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Natural Language Processing (NLP) technology is transforming the way that businesses interact with customers. With its ability to process human language, NLP is allowing companies to process customer data quickly and effectively, and to make decisions based on that data.
The field of NLP has been around for decades, but recent advances in machine learning have enabled it to become increasingly powerful and effective. Companies are now able to analyze vast amounts of customer data and extract insights from it. This can be used for a variety of use-cases, including customer segmentation and marketing personalization.
Akkio, an end-to-end machine learning platform, is making it easier for businesses to take advantage of NLP technology. In this post, we will explore the various applications of NLP to your business and how you can use Akkio to perform NLP tasks without any coding or data science skills.
NLP is a branch of Artificial Intelligence that deals with understanding and generating natural language. It allows computers to understand the meaning of words and phrases, as well as the context in which they’re used.
For instance, for a machine to understand the above sentence, it would first normalize it, taking into account the grammar and syntax, then break it down into its components with tokenization, and finally analyze it and extract the meaning with various algorithms. Bag-of-words, for example, is an algorithm that encodes a sentence into a numerical vector, which can be used for sentiment analysis.
The “bag” part of the name refers to the fact that it ignores the order in which words appear, and instead looks only at their presence or absence in a sentence. Words that appear more frequently in the sentence will have a higher numerical value than those that appear less often, and words like “the” or “a” that do not indicate sentiment are ignored.
More complex sub-fields of NLP, like natural language generation (NLG) use techniques such as transformers, a sequence-to-sequence deep learning architecture, to process language. Transformers take a sequence of words as input and generate another sequence of words as output, based on its training data.
By extracting meaning from written text, NLP allows businesses to gain insights about their customers and respond accordingly.
Now that we've explored the basics of NLP, let’s look at some of the most popular applications of this technology.
Chatbots are the most well-known NLP use-case, which captured the public imagination long before the advent of applications like Siri and Alexa. In fact, the first chatbot, ELIZA, was developed by an MIT professor in 1966.
Today, NLP has invaded nearly every consumer-facing product from fashion advice bots (like the Stitch Fix bot) to AI-powered landing page bots. With Stitch Fix, for instance, people can get personalized fashion advice tailored to their individual style preferences by conversing with a chatbot.
Akkio's no-code AI platform lets you build and deploy a model into a chatbot easily. For instance, Akkio has been used to create a chatbot that automatically predicts credit eligibility for users of a fintech service.
Every Internet user has received a customer feedback survey at one point or another. While tools like SurveyMonkey and Google Forms have helped democratize customer feedback surveys, NLP offers a more sophisticated approach.
Natural language processing can help to quickly and accurately process and analyze customer feedback – such as restaurant reviews or product reviews – in order to determine sentiment and other factors about the experience that customers had with the brand, product or service.
Applications range from automatic sentiment analysis of online reviews (as widely used by Amazon) to AI-powered analytics of customer feedback in a call center (used by companies such as Verizon and T-Mobile).
In one case, Akkio was used to classify the sentiment of tweets about a brand's products, driving real-time customer feedback and allowing companies to adjust their marketing strategies accordingly. If a negative sentiment is detected, companies can quickly address customer needs before the situation escalates.
The average person sends and receives 121 business emails per day. We already spend far too much time managing our inboxes – and that’s before spam comes into the equation.
To combat this, companies are turning to natural language processing to automatically filter out unwanted emails. By training an NLP model on a dataset of past emails, it can learn how to accurately classify incoming emails as “spam” or “not-spam.”
Email service providers have evolved far beyond simple spam classification, however. Gmail, for instance, uses NLP to create “smart replies” that can be used to automatically generate a response.
It also automatically stars, marks as important, or even suggests emails you may want to follow-up on. This all helps us to manage our inboxes, so we can focus on the emails that really matter.
In our globalized economy, the ability to quickly and accurately translate text from one language to another has become increasingly important. NLP algorithms focus on linguistics, computer science, and data analysis to provide machine translation capabilities for real-world applications.
The use of NLP for language translation traditionally involved rule-based machine translation, while more sophisticated methods use semantic analysis, named entity recognition, and information extraction models to produce better results.
These models can be written in languages like Python, or made with AutoML tools like Akkio, Microsoft Cognitive Services, and Google Cloud Natural Language.
Beyond obvious use-cases like simply translating documents with the likes of Google Translate or DeepL, machine translation can also be used to improve datasets used in text summarization algorithms, social media monitoring, question answering, virtual assistant applications, and more.
