By 2022, chatbots are expected to cut business costs by $8 billion. Intelligent chatbots, leveraging machine learning, natural language processing (NLP), natural language understanding (NLU), natural language generating (NLG ), and other artificial intelligence technologies, will not only reflect on a business’s bottom line but also impact the way we communicate — in just 2 years, chatbots will become a more frequent chat companion than our partners!

But are NLP chatbots the ultimate solution to every business problem, and should you follow the buzz around this tech solution? There’s no doubt artificial intelligence has the power to help you outperform the competition — but the key is not the technology itself, but how you use it.

In this article, we’ll help you understand the potential of NLP chatbots, what they offer, and how their natural language processing capabilities can help your business become more competitive.

Let’s first look at different types of chatbots.

Is an NLP chatbot a good solution for your company? NLP chatbots in customer care and business operations

Rule-based chatbots vs. NLP chatbots

There are two types of chatbots businesses use — rule-based and AI-powered. Rule-based chatbots react to sentences that contain specific words. In simple terms, they serve as an extension to an F.A.Q. library or knowledge base on a company’s website and can answer simple questions phrased in a certain way, such as queries about your company’s working hours, office location, offer, etc.

Rule-based bots

Rule-based bots don’t require high-level tech knowledge and are often available as off-the-shelf solutions. They operate on predefined rules and keywords, making them straightforward to set up and use without needing advanced technical skills or using complex programming language. As off-the-shelf solutions, rule-based bots are generally more affordable and quicker to implement compared to custom chatbots powered by natural language processing, machine learning, or other AI technologies. However, they are designed for simple tasks and don’t have the advanced capabilities of AI-powered virtual assistants, which require technical expertise to develop and maintain.

Intelligent chatbots

Intelligent bots are often based on natural language processing (NLP) and/or machine learning, and can learn and understand the situational context of a question. They’re often custom-made to ensure they’re perfectly aligned with the company’s needs and internal processes. These natural language processing chatbots can hold natural language interaction and even read human emotions — based on the words and punctuation marks users use. When leveraging machine learning algorithms, they can learn from ongoing customer interactions and improve with time. They are a better solution for high-traffic websites where user queries are more complex, and many customers need to be served at the same time. However, artificial intelligence chatbot development requires a skilled team of data scientists and developers and a much bigger budget.

Before we discuss the capabilities of artificial intelligence chatbots and real-life examples of such solutions in more detail, let’s first ensure a clear understanding of natural language processing.

NLP chatbot development — are chatbot using natural language good?

What is natural language processing?

Natural language processing (NLP) is a branch of artificial intelligence that focuses on the interaction between computers and humans through natural language. By leveraging computational linguistics and machine learning, NLP enables machines to understand, interpret, and respond to human language in a way that is both meaningful and useful. An NLP chatbot utilizes these technologies to comprehend the situational context of a question, recognize human emotions, and interpret complex metaphors.

Unlike rule-based bots, a natural language processing chatbot can hold nearly human-like conversations, providing precise and context-aware responses, which enhances customer interactions and improves user satisfaction. This advanced capability makes NLP chatbots a valuable asset for businesses aiming to deliver high-quality, personalized customer service.

Now, let’s take a look at how AI bots can contribute to your business and why it makes more sense to build an NLP-based bot than grab an off-the-shelf solution.

What can an artificial intelligence bot do?

AI bots can help businesses with multiple tasks. NLP technology enables AI bots to perform tasks such as understanding customer queries and providing accurate responses.

Increase sales

As bots are available at any time, they are right there when a customer needs help. Based on user input they can help customers in various areas — from choosing a relevant item in a shop, to helping find a solution to a problem.

Unlike pre-built bots, AI-powered chatbots are able to answer more precisely in a short period of time as they can take into account the context of a question and won’t need to ask additional questions to uncover intent, which would be the case for pre-built, off-the-shelf solutions. Thanks to natural language processing, such bots can better understand customer queries and hold a nearly human-like conversation flow, providing high-level support.

AI chatbots: artificially intelligent chatbots can help you increase sales

Keeping lead response time low

According to Harvard Business Review, lead response time is a critical metric for businesses to convert leads into clients. The customer will wait no longer than 5 minutes before losing interest in a provided solution. Bots help answer customer questions in no time whatsoever!

Human-like interaction with natural language processing

Let’s take a look at a real-life example. PayPal decided to pass on a pre-built solution and implemented its custom NLP-powered bot to provide more precise answers to its clients straight away. Normally, a rule-based chatbot would ask additional questions to narrow down the context of the question asked.

An NLP-based bot, leveraging natural language understanding, can identify user intent and provide an answer much faster in comparison to its less advanced off-the-shelf substitutes.

Scale your customer service effort (chatbots work 24/7)

Around 265 billion support tickets are submitted every year resulting in $1.3 trillion direct costs. While bots will not replace a support department fully, they will certainly reduce costs as human assistants maintain their focus only on the most crucial tasks — those where a human being is required.

