Using AI to handle human interactions is a well-established field. Natural Language Processing presents some stimulating and unique opportunities. With this, users can glide through a large proportion of new data and use it for optimizing cost and boosting outcomes. Businesses and public organizations have been applying machine technologies and data science learning for a while. And, NLP is one of the quickest-evolving technologies in AI.
A report by Statista shows that the NLP market is going to increase to 43.9 billion dollars by 2025. But why is Natural Language Processing becoming so popular with time and how can it help businesses? These kinds of questions are largely common. In this article, we are going to take a look at the beneficial use cases of NLP.
What’s Natural Language Processing?
Natural Language Processing comes under the artificial group of technologies that includes machine learning, too. It is a technology that is used by the computer to read, translate, or summarize any text, regardless of whether it is in audio or visual form, to figure out what it implies.
Through NLP technology, a computer can understand natural or human language. NLP uses semantics and syntax to help systems and computers think like humans depending on the data they receive, then give a relevant response. Think how sites like Google can give you relevant results even if the search term is not phrased or spelled correctly- that is NLP at work there.
To provide users with the best browsing experience, Google uses Natural Language Processing to identify the intent or context of search engines. This means that your digital marketing team can use NLP marketing campaigns to ensure that your messages are reaching out properly to the target audience- organizations of people who will benefit from the content, service, or product.
If you are looking for data integration services to implement NLP in Marketing, look no further than DATAFOREST data integration services. We can help you to connect all the platforms together to have a seamless data flow in your marketing campaigns.
We follow the below steps to do so:
- Connect all external data sources using the API integration with your external suppliers, delivery services, e-commerce stores, physical stores, etc.
- Then comes the ETL process where we clean the data so that you can get the best available information and derive actionable insights from the same.
- Then with the help of data from your HR, ERP, and CRM systems, DATAFOREST team integrates the internal data sources using Natural Language Processing.
- With proper configuration done by our team, you get real-time information. We have the NLP capability to process terabytes of data within seconds to make quick decisions.
To get the best data-driven marketing for your organization, contact DATAFOREST team today. Our Natural Language Processing solution will help you optimize the customer base to get user personas and what they are likely to buy next.
What are the Tasks of NLP?
Many NLP tasks involve semantic and syntactic analysis, used for breaking down human language into chunks that are machine-readable.
The syntactic analysis also called syntax or parsing analysis identifies the syntactic structure of a text and the relationship dependency between words, displayed on a diagram known as a parse tree.
The semantic analysis in Natural Language Processing emphasizes identifying the language’s meaning. Nevertheless, since language is ambiguous or polysemic, semantics is taken as one of the most challenging areas in Natural Language Processing.
Below we are listing the sub-tasks of both syntactic and semantic analysis in Natural Language Processing.
1. Speech Recognition
Speech Recognition Tech uses NLP to transform spoken language into a format that machines can understand. Technology is an important part of virtual assistants, such as Alexa, Siri, Watson Assistant, and Google Assistants. IBM’s Watson Natural Language Understanding is a perfect example of extracting sentiments in a quick manner.
2. Part of Speech Tagging
Part of speech tagging includes adding a part of the category to every token within a text. A few common PoS tags in Natural Language Processing are nouns, adjectives, pronouns, prepositions, conjunctions, intersections, among others.
PoS tagging is used to identify relationships between words and thus, learn the meaning of the sentences using NLP.
3. Word Sense Disambiguation
Word-Sense Disambiguation is a task to identify the right meaning of a word using unstructured data. For instance, in the sentences, ‘The dog was barking at the mailman’ and ‘The barks of trees are used as machine’. Both sentences use ‘bark’ but have a different meaning. Word-Sense Disambiguation is crucial for tasks, like question answering in Natural Language Processing.
4. Named Entity Recognition
One of the most well-known tasks in semantic analysis is Named Entity Recognition, which includes the extraction of entities from within a text. These entities are names, places, email addresses, organizations, and more.
For instance, the sentence, ‘Sam gave Cecilia two oranges at school on Monday’ will be changed to ‘[Sam] name gave [Cecilia] name [two] number oranges at [school] organization on [Monday] time. When it comes to Natural Language Processing, NER is an important topic in fields, like knowledge representation and information retrieval.
