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.

Text-analytics tools enable marketing executives to quantify and analyze the sprawl of "unstructured" information, too: survey verbatims, call-center notes, and other customer communications, along with the ever-growing Internet-based consumer-generated content.

Because this textual information traditionally has been reviewed manually—one document at a time—its full potential has gone untapped in terms of assessing customer satisfaction, illuminating trends, and revealing root causes of problems or opportunities for improvement.

Even the direct customer feedback so frequently collected on open-ended survey comment cards has sat mostly dormant. Representative sampling of this data was standard procedure, as it just wasn't possible for a company to sift through thousands of inputs or to then relate them to one another and other data sources to produce useful insight. These verbatims often hold valuable insights that cannot be reflected in structured survey data—information such as intentions to buy or return products or to change providers.

Marketing executives need methods to analyze all of the information gleaned from both inside and outside their enterprises to more accurately measure customer satisfaction and react accordingly. Text analytics makes it possible.

Based on natural language processing (NLP) capabilities, text-analytics software fundamentally transforms sentences and paragraphs into reports, calculations, and presentation views that provide a true, 360-degree view of customers' desires, emotions, and future behaviors.

Turning Words Into Action

Customers are constantly talking to and about a company. With text analytics, a company for the first time can harness the complete array of those communications and automatically transform them into intelligence that is useful by anyone across the enterprise. Dashboards or reports illuminate crucial intelligence on products, services, competitors, and consumer trends, enabling agile response to competitive developments and revealing timely opportunities for new programs.

With text analytics, a company could derive sentiment trends from open-ended customer surveys as well as yes-or-no questions. Analysis might uncover a tendency among certain customers to switch products under certain conditions—providing the company with an important forward-looking view that can inform efforts to head off customer churn.

Or a company might use text analytics to compare perceptions of its products over time versus those of its competitors. Analyzing unstructured information such as survey text alongside structured, historical transactional data, the company could drill down into the business intelligence to understand (and ultimately eliminate) the specific customer issues that are driving negative perceptions.

How Well Are You Doing?

The third step in measuring customer satisfaction is implementing an instrument for ongoing assessment and improvement.

Toward this goal, many companies are adopting customer-score calculators that quantify the value of their individual customer relationships. One such example is the Net Promoter Score (NPS)—based on the question, "Would you recommend us to a friend or colleague?" Customers answer on a range from 0 (least likely to recommend) to 10 (most likely to recommend).

The unstructured comments that tools such as NPS generate must not be disregarded. With an integrated capability for text analytics, a company can read, analyze, and appropriately react to the entirety of the valuable information available to it—both structured and unstructured, both within and beyond the enterprise.

In fact, the proliferation of internal tools like NPS and especially external, online forums for rating customer experiences makes text analytics increasingly important. Consumers are no longer just telling their neighbors about their favorite and not-so-favorite products and brands—they are telling the world, by posting comments on online forums. and, for example, allow consumers to post comments about their travel-related experiences—valuable information for companies and their competitors. Joining a trend among retail Web sites, Wal-Mart Stores recently enabled its online shoppers to review and rate their purchases at

The competitive pressure to improve customer satisfaction makes measuring and analyzing customer feedback ever more important.


The ability to quickly and easily convert unstructured textual information such as survey verbatims into rich, useful business intelligence enables a company to gain an unprecedented understanding of customer satisfaction. In this way, text analytics is separating leaders from their competition across industries as diverse as retail, financial/insurance, high technology, and hospitality.

Text analytics turns customer satisfaction into a rich, quantifiable metric of wide-ranging usefulness across a company's end-to-end operations.

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Tony Lopresti is vice-president of marketing at Clarabridge (, which enables companies to transform text into valuable information. He can be reached at