In the healthcare industry, machine translation can help quickly process and analyze clinical reports, patient records, and other medical data. This can dramatically improve the customer experience and provide a better understanding of patient health.
We’ve all used predictive text while typing on a smartphone keyboard. It’s one of the most widely used NLP applications in the world, with Google alone processing more than 40 billion words per day.
Predictive text uses a powerful neural network model to “learn” from the user’s behavior and suggest the next word or phrase they are likely to type. In addition, it can offer autocorrect suggestions and even learn new words that you type frequently.
At the core of these models often lies a “Markov chain model” that predicts the probability of the next word based on what has been typed before. For example, the word “Good” at the start of a sentence written at 9 a.m. will likely be followed by “morning,” while the same word at the end of a sentence will more likely be followed by “luck.”
By analyzing billions of sentences, these chains become surprisingly efficient predictors. They’re also very useful for auto correcting typos, since they can often accurately guess the intended word based on context.
Machine learning algorithms are typically fueled by structured datasets. However, most data is unstructured – books, news articles, user comments, customer reviews, emails, and so on. This can make it a challenge for companies to gain insights from this data.
Luckily, NLP can help with this problem. By converting the text into numerical vectors (using techniques like word embeddings) and feeding those vectors into machine learning models, it’s possible to uncover previously hidden insights from these “dark data” sources.
For instance, given thousands of unstructured documents about a publicly-traded company, such as quarterly reports, earnings transcripts, analyst notes, and customer reviews, NLP can be used to automatically identify and track key metrics such as sales growth, customer sentiment, and competitive differentiation.
Texting is convenient, but if you want to interact with a computer it’s often faster and easier to simply speak. That’s why smart assistants like Siri, Alexa and Google Assistant are growing increasingly popular.
These voice assistants start with speech-to-text, recognizing the utterance and converting it into text. It then commonly uses intent classification to classify the text data input as a “question” or an “action” and retrieves a response from a predefined database, using fuzzy matching, or generating a new response via natural language generation.
These assistants can also track and remember user information, such as daily to-dos or recent activities. This is one of the more complex applications of natural language processing that requires the model to understand context and store the information in a database that can be accessed later.
At the same time, there is a growing trend towards combining natural language understanding and speech recognition to create personalized experiences for users. For example, AI-driven chatbots are being used by banks, airlines, and other businesses to provide customer service and support that is tailored to the individual.
Personalized recommendations have become a major source of revenue for online retailers, as they understand that customers aren’t always looking for what’s on sale or in stock — they often want to find something that pleasantly surprises them.
In fact, Amazon's recommendation engine is responsible for 35% of the sales from its e-commerce platform.
NLP can be used to generate these personalized recommendations, by analyzing customer reviews, search history (written or spoken), product descriptions, or even customer service conversations.
This information can be used to accurately predict what products a customer might be interested in or what items are best suited for them based on their individual preferences. These recommendations can then be presented to the customer in the form of personalized email campaigns, product pages, or other forms of communication.
Having access to a tool doesn't necessarily mean you should use it. But when it comes to NLP, the benefits are too compelling to ignore.
NLP is becoming increasingly essential to businesses looking to gain insights into customer behavior and preferences. By applying NLP techniques, companies can identify trends and customer feedback in order to better understand their customers, improve their products and services, create more engaging content, and analyze large amounts of unstructured data.
For example, NLP can be used to analyze customer feedback and determine customer sentiment through text classification. This data can then be used to create better targeted marketing campaigns, develop new products, understand user behavior on webpages or even in-app experiences. Additionally, companies utilizing NLP techniques have also seen an increase in engagement by customers.
NLP also helps businesses with large amounts of unstructured data, such as social media posts, emails and support tickets. By automating the processing of such data, companies can save time and resources by focusing on their core business functions.
Natural Language Processing is becoming increasingly important for businesses to understand and respond to customers. With its ability to process human language, NLP is allowing companies to analyze vast amounts of customer data quickly and effectively.
Akkio's platform makes it easy to apply NLP models to any business without the need for coding or data science skills, and even without having to pay for model training. Companies can use this technology to gain valuable insights from customer feedback, create personalized recommendations, filter emails, automate customer support, and more.
As Christina Valente, a Senior Director of Product Operations explains, “before Akkio ML, projects took months-long engineering effort, costing hundreds of thousands of dollars. With Akkio, we are able to build and deploy AI models in minutes, with no prior machine learning expertise or coding.” Sign up for a free trial of Akkio and see how NLP can help your business.