There's new interest in solving an age-old corporate problem: how to measure customer satisfaction. There are new tools for doing so, too.
Companies for years have surveyed their customers and prospects to measure customer satisfaction, but honing in on the true voice of the customer traditionally has been a tricky task.
A company could ask good, open-ended questions and gain valuable feedback—but then have no efficient method to read, process, and act on all of the raw intelligence collected in customer surveys. Or, to ensure hastier, more manageable analysis, the company could ask much more limited questions—but acquire only limited insight into customers' feelings and behaviors.
The emergence of "customer insights" or "customer intelligence" departments within large corporations indicates that companies are more determined than ever to accurately assess customer opinions, desires, and moods.
New text-analytics tools enable companies to succeed in that endeavor—to move away from manual processing of text, such as survey verbatims, to automatically converting large amounts of text into useful customer intelligence.
Straight From the Source
Integrated, end-to-end text-analytics solutions have emerged that are built precisely to analyze and quantify all of the data available to a company, providing it with a complete understanding of customer satisfaction.
In the past, companies have based business decisions almost solely on "structured" data—the checkboxes in surveys, transactions from customer relationship management (CRM) systems, and various point-of-sale systems that can be most readily represented in rows and columns of relational databases and spreadsheets.