Everyone is talking AI and specifically Chat GPT, however, one if its older cousins is equally exciting and is arguably having a greater impact.
This is the use of Natural Language Processing (NLP).
This technology is revolutionising the way businesses can interact with customers, making the customer experience more intuitive, highly personalised, and for both the customer and business much more efficient.
At Beyond, I am often asked about NLP and its applications for our clients businesses so I thought it would make for a good blog post to provide a short and sweet primer covering off what it is, how it works and where you can use it.
Understanding Natural Language Processing (NLP)
Definition of NLP
Natural Language Processing (NLP) is a branch artificial intelligence (AI) that basically uses machine learning techniques to allow computers to understand, process and communicate with human language.
Simply put it takes the rules we all use intuitively in constructing our languages, be it written or spoken, and creates statistical models such that our computers can both recognise and understand language as well as generate language itself.
Sounds a bit familiar?
That’s because all the clever research and development into NLP is what has subsequently enabled what we are witnessing today in terms of Generative AI.
Whilst GenAI is still pretty new, NLP has been around in our day-to-day lives for a while, in search engines, voice operated devices like our phones and chatbots for customer service.
At Beyond, NLP has been playing an increasing role in many of our clients digital transformation efforts playing an important role in the redesign and optimisation of key customer experience processes and helping to improve employee productivity.
How does NLP Work?
NLP is a combination of lots of statistical methodologies.
The way I explain it is: start to imagine all the different rules, and exceptions to those rules, that you would have to write down if you were trying to write a manual for someone to learn a language.
What you end up with is layer upon layer of information and rules.
If you understand some of the concepts that go into NLP it should start to make more sense.
Typically, NLP solutions follow five key steps to analyse language.
1.Lexical Analysis - This step breaks down language into smaller chunks like paragraphs, sentences, phrases, and words. It also categorises words into parts of speech like nouns or verbs and identifies “morphemes”. To the uninitiated, these are the smallest meaningful units of language. For example, in the word "unimaginable," "un-" and "-able" are bound morphemes, while "imagine" is a free morpheme.
2. Syntactic Analysis - This step checks the grammatical structure of sentences to ensure they are correctly formed. For example, “Alex strategised the data project” is grammatically correct, while “Alex do data project” is not.
3. Semantic Analysis - Semantic analysis works on the meaning of words and sentences. Its job is to understand what the words mean. For example, “Data insights speak to Alex” is grammatically correct but doesn’t make much sense.
4. Discourse Integration - This step looks at the context of the entire conversation to understand ambiguous language. For example, in the sentences “The report provided by Beyond was insightful. It helped Alex in making strategic decisions,” discourse integration helps us understand that “It” refers to the report.
5. Pragmatic Analysis - Pragmatic analysis interprets the intention of the meaning behind words, not just the literal meaning. For example, “Beyond turns data into gold” is a metaphor, and pragmatic analysis helps us understand that it means Beyond effectively transforms data into valuable insights.
As you imagine or think through each of these steps you can begin to imagine the layers of rules, databases of words and definitions that need to come together to essentially break down a piece of spoken or written language into its constituent parts to enable a computer to use its logical processing power to arrive at the meaning.
Conversely the process in reverse is what enables it to generate its own language in response.
The more technically savvy will probably be heavy breathing right now as I may well have over simplified it too much for some, but these are the basics that you need to know to grasp the concept.
NLP techniques can be categorised into rule-based, statistical, machine learning, and deep learning approaches. Each of these techniques plays a crucial role in different NLP applications.
By way of a quick explanation:
Rule-Based Approaches: These techniques rely on predefined linguistic rules and patterns to process language. They are highly accurate for specific tasks but can be inflexible and require manual updates.
For example a rule-based system might recognise dates in text by following rules that identify patterns like "DD/MM/YYYY".
Statistical Approaches: These methods use probabilistic models based on large amounts of language data to identify patterns and make predictions.
They are more flexible than rule-based approaches but require significant amounts of data (preferably accurate!) to be effective.
So to explain further, a statistical based model might determine the likelihood that a word follows another word based on frequency data from a large amount of text it relies on for learning, like predicting "morning" often follows "good".
Machine Learning Approaches: These use algorithms that learn from data to improve their performance over time. ML approaches can handle a wide range of NLP tasks and adapt to new data without explicit programming - hence the learning moniker.
A machine learning model would be used to classify emails as junk by learning from a dataset of labelled examples.
