When we speak about customer service, we often think of human interaction. But nowadays, companies have experimented with different ways to connect with customers for a better service. If one has an issue with a product or service, they can email the company, call the 1-800 number, chat through social media, use the Chatbot on the website, or meet a representative in person. Some ways, including 1-800 and Chatbot, have incorporated Natural Language Processing into the service.
Natural Language Processing is a sub-discipline of AI that converts text into data. Computers learn to recognize speech and text before generating a response in speech or text. Search engines and personal assistants, such as Siri, Alexa, and Google Home, use NLP to understand human language better. The idea is to provide better service and ease requesting and providing information to humans.
The many applications of NLP
You may have noticed it now that there’s an email filter at Yahoo and Google that categorizes emails as spams. This is using NLP to let the computers learn what a spam message is and what is not. Similarly, the NLP group at MIT can identify fake news through NLP applications. Meanwhile, Amazon created Amazon Comprehend Medical to predict diseases based on electronic health records and the patient’s own speech.
Some other applications around us are content creation by Curata, YouTube, and Netflix. The idea is to utilize NLP in a word search and calculating the likelihood that the user would like related content based on the previous search.
In healthcare, there is also a medical transcribing software by Nuance called Dragon. The software writes out what a doctor says as a transcription on the computer.
Some big tech companies’ offers in NLP
Google Cloud has provided an NLP Application Programming Interface (or API). API is a platform that helps to take your input requests, deliver it to a system, and then bring back the response. MuleSoft showed a short explanation of what an API is.
Meanwhile, Facebook has a built-in NLP in its pages, and Amazon offers Amazon Comprehend to understand texts. As the website said, “you pay only for what you use, and there are no minimum fees and no upfront commitments” for using Amazon Comprehend.
Apple has a Worldwide Developer Conference (WWDC) every year that discusses NLP in action. You can find the 2020 recording here. NLP in Apple has been divided into several topics: tokenization, language identification, linguistic tags, text embedding, and natural language models.
The two types of applications: customer-facing and employee-facing
The customer-facing app has customers as users. It is often the case that there is only one comprehensive customer-facing app, but there are 10s to more than 50 employee-facing apps to handle the feedback.
Imagine a department store that needs both a customer-facing app and employee-facing apps. In the eyes of a customer, they only need an app that takes a voice command, translates it to search using NLP, inventory checks, and tells the customer which floor and aisle to get the product. Meanwhile, the employee-facing apps would be a combination of an app to take online orders, an app to take orders from customers in-store, an app to update inventory at the store, an app to check inventory at the warehouse, an app to track delivery trucks, and an app to track shipping to customers.
Another example of an employee-facing app would be Dragon from Nuance that increases medical doctors and NLP productivity that is used to evaluate customer feedback on product reviews.
NLP application during Launch, Mature, and Growth business stages
During the early product stage, your company will focus on building infrastructure and related algorithms to make the business possible. Here is when you can start implementing NLP algorithms and acquire data from public sources to train computers. Kaggle and GitHub are a community and a platform you can explore further on AI and machine learning.
As your business grows, you begin to accumulate data from customers (what they like to buy, what service or product they’d likely purchase next after the first one, what Frequently Asked Questions or FAQs they have, and what kind of help they need). Here you can use your trained NLP algorithms to help build better customer service for the users. Some tools may include Chatbot, Autocomplete searches, and Automatic word translation.
In the business world, it is necessary to keep growing and evolving. Companies like Nokia and Blackberry showed us how reluctance to change caused major failures in their businesses. Hence, in the Growth stage, your business needs to explore new fields while focusing on a certain customer segment. For example, you wanted to expand more engagement from Gen Z and incorporate social media into your business. You upload content on Tiktok so that you better reach that particular segment. In the Growth stage, you can use NLP to ease reaching out to customers with new offers. For example, your business uses features similar to Personal Assistant (Siri, Alexa, Google Home) on your website for your customer to inquire about new products and services.
Another way to utilize NLP for your business in all three stages is to build a review platform for your company’s evaluation. Users tend to purchase a product or a service based on referrals and reviews. By having a review platform, you can take advantage of showcasing your competitive advantage. You can use NLP to study the sentiment of reviews and to respond to each review.
The user experience of NLP
NLP enhances UX design with better services. Previously, a standard car operated with manual button clicks on knob turns. Some of today’s cars have voice-automated instructions that the car can recognize and interpret as an instruction. With NLP that trains computers to learn, the car can know the instructions even when communicating in low volume, high volume, or different accents.
A better UX design also is enabled through predictive text that we already see in Grammarly and Google (Gmail and Google search engine).
The next step of NLP would be to understand context and emotion through text and speech. An ideal result is to create a more user-friendly software that understands context and emotion. To reach that stage, NLP researchers used data of voice recordings from many different places, including personal assistants (Alexa, Siri, Google Home, for example). Other sources of data would be product reviews. For example, apps reviews, Goodreads reviews, Yelp reviews, etc. where computers can aggregate moods and sentiments from the words customers put in.
Downturn Effect: How AI and NLP Can Make Your Business Thrive
During an economic downturn, we must think about what is important for the business. Technology is always a good investment because we will need it in the future; technology also improves efficiency in the long run. Your company can rent computers and developers and use existing NLP tools (such as Chatbots) for the business.
Another thing you might want to consider is to invest in cloud computing. Big tech companies such as Amazon, Microsoft, Google, and IBM offer different packages and facilities to support your company.