Business Process Optimization with NLP: Capabilities and Use Cases

The Essentials of NLP

A branch of artificial intelligence, NLP is a cutting-edge technology that leverages computational linguistics and machine learning models to enable computers to understand written text and spoken words — including all its sentiments — in the same way humans do it. Among other capabilities are translating texts from one natural language to another, rapid text analysis and summarizing, automatic text generation, and responding to voice commands.

One of the key phases in NLP functioning is data processing. In order for a computer to clearly understand and interpret key meanings, the text should be “cleaned” and prepared. Here are the techniques used to do that and further process the text:

• Tokenization: fragmenting text into small parts (tokens); for example, phrases or words
• Stop words elimination: removing the words that do not bring meaningful contribution (articles, conjunctions, prepositions)
• Stemming and lemmatization: grouping words based on their roots
• Part-of-speech (POS) tagging: setting grammatical categories such as noun, verb, adjective, and adverbs to clarify the relationships between text parts
• Syntactic parsing: using grammatical rules to identify the sentence structure and hierarchy
• Named entity recognition (NER): detecting and grouping entities like personal names, dates, organizations, locations, and more
• Sentiment analysis: identifying emotions and sentiments in a text (positive, negative, neutral)
• Text summarization: analyzing the whole text and preparing a short summary of the key ideas.

Aside from all the techniques used to power the analysis, these mechanisms also need continuous training on large amounts of data to perform the tasks according to the specifics of the business. 

NLP Use Cases in Business

NLP offers many powerful capabilities that enable computers to effectively process unstructured, text-heavy data and analyze it to deliver valuable insights. Such findings are further used by businesses to solve tailored, domain-specific tasks.

Customer Support

Customer demands are becoming higher with each passing year, and here is the proof. According to Zendesk, 60% of clients expect a business to meet all their needs, even the most sophisticated ones. Moreover, 61% of respondents say they would go to a competitor after one poor experience. With this in mind, investing additional resources to revamp customer service and support certainly seems like a reasonable — if not essential — idea, and here is where technologies like NLP can help.

Namely, NLP solutions process customer interactions to assist businesses in surfacing what people like and dislike about their products and services. All possible gaps and issues can be found through live messaging, phone call recordings, service tickets, and social media chats. With such findings in place, companies can make relevant changes in their work, notably enhancing customer experiences.

On top of performing customer feedback analysis, NLP serves as an intelligent virtual assistant. Trained on massive amounts of data, including industry-specific language, NLP-powered chatbots can handle simultaneous customer queries in a tailored way. From the first points of engagement that include answering about products and services to recommending similar items and delivering instructions — chatbots are here to assist. For example, Belfuis, a Belgian insurance bank, uses an AI-powered assistant to classify clients’ claims — either to address them personally or transfer to the suitable human agent. The chatbot helped the company slash call volume by 20%, reduce response time, and get better visibility into potential knowledge gaps.

Benefits you’ll receive: Accurate and fast responses, 24/7 support, positive tone of communication, personalized approach — all this leads to improved overall productivity in the support center, higher customer satisfaction, and decreased churn rate.

A case in point: Infopulse helped a large gas and oil company speed up the procurement process with a smart chatbot. Underpinned by an advanced NLP platform, an intelligent AI assistant automates 75% of all responses, provides 24/7 support, and enables 10 times better effectiveness compared to human experts.

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Social Media Monitoring

Social media provides a vast layer of knowledge about what customers think and want, and NLP effectively uses it to extract actionable information for businesses. By analyzing reviews, comments, threads, and other sources, NLP can gauge customer sentiment, i.e., detecting and categorizing customer opinions expressed in text or in an audio/video. Advanced social media analysis can also help you with predicting trends, analyzing competitors, and evaluating brand recognition.

To wit, Breadfast, an online eCommerce platform, capitalized on social listening monitoring to:

• Monitor online conversations and immediately address customer complaints
• Analyze client sentiments and revamp their product offerings
• Identify market trends and get a jump on competitors

Benefits you’ll receive: Brands can detect complaints and proactively manage their reputation, as well as surface trends and common topics of interest for further product and service recommendations and improvements.

Recruitment

Hiring new employees has never been cheap. According to SHRM, the average cost per hire is about $4,700, with the total cost being three or even four times the salary associated with the post. So, if you recruit a person getting $70,000, you might spend $210,000 just to fill that position. NLP, with its intelligent resume scanning ability, helps you automate and refine the candidate search process, effectively reducing the amount of resources spent per position.

Such a mechanism can analyze hundreds or even thousands of candidate profiles without any human intervention to extract the relevant information and find the most suitable candidates. Powered by NLP, the system also automatically ranks the candidates according to the pre-defined parameters and gives recommendations on each of them.

One of the examples is Celential.ai. This tool identifies which skills a candidate has, even if they don’t publicly post them. This is done through the analysis of the tech stack of the organizations they have previously worked for and the skills enumerated on their coworkers’ profiles.

