Text analysis is still relatively new for most companies. What do we know so far about what has worked?
The global text analytics market is expected to reach $29.42 billion by 2030, growing at a CAGR of 17.8% from 2021 to 2030, according to Allied Market Research†
Text analysis identifies trends, topics, and patterns by parsing and analyzing written and spoken text. It helps companies better engage with their customers and has been proven to save time in business sectors where analyzing large amounts of written and spoken information is critical.
At the same time, many companies are still figuring out how best to use text analytics.
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What are some of the best use cases that have emerged for text analytics?
Top 5 Text Analytics Use Cases
Customer sentiment analysis
Call centers and customer service desks use speech analytics to analyze the verbal exchanges between customers and agents. The analytics use natural language processing to analyze the words spoken between agents and customers. Analytical algorithms also analyze the intonations and inflections of customers’ voices, which convey sentiment.
This helps businesses know which customers they are at risk of losing, as the text analyzer is programmed to detect emotions such as happiness or anger.
Social media
Companies apply text analysis of the written word by analyzing social media posts on Twitter, blogs and online forums.
Analyzing these social media posts can give businesses an early indication of whether a recent product or product promotion is well received and whether customers are satisfied with the company and its products and services.
Companies use this feedback to improve their products, optimally position their marketing campaigns, and reach customers they believe are disappointed. Collectively, these efforts contribute to monetization while reducing customer churn.
Legal Discovery
It wasn’t long ago that law firms hired temporary workers to scan thousands of documents and identify key lawsuit terms that lawyers could refer to later when drafting their cases. The process was time consuming, expensive and long.
Text analysis has changed all that.
Today, a text analytics program can process thousands of emails and documents in two or three days — returning a subset of the information that includes the topics and terms relevant to the case, while eliminating information that isn’t relevant.
Academic and scientific research
An academic research institution, life sciences company, or pharmaceutical company can spend weeks and even months going through all the research papers, dissertations, experiments, treatises, and journals that exist around the world on a particular topic.
Most of these organizations now use text-based analytics to remove documents, recordings, etc. that they deem irrelevant to their informational searches. They do this to save time and money, as well as speed up the time to results.
HR recruitment
As part of the corporate hiring process, more HR departments are using text-based analytics to screen applicants based on comments candidates have posted on social media.
HR uses text analysis to reduce the number of candidates for a particular position so that the ‘best fit’ candidates can be identified in advance. This reduces the amount of manual time spent on the recruiting process.
What we’ve learned from the best use cases so far
The best use cases for text analysis do two things: they reduce the amount of manual work required to read through and filter out information irrelevant to what a company wants to know, and they help analyze the verbal and written communications to help companies better understand and interact with these individuals.
While there’s some debate as to whether NLP-powered applications like website chat or automated attendants are text analytics, I’d say it is. They may not be the most talked about text analytics reporting methods, but they are integral parts of real-time business processes that can only be facilitated by text-based analytics.
For businesses without an active text analytics program, the best place to start is with chat and automated phone systems. Both use cases are already in the mature stages of implementation.
The next step is to see where other text analysis use cases (e.g. document screening) make sense. In all cases, companies of all sizes and industry sectors should look closely at text analytics, as business is still largely done through the spoken and written word.
