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
Bringing 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.
JetBlue 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.
Rosetta 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.
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