Which one is best for chatbots?

by Disha Sinha

October 17, 2021

Chatbots are the hottest automated technology in this highly competitive consumer-driven marketplace. Every business needs a competitive advantage to meet the needs of consumers and instantly wants the ultimate customer satisfaction in order to achieve higher customer engagement. But there are two different approaches to effectively creating user-friendly chatbots: NLP and machine learning. AI-based chatbots behave differently with these two different approaches. Yes, it is overwhelming for a beginner to know all about these technical elements. Let’s explore which one is best for chatbots, NLP, or machine learning.

On the one hand, chatbots are known to harness NLP or natural language processing to gain a clear understanding of the demands, issues, concerns and many more of unique and personal target customers. NLP engines are popular for rigorously using machine learning to record user input to generate the necessary entities and understand dilemmas to reduce potential failures. NLP is known to be semantically sensitive, which means that it focuses on personal and real content instead of just understanding keywords. Businesses receive greater customer satisfaction with NLP chatbots because they generate an intelligent and crisp visualization of the reasoning behind every solution generated. It is easy for a team to track and trace potential errors to resolve issues effectively and efficiently.

On the other hand, machine learning for chatbots needs large datasets to train AI-based chatbots to match exact patterns of consumer questions and efficiently generate the right outcomes. Machine learning models don’t need to understand natural human language and feelings to function properly. It only takes a huge volume of different types of data to gain limited access to the level of precision. Thus, it can offer potential opportunities to affect the overall performance and quality of AI chatbots to the target audience. Machine learning and AI algorithms cannot help developers solve a problem so quickly due to AI behavioral patterns. This can have a serious impact on a company’s brand with a drastic effect on consumer engagement.

NLP chatbots bridge the gap between AI algorithms and human language through intelligent learning, while machine learning chatbots must learn how to generate the necessary inputs. Machine learning chatbots can fail to convert potential consumers and provide intelligent responses to queries when there is a lack of data to understand, while NLP chatbots can easily identify critical contexts and understand what the consumer wants to know.

That being said, there is a growing demand for the availability of more interactive chatbots in the tech-driven marketplace to ensure high customer engagement. Thus, NLP and machine learning should be combined to generate smarter computer systems to develop the necessary key areas such as emotion, logic, reasoning, slang, trending terminologies and many more. . More funding in advanced technology can introduce more advanced chatbots to the business world without increasing customer service support staff as well as operational costs.

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