Content Analytics is a process of website data collection that allows to track visitors’ actions with the aim of adjusting and improving content strategy.
Content analytics is based on a certain framework to process a large amount of data to get insights into how effective the content strategy is.
The Purpose of Content Analytics
The purpose of installing content analytics software is to display a complete picture of user interaction with the content — from acquisition channels to measuring content effectiveness and ending with user behavior on a site. This picture allows to:
- Explore audience deeper
- Understand how effective a current content strategy is
- Define pool of tactics for experiments (for example, A/B testing of headlines or any page element)
Real-time analytics also shows what content should be promoted here and now for receiving instant value from trending content (used in media).
Overall, installing content analytics software makes content marketing more effective. It allows content teams to focus on developing content capable of acquiring target audience and converting it into subscribers/customers.
Historical data and Real-time data
Content analytics mainly collects and processes historical data from the site, but sometimes it can provide real-time data (for companies that need to make decisions fast — for example, news websites).
What does content analytics report?
Content analytics measures indicators in 4 categories:
- Content performance
Here it shows how well the content is contributing to achieving business goals through such metrics:
- Number of unique visitors
- Number of returning visitors
- New vs. returning visitors ratio
- Time spent on page
- Number of likes, shares
- Readability score
- Headline effectiveness — CTR (click-through-rate)
- User behavior
Tells how users behave on a site — their preferences and the way they interact with content and separate elements.
- Visitor path
- Interaction with individual page elements
- Acquisition channels
Shows where readers come from.
- Acquisition channels (direct, organic, paid, referral, social)
- Shows which campaigns worked best
- Shows which keywords drive traffic
History of Content Analytics
In the pre-internet era, when no one knows what online content is, media companies used various offline methods to measure audience engagement.
A theory that pressed upon the importance of audience research was created by Daniel Starch. He first stated that advertising has to be seen, remembered, and actionable. Daniel, along with his like-minded companions, surveyed people on the streets to find out if they remembered ads in the publications they read.
The post-war economic expansion gave rise to qualitative audience research — at that time focus groups emerged.
Undoubtedly, a breakthrough in qualitative audience research belongs to Nielsen, the developer of Nielsen Ratings — a system that allowed to track what TV channels people watched. The device he invented was called “Audimeter.” They attached it on a television to record the channels viewed.
In 1978, Nielsen introduced “People meter” — an upgraded device about the size of a book. It came with a special remoter that identified the viewer’s age and sex. Those two devices together gave insights into individual viewing habits (who watched what and when).
With the advent of the Internet, audience research came to a new level. Content production as a part of digital marketing requires content analytics to come hand-in-hand to make the whole process effective.
Today many companies integrate content analytics tools to understand how effective their content strategy is.
Media companies that transformed into digital media also use content analytics software, mostly real-time analytics, to analyze user behavior in real time.
Web analytics vs. Content analytics
Content analytics as a term is not widely known yet. Many companies that do content marketing, as well as media companies are in the stage of area researching. Many integrate web analytics tools (like Google Analytics) for the purposes of analysing content performance.
Web analytics is a more broad term. Web analytics tools make all the same things as content analytics tools. The difference is in the amount of data — it’s very large in web analytics software. Usually, such tools have problems with the quality of data delivered, especially during the breakdown of data. There are also issues with behavioral data delay.
Content analytics allows to look at the user interaction with content more narrowly. Content analytics tools are designed specifically to measure the effectiveness of content and focus on most important metrics in this relation.
To make the use of a tool as convenient as possible, content analytics tools place an emphasis on very comprehensive data visualisation. This directly influences a content team’s ability to make fast and informed decisions.
Content analytics tools
User behavior analytics
Problems with content efficiency measurement
The most important problem with using content analytics tools is an inability to define which metrics should be tracked to improve content marketing.
Today market is represented by a handful of tools that can be used for content analytics.
To pick up the most appropriate solution, a company has to think about what metrics are essential to track in their case.