Increased usage of NLP in analytics and BI

Introduction

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.
For example:

• If you have a large set of customers who have purchased your product or service and want to know which ones are likely to purchase again in the future (or become promoters), then it would make sense for you to use NLP techniques such as topic models and sentiment analysis on their customer support email conversations with your company’s representatives so as not only understand what issues they may have had with their purchase but also identify any trends across these interactions regarding how satisfied each individual customer was throughout this process. This could help inform how much effort should go into resolving their specific problem(s) vs addressing general concerns shared across multiple individuals’ interactions with one another via email exchanges between themselves or between themselves plus others outside the organization itself;
• Another example would be using NLP algorithms like word embedding techniques which allow us to map words onto vectors representing semantic concepts contained within them such as whether someone said something negative about something positive (e.,g., “this movie sucked”) versus vice versa (i.,e., “this movie rocks”). Once we’ve mapped all our relevant words onto these vectors then we can start analyzing relationships between them based upon proximity between certain pairs within our dataset – which could prove useful if we wanted to find out about topics where users talk about both positively and negatively related topics together frequently enough so that these two categories might actually belong together under one umbrella category rather than being separated into two separate ones based purely upon individual preferences rather than actual usage patterns observed over time among users who interact regularly when posting comments online at various websites where people gather daily around common interests such as movies.”

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.

Conclusion

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!