The digitized business ecosystem has evolved as a space where humans increasingly engage with machines. There’s no denying that chatbot development has been the ultimate game-changer in almost all industry verticals. Walking in the shoes of a developer, you’d find it overwhelming to know how these digital companions have transformed business interactions with customers. With personalized recommendations, instant support, and now human-like conversations, AI-powered chatbot development has significantly streamlined interactions.
Well, in the backdrop of the evolution of powerful chatbots, the NLP technology stands tall. Did we have virtual assistants that understand our emotions, detect intentions, or comprehend nuances a decade back? NLP, a specialized branch of AI, empowers chatbot development and enables bots to engage customers with human-like conversations. It’s time to explore the role of NLP in the development of intelligent chatbots.
What is Natural Language Processing (NLP)?
NLP is a subsection of AI that empowers chatbots to comprehend human sentiment. The words or vocabulary we use during conversing with chatbots carry our emotions. Since NLP is based on deep learning, it helps computers derive the actual meaning of these human senses.
AI-powered chatbots are capable of understanding the context, intent, and emotion behind human interactions. With smart chatbot development, they generate human-like conversations that mimic real-life humans.
This ability to understand human emotions makes NLP different from search engines or other algorithms. NLP doesn’t rely just on a few keywords. Rather, they help chatbots understand the real intent behind the conversation.
Once NLP identifies the intent and conveys the same to the bot, they respond like humans, based on how developers program them.
How does NLP work in chatbots?
Humans use natural language while communicating with each other. Machines, on the other hand, use programming languages while interpreting inputs from humans. Blending these two primary concepts, Natural Language Processing fosters seamless human-to-machine interaction. This implies that people can directly communicate with machines without knowing programming languages.
With NLP-backed chatbot development, bots gain the liberty to obtain information and process the same from verbal or written inputs from customers.
Chatbot developers work on NLP models, empowering machines to decode human interactions and even respond to them like humans. They can identify context and reply based on the intent of their users.
Intelligent chatbot development holds tremendous potential in customer interaction and engagement. Naturally, businesses are integrating their support systems with these intuitive bots. Let’s have a look at the progressive growth trajectory of the global chatbot market.
The graph reveals that the global chatbot market is set to reach the milestone of $1.25 billion in 2025. In 2016, it stood at just $190.8 million. This shows the exponential growth potential of AI bots.
How does NLP help in developing intelligent bots?
Before exploring the role of NLP in chatbot development, let’s take a look at these statistics.
- 40% of large businesses already implemented chatbots in some form back in 2019.
- 46% of these businesses use NLP for detecting text or voice. Another 14% use NLP for streamlining their customer services.
- 10% of the surveyed organizations use NLP-backed bots for data analytics.
During chatbot development, NLP engines largely use machine learning and deep learning technologies. Here’s why NLP helps in intelligent chatbot development.
- Recognizing intent
Businesses deploying smart bots have customers who reach out to their helpdesk with specific intents. Depending on the industry, the nature of this intent significantly varies. For instance, a customer looking for the best pizza corners in a food delivery app would have a different intent than someone shopping for medicines.
Thanks to chatbot development using NLP, users now largely bank on smart technology to identify their intention and complete the sentence during the search. This implies that NLP takes care of the words, conjunction, grammar, plurality, and other natural elements of human speech.
On identifying the intent of each user, smart bots carry out appropriate actions. Typically, AI bots are trained with a basic set of intents associated with the business they are used for.
Being developers, you need to come up with separate NLP models to address different intents.
- Working on entities
Entities refer to words or data related to any product, location, place, time, person, or anything as such. During chatbot development, NLP is used to identify specific words from users. As programmed, they match these words with available entities and collect the programmed ones to complete a task.
- Simplifying interactions
With Natural Language Processing, language no longer happens to be a barrier as customers interact with bots. The chatbot development process involves using NLP to simplify conversations.
- NLP-backed smart bots check common nouns and remove capitalization, so that they can recognize the user input accurately.
- AI bots with NLP continue to add new words to their vocabulary and transfer the refined stock of words to other bots.
- During chatbot development, NLP helps in understanding grammar, conjunction, and tenses to simplify conversions with customers.
