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25 examples of NLP & machine learning in everyday life


The Team at CallMiner

April 28, 2023

NLP Machine Learning Customer experience
NLP Machine Learning Customer experience

Machine learning (ML) is an artificial intelligence (AI) technology used to recognize patterns, learn from data, and make decisions automatically — without human intervention. Natural language processing (NLP), on the other hand, is a form of AI that enables machines to interpret and understand human language. These technologies are commonly used together for an increasing number of applications, such as:

…and many others

For example, the CallMiner platform leverages NLP and ML to provide call center agents with real-time guidance to drive better outcomes from customer conversations and improve agent performance and overall business performance.

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In many applications, NLP software is used to interpret and understand human language, while ML is used to detect patterns and anomalies and learn from analyzing data. With an ever-growing number of use cases, NLP, ML and AI are ubiquitous in modern life, and most people have encountered these technologies in action without even being aware of it.

Let’s take a look at 25 examples of NLP and ML.

25 examples of NLP and machine learning

1. Sentiment analysis and emotion analysis are driven by advanced NLP. “For instance, where sentiment analysis would reveal a comment regarding your brand on social media to be positive or negative, emotional analysis would strive to determine how the poster was actually feeling when they mentioned your brand in the first place. This key difference makes the addition of emotional context particularly appealing to businesses looking to create more positive customer experiences across touchpoints.

“Evaluating sentiment along with behavioral metrics arms companies with the insights needed to identify drivers of customer satisfaction and loyalty and take the appropriate action in the moment to create exceptional customer experiences. In the contact center, sentiment and emotion analysis helps agents understand how callers are feeling and respond appropriately to make a positive impact and improve customer satisfaction. Emotion analysis is also crucial for identifying vulnerable customers and gaining insights on the most effective ways to handle vulnerable customers to achieve successful outcomes.” - The ultimate guide to sentiment and emotion analysis, CallMiner; Twitter: @CallMiner

2. NLP can filter email for spam and categorize content. “Of more than 300 billion emails sent every day, at least half are spam. Email providers have the huge task of filtering out the spam and making sure their users receive the messages that matter.

“Spam detection is messy. The line between spam and non-spam messages is fuzzy, and the criteria change over time. From various efforts to automate spam detection, machine learning has so far proven to be the most effective and the favored approach by email providers. Although we still see spammy emails, a quick look at the junk folder will show how much spam gets weeded out of our inboxes every day thanks to machine learning algorithms.” - Ben Dickson, How machine learning removes spam from your inbox, TechTalks; Twitter: @bdtechtalks

3. NLP makes chatbots feel more effective. “Say you have a chatbot for customer support, it is very likely that users will try to ask questions that go beyond the bot’s scope and throw it off. This can be resolved by having default responses in place, however, it isn’t exactly possible to predict the kind of questions a user may ask or the manner in which they will be raised.

“When it comes to Natural Language Processing, developers can train the bot on multiple interactions and conversations it will go through as well as providing multiple examples of content it will come in contact with as that tends to give it a much wider basis with which it can further assess and interpret queries more effectively.” - Mitul Makadia, 5 Reasons Why Your Chatbot Needs Natural Language Processing, Towards Data Science; Twitter: @TDataScience

4. Smart assistants like Siri, Alexa, and Cortana use NLP to understand commands and formulate responses. “It allows machines to discover patterns that they can then use to produce meaningful responses and adequate actions. For instance, it enables computers to analyse a question asked by a human to understand what data is required, then refer to the relevant source of information to obtain that data and finally, based on that information, tell a human what the weather is going to be the next day.” - What Makes Your Smart Speaker Smart?, Toppan; Twitter: @ToppanDigital

5. Search engines use semantic search and NLP to identify search intent and produce relevant results. “Many definitions of semantic search focus on interpreting search intent as its essence. But first and foremost, semantic search is about recognizing the meaning of search queries and content based on the entities that occur.

“Semantics = theory of meaning. But ‘meaning’ is not the same as ‘intention.’ The search intent describes what a user expects from the search results. Meaning is something else. Identifying meaning can help recognize search intent, but is more of an additional benefit of semantic search.” - Olaf Kopp, What is semantic search: A deep dive into entity-based search, Search Engine Land; Twitter: @sengineland

6. NLP is used for other types of information retrieval systems, similar to search engines. “An information retrieval system searches a collection of natural language documents with the goal of retrieving exactly the set of documents that matches a user’s question. They have their origin in library systems.

