the most frequent use of chatbots on business websites like Amazon, product delivery websites, and applications as well. For instance, you’re shopping for a computer table online, and after endlessly looking for the right fit, a chatbot appears to assist you with similar products that you might like. The kind of chatbots could very possibly appear in text or voice forms.

 

However, with time everything has changed, and the same is the case with chatbots as nowadays conversational AI companies are making them smarter with the integration of Artificial Intelligence (AI), Natural Language Understanding (NLU), Machine Learning (ML), Natural Language Processing (NLP), to provide responses identical to human conversations. The primary work of chatbots is completely dependent upon how they are being programmed and the level of integration of machine learning solutions and conversational AI solutions.

 

Types of chatbots

 

On the grounds of conversation style, the chatbots can be subcategorized as declarative chatbots and predictive chatbots. Here we will understand the basic definition of the two types of chatbots along with their working.

 

• Declarative chatbots:

 

These are the basic kind of chatbots that you get to see almost everywhere. In compliance with the frameworks of NLP (Natural Language Processing), they carry out an individual function at once. Machine Learning solutions are often used on minuscule levels in order to supply realistic responses to queries generated by humans. 

Any customer communication with this kind of chatbot is pretty structured and specific and majorly revolves around generic queries and Frequently Asked Questions (FAQs). The best part that machine learning companies focus on is that these chatbots can manage to answer common questions like the ones related to business hours, transactions, pricing of particular services, and more. Due to the use of NLP, it offers solutions to users in a conversational way. 

 

• Conversational chatbots:

 

As the name suggests, these chatbots are the ones that have a mastery of communication and a good understanding of customers’ needs. This kind of chatbot is mostly used in digital or virtual assistants. The most intriguing part about them is that they offer personalized, in-depth answers to almost all customer queries, until and unless you are not asking for the formula to make a bomb; not kidding. 

 

Most conversational chatbots are aware of the question’s context and use Natural Language Understanding (NLU), Machine Learning (ML), and Natural Language Processing (NLP) together to keep learning and improvising. The most astonishing success that an artificial intelligence development company with such digital assistants achieved is the fact that they make conversations super-easy and relatable. By analyzing where the user’s question is coming from, they’re able to link it with the information available. To conclude, Siri and Alexa happen to be the two most popular digital assistants in the international market. 

Such complex and advanced assistants are made available by Artificial Intelligence solutions company. Other than these two, chatbots come in a multitude of categories that include but are not limited to:

 

• Script-based:

These ones are level-one chatbots in terms of basic functioning. They reply to the questions of the users with the help of a decision tree to deliver pre-written answers for all the common questions. These chatbots offer a menu, in most cases, that can be used by the customer to get quick information regarding things like their balance, account details, and more. 

 

• Keyword-based:

 

As the name suggests, these chatbots are a bit better than script-based ones and work on keywords. They mostly work on a blend of AI and a keyword to identify what the customer is seeking answers for. They offer a bit more personalized answers to the customers for their queries. However, they can be a bit tricky for the customers if they fail to use the right keywords. 

 

• Hybrid:

 

These chatbots are a combination of script-based and keyword-based chatbots. They offer the customers both ways – either choose from the menu option or just simply type their queries based on keywords they want to use. 

 

• Contextual:

 

These chatbots are the ones that are developed by top conversational AI companies and Machine learning companies. They use the abilities of both Artificial Intelligence and Machine Learning that lets them remember user interactions and make improvements based on the same. Such kinds of chatbots usually don’t’ put keywords to their use and stay super-associated with user information to derive the best resolutions to the queries raised. 

These turn out to be the most readily available chatbots in the technology marketplace. However, to make them work properly, the artificial intelligence solutions companies feed them enormous amounts of data. 

 

• Voice-based: Voice-based chatbots are the ones that register the user queries via their voice and reply back in the same way. Some examples of best voice-based chatbots developed by top conversational AI companies are Alexa by Amazon, Siri by Apple, and Google Voice Assistant by Google. Customers can simply have conversations with these chatbots in a vocal format and get answers from all around the web based on their access to the internet. These chatbots utilize text-to-speech and voice recognition technologies to identically produce human conversations.

