What is NLP and Why is it Important for Business?
Exploring the Evolution, Applications, and Benefits of Natural Language Processing in Business.
Natural Language Processing (NLP) is a hot topic these days, especially with the rise of large language models (LLMs) and generative AI (GenAI).
But, there’s much more to NLP than these recent advancements.
Today, it also delivers significant business value through a range of applications: chatbots and sentiment analysis are some of best known examples, but there are many more.
So. let's explore what NLP is and why it's crucial for modern enterprises.
What is Natural Language Processing?
Natural Language Processing (NLP) is a branch of AI (combining computer linguistics with machine learning) that allows computers to understand, interpret, and act on human language.
“Natural language” refers to the way humans naturally communicate with each other, which includes spoken and written forms.
Although we typically take for granted our ability to easily understand natural language, this task has historically been very challenging for computers due to the complexities and nuances involved.
What makes NLP so difficult?
Lets look at some examples of what makes language so complex.
Note: this is not meant to be an exhaustive list, just to give an appreciation for the difficulty of the task from a computing point of view.
Ambiguity: the word "predictable" can have different connotations based on context. In a car review, "predictable" is positive, suggesting that the car handles in a reliable and expected manner. However, in a movie review, "predictable" is most definitely negative: implying that the plot lacks originality.
Idiomatic Expression: phrases like “spill the beans” or “break the ice” are not meant to be taken literally, these caused early NLP systems difficulties.
Variations in Language Use: consider all the ways in which we can greet someone “hello”, ‘hi”, “hey”, “good afternoon”, etc.
Context dependance: in the sentence “the chicken is ready to eat”, it’s not clear if the chicken is ready for its meal, or if the chicken is the meal.
With this in mind, we can start to understand some of the complexities at hand, and understand why some of the early rules-based systems would struggle.
History of NLP
Key takeaways: by examining the diagram above we can start to appreciate that.
NLP has progressed significantly due to the increasing availability of data and the development of more complex models.
Initially, rules-based systems from the 1950s to the 1980s struggled with the complexities of language because they relied on manually encoded rules.
In the 1980s and 1990s, statistical methods improved NLP by using probabilistic models, but it was the explosion of internet data in the 2000s that really accelerated progress.
Machine learning algorithms could now train on vast datasets, leading to better text classification and sentiment analysis.
The deep learning era from 2010 to 2017 saw neural networks making substantial advances in language understanding.
Today, large language models (LLMs) like GPT and BERT leverage enormous datasets and advanced computational power to achieve unprecedented accuracy in generating and understanding human language.
This history shows that more data and more sophisticated models have been crucial for overcoming the inherent challenges of NLP.
One important point to make
Although the latest technologies (i.e. LLMs) are more advanced (across a wide range of areas), they are not always the most suitable for all tasks.
The final decision on what best suits any given use case ultimately depends on a range of factors, such as diversity of the language, training data available, computing resources, data privacy and cost.
Benefits and Applications of NLP for Business
NLP’s importance to business cannot be overstated: its applications span various sectors, driving efficiency, enhancing customer experiences, and unlocking new insights from data.
While we highlight some specific business areas, it’s crucial to understand that NLP can be applied wherever a business uses data in an unstructured or natural language form. This includes emails, customer reviews, social media posts, chat logs, support tickets, contracts, reports, survey responses, and more.
Essentially, every business interacts with multiple forms of natural language data.
But NLP techniques can also benefit structured data, particularly when it is a high-dimensional form.
For example, applying NLP techniques such as word embeddings to high-cardinality categorical data can uncover patterns and relationships that traditional methods might miss.
In short, the applications and business value of NLP are widespread.
Next. we examine some of the key business areas that are common across industries and show how NLP can have an impact:
1. Enhanced Customer Service
Customer service is a critical aspect of any business, often associated with challenges such as long wait times, repetitive inquiries, and the need for round-the-clock support.
NLP can significantly enhance customer service in various ways, particularly when dealing with unstructured data in natural language form.
Examples:
Using chatbots to handle basic customer inquiries
Automating the categorisation and prioritisation of support tickets
Providing real-time language translation for international customer support
Extracting key information from customer emails for faster response
Offering self-service options through automated FAQ systems
Analysing customer interactions to identify service improvement areas
Personalising responses based on customer history and preferences
2. Operational Efficiency
Within any business there are numerous routine tasks that involve interacting with natural language text: emails, documents, etc.
Not only are these tasks time-consuming, they’re also prone to human error.
NLP can automate these processes (or provide guidance to humans), reducing the time employees spend on them and improving overall productivity.
Additionally, these tools can help to maintain high levels of consistency across different teams, which is crucial for high operational standards.
