Natural language processing (NLP) is a branch of artificial intelligence that enables computers to understand human language. It’s also called computational linguistics, and it has many applications in business intelligence (BI). The key benefit of using NLP is that it allows you to analyze unstructured data such as text, audio, and video files–something that traditional BI tools don’t support very well. This article will explain how NLP can help you improve your customer experience using examples from real-world use cases.
NLP can take analytics to another level
NLP can take analytics to another level. In this article, we will see how NLP (Natural Language Processing) is being used in analytics and BI to extract insights from unstructured data like text, images, audio, and video.
Text mining is basically data mining with text as the source material
Text mining uses statistical, linguistic, and machine learning techniques to analyze unstructured data in order to find hidden patterns and relationships within it.
Text mining can be used to extract information from unstructured data such as news articles or emails that would otherwise be difficult or impossible to obtain using traditional database tools. The goal of text analysis is often to predict future events based on historical trends found in large amounts of textual content (i.e., social media posts).
The insights derived from doing text mining are more holistic and complete than those that can be produced by analyzing numerical data alone.
Text mining is an analytical process that allows you to extract meaning from unstructured data. Text mining can be used to analyze text documents, emails, social media posts, and other forms of digital content. The insights derived from doing text mining are more holistic and complete than those that can be produced by analyzing numerical data alone.
Sentiment analysis helps in getting insights from customer feedback.
Sentiment analysis is a way to analyze the emotional content of texts. It can be used to understand the tone of customer feedback, and it can also help you better understand how customers feel about your company and products. In this way, sentiment analysis can help you make better business decisions by providing insights into what people are saying about your brand online.
To understand how sentiment analysis works, let’s take a look at an example:
You run an e-commerce site with multiple product categories such as clothing or electronics–anywhere from hundreds to thousands of products in total. Each day new reviews come in for these items on review sites such as Amazon or Newegg (or even Yelp). You want to know which ones are most popular overall but also which ones have garnered negative reviews so that they may need special attention from customer service reps before being sold again online through another channel like eBay.”
Using NLP in customer support can help identify the root cause of problems, resolve them faster, and increase customer satisfaction.
NLP can be used in customer support to identify the root cause of problems, resolve them faster, and increase customer satisfaction.
In customer service, it’s common for employees to receive calls from customers who are angry about an issue they’ve had with a product or service. As a result, these agents spend most of their time trying to calm down disgruntled callers rather than actually solving their problems. This is where NLP comes in: if you have access to data that shows which issues are causing most complaints (and thus consuming most agent time), then you can prioritize those issues and give them more attention from your team members.
What if you could get results for your questions without having to download any data or write queries?
You’re probably familiar with the concept of NLP, or natural language processing. It’s a computer science field that studies how humans communicate and understand language, then applies those findings to create computers capable of understanding human speech.
In BI and analytics, NLP allows you to ask questions like: “What are my top customers?” Or “How many sales did I make last quarter?” Instead of having to download data or write queries, you can simply type your question into a tool like Google Sheets or Excel and get answers back immediately in plain English–no coding required!
Natural language processing and business intelligence go hand in hand when it comes to improving customer experience.
NLP is a very powerful tool that can be used in various ways. It helps you get insights from data, answers your questions, and improves customer experience.
NLP can help you improve customer satisfaction by identifying the right customers for your business and providing them with personalized experiences that meet their needs. It also helps you identify potential issues so you can fix them early on before they become problems for the company or its customers.
In this article, we’ve covered some of the benefits of NLP in analytics and BI. We hope that you were able to learn something new about how NLP can be used for business intelligence purposes!
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