Text Analytics Drives Customer Insight

By uniting intelligence from customer surveys and social media with conventional business intelligence, companies are moving beyond transactions to understand customer behavior.



Customers today are not just customers -- they are influencers and social networkers. Across the Web, at any hour, they are sharing observations about your company's products and services. They are doing the same about your competitors. Customers amplify their single voices when they blog, write online comments and reviews, and participate in communities such as Facebook and Twitter. Through use of search engines and social networks, they reveal clues to their buying intentions, and in doing so, create a potential gold mine for customer intelligence. These new modes of customer behavior make it essential for companies to move beyond traditional ways of gathering, analyzing and acting on customer information.

In most organizations, transaction data is still the raw material of customer intelligence, and to be sure, advances in the depth, breadth and timeliness of transaction data analysis guarantees that it will continue to deliver competitive advantages. These have come through use of tools and systems for business intelligence (BI), data mining, data visualization, customer relationship management (CRM), information integration, data warehousing and more.

However, what's firing the imagination in many organizations these days is the potential of search, text and social network analytics for understanding and predicting customer behavior. And not just the behavior of single customers: Social network analysis is also about how customers influence one another, how they create new "segments" of shared interests, and how they share satisfaction and dissatisfaction with products or services. Interest in getting the most out of this kind of information is pushing demand for tools and services for analyzing and visualizing search, text and other non-traditional analytics -- as well as a need for the ability to integrate the results with BI.

Analyzing Unstructured Content

JetBlue AirwaysBringing the worlds of structured and unstructured information together is essential to moving an organization's customer intelligence beyond transactions and into richer data sets common in the world of customer behavior.

Needless to say, textual information, including forms, letters, survey responses, warranty cards and more, has long been part of customer data sets. It has also been difficult to access and analyze in a timely fashion. But now that this information is primarily in digital form, organizations can gain significant value by automating processes and using software to increase the speed and scale of text analysis.

"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. Different tools have different strengths. 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 HotelsJetBlue Airways uses text analytics software from Attensity to analyze the large volume of e-mail messages it receives from customers. By matching specific comments and comment patterns with structured data, airline personnel can solve problems rapidly, before they jeopardize the carrier's satisfaction rating.

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 quickly spots positive and negative comments that can then be correlated with specific hotels, facilities, services, rooms and employee shifts. The feedback drives immediate customerservice response, with outbound calls or letters to acknowledge and apologize for problems and perhaps offer discounts to win over disaffected customers. More importantly, chain and facility managers track trends in customer satisfaction and spot problems -- as well as best practices -- tied to particular properties, departments or employees.

RosettaStoneRosetta Stone, the provider of technology-based language-learning solutions, uses IBM SPSS text analytics software to analyze answers to open-ended questions in surveys of current and potential customers. Along with text, the participants provide structured data in the surveys, such as identification information and product purchasing codes. This information is correlated and integrated with text analysis. The company uses the resulting insights to drive decisions on advertising, marketing and product development as well as strategic planning.

Detecting Customer Sentiment

Demand for text analytics has raised the profile of specialized vendors such as Attensity, Clarabridge and Overtone for doing trended and basic root-cause analysis of why customers are commenting as they are. SAS, IBM's SPSS, SAP (Inxight) and Tibco (Insightful) are prominent vendors offering tools for analyzing text for predictive insights.

One application of text analytics currently getting a lot of attention is "sentiment analysis," which lets organizations discover positive and negative comment patterns in social media, customer reviews and other sources. Lexicons, word extraction, pattern matching, and other tools and approaches are used to develop the knowledge. As practiced at JetBlue, Choice Hotels and Gaylord Hotels, and Rosetta Stone, organizations may also apply these techniques to customer satisfaction surveys to analyze trends in what satisfies or dissatisfies customers.

Sentiment analysis can give organizations a view into hidden factors that may be having a big impact on customer loyalty and churn. The analysis also helps them determine what steps to take to both satisfy customers and reduce expenses by alleviating the need to interact with the contact center. Lexalytics, Nstein and Teragram (a division of SAS) are vendors that have text mining specialization for sentiment analysis.

To address contact center needs, some vendors are looking at sentiments expressed in other ways. Verint Systems, for example, offers applications that analyze recorded calls for changes in customers' voice volume or the frequency with which they use certain words and phrases; the knowledge helps organizations detect, through words, what issues are currently important or, alternatively, have dropped off the radar. Some organizations are even employing sophisticated presentation tools to visualize the customer experience, using heat maps and other graphics to understand the importance of factors such as voice volume and seriousness of tone.

Without text analytics, organizations would have to employ armies of clerks to sift through large volumes of customer comments in surveys. Gaylord Hotels, for example, contracted a third-party service firm to handle that task, but it took the firm eight weeks to report aggregated results. Using Clarabridge software, Gaylord now obtains verbatim results and trend information overnight, so it can immediately address service problems and make "rescue" calls or e-mail contact with disaffected customers.

Text analytics can also yield insight from valuable sources that would otherwise go untapped…

To read the rest of this article, download "Text Analytics Drives Customer Insight". The full report includes more on JetBlue, survey-based research and three suggested steps on using text analytics. The report downloads automatically for registered users who are logged into the site. New users can register at no charge.

David Stodder is an independent analyst, writer and researcher focused on innovative uses of information to achieve business objectives. Along with heading up his own firm, Perceptive Information Strategies, he is a Research Fellow with Ventana Research.

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