 

 

Characteristics Of An Ideal Chatbot

 

An ideal chatbot should possess these key characteristics:

• Contextual and Outstanding Conversational Skills:

 

Chatbots must be able to interpret not just the language but also the context. Machine learning companies are providing machine learning solutions to ensure that dialogue exchange doesn’t get boring, by keeping it interactive enough to make the users feel as if they’re talking to a friend. This is possible with the help of Bots via the usage of NLP (Natural Language Processing)

 

 They will be able to comprehend context without asking validating questions and also comprehend text interchange in a variety of languages. This will place focus on understanding the intent of the words used and delivering appropriate responses or solutions. 

 

Chatbots must also be able to comprehend and reply to messages on various channels, such as SMS, Facebook, Twitter, and website chat windows.

 

The most effective chatbots actively seek additional information and ask a series of clarifying questions when there is a complex discussion or long questions and queries.

 

• Independent Logic and Reasoning:

 

The chatbot should be able to reason on its own without the assistance of a live agent or human being. Being able to read a customer’s mood and respond appropriately is a crucial part of providing excellent service.

 

This will allow the chatbot to understand and decide what steps to take for the benefit of the consumer and your company.

 

Bots built with AI by artificial intelligence solutions company and machine learning solutions can understand user discussions and respond in real-time. Conversational robots can adapt their demeanor and vocabulary to provide unique interactions.

 

• CRM Integration:

 

To manage real-time work and choreograph workflows as complicated as 10 steps that span many systems, a chatbot should be tightly integrated with Customer Relationship Management (CRM). 

 

This allows the chatbot to better manage your company’s interactions with current and potential consumers, resulting in increased client acquisition, retention, and sales growth.

 

• Interactive interface with excellent UI/UX Design:

 

The whole idea of having a chatbot for your business is to encourage people to use it. If the interface and the working of chatbots are complicated or difficult to operate, it defeats the main purpose and makes discussions redundant. 

 

The design must be straightforward and intuitive so that consumers may easily use them to discover answers.

 

• Pre-configuration and ongoing maintenance:

 

The chatbot should be pre-configured to handle common questions from clients regarding a specific industry. This will allow the chatbot to handle common consumer inquiries about a specific industry. 

 

When trained, chatbots provide numerous advantages. Regular updates and training of chatbots through machine learning solutions and conversational AI solutions will make it more powerful and as a result, it will handle inquiries and interactions more smoothly. 

 

• Data Exploration Freedom: The chatbot should be able to investigate a large amount of data in order to acquire insights from any source, structured or unstructured. Providing this database is where an artificial intelligence solutions company comes in.
  
  Finally, an ideal chatbot should have enhanced conversational capabilities, emotional intelligence, pre-configuration, and data exploration flexibility. You can design a chatbot that effectively engages users, saves time and money, and generates leads and income by following these best practices.

 

Understanding Conversational AI

 

Conversational AI companies provide a set of technologies that can understand speech and text inputs and reply to them.

Conversational AI is a sort of artificial intelligence that enables customers to communicate with computer programs.

The key purpose is that the consumer’s verbal interaction is understood, even if expressed differently so that a task can be completed.

This technology is used in customer service to engage with customers in a human-like manner.

The conversation can take place via a bot in a message channel or a voice assistant on the phone.

 

There are various Conversational AI solutions but the most popular is Conversational AI chatbot. 

It can answer typical user questions as well as identify and classify the purpose of consumer complaints, allowing for faster and more effective issue resolution.

 

Chatbots need the assistance of conversational AI to be able to comprehend human-written texts. It could be said that conversational AI makes Chatbot seem intuition based.

 

Speech recognition is put to use along with ML (Machine Learning), to successfully understand what a user is saying. It’s very crucial to understand where the user is coming from, his feeling attached to the query as well as the urgency of the situational context. Conversational AI uses a multi-channel approach to ensure queries are being resolved in the best way possible.

 

Conversational AI companies based on your need can extend technology from simple language to advanced machine learning solutions.

