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Why Do You Need NLP and Machine Learning for Your Chatbot? | by Ashok Sharma | Jul, 2021


When you visit any website, you may have noticed that a pop-up usually appears at the bottom right of the screen. It greets you and welcomes you, and offers assistance while you surf the site. This popup is a Chatbot, also known as a Conversational Marketing Chatbot.

These Chatbots are one of the best marketing strategies adopted for enhancing the user experience and business growth. Today’s Chatbots are powered by cutting-edge technologies like NLP (Natural Language Processing) and ML (Machine Learning) that enable businesses to leverage automation to conduct smooth interactions with customers in a more human way.

Many brands often use regular Chatbots. These bots are built based on decision trees, but fail to impress the consumer, resulting in poor customer experiences. In addition, people sometimes complain that Chatbots don’t understand what they’re trying to say.

For such problems, Natural Language Processing and Machine Learning prove invaluable. Chatbots based on NLP and ML can efficiently determine the appropriate context for the suitable applications. This way, it offers a user–friendly interface for consumers. Moreover, these technologies give the appearance to end-users that a human is answering on the other side.

Aside from these, NLP-ML bots can do a variety of other things, such as document analysis, machine translation, and differentiating contents. We’ll explore why NLP and ML are needed in these bots in more detail.

Chatbots and its Types

Chatbots are software programs commonly known as “bots” that interact with end-users through an automated chat interface. For example, a programmed Chatbot interacts like an online customer service executive giving you instant replies.

Chatbots are used for different purposes, but primarily, they are used in customer services.

1. How Conversational AI can Automate Customer Service

2. Automated vs Live Chats: What will the Future of Customer Service Look Like?

3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

Chatbots are generally divided into two types, fully automated and semi-automated.

  • A decision tree or rule-based
  • ML, NLP, or AI (Artificial Intelligence) based

A decision tree or rule-based bot works based on predefined keywords or scripted actions. Simple customer service requests are handled by rule-based Chatbots in e-commerce applications. They are easy to build, simple to use, and accomplish routine tasks.

On the other hand, ML and NLP-based Chatbots use the latest techniques to converse more naturally.

Machine learning chatbots learn from the input they receive. By utilizing natural language processing, it achieves intelligent learning that considers any interaction between computers and human language.

Let us find out how ML and NLP make your Chatbot experience better-

  1. The task of NLP Engines — NLP engines comprehensively employ Machine Learning for parsing user input to extract the required entities and understand user intent. Chatbots powered by Natural Language Processing can parse several user intents to reduce the failure rate.
  2. Intent Recognition — User inputs from a Chatbot are segmented and compiled to user intent through little words. NLP scrutinizes complete sentences by understanding the meaning of words, placing, conjugation, form, etc. Hence, it converts a sentence or paragraph into a simpler one.
  3. Handling Entity — Entities are made up of fields, data, or words depicting an object like date, time, place, location, description, etc. The chatbots identify words from users and match with the available entities or collect other entities for completing a task.
  4. Capitalizing Nouns — NLP-based Chatbots remove capitalization from common nouns and identify proper nouns from speech/user input.
  5. Increasing Vocabulary — NLP continuously adds new synonyms to the bots and uses Machine Learning to improve the chatbot vocabulary, also transferring vocabulary.
  6. Understanding Tense of the Verbs — NLP-MI Chatbots can learn different tenses and conjugation of the verbs through the tenses.
  7. Contractions — These bots expand the contractions and remove apostrophes in between the words.

The Context behind Customer Data Lacks Generalization

Each customer interaction is unique, holding its own connotations and intents. As a result, marketing teams are under pressure to understand customer requests accurately.

Many businesses use generic Chatbots that use NLP techniques. Yet these Chatbots fail to convert potential customers, acquire them, and deliver precise, intelligent responses.

It is where NLP-powered Chatbots come into play.

  • These bots can identify specific contexts and understand customers with different intents.
  • Powered by Machine Learning, the bots can be trained to make better predictions that align with appropriate responses.
  • NLP-powered Chatbots help in recognizing the essential parts of your customer’s responses.
  • They match the intent in those messages with product lists and content feeds to offer better recommendations.

Identifying Context And Intent Will Increase Conversion Rates

Generalized natural language processing is helpful in generic contexts such as finding a location or checking the weather, etc. However, it will not be able to answer any specific queries made by customers.

  • Domain-specific NLP is best suited for intelligent identification and learning how a customer of a particular brand enquiries.
  • Every business has different customer intents and purchases records. Refined data is hard to find and analyze. There might not be a lot of examples of data for training the bot initially.
  • Generic NLP solutions fall short of offering mechanized solutions that can automate point-to-point conversations with your consumers.
  • Domain-specific NLP provides improved customer experience and marketing performance (good ROI).
  • Advanced machine learning through the power of conversational intelligence techniques will help you in achieving a human-like approach.
  • ML creates context and intent for the niche markets. These are categorized by examining each scenario and conversation.

Issues Faced By Marketers and Its Solutions

One of the primary concerns for many businesses and marketers while launching a marketing Chatbot is the absence of useful information or data. There is no data to start with, and even if there are some, it lacks variations. Therefore, it becomes difficult to develop a Chatbot that understands how the customer interacts.

Solutions — This is where Machine Learning comes to the rescue. ML helps with generating variations and offers a better solution.

  • It offers a way out by classifying contexts and intents followed by creating more precise data in less time.
  • ML techniques coupled with Domain-Specific NLP help in creating Intelligent Chatbots.
  • A specific Machine Learning technique known as generative adversarial networks helps in achieving the objectives mentioned above.
  • Automated systems based on ML are designed to train each other.
  • It can take a brand’s limited data as input, predict over one million variants, and match it to the most appropriate responses.
  • ML-NLP-based techniques generate useful information from a limited set of data in less time.

It means your chatbot will respond accurately with the best suggestions to your customers.

Advantages of NLP and ML Based Chatbots

  • It reduces the false-positive rate due to accurate interpretation.
  • Identifies failures in user input and offers a solution by resolving conflicts through statistical modeling.
  • Uses comprehensive communication when dealing with user responses.
  • Has the capability to learn faster and address the gaps observed while developing a solution.
  • Achieve natural language capability through lesser training data inputs.
  • Ability to reuse or re-purpose the input training data for learning in the future.
  • It offers straightforward and straightforward corrective measures for false positives.

Not Just Limited To Retrieve Information

Chatbots built with NLP and ML are not limited to CRM (Customer Relationship Management). They also work with back-end systems of enterprises, such as application access, configuration management, service catalogs, workflow management, identity management, etc.

This leads to resolving service requests that require end-to-end integration of interconnected business workflows in a better way.

Using RPA (Robotic process automation) technology, you can troubleshoot email, unlock accounts, create strong and unique passwords, install antivirus software, use anonymous browsers, use two-factor authentication, reset passwords, manage what access your apps have, and resolve VPN connectivity issues.

Businesses are now recognizing the value of AI chatbots in automating processes. Several large companies are silently implementing such technologies globally.


NLP-ML-powered chatbots help improve your business processes and elevate customer experience to a higher level. When you automate the customer interaction process, you indirectly increase your chances of improving overall growth and productivity by manifold.

It provides advantages in terms of cutting-edge technologies that help survive in the competitive market; thus, saving time, effort, and costs that further guarantees customer satisfaction and better engagements in your business.

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