- Sometimes, users use contractions while communicating with businesses. Bots are trained to expand these contractions to elicit the accurate meaning of their conversation. Besides, they remove apostrophes between words to foster better understanding.
- Advanced NLP capabilities in chatbot development include machine translations, document analysis, and distinguishing contents.
- Intelligent and contextual conversation
The secret to smart chatbot development lies in training machines to understand user intent and come up with contextual responses. While processing queries, NLP engines evaluate the following elements.
- Utterance: A user may convey a sentence in different ways. That chatbot evaluates these sentences based on the nature of utterance to generate responses.
- Intent: As we discussed, the chatbot development process involves intent identification. Accordingly, bots identify the problem statement and help the users.
- Context: Context is yet another parameter that enables bots to identify what the user is talking about.
- Session: The session records both the points during user conversations, including the initial and end points of the interactive period.
The chatbot development process involves programming responses based on the above-mentioned elements.
4 ways NLP can streamline conversations on your bot
Do you know that as much as 62% of customers prefer interacting with chatbots rather than humans? This is largely due to their instant response, accuracy, and spontaneous response.
With the majority of your audience inclining to machines, it’s time to give your chatbot development process a second thought. In case it still lacks NLP integration, you’ll soon fall behind your competitors.
Explore four ways in which NLP can streamline conversations on your chatbot to engage customers.
- Converse naturally across different languages
Before the inception of NLP, the primary hurdle for chatbots to identify user intent was the multiplicity of ways in which customers provide their inputs. Developers have worked long enough on chatbot development to train them with the human language. As a result, even system-generated responses from chatbots are contextual and you’d find them understanding emotional nuances.
Thanks to NLP, developers have succeeded in establishing a connection between human-oriented texts and system-generated responses.
NLP is capable of differentiating different types of customer requests. A personalized approach in responding to these requests significantly enhances customer experience. To be specific, chatbot development using AI enables these tools to interpret the following elements.
- Text structures
- Language semantics
- Speech phrases
- Unstructured data
- Morphemes across languages
- Abbreviations and colloquial language
- Better customer satisfaction
Instant response from online platforms and eCommerce sites is what millennials expect today. The use of NLP in chatbot development empowers these tools to analyze questions and prioritize the same based on their complexity. As a result, bots respond contextually and instantly, delivering better customer satisfaction.
Particularly, faster response from businesses goes a long way in fostering customer trust. This eventually translates to greater business volumes. Smart bots have been a trendsetter in the eCommerce sector, with established online retailers like Ubuy embracing the technology.
With dedicated bots, customers get the time and attention they deserve on your platform. Online retailers including eCommerce brands have experienced higher customer retention rates. Besides, these smart tools help in mitigating the cost and efforts involved in new customer acquisition.
- Reduce customer service costs
Isn’t it impressive to learn that businesses can slash as much as 30% of their customer service expenses by integrating chatbots?
Regardless of the industry you operate in, you’d factor in customer service costs while equating your profitability. Using NLP during chatbot development implies minimal human involvement. This helps retailers curtail customer service costs. Why not integrate AI-powered bots to carry out mundane or repetitive tasks? This approach would boost efficiency at your organization, besides streamlining workflows.
- Contextual engagement
Customers hate being redirected from one agent to the next when they reach out to your business to resolve their issues. In the worst scenario, many of them end up without support from a live agent. This bitter experience can prove detrimental to your business, leading to customer loss.
Remember, 91% of unhappy customers never come back. Using NLP in chatbot development leverages contextual engagement. This implies that smart bots evaluate the background information of the users and reply contextually. Besides, human agents get to know the context, so customers need not repeat their problems time and again.
NLP has revolutionized automated conversations, bridging the gap between human and machine-oriented communications. Thus, chatbot development involving NLP should be on the radar of proactive developers for at least the next decade.
As NLP continues to evolve, developers are experimenting with advanced technologies to enhance their amazing capabilities. With enhanced language models, sophisticated algorithms, and better semantic interpretation, chatbots will continue to replicate human responses. No wonder, eCommerce brands and businesses operating digitally can exploit the advantages of smart chatbot development.