 

Top conversational AI companies’ platforms are typically comprised of chatbots and/or voice assistants that employ natural language processing (NLP) and/or natural language understanding (NLU) to comprehend user inputs and carry out conversation-like interactions.

 

Siri, Cortana, and Alexa are a few conversational AI chatbot or you can say conversational ai solutions examples. A chatbot can use or not use conversational AI technology, depending on its complexity level.

 

What is a crucial point of differentiation for conversational AI?

 

The primary difference between the working model of chatbots and conversational AI is the usage of NLU (Natural Language Understanding) coupled with ML (Machine Learning) to keep up with human-level interactions.

 

NLP (Natural Language Understanding): It happens to be a technology that allows a computer to comprehend text and speech-based cues to carry out a realistic conversation with users.

 

NLP examines speech and writing patterns to determine what a consumer is saying in order to decipher their intent. It learns to account for mistakes in grammar, typos, intonation and syllable stress, accents, and so on.

 

After determining the customer’s purpose, machine learning & conversational AI technology formulates a response.

Machine learning is the process through which machines (computers) parse data, learn from that data, and then use what they’ve learned to provide meaningful replies.

 

So, it uses both Natural language processing and machine learning technology to convert human interactions into a language that machines can comprehend and then generate a response based on information taken from a specific knowledge base.

 

The knowledge base in an organization is unique to the company, and the business’ conversational AI software learns from each encounter and adds new information to the knowledge base. Which results in constant involvement of technology.

 

What Is the Process and Working of Conversational AI?

 

Conversational AI has two functions: 

 

Natural language processing: This is how artificial intelligence breaks down information to grasp the various parts of what is being said. 

 

Intent detection: Using machine learning from past interactions, artificial intelligence matches the language processing details to discover the fundamental intent of what was said.

 

Entity extraction: Artificial intelligence is always improving itself as it was in the case of intent detection, it employs learnings to extract any specific objects/items that is being discussed in the whole conversation.

 

For example, if you own a shipment company and a customer contacts customer service for their order’s tracking information, the chatbot will be able to

(1) parse the message,

(2) determine that the consumer is interested in ‘tracking a shipment,’ and

(3) identify the box to be tracked. 

Based on how the AI has been trained, the chatbot can answer by directing the user to an effective resolution pathway.

 

Different Types of conversational AI technology

 

Chatbots

 

All Chatbot does not use Conversational AI

They are computer programs that mimic human communication. They assist clients in obtaining immediate responses around the clock or successfully routing them to the appropriate department to handle their concerns. 

Chatbots might employ conversational AI solutions as they tend to produce more realistic and relevant results due to thier training using natural language processing (NLP) models.

 

Voice assistants

 

Voice assistants are software programs that do activities in response to voice commands. To interpret the command and give the necessary outputs, it employs voice recognition, voice synthesis, and language processing algorithms. 

These apps are created by artificial intelligence solutions companies that first comprehend voice commands and then accomplish tasks for the user based on those commands. 

The primary benefits of voice assistants are as follows:

• Because they can be used hands-free, they are popular among those with disabilities.
• They, like chatbots, can recognize a wide range of languages.

 

Virtual assistants

 

Virtual assistants are AI-powered chatbots that assist you in performing specific activities. They are powered by NLP and NLU models, which makes their output more tailored, accurate, and engaging.

 

How to Use Conversational AI

 

1. Determine your objectives and use case.

 

You won’t know if your conversational AI endeavor is successful until you know what you hope to achieve from it.

 

Do you need conversational AI for customer service, sales, or marketing? Be explicit about your goals and the challenges for choosing which conversational AI technology is ideal for your organization.

 

For instance, your main complaint is that your support personnel are spending time answering basic queries when you need them to handle difficult customer inquiries. What types of conversational AI would be most effective in resolving this issue? Perhaps it’s a hybrid of voice assistants that provide automatic responses to common questions and rule-based chatbots that can answer FAQs.

 

Before proceeding with implementation, define your customer service goals and key performance indicators (KPIs). That way, once your conversational AI strategy is in place, you can assess its success.

