Text analytics is a process that takes advantage of a vast amount of unstructured textual data collected and extracts pertinent business insights automatically by leveraging artificial intelligence, machine learning, and natural language processing.
The value of structured data is formidable and is now widely acknowledged. However, the value of unstructured textual data remains largely untapped. The rise of digital channels has created massive volumes of data that organizations face difficulties in collecting, correlating, and analyzing. This presents a huge challenge for customer-centric organizations, as they need to take timely action to harness this data and improve business performance. The rise of mobility, digital channels, and the complexity of their interactions are creating new challenges for organizations that are trying to respond to the changing customer needs. This is where text mining and text analytics come into play.
What is text analysis?
Text analysis is an automated process that uses the techniques of text mining and text analytics to analyze and interpret a vast amount of data. Some of the leading use cases include sentiment analysis, natural language processing, and document classification. Although text analytics and text mining are related and often complement each other in the application, text analytics is about computational linguistics and knowledge management whereas text mining involves data mining and statistics.
The goal of text analysis is to derive quality insights from text or words. It excludes the semantics in the text by answering questions about the frequency of words spoken and the length of sentences. Understanding the context and usage of the text is the goal of natural language processing. It helps identify the intention behind a sentence along with human linguistic habits.
Text analytics methods and approaches
Advanced text analytics for better customer insights
The ability to analyze unstructured textual data has allowed many industries to improve their product user experience and make faster and better decisions with text analytics.
Improve customer service
From online reviews, social media profiles, chatbots, customer surveys, etc., companies can solicit customer feedback. When combined with text analytics tools, companies can improve their customer experience by gaining deeper insight into their users. Sentiment analysis and text mining can help companies identify the most critical and real-time issues faced by their customers. These insights can be used to improve operations and increase customer satisfaction.
Expanded market research
Market research is an important part of strategic planning, but analyzing large amounts of text data available is time-consuming and inefficient. With text analytics, enterprises can easily gather and analyze all the available text data. A comprehensive text analytics solution can easily collect and analyze large volumes of unstructured data, identify market trends, acquire competitive and customer insights, analyze customer responses, etc. Additionally, NLP can transform the way digital marketing functions. It can be used to improve the efficiencies of various marketing activities, such as analyzing the impact of social media and optimizing SEO.
Sentiment analysis helps sales and marketing teams determine how their clients feel about their brand. This type of analysis, which is commonly referred to as opinion mining, is conducted through social media sites to find out what their readers think about a given topic. A significant amount of new-age tools used for sentiment analysis are geared toward recognizing and analyzing emotions like gladness, misery, etc. These tools can help marketers make informed decisions and improve their sales.
Text analytics enables organizations to collect and analyze available feedback, identify process-product-service issues, and drive operational improvements. Before implementing a customer journey analytics strategy, it is important that organizations practice data democratization. In large organizations, data collected through employee and customer activities are often stored in silos. And democratizing data will improve customer journey analytics.
There is a vast amount of unstructured data that goes untapped and missed opportunities to take advantage of the analytical insights to improve business intelligence necessary to achieve a crucial competitive edge.
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