Examples:
Drafting standard reports based on data inputs
Managing and categorising large volumes of text (such as emails) efficiently
Extracting and organising data from scanned documents
Automating the transcription of meeting notes
Automating routine administrative tasks
Automatically summarising lengthy documents for quick review
Generating automated responses for common internal queries
Enhancing search functionality within enterprise databases
3. Market and Competitive Intelligence
Keeping up with market trends and competitor activities is essential for strategic planning and staying competitive.
But, the volume of information available can be overwhelming: NLP can analyse vast amounts of text data from a variety of sources: market reports, news articles, and social media to gain actionable insights.
This helps businesses make informed decisions and stay ahead of their competitors.
Examples:
Monitoring social media for competitor activity and customer sentiment
Tracking keyword trends and search engine results for market shifts
Extracting insights from industry reports to inform strategic decisions
Analysing news articles to identify emerging market trends or competitor strategies
Monitoring patents and publications for technological advancements
Conducting sentiment analysis on financial news to predict market movements
4. Improved Data Analysis and Enrichment
Businesses generate vast amounts of unstructured data daily, which can be difficult to analyse using traditional methods.
NLP can sift through this data to extract meaningful insights that might otherwise go unnoticed.
By automating the analysis of reports, emails, and other documents, businesses can make crucial decisions faster
Examples:
Extracting key information from customer feedback and reviews
Analysing email communications to identify common themes and issues
Enriching databases with structured information extracted from unstructured sources
Transforming unstructured survey responses into actionable insights
Automating the extraction of data from invoices and receipts
Enhancing CRM systems with enriched customer profiles
Identifying trends and patterns in historical data
Converting raw data into visualisations for better comprehension
5. Personalised Marketing and Customer Insights
Understanding customer preferences and behaviour is crucial for effective marketing.
NLP helps businesses gain deeper insights into their customers by analysing their interactions, preferences, and feedback.
This allows the creation of highly personalised marketing campaigns and improved product recommendations, which enhances customer engagement and loyalty.
Examples:
Analysing customer reviews to understand preferences and pain points
Personalising email campaigns based on customer interaction data
Improving product recommendations through detailed analysis of customer behaviour
Tailoring website content to individual user preferences
Segmenting customers for targeted marketing campaigns
Creating dynamic and personalised advertisements
Analysing social media interactions to gauge brand sentiment
Customising loyalty programs based on customer purchasing patterns
6. Enhanced Compliance and Risk Management
For industries heavily regulated by compliance requirements, ensuring adherence to regulations can be a complex and time-consuming task.
NLP can play a crucial role in automating the analysis of communication and documentation to ensure compliance.
By identifying potential compliance breaches and managing risks, NLP helps businesses avoid costly penalties and maintain regulatory standards.
Examples:
Scanning financial documents for compliance with regulatory standards
Monitoring external sources for updates to regulations or policy changes and alerting relevant departments to take action
Analysing legal documents to ensure compliance with industry regulations
Automating the detection of sensitive information in communications
Conducting automated audits of business processes to ensure regulatory adherence
Identifying potential fraud through transaction analysis
Considerations for Implementing NLP
To implement NLP solutions effectively in your business, it's important to address several key factor that affect the type of solution to use, as well as ensure effective adoption and integration.
Data Quality and Quantity
High-quality, large datasets are essential for training effective NLP models. Ensuring your data is clean and relevant is crucial for success.
Language Complexity
Understanding context, slang, and nuances in human language can be challenging. Choosing the right NLP solutions to address these complexities is important.
Integration with Existing Systems
Integrating NLP solutions with legacy systems and workflows can be challenging. It’s essential to plan for smooth integration to avoid disruptions.
Privacy and Security Concerns
Ensuring data privacy and security in NLP applications is critical. Be mindful of regulatory requirements and best practices for data protection.
Ethics and Bias
Ethics and bias are key considerations in any NLP implementation. These models can inadvertently learn from biases that are present in the training data: leading to unfair or discriminatory outcomes. Therefore, it is essential to identify and mitigate bias in these models.
Continuous Improvement
NLP models require ongoing training and updating to maintain accuracy and relevance. Continuous improvement is necessary to keep your NLP applications effective.
Conclusion
NLP is a vital tool that helps computers understand and process human language.
While recent advancements like LLMs and GenAI have brought NLP into the spotlight, there’s much more to it than these recent advances.
All businesses are interacting with a range of natural language text (emails, documents, customer information, etc.), and as such there are huge opportunities to benefit from the use of NLP: whether its enhancing customer service, streamlining operation or gaining insights from data.
For those wishing to learn more or seeking guidance, we offer a full range of NLP consulting services, so please get in contact,