 

2. Obtain the backing of stakeholders

 

The initiative’s next stage is to gain support. When proposing your concept to stakeholders, make sure your reasons are in alignment with top business objectives. Consider the significance of:

 

• Understanding customer needs: Show how conversational AI technologies learn about customer wants, behaviors, and preferences. Also, explain how this improves customer experience.
• Increasing agent satisfaction: Highlight the beneficial effects AI can have on your agents. Spending less time on repetitious chores boosts production as well as employee satisfaction.
• Obtaining a satisfactory return on investment: Decision-makers will require specific ROI predictions. Learn how to compute, frame, and present the ROI metrics of AI initiatives using tools like Dataiku and Nexocode.

 

The success of your conversational AI program is dependent on the level of support it receives throughout your organization. According to the Deloitte State of AI report, AI projects will fail unless firm leaders establish a core, overarching business strategy to fulfill the vision.

 

3. Establish your budget and resources.

 

Consider how much money and resources your company can devote after selecting how you want to use your chatbot. Because it works right out of the box, a no-code alternative is ideal for enterprises with small development staff. More complicated use cases necessitate more budget and resources.

 

4. Think about your current infrastructure.

 

Next, look into your current communication methods and infrastructure. Choose a conversational AI technology that is simple to integrate with your existing customer support or sales CRM. For a strong omnichannel experience, you’ll want the bot to interact with the channels you already have and effortlessly step into ongoing discussions.

 

5. Select and connect your customer service or sales CRM.

 

Determine the requirement of any more tools. What investments in conversational AI platforms have you already made (if any)? Use any current architecture to add value while lowering expenses. Is it compatible with your present systems?

 

For example, if you have already designed a messenger app for your business, you can create a chatbot that integrates with it rather than creating a similar product from scratch. 

 

6. Examine data to assess performance

 

Collect statistics and customer feedback to assess the bot’s performance. The bot can collect client information and analyze how individual responses perform during the chat. This will show you what clients like about AI interactions. Also, assist you in discovering areas for improvement or evaluate if the bot is a good fit.

 

 

Conversational AI Versus Chatbots And the Future

 

Conversational AI differs from chatbots in that it can identify speech and text inputs and engage in human-like discussions. Chatbots are conversational artificial intelligence, but their capacity to be “conversational” varies depending on their development. As previously stated, conversational AI companies are revamping chatbots for better communication and endless opportunities.

 

Chatbots and conversational AI are two concepts that are quite similar. But they are not the same and are not interchangeable. While both can follow instructions and respond appropriately. However, chatbots operate on a predefined flow, but conversational AI apps can learn and intelligently update themselves as they go.

The future is an AI-powered chatbot facilitated by conversational AI companies to make it more conversational.

 

A chatbot will be conversational if it 

 

• Work smoothly across several media, such as online, mobile, and social apps.
• Record the whole conversation to facilitate smooth bot-to-agent transfer to ensure no loss of client’s original concern.
• Also,  there will be no repetition at the time of transfer of discussion to live agent.
• Make sure that each interaction is part of a wider dialogue that spans a lifetime of engagements with the organization.

 

This is in contrast to siloed chats, which begin and end each time a consumer contacts (or switches channels). The elimination of siloed chats leads to a more seamless experience for both customers and agents.

 

Chatbots are software for automated, text-based communication and customer service; combining it with conversational AI that replicates a natural, human connection and conversation with customers results in creating one of the best conversational ai solutions.

 

As a result, many businesses are shifting to a conversational AI approach. It provides the advantage of creating an interactive, client-centric experience. Many companies have started to use automation and conversational interfaces, demonstrating that demand for conversational AI companies, artificial intelligence development companies and machine learning companies is increasing.

Furthermore, the best economic advantage of conversational AI-powered chatbots is their ability to learn. Archiving and analyzing customer contact data over time collects a huge record of useful insights which includes:

• comparison of its own successful and unsuccessful approaches to customer service,
• feedback on an existing or a newly launched product and services

Those who adopt this technology quickly will pave the way for a new method of engaging with their customers.