What is a natural language processing (NLP) chatbot?

Overview of NLP chatbots

Imagine you’re on a website trying to make a purchase or find the answer to a question. As soon as you arrive on the page, a chatbot pops up.

“Not another one of these,” you sigh to yourself.

The experience dredges up memories of frustrating and unnatural conversations, robotic rhetoric, and nonsensical responses. But you give it a go anyway (what have you got to lose, right?). You type in your search query, not expecting much, but the response you get isn’t only helpful and relevant — it's conversational and engaging.

It encourages you to stay on the page, complete your purchase, find out more about the business, or sign up for a subscription. In other words:

Natural language processing (NLP) chatbots provide a better, more human experience for customers — unlike a robotic and impersonal experience that old-school answer bots are infamous for. You also benefit from more automation, zero contact resolution, better lead generation, and valuable feedback collection. 

How do you get your own NLP chatbot?  Let’s start simple.

What is natural language processing?

NLP helps computers understand how humans speak. Using artificial intelligence, these computers process both spoken and written language. They then generate responses like a human would.

Any business using NLP in chatbot communication can enrich the user experience and engage customers. It provides customers with relevant information delivered in an accessible, conversational way.

Real-time chat can help you convert more customers. It adds value to the customer service experience while improving ordering processes, tracking metrics, and informing data analytics.

NLP chatbots' abilities include:

• Recognizing user intent: This allows chatbots to classify the input and determine what the user wants.

• Identifying entities: Chatbots scan text and identify fundamental entities. They group real-world objects like people, places, or businesses before classifying them into categories.

• Vocabulary expansion and transfer: NLP chatbots improve their vocabulary using Machine Learning (ML). This encompasses synonyms, similar phrases, and colloquial references.

• Noun recognition: Chatbots equipped with NLP can differentiate common nouns from proper nouns and correct capitalization issues accordingly.

• Verb tenses: Determining verb tenses and conjunctions helps NLP bots achieve natural responses.

NLP chatbot adoption is growing. Investing in it now can keep your business competitive.

Traditional chatbots vs. NLP chatbots

Traditional text-based chatbots learn keyword questions and the answers related to them — this is great for simple queries. When a user types in a question, the automated answer pops up. However, keyword-led chatbots can’t respond to questions they’re not programmed for. This limited scope leads to frustration when customers don’t receive the right information.

On the other hand, NLP chatbots use natural language processing to understand questions regardless of phrasing. They don’t need an exact keyword or keyphrase match.

NLP chatbots use AI to learn “on the job” from each customer interaction. They continue to become more intelligent and efficient over time.

NLP chatbots can also pick up on spelling and language mistakes. They identify misspelled words while interpreting the user’s intention correctly.

Regular chatbots can’t understand intent. They don’t even differentiate between questions and statements. NLP chatbots have become more widespread as they deliver superior service and customer convenience.

How do NLP chatbots work?

NLP runs on a combination of computational linguistics, machine learning, and deep learning models. It's part of what's often called the three pillars of conversational AI.

The three pillars are:

1. Natural language processing

2. Natural language understanding

3. Natural language generation

These pillars empower computers to absorb human language. They make examining, categorizing, and processing that information possible.

Intelligent chatbots understand user input through Natural Language Understanding (NLU) technology. They then formulate the most accurate response to a query using Natural Language Generation (NLG). The bots finally refine the appropriate response based on available data from previous interactions.

The best conversational AI chatbots use a combination of NLP, NLU, and NLG for conversational responses and solutions.

Here’s how it works:

1. Input gathering: The user provides input through a website or an app in either text or voice format.

2. Input analysis: Based on the type of input, different technologies perform input analysis. If it’s text-based, the conversational AI solution interprets the meaning using NLU. If it’s speech-based, it analyzes the data through a combination of Automatic Speech Recognition (ASR) and NLU.

3. Response management: During this stage, Natural Language Generation (NLG) formulates a response.

4. Response refinement: Finally, machine learning algorithms use the data to refine chatbot responses and guarantee future accuracy.

What are the benefits of NLP in chatbots?

