What Is Content Analytics and Who Needs It?


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There are many different kinds of analytics in today's world of technology and information management: web analytics, predictive analytics, social analytics, etc. If you work closely with a web CMS or an ECM system, you have probably run into the notion of content analytics. But chances are good that no one ever bothered to explain what it is exactly and how one uses it.

In general, analytics is about gathering meaningful insights from batches of data from various sources; the Big Data, if you wish. For example, if you've used Google Analytics, you were able to gather such insights as the number of visitors to your website, the number of clicks a piece of content received, and the location of your audience.

When content is the heart of your business-and for many organizations it is-the journey doesn't stop at web analytics. More and more often the interest grows beyond those simple numbers and into the land of content analytics as a separate yet connected discipline.

Let's say you have a web CMS and you're able to push out content based on some business drivers and goals that you have established. Now what?

In the early days of WCM's evolution, simply getting the content out to the website without having to call in all the favors you had in the IT department was a huge accomplishment. Schedule a press release to go out next Monday at 6 a.m. PST-even better, the sweet taste of success!

But that was in the early 1990s. Nowadays, for better or for worse, things are a little more complicated. We are not just authoring and publishing content anymore; we also want to make sure the content is useful to our audiences and that they respond to it in a way that serves our purposes. Content-related patterns and trends, actionable insights, key performance indicators, benchmarks, etc., that are available via content analytics may help to drive an organization's growth or to measure and reach some other business goal. And this is why we start tinkering with content analysis.

Content analysis in an enterprise is rather intertwined with many other notions, such as personalization, segmentation, and dynamic delivery of content that is relevant to specific people at specific times on specific devices.

Having more content doesn't necessarily mean you are better off. In fact, the more you have, the more work you need to do to analyze and process your content, making sure you have intelligent analytics tools to help you do that. This is why Business Intelligence and Big Data are not just industry buzzwords (well, at least not in most cases). Data aggregation, analysis, reporting, visual mapping in charts and dashboards, and insightful meaning are all part of the modern lives of content managers, strategists, and marketers. In other words, if you cannot measure and analyze content, how do you know it is working? How do you know it is delivering the message you intend to the audience you need?

Content analysis, despite its long history-going all the way back to early 1900s in the field of linguistics-is not easy to do. Not only are you likely to have to find a specific tool that was designed for this job, make sure that the built-in search engine is powerful enough, and implement what needs to be in place, you will also need to streamline many of the internal institutional processes you have (or don't have) in place. Metadata management, tagging and classification, ontologies, and controlled vocabularies are prime examples.

Moreover, if you start taking content analytics more and more seriously, you will start delving into such topics (and related technologies) as contextual discovery, semantic profiling and analysis, language and regionalism variation detection, sentiment analysis, predictive analysis, natural language queries, multilingual search, linguistic modeling-the list can go on and on.

In the end though, the goal is not to chase after the seemingly cool features and functions, but to establish your content strategy first (supported, of course, by overall business goals and objectives), and then look into selecting an analytics tool that helps you reach your goals. Leave the unnecessary bells and whistles to the software vendors for their next rounds of sales demos.