Benefits of Natural Language Processing in Business & Beyond

Natural language processing (NLP) combines the studies of data science, computer science, and linguistics to understand language much like humans do. Simply put, NLP breaks down human language so that it can be understood and processed by machines.

SaaS text analysis platforms, like MonkeyLearn, are making it easier than ever for businesses to harness the power of NLP and machine learning to automatically process text data in huge quantities, to ensure they get the most out of their unstructured data.

You might already have heard of NLP machine learning techniques, like sentiment analysis, which allows companies to monitor what customers are saying about their brand on social media, online review sites, and all over the internet, automatically and in real time.

And using NLP techniques, like topic analysis, on CRM data, emails, and customer service interactions can help follow the entire customer journey to understand what your company is doing well and what you can do to improve the customer experience.

Let’s go over the major benefits of NLP in business.

Natural Language Processing (NLP) Benefits

The benefits and use cases of NLP are large and impressive, and only growing by the day. What can natural language processing do for your business?

  1. Large-scale analysis
  2. Objective and accurate analysis
  3. Reduce costs
  4. Automate processes in real time
  5. Improve customer service
  6. Improve marketing and decrease customer churn
  7. Use human resources for the important things
  8. Tailor models to your business
  9. Get meaning from unstructured data

1. Large-scale analysis

Machine learning NLP technology allows for text analysis at scale on all manner of documents, internal systems, emails, social media data, online reviews, and more. Process huge amounts of data in just seconds or minutes, that would take days or weeks of manual analysis.

Furthermore, SaaS platforms can scale up or down immediately to meet your needs, so you have as much or as little computational power as you need.

2. Objective and accurate analysis

When performing repetitive (and frankly boring) tasks, like reading and analyzing surveys and other text data, humans are prone to mistakes or may have inherent biases that can skew the results.

Machine learning NLP models can be trained to the language and criteria of your business, often in just a few steps. So, once you have them up and running, they perform much more accurately than humans ever could. And you can tweak and continue to train your models as the marketplace or language of your business evolves.

3. Reduce costs

Because NLP SaaS models work at whatever scale you need, you can choose a plan that aligns to your current or projected data growth. You’d need at least a couple of employees working full-time to accomplish manual data analysis but with NLP SaaS tools, you can keep staff to a minimum. If you compare additional hire costs to MonkeyLearn’s pricing, for example, the savings are clear.

4. Automate processes in real time

NLP text analysis tools work 24/7, in real time, so you can streamline processes and perform regular and constant analyses.

Perform social listening all over the web with techniques, like aspect-based sentiment analysis to find out the sentiment (Positive, Neutral, Negative) of your customers toward different “aspects” (categories, features) of your brand.

Aspect based sentiment analysis.

You’ll know right away when customers are having problems with your product or service, remain on top of emerging trends just as they arise, and follow your brand image as it rises (or falls) over time – even use brand monitoring for competitive analysis to find out what your competition may be doing better than you.

Other NLP techniques, like intent classification, can automatically analyze text for the reason behind the writing, so you can deal with them accordingly. This pre-trained email intent classifier, for example, automatically sorts email responses into the categories, Interested, Not Interested, Unsubscribe, Autoresponder, Email Bounce, and Wrong Person:

Test with your own text

Results

TagConfidence
Interested49.3%

5. Improve customer service

Using the techniques above, and more, you can automatically analyze and sort customer service tickets by topic, intent, urgency, sentiment, etc., and route them directly to the proper department or employee, so you never leave a customer in the cold.

MonkeyLearn integrations with CRM systems, like Zendesk, Freshdesk, Service Cloud, and HelpScout are a great help to automatically manage, route, even respond to customer support tickets. And performing sentiment analysis on past and current customer service interactions will ensure your service staff is working up to your standards, always at the top of their game.

6. Improve marketing and decrease customer churn

Natural language processing is having a huge impact on marketing. When you put NLP to work to understand the language of your customer base, you’ll have a better understanding of market segmentation, be better equipped to target your customers directly, and decrease customer churn.

7. Use employees for the important things

With all the human hours you’ll save by automating processes and using data analysis to its full potential, your employees will be able to focus on what matters: their actual jobs. Furthermore, when you remove tedious, repetitive tasks, your employees will have less boredom fatigue, and increased focus.

8. Tailor models to your business

SaaS NLP platforms allow you to custom-tailor models to the language, criteria, and specific needs of your business. And user-friendly tools mean you don’t need a data science or engineering background to use machine learning to its full potential.

9. Get real insights from unstructured data

The unstructured data of open-ended survey responses and online reviews and comments requires an extra level of analysis – you have to break down the text so it can be understood by machines. But AI-guided NLP tools can make it easy.

No more guesswork or simple, cursory analyses. Machine learning allows you to really dig into unstructured text for data-driven, real-world, immediately actionable insights.

Closing

Machine learning NLP analysis can be a huge help to any business, to save time and money, streamline and automate processes, and make real-time, data-driven decisions. And with easy-to-use and easy-to-implement SaaS tools, you no longer need a data science background to put machine learning to work for you.

MonkeyLearn’s suite of powerful text analysis tools can ensure you always get the most out of your text data, and check out MonkeyLearn Studio to see what data visualization can do to make your data easier to understand and explain.

Take a look at MonkeyLearn to learn more, or sign up for a demo, and we’ll walk you through even more benefits of NLP.

Rachel Wolff

December 11th, 2020