Chatbots are ideal for customers who need fast answers to FAQs and businesses that want to provide customers with information. They save businesses the time, resources, and investment required to manage large-scale customer service teams.

Conversational AI allows for greater personalization and provides additional services. This includes everything from administrative tasks to conducting searches and logging data.

An NLP-enabled chatbot can:

• Respond to written or spoken commands

• Scan and summarize large pieces of text in real-time

• Create opportunities for smoother, labor-saving business operations 

• Reduce human-to-human interaction 

• Simplify mission-critical tasks

• Save costs and increase team productivity in the workplace

NLP also has social benefits. The AI can identify propaganda and hate speech and assist people with dyslexia by simplifying complicated text.

Industry use cases & examples of NLP chatbots

You now have a broad understanding of how NLP chatbots can help businesses. But let’s dig deeper. Here are some examples of how different chatbots can help specific industries: 

B2B examples 

B2B businesses can bring the enhanced efficiency their customers demand to the forefront by using some of these NLP chatbots. 

Event booking chatbots

Event-based businesses like trade shows and conferences can streamline booking processes with NLP chatbots.

When your conference involves important professionals like CEOs, CFOs, and other executives, you need to provide fast, reliable service. NLP chatbots can instantly answer guest questions and even process registrations and bookings. 

Customer service chatbots

Customer service chatbots answer queries 24/7. That’s important for B2B SaaS companies with global clients.

For instance, if a vital tool like a CRM experiences an outage in the middle of the night, an AI-powered chatbot can field questions and provide updates to concerned subscribers. These bots reduce the need to wait in call queues. They even maintain a consistent upbeat tone that never wavers, even when a customer is upset or difficult. 

Pricing chatbots

When B2B prospects contact you, they want the most information as quickly as possible.

An NLP chatbot can provide valuable data like pricing information or pass along special promotions. The bot can also capture contact information and forward it to the sales department for a follow-up. 

B2C examples

When serving consumers, businesses do a lot with NLP chatbots. Here are just a few chatbot use cases in the B2C sector. 

Product recommendations

Product recommendations are typically keyword-centric and rule-based. NLP chatbots can improve them by factoring in previous search data and context.

They’re capable of routing customers to appropriate products while providing answers that move prospects through the sales funnel. Michael Kors uses its website’s NLP chatbot to direct customers toward existing offers and recommend products. It helps customers find what they need at the best price before moving them to the ecommerce store for checkout.

Customer sentiment analysis

NLP chatbots identify and categorize customer opinions and feedback. They also monitor complaints and any common topics of interest. Intel, Twitter, and IBM all employ sentiment analysis technologies to highlight customer concerns and make improvements.

Inventory chatbots

Consumers can ask NLP chatbots about inventory availability. The chatbot then accesses your inventory list to determine what’s in stock. The bot can even communicate expected restock dates by pulling the information directly from your inventory system. 

Banking chatbots

Banking customers can use NLP financial services chatbots for a variety of financial requests. This cuts down on frustrating hold times and provides instant service to valuable customers. For instance, Bank of America has a virtual chatbot named Erica that’s available to account holders 24/7.

Some financial services bots help with include:

• Balance inquiries

• Sending funds

• Monitoring investments

• Applying for loans or credit cards

• Checking the status of a check

• Managing auto payments

Outside Business Examples

Chatbots also have uses outside the direct sales market. Companies use NLP chatbots for some truly creative endeavors.

Here are a few examples:

Chatbots for insomniacs 

Casper has developed chatbot software named Insomnobot 3000. It keeps insomniacs company if they’re awake at night and need someone to talk to.

Chatbots for game lovers 

Disney used NLP technology to create a chatbot based on a character from the popular 2016 movie, Zootopia. Users can actually converse with Officer Judy Hopps, who needs help solving a series of crimes. 

Helping give voice to marginalized communities 

Children's charity UNICEF uses NLP chatbots in a different way. The bot, known as U-Report, collects large-scale data from people living in developing nations. It gathers information through polls on urgent community needs.

