The potential of text analytics is much bigger than that of social media. In fact, some of its application may surprise you. Text analytics help to uncover insights in collections of text. This process gives companies relevant data about consumers, products, businesses and potential clients using techniques such as duplicate detection, clustering, and machine-learning.
The biggest users of text analytics are retailers, which account for one-third of the market, which is expected to be $6.5 billion by 2020. DiscoverText is a cloud-based, collaborative analytics solution that includes dozens of text mining features to help businesses make better decisions quickly and accurately. It has applications in various industries, including education, voice of the customer, HR analytics, and government.
DiscoverText solutions combine flexible software algorithms with human-based coding to provide businesses a more efficient framework for conducting reliable and accurate large-scale analyzes. Pulling actionable pieces of information from mounds of text, the platform analyzes records to predict failure based on maintenance reports and identify indicators of problems. The software can also merge data from various sources to combine information from multiple data channels.
Tapping into Unstructured Data
There are rich supplies of text, both inside and outside an enterprise, waiting to be analyzed. With tools such as DiscoverText, it is possible to analyze an entire article within minutes. In fact, some businesses in the consumer industry are already tapping into the software to gather information from the troves of feedback made by their customers in social media, email and surveys. It helps companies make informed decisions about markets and customers by streamlining the gathering and presentation of important information.
DiscoverText plays a role in the social media and survey research space. It merges data from social networking sites such as Facebook and Twitter with data from SurveyMonkey. It allows users to search and retrieve data day forward and from the complete history of Twitter using Gnip’s historical PowerTrack rules. The solution includes five pillars of social data text analytics: search, filter, clustering, human coding, and machine learning.
• Search. Using keywords, DicoverText allows search and discovery of raw data. It even trains a machine-learning algorithm to recognize social media data as relevant.
• Filter. It filters data and stacks multiple filters to facilitate a more refined search.
• Clustering. It removes duplicates through automated clustering based on the similarity of items, giving users a high-level sense of social data landscape.
• Human Coding. It provides a better web-based interface to crowd source social data quickly. Featuring basic research tools, it measures inter-rater reliability and resolves human coder differences for better machine learning.
• Machine Learning. With iterative techniques involving human coders and machines, DiscoverText increases the ability of both to learn and improve over time.
Aside from analyzing social data, companies can also benefit from analyzing their customer feedback. For years, companies have not realized the point of text analytics. Fortunately, now they get it. Text analytic solutions, such as DiscoverText, are useful for identifying the emerging topics in social media. They are helpful in sorting through and categorizing large volumes of content. Insights gained from such solutions support decision making in sales, human resources, marketing, customer development, product development and other areas that link to customers ultimately.