Companies such as JetBlue have used it to mine e-mails and other customer feedback for insight.
There's nothing like an ice storm and angry customers to focus the mind. JetBlue Airways went through a trial by ice in February 2007, when a storm grounded flights at New York's Kennedy Airport and left its passengers stranded on the runway for hours. During the crisis and its aftermath, the volume of e-mail to the company soared from 400 a day to 15,000. Personnel couldn't possibly read and report on all the messages in a timely way--but the company needed to respond.
Not long before the storm, JetBlue had begun to explore text analytics, intrigued by new technology that promised real linguistic analysis that could mine for concepts, not just do the simplistic "counting words" approach. In the heat of the crisis, one text analytics vendor, Attensity, analyzed all the e-mail messages JetBlue had received and produced a report in two days. The report proved critical to the development of JetBlue's customer bill of rights, says Bryan Jeppsen, a JetBlue senior analyst.
The airline went on to integrate Attensity with other customer analysis. It uses Satmetrix's Net Promoter metrics, which gives JetBlue a score based on the percentage of customers classified as "detractors," "neutrals," or "promoters." That score is useful because it's clear, simple feedback everyone in the company understands. But "executives want to know why the score has moved up or down," Jeppsen says. "Was it because planes left on time or were late? Was it the average fare? The only ones who really know are the customers." By using Attensity to match specific comments and comment patterns with structured data such as arrival times, fares, crews, and gates, the airline thinks it can solve problems more rapidly.
Customers today aren't just customers--they're influencers and social networkers. Across the Web at any hour, they're sharing observations about your company's products and services, and those of your competitors. Customers amplify their single voices when they blog, write online comments and reviews, and participate in communities such as Facebook and Twitter. These new modes of customer behavior make it essential for companies to move beyond traditional ways of gathering, analyzing, and acting on customer information.
Customers also continue to reach out in conventional ways, such as feedback surveys and letters to the company. But the big difference today is that those surveys are often digital and the "letters" are e-mail--meaning it's in a digital form that should be easier to analyze.
In most organizations, transaction data is still the raw material of customer intelligence, and advances in the depth, breadth, and timeliness of transaction data analysis can help companies deliver competitive advantages. However, what's firing the imagination in many organizations these days is the potential to apply analytics to search results, text, and social network content to better understand customers and predict their behavior.
Analyzing Unstructured Content
This effort doesn't replace the work companies do to analyze structured data. Bringing structured data that measure hard facts such as transactions together with unstructured information such as e-mails and surveys that measure sentiment is essential to understanding customers. Of course, textual information, including forms, letters, survey responses, and warranty cards, has long been part of customer data sets. But it's the digitizing of this information that has interest in text analytics on the rise.
Text analytics, like data mining, is an umbrella term that covers a range of techniques and practices, including natural language processing, text mining, relationship extraction, classification and tagging, visualization, modeling, and predictive analysis. Compared with structured data analysis, text analytics is by nature less precise and complete; "good enough" is often the rule. Thus, text analytics can be most valuable when used in tandem with structured data analysis, particularly when an organization wants to combine or correlate customer predictors found in data and text.
Choice Hotels and Gaylord Hotels are both using text analytics software from Clarabridge to quickly make sense of thousands of customer satisfaction surveys gathered each day. The software spots positive and negative comments that can then be correlated with specific hotels, facilities, services, rooms, and employee shifts. The feedback drives immediate customer service response, with outbound calls or letters to acknowledge and apologize for problems and perhaps offer discounts to win over upset customers. More important, chain and facility managers track trends to spot problems and best practices. For example, Gaylord Hotels found out from using text analytics of post-visit surveys that noisy rooms weren't the most common problem, but they were among the most serious because they were most correlated to a "wouldn't return" or "wouldn't recommend" response.
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