Benefits you’ll receive: The automation of the recruitment process with NLP brings businesses better efficiency, enhanced accuracy, and slashed costs.

Market Research

The amount of data created year over year is enormous. For example, it is projected there will be 181 ZB of data by 2025, and a user will need over 180 million years to download all the data from the internet. Sounds shocking, doesn’t it? Data structuring, processing, and analysis have never been a piece of cake, and now as the amount of data increases, companies need more sophisticated mechanisms to handle it all effectively.

NLP solutions can help in this regard. When underpinned by natural language processing, you can analyze thousands and even millions of different sources for your in-depth market research. This might include surveys, reviews, social network posts, and reports. The findings can be further used to uncover industry trends, analyze competitor strategies, identify customer needs, explore market segments, and more. MonkeyLearn is one of the NLP tools that run deep market analysis and deliver actionable insights that are instantly visualized. The platform turns reviews and surveys into business improvements, helping generate more leads.

Benefits you’ll receive: By understanding better the needs of clients, you will be able to come up with hyper-personalized offerings, increasing customer satisfaction. Deep knowledge of your competitors will be instrumental in optimizing your strategies, giving you a competitive edge.

Legal Document Analysis

Another field where NLP perfectly fits in is legal document processing. Automatic AI mechanisms navigate through huge volumes of legal documents to scan and retrieve the relevant information, all while enhancing accuracy and efficiency for human workers. NLP use cases around legal document data analysis include:

• Named entity recognition: extracting document type, aliases, parties, addresses, and dates from contracts
• Legal relationships extraction: automatically identifying relationships between parties, whether they are mentioned by their names or aliases
• Legal clause identification: accurately defining different types of legal clauses and extracting the key information on clauses
• Legal de-identification: anonymizing and masking sensitive data in documents and images.

Amazon Comprehend is an example of how NLP is used to redefine document processing. This service automatically extracts insights from legal briefs packets like court records and contracts as well as redacts personally identifiable information (PII).

Benefits you’ll receive: Money and time savings, utmost accuracy, faster document processing, new levels of productivity — the benefits of NLP contribute to any legal business’ digital transformation.

NLP Advancement: Large Language Models

With the course of time, NLP has developed to address more complicated business tasks. One of the most exciting know-hows in this field is large language models (LLMs). On top of understanding human-like language, they are able to generate it as well.

To accurately perform complex language tasks such as chatbot conversations, new content creation, and language translation, these models are trained on massive sets of data. Besides applying additional computing resources, this might also mean leveraging extensive ML expertise from a third-party service provider. The allure of LLMs is that you can train one model to perform multiple NLP tasks — no need to create several separate models. Such “transfer learning” helps significantly improve the performance of NLP models.

Generative Pre-Trained Transformer Models

Commonly referred to as GPT, these models are able to complete tasks like writing texts, answering questions, and creating original content. Articles, poetry, even computer code — GPT models can compile all these artifacts in a strikingly similar way to those made by human counterparts.

Among the examples of such models is OpenAI’s ChatGPT, which makes use of NLP techniques to deliver automated responses to users, whether it is a simple question or a deep conversation. Empowered by contextual language modeling, ChatGPT analyzes not just current messages, but the entire context of a conversation before providing a response, which makes it sound more natural, coherent, and personalized when compared to other models. By learning from past responses, this conversational agent can generate even better responses over time.

High accuracy is achieved through a 175-billion parameter transformer-based neural network that enables ChatGPT to handle complex language constructs. Also, ChatGPT can be customized for your particular industry, completing customer inquiries more efficiently and in real time.

Github Copilot is another innovative LLM that can drive business value through automation. Trained on all languages appearing freely in public repositories, Github Copilot helps developers write code by making autocomplete-style suggestions. This is done through analyzing the context in your files and your natural language comments describing what the code should do.

Bidirectional Encoder Representations from Transformers Models

Another type of LLMs, BERT, is designed to interpret ambiguous language or predict a word in a blank by using left-to-right and right-to-left contexts. This is exactly how the Google search mechanism works, suggesting to us words and phrases for auto-completing queries. Trained initially on English-version Wikipedia and the Brown Corpus, BERT now continues learning in an unsupervised manner from unlabeled texts.

Robustly Optimized BERT Approach (RoBERTa) and A Lite BERT (ALBERT) are BERT variants used to perform NLP tasks with optimized performance, and while maintaining high accuracy. Other BERT model examples include ELECTRA (Efficiently Learning an Encoder that Classifies Token Replacements Accurately), XLNet, and DistilBERT.

Where Do We Go from Here?

With the emergence of LLMs, natural language processing has gained even more popularity, resulting in benefits like enhanced efficiency, business workflow optimization, and improved client experience. However, the process of obtaining them is no one-day venture, but a full-cycle project that will require teaming up with a seasoned provider of NLP solutions. Such an expert will perform a detailed analysis of your business case and IT infrastructure, create a robust NLP implementation strategy, build out a new or refine an existing NLP system, and provide continuous support.