Personalized experience based on user input

AI-powered bots are able to collect user information from various platforms to later build a comprehensive 360° customer profile. The information collected can be used to make more personalized offers. As a result, the relevance of in-bot product suggestions increases, so does customer satisfaction from online shopping. This is also a starting point for brands to build their customer loyalty. Natural language generation helps AI bots create personalized offers and responses, enhancing the overall user experience.

Source of customer data

The more information you collect about your customers, the more accurate understanding of their needs you and your employees will get. As a business owner, you can use the information bots collect to derive some insight and build a hypothesis on the demand for your products.

Your bot will also benefit from interacting more with customers, and as a result, collect more data — it will identify new patterns and provide more precise answers in the future. Please bear in mind — this applies only to NLP-powered bots, pre-built rule-based bots are not able to derive patterns from previous answers.

using natural language processing, NLP chatbots can imitate human speech

Use cases of AI bots

PayPal (finance)

PayPal uses machine learning to identify fraud as well as answer clients’ questions related to declined payments, unauthorized charges, account hold limitations, charges, processing time of a given payment and more. Thanks to natural language processing capabilities, the bot is able to deduce the context for the asked question and give the answer right away. PayPal’s NLP chatbot not only answers client questions but also helps in identifying fraud, providing a more human-like experience and enriching user interactions.

So you might think, if PayPal made it, so should I! However, if you don’t have a huge user base there is no point in such a solution at all. Instead, you can be smarter and choose a less “sexy”, but not less efficient AI solution — a recommender system.

In fact, if you have been gathering data about your customers’ behaviour before, you don’t have to overcomplicate it and make use of what you already have — data — to boost conversion and customer satisfaction.

With a recommender system, you would be able to motivate customers to buy more by offering items they have considered before but haven’t bought or suggest complementary products to already purchased ones. This way, you are not asking customers what they need to solve or find — you already know what they want, and can surprise them by sending them suggestions and offers that match their needs.

Amazon is one of the companies using predictive analytics to increase purchase volume. Check more about recommender systems in this article.

PayPal — real-life examples of NLP chatbot technology

Hugo Legal AI (legal)

HugoLegal has democratized access to legal support in Estonia — thanks to HUGO, people who can’t afford access to legal support now access it for free through a bot. However, more complicated cases are passed over to law firms — it helps lawyers decrease customer acquisition costs and for clients, to match them with the right profile of a lawyer. Hugo.legal has already matched 21,5k cases with law firms and at the same time, has generated over 1000 appeal documents for those who can’t afford legal help.

Hugo Legal uses NLP chatbots to match clients with the right lawyer and generate appeal documents. A fair enough chatbot is a great tool to establish communication with a user and match them with the right lawyer. It is the core of Hugo.legal business. But if you already have information about your users, you don’t need a chatbot to make a perfect match that would be based on multiple data points. Instead you can rely on predictive analytics and build your first predictive model in a few months.

BMW Bot (uutomobile/retail)

BMW has harnessed NLP bots to assist clients in the process of choosing a car — it guides them through a variety of models, trims, prices, specifications, and options. These NLP bots use artificial intelligence and natural language processing techniques to interact with users in a human-like manner. The bot also acts as an assistant for the less experienced drivers by describing the use of particular car elements and provides some explanation on how they are different from those available on the market.

In this case, the chatbot is a nice-to-have tool to surprise customers. And let’s face it — BMW can do that on top of other AI and more traditional non-tech solutions.

Apart from chatbots giving suggestions, companies can use AI to reduce churn. With AI, you can also predict when users are unlikely to pay a given price for the product — this is especially beneficial for subscription businesses where customers can opt out and choose a competitive solution in short instance. By using predictive analytics, you will be able to predict which users are likely to churn and provide them with some discount or special offer and simply give them more of your attention. AI goes much beyond chatbots!

Lidl’s Winebot

The Winebot called ‘Margot’ helps Lidl’s clients make the right choice for wine. This natural language processing chatbot can provide a description of the origin of wine and its history and send some recommendations on food pairing — far better than a shop assistant could!

How would this bot differ from a simple pre-built recommendations bot? It constantly learns and identifies a sentiment. For instance, over time the Bot has learnt that users mean 5 pounds when writing £5, 5 quid, a fiver, and even “around five-ish”.

The bot is available in multiple places in Lidl’s ecosystem — not only on Facebook, but it can also be discovered on website pages, where users need it the most.

So what are the alternatives? Let’s come back to recommender systems again. 59% of shoppers who have experienced personalization say it has a big influence on their purchase decisions. Instead of asking what wine your client wants to buy, you can use AI to analyze their previous purchases and impress them by suggesting their favorite Chardonnay they have bought before at some discount — to keep your loyal customers happy.

Air France’s Lucie

AirFrance has created an AI chatbot called Lucie to provide clients with their personal AI-powered travel agent. Acting as a virtual assistant, Lucie accounts for the context of asked questions and a customer’s mood to provide a wide range of recommendations for a trip.

The bot asks customers about their dream holiday, and after getting some description, it sends articles, videos, places of interest, and events happening at the destination. This is a whole new experience when booking a flight!

This is a great solution to engage recurring customers and Air France did it the right way.

