Natural Language Processing: Exciting & Futuristic Use Cases




Natural language processing (NPL) can be described succinctly as, “teaching computers to understand human languages.”

While the details are considerably more complex than that, the end goal rings true.

Despite many use cases for NLP, people tend to hone in and rally around chatbots.  However, the ability for computers to learn, understand, and speak human language opens up countless opportunities for artificial intelligence, data science, and software applications.

Here are some of the tech world’s most exciting natural language processing use cases.

Decreasing Subscription Cancelations

What if you could train a computer to listen to a customer’s feedback and predict (with great accuracy) whether the customer will cancel their account?

Text classifiers are doing this work for major corporations in the US. Programs are looking for specific words, message length, and overall sentiment, then pairing it with other available data to predict, “Kathy is going to cancel her cable this month.”

Why this matters: Text classifiers enable businesses to forecast gains and losses and prioritize high-alert accounts. They also allow companies to see patterns in their customer base. For example, if they get a flood of cancel-characteristic messages, they can look at the market and make informed assumptions about why they’re happening.

Monitoring Conversations About Your Brand

Can businesses measure the effectiveness of their marketing initiatives beyond standard metrics? Could a business understand how their audience feels towards their latest commercial? There are advertising technology companies championing this idea with brand sentiment monitoring.

The gist? Robots crawl the web looking for brand mentions and analyze the context surrounding them. These software applications can tell whether people are talking about your brand in a positive, negative, or neutral way.

Why this matters: Being able to understand how users feel about your brand seasonally, after certain commercials, or after product launches can help verify which marketing initiatives are working and which are not.

Predicting No-Show Rates

It’s safe to say that the patients who miss their doctor’s appointments have some characteristics in common. These commonalities paired with NLP programs can help predict no show rates.

Here’s how it works. A nurse or admin takes notes during your doctor’s appointment and files them into a program. Certain notes are considered an indicator of whether a patient is likely to miss their next appointment. Some indicators may be unreliable transportation, self-reported health habits, or tardiness to their most recent appointment.

Why this matters: There is massive waste in the healthcare industry. Reducing even a small portion of unused appointments can save millions and give people better access to healthcare.

Freeing Up Human Resources

For most companies, there is a core set of problems their users face. Things like password reset, canceling a purchase, re-ordering something all fall into this category. These whoopsies happen so frequently that it starts to become an opportunity to let technology take over. Enter chatbots.

Chatbots can understand a fair amount of English and respond in helpful ways, especially to standard requests.

Why this matters: Chatbots reduce the need for humans to manage simple requests, which allows humans to solve more complex issues.

Communicating With Someone From Japan

There are hundreds of eCommerce platforms that allow you to buy and sell things around the world. But what happens when the buyer and seller don’t speak a common language?

Enter natural language processing software.

Most enterprise-level peer to peer eComm platforms have built-in contact forms that can translate languages, allowing international transactions and pleasant user experiences.

Why this matters: Being able to communicate with anyone, in any language opens doors and creates massive opportunities across business, entertainment, and education.

Preventing Online Bullying

Instagram is one of the social media platforms commonly associated with bullying. If Instagram wants to continue its reign of social media, they’re going to have to find ways to support their users.

In late 2019, Instagram announced a new AI feature that will warn users when they’re about to post a “potentially offensive” comment. Users will then have the choice to “edit caption” or “share anyway.”

Why this matters: Bullying online, especially on social media, has been linked to mental health issues and low self-esteem in teenagers (and adults). If this NLP feature can reduce some of the cruelness, it’s worth a shot.

Scoring Creditworthiness in Developing Countries

By leveraging NLP, banks in developing countries can now assess the creditworthiness of clients with little or no credit history. NLP algorithms analyze social media activity, reasons for loan applications, stated collateral, and other factors to derive insights into their habits, peer networks, and the strength of their relationships.

After analyzing thousands of client-related variables, the software generates a credit score that’s highly predictive.

Why this matters: Credit scores are only as good as the data you can collect, but there is little standardized data in developing countries. By measuring data and language, computers can help people across the world secure loans and assets.

The Future of Natural Language Processing Use Cases

As NLP advances and computers become better at learning English, new software and AI will become seriously futuristic. Some of the most promising examples include:

As with all technology, we’re excited to see what other natural language processing use cases debut in the next decade. The ability for computers to understand our language may be one of the most valuable intelligence initiatives of the century.