Text analytics is still relatively new to most companies. What do we know so far about what has worked?
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Text analytics identifies trends, themes, and patterns by parsing and analyzing verbal and written texts. It helps companies interact better with their customers and has proven to be a time saver in business areas where analyzing large volumes of written and spoken information is critical.
At the same time, many companies are still strategizing 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 use cases for text analytics
Customer sentiment analysis
Call centers and customer service desks are using voice analytics to analyze verbal conversations between customers and agents. Analytics uses natural language processing to analyze speech between agents and customers. Analytical algorithms also analyze the intonation and content of the customer’s voice to convey sentiment.
This helps companies know which customers they are at risk of losing, as text analyzers are programmed to detect emotions like happiness or anger.
Social media
Companies apply textual analysis of written words by analyzing social media posts on Twitter, blogs, and online forums.
The analysis of these social media posts can provide companies with an early indicator of whether a recent product or product promotion is well received and whether customers are satisfied with the product. company as well as its products and services.
Companies use this feedback to improve their products, optimally position their marketing campaigns, and reach customers they believe they are not happy with. Collectively, these efforts contribute to revenue generation while reducing customer churn.
Legal discovery
It wasn’t long ago when legal firms hired temporary staff to scan through thousands of documents and identify key litigation terms that attorneys could later refer to as they craft their case. . This process is time-consuming, expensive, and lengthy.
Text analytics changed all of that.
Today, a text analysis program can power thousands of emails and documents in two or three days – returning a subset of information containing topics and terms relevant to the case, and remove inappropriate information.
Scientific and academic research
An academic research organization, a life science company, or a pharmaceutical company can spend weeks and even months going through every research paper, thesis, experiment, thesis, and journal. may even exist around the world on a given subject.
Most of these organizations now use text-based analytics to remove documents, audio recordings, etc that they consider irrelevant to their information searches. They do this to save time and money, and also to speed up time for results.
Personnel recruitment
As part of a company’s hiring process, many HR departments are using text-based analytics to screen job candidates based on comments the candidate has posted on social media. festival.
Human resources departments use text analytics to analyze the applicant pool for a given position so that the “best fit” candidates can be identified in advance. This reduces the amount of manual time spent in the recruitment process.
What we’ve learned so far from the best use cases
The best use cases for text analytics do one of two things: They reduce the amount of manual work required to read through and sift through information unrelated to what the company wants to know, and they help. analyze people’s speech and written communications so companies can better understand and relate to these individuals.
While there is some debate as to whether NLP-based applications such as website chats or automated phone answerers are text analytics, I would argue that they are. 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 supported by analytics based document.
For companies that don’t have an active text analytics program, the best place to start is with automated phone and chat systems. Both use cases are already at the mature stage of implementation.
The next step is to see what other text analytics use cases (e.g., document screening) make sense. In all cases, companies of all sizes and industries should scrutinize text analysis because business is still primarily conducted through speech and writing.