“These systems assist users in finding the information they require but it does not attempt to deduce or generate answers. It tells about the existence and location of documents that might consist of the required information that is given to the user. The documents that satisfy the user’s requirement are called relevant documents. If we have a perfect IR system, then it will retrieve only relevant documents.” - Chirag Goyal, Part 20: Step by Step Guide to Master NLP – Information Retrieval, Analytics Vidhya; Twitter: @AnalyticsVidhya

7. NLP is an essential element of text analytics. “Text analytics is a computational field that draws heavily from the machine learning and statistical modeling niches as well as the linguistics space. In this space, computers are used to analyze text in a way that is similar to a human's reading comprehension. This opens the door for incredible insights to be unlocked on a scale that was previously inconceivable without massive amounts of manual intervention.

“Text analytics is a perfect fit for areas in which information of value can be found buried under a large amount of less valuable information. Researchers would normally need to engage in lengthy and extremely arduous discovery processes to unearth such insights from so-called "unstructured" text. This translates to high costs and low rewards on a regular basis. Text analytics challenges such an approach by offering a viable means of doing the same without requiring human beings to pore over mountains of unstructured words, phrases and documents.” - What is text analytics and how does it work?, CallMiner; Twitter: @CallMiner

8. Product recommendations are more relevant when powered by NLP. “Product recommendations are usually keyword-based. What you type in is what you will get as a result. On the other hand, NLP can take in more factors, such as previous search data and context. These factors can help in search results being more specific.

“It also helps the retailers to keep the visitors interested by recommending the right things to them. If you show products that fit the customers' needs - it will reduce site abandonment and increase the number of purchases. Amazon has stated that the purchases made through the recommendation that their site gave increased their revenue by 35%.” - How eCommerce uses Natural Language Processing (NLP) in 2022, Brouton Lab; Twitter: @BroutonLab

9. Autocorrect relies on NLP and machine learning to detect errors and automatically correct them. “One of the features that use Natural Language Processing (NLP) is the Autocorrect function. This feature works on every smartphone keyboard regardless of the brand.

“It is specially programmed to generalize all the correct words in the dictionary and looks for the words that are the most comparable to those words not in the vocabulary.” - Antony Lia, Building an Autocorrect Feature using NLP with Python,; Twitter: @sectionio

10. NLP can help you improve customer service. “Customer interactions aren’t always about a single topic. Thankfully, natural language processing can identify all topics and subtopics within a single interaction, with ‘root cause’ analysis that drives actionability. The same goes for different customer channels.

“A fully-integrated experience management tool with natural language processing can scour everything from emails and phone calls to reviews on third-party websites, and learn where customers are finding friction – both on an individual basis and at scale – by analyzing human language.” - Natural Language Processing (NLP): A full guide, Qualtrics; Twitter: @Qualtrics

11. NLP can be used for question-and-answer systems. “Question Answering (QA) is a research area that combines research from different fields, with a common subject, which are Information Retrieval (IR), Information Extraction (IE) and Natural Language Processing (NLP). Actually, current search engine just do ‘document retrieval’, i.e. given some keywords it only returns the relevant ranked documents that contain these keywords. They do not provide a precise answer to that. Hence QAS is designed to help people find specific answers to specific questions in restricted domain.

“QA systems are classified into two main categories, namely open-domain QA systems and closed- domain QA systems. Open-domain question answering deals with questions about nearly everything such as the World Wide Web. On the other hand, closed-domain question answering deals with questions under a specific domain (music, weather forecasting etc.) The domain specific QA system considers heavy use of natural language processing systems.” - Shivani Singh, Nishtha Das, Rachel Michael, and Dr. Poonam Tanwar, The Question Answering System Using NLP and AI, International Journal of Scientific & Engineering Research; Twitter: @ijser_research

12. When you use tools like Google Translate, you’re using an NLP-powered tool. “Machine Translation simply refers to converting one human language to another based on context, grammar, etc. Google translator is the widely used software by Google to translate human languages. If you are a traveler, you must have used the Google translator app.” - Natural language processing and its use in machine translation, Q Blocks; Twitter: @blocks_q