Deep Learning Approaches: More advanced approaches use neural networks with multiple layers to model complex language patterns.
They are used for tasks like speech recognition and natural language generation but require masses of processing power and large datasets for training.
An example here that you will be familiar with are the models which power virtual assistants like Siri or Alexa.
Applications of NLP in Retail and Consumer-Facing Businesses
I’m not going to go into a huge amount of detail here with regards the how, but in terms of what can you use NLP for in your business, here is a list of the typical examples:
Customer Service and Support
Chatbots and virtual assistants are optimsing customer service by providing instant, 24/7 support. For example, lots of retailers are using a customer service chatbot to handle common inquiries, freeing up agents to deal with the more complex issues. Similarly, customer reviews can be analysed using NLP to gain insights into customer satisfaction and areas for improvement.
2. Personalised Marketing
NLP helps scale up personalised marketing by analysing customer feedback and social media interactions. Some of our clients at Beyond, for instance, use sentiment analysis to tailor their marketing campaigns based on customer feedback. NLP can be used to manage and generate content helping to make sure that marketing messages are as relevant and engaging as possible to different groups of segments of customers.
3. Product Search and Recommendation
Enhancing search functionality on e-commerce site is a really useful application of NLP. One of our client's recommendation engine uses NLP to suggest products based on user behaviour and preferences. Whilst a client in the B2B space applies similar technology to recommend the right product and services, providing a personalised and simplified user experience that helps their customers through what would otherwise be a complex purchase cycle.
4. Market Analysis
Retailers can use NLP to analyse customer reviews and social media to identify market trends and consumer preferences. One our clients is running large product sampling programmes as part of its R&D and new product development. We help them leverage NLP to perform trend analysis, helping them stay ahead of trends and customer demands.
5. Voice-Activated Shopping and Interfaces
Voice assistants like Alexa and are becoming more integrated in the shopping experience. My Alexa at home keeps tabs on my groceries and lets me add items to my shopping basket using voice commands.
6. Content Creation and Management
Automated content generation is super helpful application of NLP when done right. An increasing number of retailers now use NLP to manage their content libraries to support the development of product descriptions, blog posts, and social media updates.
7. Fraud Detection
NLP can be excellent at identifying fraudulent activities by analysing language patterns. PayPal, for instance, uses NLP to detect and prevent fraud, safeguarding both the company and its customers.
Benefits of NLP for Retail and Consumer-Facing Businesses
The benefits of NLP when you consider the example applications above are all pretty self explanatory. The main drivers or benefits we see from our clients typically fall into the following categories:
Improved Customer Engagement or Experience
NLP based solutions such as chatbots can improve the customer experience by providing personalised and timely responses, increasing customer satisfaction and loyalty. However, we are all familiar with the frustrations of chatbots and automated experiences that fall short, so take care not to assume that automation is what your customers want. I’ve heard on the grapevine that many large organisations are becoming increasingly cautious of automated service experiences as the damage caused by a poor experience can often outweigh the benefits.
Operational Efficiency
By automating repetitive tasks and providing insights for decision-making, NLP based applications can improve operational efficiency, getting things done faster, more accurately and often taking away boring repetitive task away from people allowing them to focus on more value adding tasks.
Competitive Advantage
By getting creative with how you use NLP technology you can use it to develop imaginative ways to differentiate your business offering to stay ahead of the competition. Companies like Apple have done this well with things like Siri and there is huge scope for sectors such as banking to seriously up their game. More often than not the best way to differentiate is through the experience
Challenges and Considerations
There are all the usual challenges you would expect to encounter when implementing NLP based solutions including:
Data Privacy and Security - As with any technology that handles customer data, ensuring data privacy and security is paramount. You must implement robust measures to protect sensitive information.
Implementation Costs - The initial investment and ongoing maintenance of NLP systems can be significant. However, the long-term benefits often outweigh the costs, if managed sensibly.
Technical Expertise - Implementing and managing NLP systems require skilled professionals who understand both the technology and the business context. Investing in talent and training is essential.
But above all, the biggest challenge is going to be the people aspect, or change management side of things. By its nature most things NLP based will end up including changes in processes and ways of working and that involves people. People and new technology bring resistance and this should not be under-estimated.
Conclusion
NLP is here and has been here for a long while now. It sits well and truly in the centre in the world of AI as the foundation behind the secret sauce of things such as ChatGPT. Ultimately, it is bringing computing closer to humans through codifying the rules that sit behind the way we communicate with each other.
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