Content management isn’t easy. Dealing with a wide range and high volume of content, especially in an age of increasingly dynamic customer-facing applications, can be daunting. But analytics can provide insights that improve content management.
Even simple tools such as Google Analytics can be a strategic plus. For example, it’s not unusual for a small percentage of a site’s page hits to pull in most of the site’s traffic. Google Analytics is great way to track website traffic and determine what content is effective and what’s not. How much time viewers spend on a page is another, the keywords that brought them there and the webpages they came from are also discoverable via Google Analytics.
Those are all pretty basic metrics, but Google Analytics also offers more nuanced reports.
It can measure bounce rate, the number of viewers who leave the site after only a single page view, which can indicate that a page is not engaging. Google Analytics can also measure the geographic footprint, which indicates potential markets as traffic expands. Content managers can use geographic footprint data to parse those markets and prioritize mobile versus desktop development.
Behavior flow is another metric available in Google Analytics. It tracks site visitors’ viewing patterns and suggests their search intent. The examination of these sources can indicate what types of advertising work best, compared to organic page views.
Google Analytics is only the beginning when it comes to analytics improving content management. Whether using Google or some other analytics tool, it is possible to automatically elevate, or reposition, popular content within a site. News websites frequently employ this strategy so that the more hits a piece of content gets, the higher up on the homepage it appears.
While not available within Google Analytics, predictive content is another valuable analytics tool in content management. Predictive content uses customer data to determine how receptive new content will be with customers. This functionality isn’t canned and generally requires a customized implementation and high interoperability in the content management system.
Predictive content tools aggregate all site traffic data over time then apply machine learning to that data. While it’s important to look at specific patterns within site traffic data, it’s even more useful to examine overall trends in site usage. For example, this analytics tool can tell you how the behaviors of viewers have subtly changed since the last time they were analyzed, as well as the trending of those changes and a deep dive into what is causing them.
Content managers can use predictive content tools to find useful insight to gauge the direction of viewer interests. It also enables them to fill the pipeline with appropriate content ahead of time, ensuring maximum viewer interest and engagement.