5. Coreference Resolution
It is another essential Natural Language Processing task. Conference Resolution is the task to find all expressions, which refer to the same entity present within the text. It is a crucial step for multiple higher-level natural language processing tasks, which include natural language understanding, such as information extraction, question answering, and document summarization.
6. Sentiment Analysis
Understanding natural language is especially difficult when it comes to opinions since humans often use irony and sarcasm. However, Sentiment Analysis can recognize subtle nuances in opinions and emotions - and decide how negative and positive they are.
When sentiments are analyzed in real-time, it allows you to monitor mentions on social media, get a sense of how customers feel about the company, and gauge the reactions to the latest marketing campaign or product launch.
7. Natural Language Generation
In language generation, a learning model is trained with text corpora that can predict the new text, which follows. For instance, language generation can output a whole new science fiction story with the help of existing science fiction stories for the purpose of training.
How does NLP Work?
Natural Language Processing helps computers understand natural language like humans do. No matter whether the language is written or spoken, NLP uses AI to take real-world input, process it, and then make sense in a way that the machine can understand. A computer has programs to read or microphones for collecting voice data, just like humans have different sensors, such as eyes to see or ears to hear. Like humans have brains for processing that input, computers have programs for processing the respective inputs. During the processing time, the input is changed to code, which the computer can understand.
Looking to develop a platform to understand user journey for your marketing team using Natural Language Processing? DATAFOREST team has done all the hard work for you. We will help you with the following:
- Monitor the time of the day when customers are most likely to purchase your product.
- Execute cross selling and check sales history.
- Give personalized offers to every customer to increase sales.
- Integrate your platform with popular online e-commerce websites.
To understand more about NLP marketing use cases, check out this case study by DATAFOREST for data-driven marketing.
What are the Natural Language Processing Methods and Techniques?
Here are the methods and techniques of Natural Language Processing.
Syntactic analysis analyzes syntax. It is also called parsing analysis in Natural Language Processing. The primary aim of the step is to extract dictionary-like or precise semantics from the text. This NLP technique compares the text to standard grammar rules for determining its meaning.
Now, let’s check out the different syntax analysis techniques used in Natural Language Processing.
- Parser Concept
It is a concept that is used for carrying out the parsing process in NLP. The software takes input data and changes it into a structural representation after it verifies it for valid syntax with the help of formal grammar. The Parser Concept creates a data structure that can be a parse tree, a hierarchical structure, or an abstract syntax tree.
- Word Segmentation
It identifies word boundaries in a given text where one word ends and another word begins. Often, words are marked as the last word in the sentence with a punctuation mark. The NLP marketing technique takes a string of text and derives word forms from it. So, when a person is scanning a handwritten document on a computer, the algorithm will analyze the page and detect the words which have been separated using white spaces.
- Sentence Breaking
It includes sentence boundaries in large texts. For instance, the NLP algorithm is fed the text, ‘The car honked. I woke up’. The algorithm can analyze the periods, which splits up the sentence using the technique of sentence breaking.
- Morphological Segmentation
It is a Natural Language Processing technique that divides words into small parts known as morphemes. The word untestably is going to be broken into ‘un’, ‘test’, ‘able’, and ‘ly’ by the algorithm as morphemes. It is particularly useful in speech recognition and machine translation.
The stemming technique in NLP can divide words with inflection in them to their root forms. For instance, in the sentence ‘The dog barked’, the algorithm identifies the ‘barked’ word’s root as ‘bark’. It is useful if you analyze the text for all instances of the word ‘bark’ along with the conjunctions. The algorithm identifies that they are the same word even if the letters are different.
Semantics includes the use of and meaning behind a word. NLP marketing applies algorithms for understanding the structure and meaning of sentences. You can find all Natural Language Processing Semantic techniques in the research paper here. The different semantic techniques include-
- Word Sense Disambiguation
It derives its meaning from a word depending on the context. For instance, you should consider the sentence, ‘The pig is sleeping in the pen.’ The word ‘pen’ has a different meaning. An algorithm that uses the method will know the word ‘pen’ refers to a fenced-in area and isn’t a writing implement.
- Named Entity Recognition
It puts words into groups. An algorithm that uses this method can analyze the news article and recognize the mentions of a certain product or company. Using the text semantics in Natural Language Processing, it will be able to differentiate between entities, which are visually the same.