Case studies:

https://www.freshworks.com/freshdesk/resources/case-study/thomas-cook/

https://www.freshworks.com/freshdesk/resources/case-study/phonepe/

https://www.freshworks.com/freshdesk/resources/case-study/7-eleven/

Best practices for building & implementing an NLP chatbot

If you’re creating a custom NLP chatbot for your business, keep these chatbot best practices in mind. 

Building an NLP chatbot

When building an NLP chatbot, get a user-friendly builder. It should also include features that serve your unique audience.

Once you have your bot, start creating its personality. Program the chatbot to speak in your brand voice. This guarantees that it adheres to your values and upholds your mission statement.

For instance, a B2C ecommerce store catering to younger audiences might want a more conversational, laid-back tone. However, a chatbot for a medical center, law firm, or serious B2B enterprise may want to keep things strictly professional at all times.

Before going live with your bot, put it through extensive testing.

Act as a customer and approach the NLP bot with different scenarios. Come at it from all angles to gauge how it handles each conversation. Make adjustments as you progress and don’t launch until you’re certain it’s ready to interact with customers.  

Integrating & implementing an NLP chatbot

Integrate your NLP chatbot into communication channels your customers use.

For example, a B2B organization might integrate with LinkedIn, while a DTC brand might focus on social media channels like Instagram or Facebook Messenger. You can also implement SMS text support, WhatsApp, Telegram, and more (as long as your specific NLP chatbot builder supports these platforms).

Once it launches, survey customers after interactions. Gauge their reactions and look for similarities. Then make adjustments as needed.

Finally, use your NLP chatbot as a data-mining tool. It gathers information on customer behaviors with each interaction, compiling it into detailed reports. NLP chatbots can even run ‌predictive analysis to gauge how the industry and your audience may change over time. Adjust to meet these shifting needs and you’ll be ahead of the game while competitors try to catch up. 

Tools for NLP chatbot development

There are a lot of NLP chatbot creation tools out there. It can be daunting to select one that works for you. That’s why we compiled this list of five NLP chatbot development tools for your review. 

Freshchat

Freshworks is an NLP chatbot creation and customer engagement platform that offers customizable, intelligent support 24/7.

Freshworks offers a seamless chat experience across a wealth of communication channels, including, but not limited to:

• Your website

• Facebook Messenger

• Line

• WhatsApp

• Apple Business Chat

• Instagram

• Mobile SMS text messages

Key features

Freshworks has a wealth of quality features that make it a can’t miss solution for NLP chatbot creation and implementation.

Some of these Freshworks features include:

• Integrating with various chat systems

• Proactive outreach to customers with targeted messaging 

• The ability to create a FAQ section

• The Freshworks SDK API for in-app messaging through your company’s mobile app

• A massive list of available integrations with third-party tools

• Enhanced security with custom SSL certificates and end-to-end encryption

• Fully GDPR-compliant

• Fully HIPAA-compliant for the healthcare industry

• Analytic reporting

• And more

Pros & cons

Some of the major pros of Freshworks, as reported online by real users, include: 

• Helpful 24/7 support 

• Enhanced security

• Responsive customer service team

• Seamless integrations

• Fully customizable

• User-friendly software

• Chat automations

Some cons reported online include:

• Occasional attachment loss in lengthy email threads

• User error creating too many notifications

It has been well noted, however, that Freshworks takes a proactive approach to asking for feedback and works to correct any issues. 

Why customers love Freshworks

Customers love Freshworks because of its advanced, customizable NLP chatbots that provide quality 24/7 support to customers worldwide.

Its security features are top-notch, with end-to-end encryption. It protects customer privacy, bringing it up to standard with the GDPR.

Customers rave about Freshworks’ wealth of integrations and communication channel support. It consistently receives near-universal praise for its responsive customer service and proactive support outreach.

Chatfuel

Chatfuel is a messaging platform that automates business communications across several channels. This includes WhatsApp, Facebook Messenger, and Instagram.

