Analytics Insight explores the differences between NLP and machine learning for chatbots

Chatbots are the hottest automated technology in this highly competitive consumer-driven market. Every company needs a competitive edge in meeting consumer needs and wants instantly for the ultimate customer satisfaction to gain higher customer engagement. But there are two different approaches to create consumer-friendly chatbots efficiently— NLP and machine learning. AI-based chatbots act differently with these two different approaches. Yes, it is overwhelming for a beginner to know all about these technical pieces of stuffs. Let's explore which one is more suitable for chatbots, NLP or machine learning.

On one hand, chatbots are known for harnessing NLP or Natural Language Processing to have a clear understanding of the unique and personal inquiries, problems, concerns, and many more of target customers. NLP engines are popular for rigorously using machine learning to register user input to generate necessary entities and understand the dilemmas to reduce the potential failures. NLP is known for being semantically sensitive which means it focuses on personal and real-life content instead of understanding just the keywords. Companies receive higher customer satisfaction with NLP chatbots because it generates smart and crisp visualization of the reasoning behind every generated solution. It is easy for a team to track and trace potential errors to fix the issues efficiently and effectively.

On the other hand, machine learning for chatbots needs vast datasets for training AI-based chatbots to match with the exact patterns of the questions from consumers and generate appropriate results efficiently. Machine learning models do not require in understanding natural human language and sentiment to work properly. It only needs a huge volume of different types of data to get limited access to the accuracy level. Thus, it can provide potential opportunities to affect the overall performance and quality of AI chatbots to the target audience. Machine learning and AI algorithms cannot help developers to solve any issue so quickly because of the behavioural patterns of AI. This may have a serious consequence to the brand of a company with a drastic effect on consumer engagement.

NLP chatbots bridge the gap between AI algorithms and human language with intelligent learning whereas machine learning chatbots need to learn to generate necessary inputs. Machine learning chatbots can fail to convert potential consumers as well as deliver intelligent responses to the queries when there is a lack of data to understand while NLP chatbots can easily identify essential contexts and understand what the consumer wants to know.

That being said, there is a rising demand for the availability of more interactive chatbots in the tech-driven market to ensure high customer engagement. Thus, NLP and machine learning should be combined together for generating smarter computer systems to develop the necessary key areas such as emotion, logic, reasoning, slang, trending terminologies, and many more. More funding in cutting-edge technologies may present the business world with more advanced chatbots without increasing customer service support staff as well as operational costs.