- Natural Language Generation
It uses a database for determining the semantics behind words and then generates new texts. An algorithm will be able to write the summary of findings automatically from business intelligence platforms, mapping specific phrases and words to features of the data in the business intelligence platform.
If you are looking to understand current potential of your business using Sentiment analysis, text classification, or text extraction, data science experts from DATAFOREST can help you get the most from your data. This is done by extracting important topics, parsing PDF docs, recognizing client intent, and more.
What are the Benefits of Natural Language Processing?
NLP marketing is crucial for businesses since it helps organizations gain insight into their brand customer satisfaction, spam detection, and brand performance. Here we are going to list the benefits of Natural Language Processing.
- Immediate Customer Service
With NLP marketing, organizations can respond to customer queries helpfully and quickly. This is vital for any business these days. Thus, every company should be aware of the opportunities that NLP systems provide, including chatbots for customer support.
AI is being used to leverage power chatbots that largely play the role of customer support to learn, understand, analyze, and respond to customer support 24/7. Chatbots are helpful if you have to engage with a larger audience and you have limited resources. You might see a seasonal spike or have to engage with your clients proactively, so they will not need to get in touch with customer support.
- Save Time and Money
Cost plays a significant role in the success of a business. If you are able to find ways of reducing expenses and improving the overall efficiency of your business, you can work on the profit margin.
With Natural Language Processing enabled chatbots, you will be able to automate all repetitive tasks without any hassle. It will eventually reduce the time and cost of your business. Chatbot Magazines study shows that you can reduce the customer service cost by up to 30% simply by implementing automotive predefined tasks with the help of NLP marketing.
To save enough time and money for business owners, DATAFOREST team provides end-to-end web app development services to develop both B2C and B2B platforms. We can build enterprise web apps, analytics dashboards, or web portals as per your requirements. Leading companies from across the world are using DATAFOREST web development services to have responsive web apps for their customers.
- Improves Conversion Rate
Conversion optimization is a crucial component of a marketing campaign. Tools and technologies are just a part of a story in terms of conversion rate optimization. Tuning the conversion funnel is not simply about implementing more tools and implementing them blindly. It’s all about using customer insights and data analytics for improving the website’s flow, thereby increasing the conversion rate.
The NLP solutions supported by tools, such as modern search functionalities and chatbots helps in improving the overall customer experience for the service, finally letting you transform your site visitors into customers.
- Perform Large Scale Analysis
NLP technologies can help sentiment analysis guide at any scale, offering insight into the structure and content of the document, emails, internal systems, online reviews, social media data, and other sources.
If you are facing issues with handling large and complex systems, DATAFOREST DevOps services can help you easily transform and scale your business. We can do the entire infrastructure audit to improve the performance and reduce overhead costs using Natural Language Processing. Clients are using our Performance troubleshooting services to early detect any bottlenecks and stability issues in their systems.
How can Natural Language Processing Help with Marketing?
Sentiment Analysis: Marketing techniques and tools used for social tracking and social listening will help marketers decide what people are writing on social media and what they feel about the brand. However, NLP marketing makes it easier for software to determine written opinions and learn about the intentions of the users.
SEO Optimization Tools: There are many SEO tools that don’t just help the user find out the phrases with more potential but also offer additional tips on competitors and NLP marketing techniques to outpace them.
To make the best out of NLP, you can utilize the data scraping services offered by DATAFOREST. It will help you in the following manner:
- Insights and Market analysis: Project managers can get all the information they want in a well-structured format. Just specify the type of information you want to be scrapped, and we will do it.
- Inventory management: Get real-time inventory information at your fingertips from all your stores. We can scrape any e-commerce platform to bring instant data like stock status, SKU, total sales, price per item, and more.
Applications of NLP for Marketing
It is crucial to measure brand awareness while planning for communication and content strategy for your product service. Human language is complicated and solutions based on older technologies to learn what customers really think about an organization is hard without NLP marketing.
Get the best out of data-driven marketing to create excellent brand positioning in the market by using qualitative data and relative insight. DATAFOREST services can help you improve the overall success of your marketing campaigns by scraping relevant information from any source like YouTube, TikTok, Instagram, Facebook, etc.