However, if you want to be a step ahead, you would use predictive analytics and not ask, but suggest the most cost-efficient option. Such enterprise booking platforms as Hotailors have already disrupted the way business travel is made by introducing AI, in particular predictive analytics, to match business travelers with the most cost-efficient destinations and avoid high-season price boosts.

How to measure the success of your NLP bot?

To track the progress of your bot, you should establish metrics to measure the success of its performance. Analyzing user inputs can help measure the success of a bot’s performance. You could consider measuring the success of your bot based on these metrics:

faster response time (your bot solves the problem of an always busy support department and doesn’t leave potential clients waiting to break their objections),
increased volume of interactions (the more customers interact with the bot, the more useful they find it),
higher rate of transaction success (this metric proves a bot can help manage transactions; apart from a finished transaction, you can also measure the increase of transaction volume in general, attributed to the bot),
more accurate responses (over time, with more data processed, you should pay attention to how a bot can iterate on customer questions and responses it provides to them),
abandonment rate (this metric helps to measure how useful a chatbot is to your users and if they find what they have been looking for thanks to a chatbot, the lower the abandonment rate is, the more effective your chatbot is),
adoption rate (it helps identify what percentage of your users started interacting with a chatbot; it also proves your marketing effort is directed at promoting the tool),
interaction time (how much time users spend interacting with the chatbot),
resolution rate (how many tickets have been closed thanks to the chatbot),
customer satisfaction (identifies how effective the chatbot is at solving customer problems and affecting their general impression of your service).

How much does it cost to build an AI chatbot?

The cost of an AI-based bot depends on many variables. It will change depending on the number of capabilities you want it to have, the number of platforms the bot will use, integrations, subscriptions, analytics, the expected number of users, and more. Choosing the right NLP chatbot platform can significantly impact both the cost and the capabilities of the bot.

Building such a solution requires a reliable team of developers able to provide not only high-quality code, but also help you build the product with the necessary capabilities. If you don’t have the required skillset in-house team, you can build your NLP chatbot with the help of a tech partner who will select the team specifically to the needs of your project and adjust it with time, as the needs change.

What to consider before developing an NLP chatbot?

Before grasping the technical aspects of introducing an AI chatbot to your business, it is first worth identifying the goal you want to achieve with its support (same as in the case of building any other AI tool). Additionally, consider how the choice of programming language can impact the development process of your AI chatbot.

The right question to ask in the beginning is, “Is my business ready for AI adoption?”

The key to successful AI implementation, whether it’s building an NLP chatbot or any other AI-powered tool, is the right approach and careful preparation. Such projects are complex and require a well-designed strategy, so if you lack knowledge or experience, your best shot might be turning to a company providing AI development services, with a proven track record of building successful AI chatbots (or AI-powered other tools).

Is it worth building my own AI chatbot? — finishing notes

Even though more human-like virtual assistants are already revolutionizing the way sales and customer experience activities are performed across industries, the majority of US (55%) customers still prefer to chat with a human being. Moreover, 36% of survey respondents would only use chatbots for simple tasks. This fact is often ignored by fans of chatbots.

Understanding human language is crucial for providing effective chatbot interactions and minimizing user frustration (that, let’s be honest, was often the case with rule-based scripted chatbots). Luckily, by leveraging natural language processing, businesses can create chatbots that provide more natural and efficient customer care, ultimately leading to better business outcomes.

UPDATE 19.06.2024

Today, the key to successfully leveraging chatbots in customer care is generative artificial intelligence, with its advanced natural language processing, natural language understanding, and natural language generation skills. Thanks to its constantly evolving abilities backed by machine learning and other artificial intelligence superpowers, virtual assistants powered by this technology can hold exceptionally natural-sounding conversations, sounding nearly like a human agent and providing your customers with support in a wide range of cases.

2024 NLP chatbots: beyond basic concepts

Using natural, human language, such an AI NLP chatbot can understand even more complex questions and not only a narrow list of queries and specific tasks the “old-school” bots were limited to. Moreover, thanks to the generative AI advancements, you can equip your chatbot with abilities such as sentiment analysis and speech recognition and allow your target audience to reach the bots through popular messaging platforms such as WhatsApp.

Beyond NLP chatbots: generative AI voice assistants

And that’s still not all. Depending on what generative AI model you use, you can even create voice assistants able to convey natural conversations and provide an appropriate response to both voice and text user queries. Such a conversational agent can understand human speech and respond to the user’s input in a way that perfectly imitates human interactions.

And it’s not about fooling your customers into believing they talk to a person. It’s about having access to a technology that allows human agents to focus on the most important cases and clients, while your intelligent chatbot, through a logical human conversation, smoothly handles simpler queries.

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If you have an idea for an AI chatbot and want to validate your vision or need a reliable team for your NLP-based chatbot development, we’re here to support you! Whether you want to leverage natural language processing technology or dive even deeper into generative AI capabilities, equipping your tool with speech recognition or other exciting skills, we’ve got your back.

Wondering if it’s time to build your own chatbot? Connect with our team of data scientists here to discuss your ideas.

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NLP & Predictive Analytics: Do You Need an AI Chatbot?