The State of Content Analytics 2015

Article ImageAt its core, content analytics is the ability to extract structured information (such as people, places, and things) from un­structured text. This information is used to track, organize, and search content. "Content analytics allows people to find what they're looking for, not what they're searching for," notes Stephen Ludlow, director of enterprise product marketing at OpenText, a leading enterprise information management organization. From a content publishing perspective, search is still a critical component of the way people interact with con­tent. The metadata extracted from unstructured content allows the user to end up in the right place.

According to IBM, more than 80% of the information landscape is comprised of unstructured content. Content analytics takes that unstructured data and attempts to structure it by extracting information (such as keywords, sentiment, and language) in an effort to measure the use and effectiveness of the content. For example, within the publishing industry, "we're able to extract the key people mentioned as well as the key concepts within articles," explains Ludlow. "This information can be used in search, to kick off business processes and to bring together both unstructured information and structured information, enabling us to do queries and analysis against the content."

As for the current state of content analytics, "We're at the stage where most marketing organizations have at least a baseline understanding of how to measure content," explains Christian Jorg, CEO of Opentopic, a content marketing platform. "The good news is that those who already measure digital content can make fast strides in analyzing the performance of that content in order to inform and optimize the development of future content."

Andrew Davies, co-founder and COO of the content intelligence software provider idio Ltd., provides an explanation for the seemingly slow adoption: "After years of predictions and promises, we are starting to see a more widespread adoption of content analytics within a variety of publishing and marketing environments-but they are usually not called by the term ‘content analytics' or ‘text analytics.' Sometimes, they are denoted by the ubiquitous ‘Big Data' label or even artificial intelligence. So, on one hand, content analytics as a distinct industry is not growing fast-but only because the vision is being realized outside of that label."

"Most of the focus in content ana­lytics, however, continues to be on the marketing front, to score higher traffic for a brand and figuring out which channel is the most effective," adds Sudhir Holla, SVP of retail at Ugam, a provider of managed ana­lytics for retail.

Davies offers a few use cases:

  • Search and discovery-showing granular and accurate results based on human language search inputs
  • Personalization-filtering and recommending content based on relevance
  • Customer intelligence-understanding customers' interests based on the content consumed
  • Editorial insight-understanding what topics are driving engagement and key performance objectives
  • Social media-understanding the topics and sentiment of readers' contributions, social media, and feedback


"In 2014, the biggest development was that content analytics itself be­came more mainstream and widely accepted. Also, because of the in­crease in power and influence of so­cial channels, the nature of analytics shifted to embrace more of text ana­lytics and semantics understanding," notes Holla.

The Google Hummingbird pro­ject-a new search algorithm with an emphasis on matching the meaning of phrases with concepts rather than just matching the individual words in a query to documents-"pushed the boundaries even further." Holla also believes the rest of the content analytics industry will follow suit, finding ways of better leveraging con­tent in the future.

Evan Carothers, co-founder and CEO of Docalytics, witnessed dis­par­ate systems becoming inter­connect­ed in 2014, "with CRM [customer relationship management] becom­ing the backbone of how many brands are building their content and analytics strategies. Also, we have seen the rise of many content production and planning tools like DivvyHQ and Kapost, as well as the announce­ment of Salesforce's Wave analytics platform," which is an industry game changer.

"For the first time, publishers have the tools and technology to under­stand the impact of content across channels, including social, blogs, 

and now even long-form content like white papers, ebooks, and case studies. Publishers can now see how not only groups and audiences engage, but also how individual, high-value pro­spects engage with different types of content," adds Carothers.

Another notable shift was the wide­spread expectation change to real time. "In earlier content analytics applica­tions, processing was always batch and usually offline-as it was normal­ly about analyzing archived content," explains Carothers. "However, cus­tomer demands on applications-and therefore organizational expecta­tions of vendors-has meant that real-time processing has become the standard in 2014."


Davies suggests that 2015 will birth greater dissemination of con­tent analytics approaches within a variety of applications. This will come from a few key drivers:

  • The macro trend of time, attention, and purchases online, thereby generating an increasing volume of content to be analyzed and retrieved
  • A consumer expectation for every aspect of life to be recorded, measured, and analyzed
  • A need for organizations to understand people (customers, prospects, advocates, critics, and policy makers, etc.)
  • A demand for personalization of content and services, which requires a real-time understanding of context, intent, and journey

"I think what we're going to find in 2015 and beyond [is] content ana­lytics providing structure to unstruc­tured information," predicts Ludlow. This includes "providing tags and oth­er information about the text found in the content. This allows organi­zations to provide analytics on large lines of unstructured text and allows them to mix and match it into their existing analytics capabilities. We're going to see more and more analytics against content as we mix and match unstructured, structured, and semi-structured information into Big Data." Ludlow believes that content analy­tics is going to be an integral part of the Big Data movement because of its unique ability to pull structured information out of unstructured text.

With that said, content analytics software still has some challenges to overcome, according to Carothers: "New data provides insight into how content is resonating across audiences, but there's still work to be done to provide actionable analytics across all channels and formats-including long-form content. It won't be until brands have access to more compre­hensive data that they'll gain truly valuable insight."