- Content Strategy
Using the right SEO tools, content team, marketers, and SEO specialists will be able to gauge if a website is visible and well-optimized in the browser for prospective customers. There are different ways to send the same message and NLP marketing technology can be used for identifying related or alternative keywords, which can be used for creating high-quality content depending on the searches of the internet users. Watson discovery is an AI platform to find automated answers for common questions.
- Customer Experience
With the help of NLP marketing systems and deep learning technology, you can collect useful data efficiently. With added business information, marketing software will offer a better customer reach with new ads and content. When you have access to specific data, it becomes much easier to create a more accurate buyer persona. Thus, it improves the accuracy of ad targeting using Natural Language Processing.
NLP marketing empowers analytics by offering important information. With this, organizations measure customer experience and enhance recommendation systems. The technology is very useful in customer service since it can be used to analyze incoming support tickets to be sent to the right support executive.
Data science experts from DATAFOREST can help you improve customer experience using NLP and machine learning services. Create the best CX with visually appealing dashboards from DATAFOREST experts.
- Targeted Lead Generation and Qualification
NLP advancements have changed businesses by offering enhanced chatbots. Chatbots are used by organizations to attend to various queries by customers every day. A Hubspot survey shows that 47% of the customers take a keen interest in purchasing products through bots. They quickly respond to the questions of customers and engage them in the process of purchasing a product or service. NLP marketing systems cannot just handle queries but also collect crucial details about visitors and analyze the needs and moods of customers for targeted lead generation.
NLP marketing chatbots make sure that the potential customers get immediate attention to make sure that quality leads are passed on that have a higher chance of being successful. You can take help of a price tracking platform developed by DATAFOREST to set up data crawling from any of your competitors. This can help you extract insights if the price drops or increases for any item.
Conclusion: Why should you adopt NLP for your Marketing Strategy?
So now you know what the real-time applications of NLP in marketing are. As we have listed everything for you on how to target lead generation, marketing, branding, and content strategy using Natural Language Processing, it’s time now to adopt it in your business.
As marketing strategies are becoming largely customer-oriented, businesses are adopting language analytics for extracting insights about customer intentions, motivations, buying journey, and more, from large quality data sets. By tapping into Natural Language Processing technology, your company will be able to better understand how the potential and the current clients perceive the company. The information you get by processing NLP tools for customer support will help your company to identify and rectify the recurring problems.
To get insight into customer behavior patterns, you can take the help of DATAFOREST services. We offer data-driven solutions, evolving efficiency with cost reduction and revenue growth. With the help of our web scraping services, you can extract and analyze valuable product and business information from more than 500 million web pages every day.
Get in touch with us today to transform your marketing campaigns with NLP and ML technology. NLP Marketing can help you excel in your business, particularly if you take on a targeted approach.
Why is NLP Important?
Natural language processing enables computers to communicate with humans in their language and scale other tasks relevant to language. It makes it possible for the computer to hear speech, read text, measure sentiment, interpret it, and decide which parts are important.
What are the Main Functions Of Natural Language Processing Algorithms?
Some of the main functions of NLP algorithms are-
- Text extraction
- Text classification
- Natural language generation
- Machine translation
What are the Major Challenges of NLP?
There are a few challenges of NLP marketing that users should be aware of.
- False positives or uncertainty can be difficult for NLP to detect
- It takes more time to develop an NLP system
- Machines find it difficult to identify words with multiple meanings
- Learning deep meaning words is difficult without using neural networks
- Misused or misspelled words can lead to problems in text analysis
- Idioms, informal phrases, and expressions present many problems for NLP
What is Natural Language Processing used for?
Natural Language Processing is good at handling nuances of natural or human language, such as informal terms, spelling errors, or regional accents, letting machines perform tasks as accurately as possible.
In what real-world applications the main functions of NLP are used?
NLP Marketing has many practical uses in real-world applications, including search engines, marketing, medical research, and corporate intelligence.
Nevertheless, there are other use cases of speech recognition in real-world applications. For instance, by adding speech-to-text abilities to business software, businesses are able to send emails, transcribe calls, and also translate automatically.
NLP has Sentiment analysis that can be done periodically and learn what the customers like or dislike about certain aspects of the business. These insights will help in making smarter decisions.
What is the evolution of Natural Language Processing?
NLP has origins in linguistics, which has been around for over five decades. Natural Language Processing was not born overnight and the research started in the 1950s. Today, it has become an integral part of many applications and continues to evolve with time.