As an increasing number of organizations have begun to explore the power of big data, they’ve seen massive improvements in revenue, informed decision-making, and customer retention. However, a critical component of this data often remains under-explored: text. Typically, a human must read through hefty volumes of text to understand and draw insights from it — a time-consuming and laborious process. In contrast, text analytics provides all the information you need within minutes and can beautifully display actionable data on your dashboard.
Text analytics, also known as text mining, uses artificial intelligence (AI) and machine learning (ML) to extract meaningful information from a large body of text. Unlike typical tabular data, which consists of rows and columns, textual data is unstructured and needs some preprocessing before you can use it to generate insights. Some of these techniques help you summarize documents, identify trends in the use of certain words, identify languages, classify text, extract entities and keywords, and perform sentiment analysis.
For instance, consider the arduousness of analyzing social media posts from customers, articles, and blogs. This is a taxing endeavor with ever-expanding source material. But with text analytics, you can quickly scan through thousands of posts and articles, and extract meaningful and actionable insights wherever your organization is mentioned.
Depending on your use case, the insights you could gain might include:
- Identifying features that users would like from your product’s next update
- How users reacted to new features you’ve released
- The current state of your competitors
- Trends in the financial market
- Commonly used words in a region to use for a marketing campaign
Below are 10 use cases that benefit from using text analytics.
Whether academic, corporate, or scientific, conducting research requires that you go through large volumes of text to figure out which methodologies, techniques, or ideas are pertinent to the problem you want to solve. Text analytics can help you filter publications by relevant topics, match the approach and methodologies to the application you need, and show you the most common words used in publications, saving time and effort.
2. Risk Management
Every business wants to manage its risks. Banks, for instance, seek to minimize risks on loans by ensuring that they’re investing in profitable businesses. Text analytics can help financial organizations keep track of companies they’ve invested in by providing insights about the company’s market performance from blogs, articles, and posts.
3. Fraud Detection
Fraud causes both individuals and organizations to lose money. Classification systems can flag fraudulent transactions, but sorting out fraudulent deals or false insurance claims from a large stack of files can be extremely time-consuming. Text analytics can help detect fraud in financial statements by searching for hidden clues in emails, insurance claims, and recorded conversations by searching for common keywords in sources like accident descriptions.
4. Business Intelligence
Business intelligence is a “set of strategies and technologies used by enterprises for the data analysis and management of business information.” Text analytics can help businesses filter out the facts they need to make informed decisions from a pool of data from different sources. You can use it to track the issues many customers complain about, collect information about competitors, and monitor changes and trends in the market to keep your business in the know.
5. Spam Filtering
Spammers are adept at evolving their writing styles to evade spam filters, even using only images in body text to do so. As a result, traditional text classification methods are no longer sufficient to prevent spam. Text analytics uses more than just text classification to help filter out these messages and keep you updated about message trends. With the latest advances in object character recognition, you can extract text from images to extend the reach of your spam filter in identifying suspicious content.
6. Knowledge Management
Knowledge management means more efficiently using human knowledge within an organization. This practice effectively manages all documentation and training materials and makes it easy to capture, store, and transfer this knowledge. Text analytics can help employees find the correct information they need by extracting key knowledge from a large volume of data. Instead of searching through tons of directories to find documents they need, employees only need to make queries and text analytics will present the relevant documents.
7. Personalized Advertising
Just as Google suggests ads to customers based on their search history and interests, businesses that leverage data to give customized sales to specific demographics provide the most suitable ads to the right people. Text analytics helps you determine which content is ideal for a particular cluster of people and shows you trending words that can draw attention to your product. This information enables you to channel your sales efforts to the right people. You can analyze a large amount of data in real time, gaining insights about how to target ads most effectively.
8. Legal Discovery
One useful application of text analytics in legal discovery is determining relevant and irrelevant evidence. Traditionally, a lawyer might have to sift through mountains of emails, messages, and articles to find relevant evidence for a court case. Instead, text analytics could extract keywords that relate to a case and filter documents to only those that contain the required information.
9. Customer Support
The customer support system is crucial to any business because it can tremendously affect customer retention. Quickly responding to high volumes of requests is sometimes impossible, leading to greater dissatisfaction among customers. Instead, you can integrate text analytics to sort support ticket types, translate complaints to the support agent’s native language, and prioritize support tickets based on customer sentiment.
10. Market Research
Market research evaluates how your product meets your customer’s needs. Text analytics can search swaths of text from product reviews to elicit understanding of the overall customer perception of the product, assess its market performance and reception, and investigate current market trends.
Keeping tabs on data from social media, blogs, articles, and surveys is extraordinarily difficult due to their sheer volume and density. Rather than relying on clicks and shares, you can use text analytics to more accurately understand what customers think of your product. Businesses can leverage text analytics to monitor product reviews on social media to make informed product or sales marketing decisions.
Whether formatted as an email, blog post, or news article, text contains vast amounts of business-critical information. Traditionally, the array and volume of resources have made gathering crucial insights an arduous process. However, the inception of text analytics has made extracting this data a practical and fruitful endeavor.
AI-based text analytics can provide real-time insights across virtually countless fields, illuminating patterns and trends and extracting information about consumer intent and sentiment. From efficient sorting of customer support tickets to filtering spam emails, to sifting through documents for relevant evidence, text analytics is a proven means of turning raw text data into a cache of actionable insights.