How to Implement Text Analytics in Business Intelligence
Text analytics is the automated process of converting unstructured text volume into quantitative data, which helps in revealing different patterns, trends, and insights. It is combined with varying types of text analytics solutions available in the market. Such a technique provides an opportunity for the business enterprise to understand the story, present behind the numbers, which help improve business decisions.
Text analytics, text analysis, and text mining are used interchangeably with the objective of analyzing the unstructured text to seek insights. Text analytics offers qualitative nature insights. Text analytics provide a helping hand in aggregating such results, thereby turning them into something visualized and quantified through various reports and charts.
Text analytics and text analytics are working together to confer the prerequisite understanding of different text types, such as social media posts, emails, customer support tickets, surveys, to name a few. It is possible to use different types of text analytics tools to understand a potential audience’s sentiment towards a specific brand on social media. It also offers a helping hand to the potential audience in understanding the primary topics in different product reviews.
Text analytics makes the best use of text analysis results for the recognition of different patterns, like a spike in negative feedback. It offers actionable insights that help make certain improvements, such as fixing the bug, which might frustrate the end-users.
How Text analytics are helpful to enterprises
Massive unstructured data amounts are produced each time. According to studies, about 510,000 new comments, 456,000 new tweets, and 156 million emails are generated daily. Hence, you should analyze and manage the information to check what is more relevant for you. Thanks to text analytics, it provides a suitable opportunity to the business enterprise for the automatic text extraction of meaning from different kinds of unstructured data, from various emails, social media posts, live surveys, and live chats, thereby turning the same into different kinds of quantitative insights.
With the recognition of patterns and trends with text analytics, the business organization will bring an improvement in customer satisfaction, diagnosing various product problems, tracking the reputation of the brand, perform market research, to name a few.
Data analytics solutions comes with a plethora of benefits. As it is scalable, it offers the opportunity to analyze massive data volume within a short period. Besides this, it provides the prerequisite opportunity to procure the results in real-time. In addition to seeking different insights that help in making other confident decisions, it is possible to resolve various problems within the due course of time.
Examples of text analytics
Customer feedback analysis happens to be one of the primary applications of text analytics in business intelligence. It includes the analysis of service and product reviews, which help find how the potential audience is evaluating the company. Also, it helps find what the potential audience needs to say about a specific brand upon the social media, processing, and optimization of the results of different open-ended responses to various customer surveys, to name a few.
Tips to get started with Text Analytics Solutions
As you are aware of the different kinds of things, which can be achieved through text analytics, you know how to start. There are various online tools available in the market that provides the prerequisite step intuitively, even if there are no programming skills. This write-up comprises different steps that help in the implementation of text analytics in BI processes:
In the beginning, it is essential to analyze the data with the aid of text analytics. Various types of pre-trained machine learning models are present, which offer the prerequisite choice to execute text analytics flawlessly. It is possible to come up with customized machine learning models to extract and classify the text. It is an ideal choice for the identification of keywords and topics in a specific field.
- It is possible to generate the custom model by following the below-mentioned steps:
- Selecting a kind of model: You can choose from Extractor or Classifier.
- Importing the data: You can make the proper use of Excel or CSV file and different kinds of third-party integrations.
Define the tags required for analysis.
Train the model by tagging various examples manually. After some instances, the specific model will begin to predictions on its own. In case you are not satisfied with the results thoroughly, you should continue to train the model. It helps in improving the accuracy.
Use of trained model for new data analysis: It is possible to upload the latest data in a batch. Besides this, you should make data with various third-party applications, which automatically analyzes the data.
Use of various BI tools for understanding the data
After completing the text analytics, it is possible to generate the data visualization of different results. Business intelligence tools such as Tableau, Looker, and Google Data Studio help produce various types of interactive and attractive charts and reports, which are useful in communicating the primary data insights.
Text analytics is suitable choice for business organizations to find meaningful details across a wide array of data resources, such as social media interactions and customer support tickets. With the aggregation of the text analysis results and use of BI tools for turning the numbers into easy-to-understand graphics and reports, text analytics is a suitable choice for recognizing various trends, patterns, and actionable insights, which enable data-driven decisions.
After the analysis of the customer feedback and examining the customer support ticket content through advanced analytics solutions, it is possible to leverage such results through text analytics. It is useful in diagnosing various opportunities for improvement and adapting the clients’ expectations and needs. The business organization is sure to reap a lot of benefits with the implementation of Text Analytics into the BI processes.
Author: Muthamilselvan is a passionate Content Marketer and SEO Analyst. He has 7 years of hands-on experience in Digital Marketing with IT and Service sectors. Helped increase online visibility and sales/leads over the years consistently with my extensive and updated knowledge of SEO. Have worked on both Service based and product-oriented websites.