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Case study 2: Text mining 1. Explain the difference between structure and unstructured data, with examples. What are the challenges associated with unstructured data? 2. How can companies overcome the challenges of unstructured data? 3. List at least 3 business applications of text mining. Explain. 4. "There is an ethical dilemma associated with text mining consumer information". Do you agree or disagree with this statement? Explain. 5. Pick a product that has been reviewed on the internet. Explain how web content mining could help the producing company improve the product. 202 Part Two lemason Teculatricture buying signals INTERACTIVE SESSION: TECHNOLOGY WHAT CAN BUSINESSES LEARN FROM TEXT MINING? buy intent to law or customer wish events it can Test mining is the discovery of patterns and rela tips from large sets of unstructured data-the reveal Specific product and service sues reactions kind of data wegenerate in e-maile phone converss to marketing and public relations efforts, and tions blog posting online customer surveys and tweets The mobile digital platform has amplified Artensitysotheare grated with JetBlue's the explosion in digital information, with hundreds ocher immer analysis tools, such as Satmetris of millions of people calling, texting searching Net Promoter met which classifies Customer "apping (ssing applications) buying goods and well- into groups that are generating positivenciave, O ng billions of emails on the Do fechack about the company Using Attesty Consumers today ate more than just consumers text analyties and with these tools, JetBlue they have more ways to collaboratr, share informi. tion and influence the options of their friends and the major ISSN Stomers and with the company peers, and data they create in doing soluvesi nificant value to businesses. Unlike structured data which are generated from events such as completing purchase transaction, unstructured data have no distinct form. Nevertheless, managers believe such data may offer unique insights into customer bchas- or and attitudes that were much more difficult to determine years ago For example, in 2007, JetBlue experienced unprecedented levels of customer discontent in the wake of a February ice storm that resulted in wide spread flight cancellations and plane stranded on Kennedy Airport runways in the US The airline Teolved 15,000 e-mails per day from customers dur. ing the storm and immediately afterwards, up from its usual daily volume of 400. The volume was so much larger than usual that JetBlue had no simple way to read everything its customers were saying Fortunately, the company had recently contracted with Attersity, a leading vendor of text analytics software and was able to use the software toama lyze all of the e-mail it had received within two days. According to dete research analyst Bryan Jeppsen Attensity Analyze for Voice of the Customer (Voc) enabled Telue to rapidly extract customer senti ments preferences, and requests it couldn't find any other way. This tool uses a proprietary technology automatically identify facts opinions, requests trends, and trouble spots from the unstructured text nt survey responses service notes e-mail messages, web forums, blog entries news articles, and other Customer communications. The technology is able to accurately and automatically identify the many different ces customers is to express their feedback (such as a negative voice, positive voice ar conditional voice which helps organizations pin- point key events and relationships such as intent to developed a customer bill of rights that addressed Hotel chains like Gaylord Hotels and Choice Hotels are using text mining software to glean insights from thousands of customer satisfaction surveys provided by their guests. Gaylord Hotels is using Clarabridge's text analytics solution deliv- ered via the internet as a hosted software service to gather and analyze customer feedback from survey e-mail, chat messaging staffed call centers and online forums associated with guests and meeting planners' experiences at the company's convention resorts. The Clarabridge software sorts through the hotel chain's customer surveys and gathers post- tive and negative comments organizing them into a variety of categories to reveal less obvious insights For example, guests complained about many things more frequently than noisy rooms, but complaints of holy rooms were most frequently correlated with surveys indicating an unwillingness to return to the hotel for another stay Analyting customer surveys used to take weeks but now takes only days, thanks to the Clarabridge software Location managers and corporate execu tives have also used findings from text mining to Intluence decisions on building improvements Wendy's International adopted Clarabridge soft- ware to analyze nearly 500,000 messages it collects cach year from its web-based feedback forum, call center notes e-mail messages, receipt-based sur veys, and social media. The chain's customer satis faction team had previously used spreadsheets and keyword searches to review customer comments very slow manual approach. Wendy's managemen was looking for a better tool to speed analysis, detect emerging issues and pinpoint troubled areas of the business at the state, regional, or corporate level The Clarbridge technology enables Wendy's to track customer experiences down to the store level related to meal quality, cleanliness, and problems speed of service Chapter 5 Foundations of Business Intelligence: Databases and Information Management 203 within minutes. This timely information helps store, regional, and corporate managers spot and address feel about their brand and take steps to respond to negative sentiment Structured data analysis won't be rendered obso- Text analytics software caught on first with gov- lete by text analytics, but companies that are able ernment agencies and larger companies with infor- to use both methods to develop a clearer picture of mation systems departments that had the means their customers' attitudes will have an easier time establishing and building their brand and gleaning Clarabridge is now offering a version of its product insights that will enhance profitability. geared towards small businesses. The technol- has already caught on with law enforcement, Sources: Doug Henschen, "Wendy's Taps Text Analytics to Mine Customer Feedback," Information Week, March 23, 2010 David search tool interfaces, and listening platforms Stodder," How Text Analytics Drive Customer Insight" Information like Nielsen Online. Listening platforms are text Week, February 1, 2010; Nancy David Kho, "Customer Experience and Sentiment Analysis," KMWorld, February 1, 2010; Siobhan Gorman, mining tools that focus on brand management, "Details of Einstein Cyber-Shield Disclosed by White House," The allowing companies to determine how consumers Wall Street Journal, March 2, 2010; www.attensity.com, accessed June 16, 2010, and www.clarabridge.com, accessed June 17, 2010 use the complicated software, but 10 properly ogy