Breakthroughs in technology that incorporate linguistics with artificial intelligence (AI) have paved the way for better human-machine communication. Think of how virtual assistants, like Apple’s Siri and Google Assistant, have improved over the years – from starting out with following simple commands to calling businesses to set appointments. These achievements are made possible by natural language processing (NLP) models, and their potential is still evolving.
What is Natural Language Processing (NLP)?
NLP is the subset of computer science that trains machines to understand human language -spoken and written – the way humans do. People’s conversations, while not always straightforward, sound natural because humans are able to respond intuitively to each other. Most people understand language cues, pauses, cultural nuances, and the various meaning behind words and phrases.
In the early days of computer linguistics modeling, AI was not able to keep up with human-generated text. But the more the training data was refined, the more the algorithms understood how human conversations work. In 2020, OpenAI developed the revolutionary Generative Pre-trained Transformer 3 (GPT-3), an NLP model which uses deep neural networks to achieve near-human levels of categorizing linguistic information.
This feature allowed AI to process information the way humans do, leading to more natural-sounding chatbots and digital assistants. Because of ever-advancing NLP models, AI tools can now write articles to mimic human journalists and solve complex customer queries or requests.
Advantages of using NLP for customer service
NLP-based chatbots
Customers now expect 24/7 assistance across all industries, from banking to e-commerce to healthcare. To meet this growing demand, companies are quickly developing customer service chatbots that are re-programmable. These tools can field basic queries as well as execute commands, such as booking appointments or flights. Additionally, these chatbots can be trained to understand industry jargon and respond to sector-specific questions and requests.
These features enable firms to save on labor costs and retrain their human agents to focus on higher-value customer relationship management, which can lower employee burnout. According to market intelligence firm Juniper Research, consumer retail investments in the global chatbot market are expected to reach USD $142 billion in 2024 from only USD $2.8 billion in 2019.
Timely customer sentiment analysis
Sentiment analysis or opinion mining is the process of identifying and categorizing emotions behind texts to gauge customer satisfaction and feedback. For example, a food manufacturing company might want to deploy NLP algorithms to scan social media sites, such as Facebook and Twitter, to understand people’s sentiments toward their newly released products.
Some algorithms can even recommend related products based on a person’s feedback, and bots can be trained to respond in real time to negative comments or complaints. Being able to successfully manage client sentiments can help companies avoid reputational losses, which are often more difficult to overcome than financial ones.
Personalized marketing that enhances customer relationship management
NLP capabilities can also be leveraged to enhance customer experience. Most people appreciate customized experiences that target their needs. For instance, online shoppers might like to have a digital shopping chatbot that can help them decide which product or service to purchase. Additionally, by analyzing social media, keywords, and browsing behavior, marketers can create highly targeted campaigns that are relatable and engaging to their target clients.
Analyzing brand mentions can also help marketers target prospects and understand emerging trends. This way, customers can have personalized landing pages or curated products on their feeds that best correspond to what they’re looking for and their demographics. Based on a 2022 survey from marketing firm Epsilon, 80 percent of consumers expect personalized experiences from retailers.
Challenges and Future of NLP
While NLP has improved dramatically over the past five years, there are still some limitations. The primary difficulty is that machines still can’t perfect human social cues, such as sarcasm, irony, or frustration. Accents and cultural nuances are yet another challenge, as algorithms are still trying to figure out how one word can have multiple meanings to different communities.
Nonetheless, experts are working hard to improve the training data for NLP models, including ensuring that machines are able to accurately identify hate speech in order not to use them. Additionally, algorithms are being taught to understand high-level concepts and abstract thinking to keep up with open-ended conversations.
How AI Superior can help
At AI Superior we enable companies to develop and implement end-to-end NLP and artificial intelligence solutions, including NLP based chatbots, and social media data analysis tools to enhance customer service processes and facilitate marketing activities.
Get in touch with us to find out how we can create a tailored solution that addresses your business needs
English 
German 
Arabic 
French 
Dutch 
Spanish