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Why AI powered Chatbots are much superior to their rule based or work flow based or click based counterparts?

Why AI powered Chatbots are much superior to their rule based or work flow based or click based counterparts?

Artificial intelligence BlueEra Softech

AI powered Chatbots vs Rule based or Work flow based or Click based Chatbots

AI Powered Chatbots are superior to Click Chatbots because:

The beauty of AI Powered Chatbots is that they allow the user to communicate naturally in free form. Users can say what they desire without having to follow a slew of buttons.

AI Chatbots do not bombard the customer with any predefined set of keyword-powered buttons. Thus do not trap users into an endless and useless conversation loop. 

They are capable of carrying out unique conversations and resolve a wide variety of customer issues. This eventually result in incremental revenue generation for the businesses.

Limiting the interaction to just button clicks can have a negative effect on user experience.

AI chatbots can understand language outside of a set of pre-programmed commands. It continues learning based on the inputs received. They can also make changes based on patterns and become smarter over time as they experience new situations. This means AI bots can be applied to a range of uses. Right from sentiment analysis to making predictions about what a visitor is looking for on your website.

Conversational AI refers to a combination of technologies like Machine Learning (ML), Natural Language Processing (NLP), Speech recognition, texts and other language technologies that allow machines to have human-like conversations. They help automate meaningful conversations and enhance the personalized customer experience. Conversational AI involves understanding and analyzing human emotions through sentiment analysis and continuous machine learning. It does so by accessing both structured and unstructured data.

It’s the evolution of human-bot interaction!!!!

Chatbot Evolution of human bot interaction BlueEra Softech

Natural Language Processing (NLP)

Natural Language Processing (NLP) allows machines to interpret human language. This is done by extracting intents (what a user wants) and entities (elements that helps in identifying user intent). It looks for statistically considerable patterns along with some factors such as synonymscanonical formsgrammarslang, and more.

Machine Learning

Machine Learning as well plays a vital role in devising a customer and his behavior centric Conversational Bot. Well, just like Natural Language Processing, ML is based on algorithms. These algorithms introduced into the system receive and analyze the data and produce the predictions. The more data they receive, the more optimized their performance is. So, as time goes by, the bot’s “intelligence” increases.

Sentiment Analysis

Sentiment Analysis is a subfield of Machine Learning (ML) and Natural Language Processing (NLP). It helps help chatbots determine emotions from textual or voice data. This key chatbot features analyzes customer data by mining thoughts, opinions, or sentiments. Conversational sentiment analysis enables a chatbot to understand the mood of the customer by sentence structures and verbal cues. Bots can use sentiment analysis to modify responses in tune with customer’s emotions and thus help segment the audience.



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