13. Auto attendants, powered by NLP, route callers to the right agent. “When you call a business and the automated attendant at the call center picks up and asks how they can be of service, did you ever wonder how the computer can understand what you are talking about? You could say anything from, “I need my account balance,” to, “I want a refund!” and magically, the system understands what you’re talking about and gets you the help you need. That’s all thanks to Natural Language Processing, or NLP for short.” - Nick D., Infographic: How Call Centers Use Natural Language Processing (NLP), Specialty Answering Service; Twitter: @SpecialtyAnswer

14. NLP can also provide answers to basic product or service questions for first-tier customer support. “NLP in customer service tools can be used as a first point of engagement to answer basic questions about products and features, such as dimensions or product availability, and even recommend similar products. This frees up human employees from routine first-tier requests, enabling them to handle escalated customer issues, which require more time and expertise.

“AI bots are also learning to remember conversations with customers, even if they occurred weeks or months prior, and can use that information to deliver more tailored content. Companies can make better recommendations through these bots and anticipate customers' future needs.” - Kathleen Walch, 5 examples of effective NLP in customer service, TechTarget; Twitter: @TTBusinessTech

15. Companies rely on NLP to monitor social media. “You’re living in the age of big data. Take social media users as an example. In 2019, there were 3.4 billion active social media users in the world. On YouTube alone, one billion hours of video content are watched daily. Every indicator suggests that we will see more data produced over time, not less.

“There is simply too much data for you to review manually. Even organizations with large budgets like national governments and global corporations are using data analysis tools, algorithms, and natural language processing.

“By using these techniques, you can understand what people are saying about your brand right now. The ability to minimize selection bias and avoid relying on anecdotes mean your decisions will have a firm foundation. That means you will make fewer mistakes as you react to a rapidly changing world.” - 6 Steps To Get Insights From Social Media With NLP, Blue Orange Digital; Twitter: @BlueOrangeData

16. NLP empowers smarter business decision-making. “Dialing into quantified customer feedback could allow a business to make decisions related to marketing and improving the customer experience. It could also allow a business to better know if a recent shipment came with defective products, if the product development team hit or miss the mark on a recent feature, or if the marketing team generated a winning ad or not. Quantified customer feedback could also inform whether or not a consumer goods company stays or parts ways with their delivery company or if a recently implemented program to improve customer service response time is meeting its goal or not.

“A business could also learn how its customers are reacting not only to its products and services, but changes in its customers’ cultural and technological landscapes that are affecting what its customers are looking for and how.” - Dylan Azulay, Discovering Customer Experience Trends with Natural Language Processing, Emerj; Twitter: @Emerj

17. NLP can apply ‘critical thinking’ to answer open-ended questions. “Reasoning NLP is the holy grail of artificial intelligence (AI). Reasoning NLP provide the ability to apply critical thinking and answer open ended questions like ‘What happens to a person who steps off the back of a pickup truck moving at 25 miles per hour?’ The ability to reason not only requires understanding concepts like space, time, inertia and mass but, also requires understanding regional, cultural and religious beliefs. Without understanding regional, cultural and religious beliefs, an AI system could inadvertently offend or alienate the consumer base that your trying to reach.” - Mitch DeFelice, How natural language processing empowers consumers, CIO; Twitter: @CIOonline

18. Grammar checking tools like Grammarly rely on NLP to help users refine their writing. “The vision for Grammarly began with spelling and grammar correction. After achieving state-of-the-art results, we’ve stepped up to higher-order aspects of communication. With features like full-sentence rewrites, tone adjustments, and fluency, we’ve gone from syntax to semantics—from mechanics to meaning. Now we’re looking even more broadly at the steps in creating and interpreting meaning. So far, we’ve focused largely on revision. Next, we want to explore how Grammarly can assist across more stages of the communication process. Could we help people express their goals and ideas with language? Could we help them understand the intended meaning behind each other’s words?” - Yury Markovsky, Timo Mertens, and Chad Mills, How Grammarly’s NLP Team Is Building the Future of Communication, Grammarly; Twitter: @Grammarly

19. NLP can aid employee experience management. “By analyzing instant messaging between employees, NLP might identify a worker who's dissatisfied with her job. If you hope to retain the employee, you could use this information to address her concerns and entice her to stay. Or, if you fear the employee's attitude might make her more likely to commit fraud or sabotage, you could watch her closely and limit her access to certain company resources.” - Listen Up: NLP Can Help Detect Fraud Evidence in Business Data, H&CO; Twitter: @HCO_USA

20. NLP can transform recruiting and hiring practices. “According to research, making a poor hiring decision based on unconscious prejudices can cost a company up to 75% of that person’s annual income. Therefore, it’s critical to hire the appropriate personnel.

