What is a natural language processing (NLP) chatbot?
Bring the power of NLP-enabled AI to your business.
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.
Do you want to talk to your experts on NLP chatbots?
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:
Natural language processing
Natural language understanding
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:
Input gathering: The user provides input through a website or an app in either text or voice format.
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.
Response management: During this stage, Natural Language Generation (NLG) formulates a response.
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.
The key priorities for CX leaders today
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.
Customize, automate, and deploy Freshworks’ free chatbot templates
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.
Freshworks Customer Service Suite
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:
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.
Why do customers rave about Freshworks’ powerful AI chat software?
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
Talk to an expert to learn which type of chatbot is right for your business
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.
FAQ
What is NLP?
NLP is natural language processing. It’s artificial intelligence that understands the context of a query. It can even comprehend misspelled words or improper usage. That makes them great virtual assistants and customer support representatives.
What is NLP vs AI?
Natural language processing (NLP) is a type of artificial intelligence that examines and understands customer queries. Artificial intelligence is a larger umbrella term that encompasses NLP and other AI initiatives like machine learning.
What industries benefit from NLP chatbots?
All service industries can benefit from NLP chatbots. This includes both the B2B and B2C sectors. Any industry that has a customer support department can get great value from an NLP chatbot.
What is an example of an NLP chatbot?
A great example of an NLP chatbot would be Freshworks, an AI-powered system that analyzes user queries and understands the context of the question regardless of keywords or spelling accuracy. It’s a chatbot that can infer intent and context from a user’s question. Other examples could be ChatGPT or Alexa from Amazon.