#NovemberNLP – Ep. 2: The Benefits of Natural Language Processing (NLP)
After a brief introduction and overview of Natural Language Processing (NLP)—as seen on our most recent #NovemberNLP article, we will now talk about the benefits of NLP.
As what we have mentioned before, NLP is a combination of computer science, linguistics, and machine learning—making it the key technology behind how we can now communicate freely with our devices using our own language (e.g.: Siri, Google Assistant, Alexa, etc.).
The fact that NLP helps machines break down human language—making the process of analysing the language’s rules and structures easier—is ultimately the reason why NLP proves to be such a great asset for businesses. Among the many benefits of NLP, below are a few that can definitely bring a positive impact on your business.
1. Analyse larger data sets
It is inevitable that a company would often come across large sets of unstructured data (documents, surveys, customer reviews, emails, news reports, social media comments, etc.) that are just too difficult for computers to process manually. It would take days, or even weeks, for conventional computers to analyse such massive volumes of text data—not to mention how this method is very prone to human errors and biases. With NLP technology, these tasks can be processed and analysed automatically using computational methods that are way more accurate and time efficient.
2. Provide a more objective analysis
When it comes to performing repetitive tasks, be it reading, processing, or analysing one too many text-based data, humans tend to lose their ability to focus on a certain point—which may cause them to make mistakes or do something based on their own personal emotions. This is exactly why computers can provide you with a more objective analysis than any humans ever could—in addition to how it can all be done in no time.
You can train NLP-based tools to analyse text-based data—no matter the amount—based on your company’s specific requirements without having to worry about unnecessary human intervention. Since the nature of NLP-based tools are to learn and adapt to your company’s objectives, you can always tweak and proceed to train them as your business evolves from time to time.
3. Streamline daily processes
Reviewing customer feedbacks regarding your products or services regularly is one of the most effective ways to improve your business strategy. However, asking employees to work full-time only to accomplish these kinds of manual data analysis can never compare to the effectiveness of using NLP tools. Connecting NLP tools to your company’s data allows you to stay updated on what your customers like or dislike about your products or services in real time, minus the hassle and cost of asking your employees to work 24/7 with the objective of merely accomplishing repetitive tasks.
4. Improve customer experience
Leaving customers in the cold—or not responding to their queries immediately—is most likely the main reason behind every company’s downfall. Detecting a potential crisis involves a comprehensive understanding of your customer’s behaviour—not just what they say about your products or services, but also why they say so. There are tons of NLP-tools available in the market which you could choose from to help you respond immediately to customers.
Chatbots and virtual assistants are two great examples of how NLP is utilized by businesses for automatic question answering, such as responding quickly and correctly to customer’s queries, accelerating response times, and providing detailed information regarding each customer’s specific requests.
5. Extract actionable insights
Analysing social media conversations, open-ended surveys, or online reviews require extra attention to details—especially when you know these places are exactly where the most valuable insights are at. However, wading through it by hand means that you would have to spend countless hours looking for insights—hoping to find actionable ones—in a sea of unstructured data. AI-driven NLP-tools can automate the whole process of sorting unstructured data, looking for the most relevant conversations, and providing you with insights that are immediately actionable.
For more information regarding how NLP is particularly utilized in Sonar’s media intelligence platforms to help us perform sentiment analysis of conversations in Bahasa Indonesia, don’t hesitate to contact us directly.
Stay tuned for our upcoming blog: #NovemberNLP – Ep. 3: NLP Process, Tasks, and Techniques
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