Key features

Some of the key features of Chatfuel include:

• AI machine learning capabilities

• Automated scheduling and publishing

• Campaign management

• Auto-responders

• Analytic reporting

Pros & cons

Some of the benefits of Chatfuel, according to users online, include: 

• Straightforward platform

• Sets up several communication paths with customers

• Responsive AI

Some of the drawbacks of this service are:

• Confusing setup process

• Might require a software developer 

• Not as many high-end features as other big names in the field

Botsify

Botsify is a chatbot builder designed for coding novices. It touts an ability to connect with communication channels like Messenger, Whatsapp, Instagram, and website chat widgets. 

Key features

Some key features of Botsify include: 

• Automated sales processes

• Scheduling features

• Chatbot creation

• Lead generation

• Proactive support

Pros & cons

Some of the pros reported for Botsify include the following: 

• User-friendly experience

• Step-by-step guidance through chatbot creation

• Library of training videos

Some of the cons associated with Botsify include:

• Complex for beginners

• Too expensive for SMBs or individual users

• Manual language translation from English

• No location sharing feature

HubSpot

Hubspot’s chatbot builder is a small piece of a much larger service. As part of its offerings, it makes a free AI chatbot builder available. 

Key features

The key features of Hubspot’s chatbot builder include:

• Creating no-code chatbots

• Lead qualifying

• Personalized replies

• Connection with HubSpot CRM

Pros & cons

Some pros associated with HubSpot include:

• A user-friendly system

• Quality interface

• Integrates with CRM

A few common cons of HubSpot are:

• There’s a learning curve

• Doesn’t have robust features

• Full suite of services can be very expensive

Landbot

Landbot is a no-code chatbot builder that helps businesses turn landing pages into lead generation and customer support resources.

It claims to always seek profitable outcomes from customer interactions. 

Key features

Some key features of Landbot include: 

• A visual drag-and-drop builder

• Analytic reporting

• Team inbox

• Chatbot templates

• Customized branding

• Workflow management

Pros & cons

Customers have reported the following Landbot pros:

• Quality customer service

• A user-friendly interface

• Solid collaboration features

Additionally, customers report the following Landbot cons: 

• A sharp learning curve

• Expensive 

• Slow speeds

The future of NLP chatbots

In the future, the NLP chatbot market will grow exponentially. Customers will become accustomed to the advanced, natural conversations offered through these services.

Chatbots will become a first contact point with customers across a variety of industries. They’ll continue providing self-service functions, answering questions, and sending customers to human agents when needed.

Businesses will gain incredible audience insight thanks to analytic reporting and predictive analysis features.

NLP chatbots will become even more effective at mirroring human conversation as technology evolves. Eventually, it may become nearly identical to human support interaction.

However, the need for human support representatives won’t end. Human reps will simply field fewer calls per day and focus almost exclusively on more advanced issues and proactive measures.

What makes Freshworks the best NLP chatbot platform?

Continued investment in research and development keeps Freshworks’ NLP chatbots ahead of the curve. That makes it easy to deliver next-level real-time customer experiences. Here’s what separates Freshworks from the competition:

Conversational AI chatbots that learn

Freshworks’ support and sales bots use AI and ML that detect intent and learn from questions asked over time.

Intent detection and faster resolutions 

Since Freshworks’ chatbots understand user intent and instantly deliver the right solution, customers no longer have to wait in chat queues for support.

Proactive customer engagement

Freshworks AI chatbots help you proactively interact with website visitors based on the type of user (new vs returning vs customer), their location, and their actions on your website. 

Intelligent agent handoff

Our intelligent agent handoff routes chats based on team member skill level and current chat load. This avoids the hassle of cherry-picking conversations and manually assigning them to agents.

Personalize customer conversations 

Our conversational AI chatbots can pull customer data from your CRM and offer personalized support and product recommendations. 

Integration with messaging channels & other tools

You can integrate our smart chatbots with messaging channels like WhatsApp, Facebook Messenger, Apple Business Chat, and other tools for a unified support experience.

Native integrations with CRM & Help Desk Software

Through native integration functionality with CRM and helpdesk software, you can easily use existing tools with Freshworks.