With more communications, interactions and discussions happening via email, blogs, text messages, Facebook, Twitter and in Web chat than ever before, organizations are realizing it is crucial to stay abreast of what is being said about their brand across these channels. Their need to extract timely, relevant, actionable information and sentiment in real or near-real time from the vast untapped body of social media conversations, emails, blogs, text messages and chats is driving the need for text analytics.

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.

VITEC can help put all of this in context - contact us!