“However, deciding what is “correct” and what truly matters is solely a human prerogative. In the recruitment and staffing process, natural language processing’s (NLP) role is to free up time for meaningful human-to-human contact.

“Here the hiring processes are streamlined, valuable insights are revealed, and participants are engaged. NLP defends against information overload and inattention, allowing a high-touch hiring process to be transformed into an enjoyable virtual tour.” - An Introduction to NLP and How it is Transforming Recruitment,; Twitter: @RecruiterDotCom

21. Insurance companies can rely on NLP to detect fraud. “According to the FBI, the total cost of insurance fraud (non-health insurance) is estimated to be more than $40 billion per year. Insurance fraud affects both insurers and customers, who end up paying higher premiums to cover the cost of fraudulent claims. Insurers can use NLP to try to mitigate the high cost of fraud, lower their claims payouts and decrease premiums for their customers. NLP models can be used to analyze past fraudulent claims in order to detect claims with similar attributes and flag them.

“For example, startup company Shift Technology has developed AI and NLP-based technology to help insurers detect fraudulent claims before they pay them out. Its software, FORCE, applies a variety of AI technologies, including NLP, to score each claim according to likelihood of fraud. The company recently signed a partnership with Central Insurance Companies to detect fraudulent claims in its auto and property sectors.” - Natasia Langfelder, 3 reasons why insurers should use Natural Language Processing technology, Data Axle; Twitter: @Data_Axle

22. NLP is increasingly used to improve cybersecurity. “As the branch of AI-based deep learning that deals with the interaction between humans and computers using natural everyday language, NLP offers a wealth of capabilities to augment human ability. NLP in risk and compliance can identify overlaps in standards and frameworks and data from an organization’s tech stack, and threat feeds to identify vulnerabilities in your security infrastructure. NLP’s ultimate objective is to “read,” decipher, and understand language that’s valuable to the end-user. In CyberStrong, NLP supports the need for automation across two of the most menial processes in risk and compliance: framework crosswalking and making security telemetry actionable from a risk and compliance perspective.” - Justin Peacock, How NLP is Transforming Cyber Risk and Compliance, CyberSaint Security; Twitter: @CyberSaintHQ

23. NLP aids in healthcare diagnostics. “Healthcare natural language processing uses specialized engines capable of scrubbing large sets of unstructured health data to discover previously missed or improperly coded patient conditions. Natural language processing medical records using machine-learned algorithms can uncover disease that may not have been previously coded, a key feature for making HCC disease discoveries.” - Natural language processing in healthcare, ForeSee Medical; Twitter: @ForeSeeMedical

24. NLP can also help to alleviate clinician burnout. “Physician productivity and motivation suffer from the glut of repetitive administrative tasks that force them to spend extra hours at the computer instead of interacting with patients. This problem even has a name — the EHR burden. NLP offers several solutions to help doctors from speech-to-text transcribing technology to simplified clinical documentation management.” - Natural Language Processing in Healthcare: Using Text Analysis for Medical Documentation and Decision-Making, AltexSoft; Twitter: @AltexSoft

25. NLP aids in compliance and fraud detection in banking. “Most banks have internal compliance teams to help them deal with the maze of compliance requirements. AI cannot replace these teams, but it can help to speed up the process by leveraging deep learning and natural language processing (NLP) to review compliance requirements and improve decision-making.

“AI can also support the people in your fraud department. It can flag potentially fraudulent transactions more accurately than humans, and then, request your team to verify if certain transactions are fraudulent or not. It also generates actionable insights which can help strengthen your fraud detection efforts.” -Artificial Intelligence in Bank Fraud Detection and Prevention, SQN Banking Systems

From sentiment analysis to recruiting and hiring practices, employee experience management, social media monitoring, grammar checking tools, semantic search, and more, natural language processing and machine learning have already touched many facets of modern life. As more advancements in NLP, ML, and AI emerge, it will become even more prominent.

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