Enhancing mobile engagement through the power of natural language processing

Mastering the unique complexities of human language has proven to be one of artificial intelligence’s (AI) toughest challenges. The ability for computers to effectively understand all human language would completely transform how we engage with brands, businesses and organizations across the world. And while machine learning (ML) and natural language processing (NLP)—both subsets of AI—can help, it is a complicated field and can be tough to know where to start when it comes to implementation.

NLP technology has been around for several decades, but businesses have been reluctant to prioritize adoption. According to IBM’s Global AI Adoption Index, nearly one in three IT professionals say their business is now using AI, with 43% reporting their company has accelerated their rollout of AI due to the pandemic. Even with recent advancements in technology making AI more accessible, many companies don’t have the proper AI skills and are struggling to keep up with the complex data landscape.

Advancements in technology have changed how customers engage with businesses, and it’s becoming more evident that customers want to be met wherever they are, in their preferred channel. One of the core benefits of both ML and NLP is that it has the capabilities of processing information and automating conversations, which can help improve interactions with customers. NLP technology not only enables these conversations to happen at scale, but it enhances customer engagement—especially critical as businesses start to shift away from high-frequency, one-way communications and toward two-way conversations.

What is NLP and why is it important?

Today, almost half of businesses are using applications powered by NLP, and one in four businesses plan to begin using NLP technology in the next 12 months. NLP is a branch of AI which gives computers the ability to understand, write and speak languages like humans do. Specifically, it is used to power a number of different applications including search engines, machine translation services, and chatbots and voicebots.

Today, most NLP models rely on ML to better understand written or spoken language. While computers and humans are quite different, their abilities to process and receive information are quite similar. Just like humans have the ability to hear things, process a conversation and respond, computers have the capabilities to collect audio and search terms from humans, interpret data and respond with either an offer or a solution.

NLP is breaking record after record in understanding human language: In the last 2 years, there have been more breakthroughs in NLP than in the past four decades. These AI algorithms now beat human performance in understanding the topic of a text and finding the answer to a random question, doing so in more than 100 languages at once.

In the world of digital ecommerce, for example, shoppers expect to enter a word or phrase into a search box and instantly see what they are looking for. These consumers also expect to receive personalized recommendations that are relevant to their search. With online shopping habits expected to continue increasing even after the pandemic, incorporating NLP technology can help meet demand and consumer preferences.

What are some practical applications of NLP?

One of the most obvious applications utilizing NLP are chatbots. In 2020, consumers spent more time on their phones than ever before, averaging 4.2 hours per day, up 20% from 2019, with two of the top three most used mobile apps being messaging apps. Due to the increase in mobile messaging, more companies are turning to chatbots and virtual assistants to answer customer questions in real-time and increase engagement.

Chatbots bring a strategic element to the omnichannel nature of the business: they interact with customers based on rules, learn from their experiences and are able to carry out a much larger number of interactions in an agile and efficient manner due to the reduction in common human errors in customer service. In order for chatbots to be successful, however, they need to use NLP to understand a customer’s question or query and respond accordingly.

A few other practical applications of NLP include:

• Intelligent search. Most search engines only work with keywords and don’t understand what people are searching for. However, there are solutions that enable users to ask regular questions, and it will review all documents to locate the answer. If businesses don't want to fall behind the curve, they should be building a vision to use their unstructured textual data to better serve their customers.

• Automatic skill and topic detection for live chat. NLP has the ability to automate skills and detect specific topics in order to hand it off to the right agent for the job. For example, NLP can be used to perform textual sentiment analysis, which can decipher the polarity of sentiments in text.

• Live agent assistance. NLP technology enables response suggestions so live agents no longer need to type the answer. This can help businesses respond to higher volumes of questions and ensure customer queries are answered quickly and accurately.

What are the benefits of NLP?

Big tech companies have a huge advantage when it comes to ML and AI. They have massive amounts of data, data centers and the budget to hire the talented software that can seamlessly process data. However, NLP can help other businesses that don’t have this advantage but still need a way to efficiently process and analyze unstructured data.

NLP can be used for a variety of different applications - and with today’s consumers purchasing more products via their mobile devices, investing in this technology is more critical than ever.

Here are four benefits of incorporating NLP into a business strategy:

• Improve customer experience. To create a more easy-to-use and personalized user experience, NLP can easily integrate into a website or mobile app. NLP can complete simple tasks like spell check and autocomplete in search bars, making it easier for customers to find what they're looking for. As a result, customers who have a positive experience navigating a business's website or app will likely return for future purchases or inquiries, ultimately increasing loyalty and reducing churn.

• Automate conversations and support. Chatbots are nothing new, but they are often the first point-of-contact for customers. NLP can increase a chatbot’s power to enable greater conversation to the point where call centers or data agents are no longer needed as the main source of communication for the customer. This can ultimately streamline business processes and reduce costs. For example, a person can call or text a business to ask questions about a recent purchase. It is likely that many people have similar questions about their purchases, which in turn would make answers to these questions repetitive. Rather than having a live agent answer all of these questions repeatedly, chatbots can use NLP to automate many of these conversations and respond to customers’ requests in real-time.

• Stay up-to-date on customer feedback. Many customers leave reviews or fill out surveys regarding a product they recently purchased, but it can be difficult for businesses to analyze this unstructured data—especially if there are thousands of reviews or survey responses. NLP allows businesses to aggregate this unstructured data and turn it into real-world actionable insights. This will help improve overall business practices, tailor product offerings and ensure customers’ needs are met.

• Block phishing attempts and identify fraud. Phishing attacks are on the rise. For many businesses, detecting these threats can be challenging. Different algorithms in the NLP field can identify and analyze a message that may be fraudulent, which can allow organizations to weed out any spam messages before they get sent to consumers. For example, if there is a URL, NLP technology can examine the link and determine if it is a scam. It can also detect fraudulent websites.

NLP will continue to gain traction, but only if organizations see its potential. Its applications can provide countless benefits to any business: It can help save time and money, enhance the customer experience, and streamline and automate processes. Understanding human language is complex, but investing in and adopting NLP technology is imperative to supporting and reaching every customer on multiple platforms.