Five Steps to Improve Marketing Effectiveness with Big Data When it comes to applying advanced analytics, the biggest opportunity for most organizations lies in the reams of text data they collect across any number of unstructured silos. The problem is that most of them can’t afford the Big Data infrastructure needed to turn all that […]
Five Steps to Improve Marketing Effectiveness with Big Data
When it comes to applying advanced analytics, the biggest opportunity for most organizations lies in the reams of text data they collect across any number of unstructured silos. The problem is that most of them can’t afford the Big Data infrastructure needed to turn all that data into an actual asset that provides customers with better service.
Rather than building out the infrastructure and then looking for the data scientists needed to turn Big Data into an actual business asset, Clarabridge, which just garnered another $80 million in funding, has developed a text analytics application coupled with a natural language interface that specifically focuses on customer experience management.
Beyond just analyzing social media streams, Clarabridge CEO Sid Banerjee says it allows organizations to also analyze customer support interactions and market research reports in order to create reports that customers can then use to target their marketing and sales activity. By making Clarabridge available as a service, it eliminates the need for customers to build their own Big Data analytics application.
As a category of software, text analytics has been around for decades. But in the last few years, a number of advances–most famously in the form of IBM’s Watson–have made it more practical to apply text analytics against massive amounts of unstructured data.
In the case of customer experience management, that data is not only being applied to increase customer satisfaction, it’s also going to drive the restructuring of organizations as the responsibility for sales shifts between different departments.
The question most organizations will need to decide is whether it makes more sense to buy an application that taps into Big Data analytics today or wait months, possibly even years, for the internal IT organization to master a range of emerging Big Data technologies. In most cases, the average line of business executive isn’t going to have much patience for the latter approach.
Michael Vizard is a seasoned IT journalist, with nearly 30 years of experience writing and editing about enterprise IT issues. He is a contributor to publications including Programmableweb, IT Business Edge, CIOinsight and UBM Tech. He formerly was editorial director for Ziff-Davis Enterprise, where he launched the company’s custom content division, and has also served as editor in chief for CRN and InfoWorld. He also has held editorial positions at PC Week, Computerworld and Digital Review.
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