Data analytics are a feedback mechanism for content providers. Used properly, data can tell you a great deal about the content you have and how readers engage with it. The key to gathering good data: Begin with the end in mind.
The content you have
Let’s start by taking a look at the content you have. Historically, content has been tied to the containers used to distribute it: a newsletter from the first quarter of 2011, an issue that came out in June 2007 or the hardcover version of a particular title, as examples.
That worked reasonably well for content storage, less well for content retrieval. I’ve met plenty of editors who specialized in serving as “content savants”, pointing to the version, issue or format in which content was first or last offered. In those settings, “content management” was the person who knew the answer to “Where could I find…?”
Many of those people are gone now – lost to retirement, job eliminations and career changes. Retrieving content now requires analytics – data that goes well beyond when and where you printed something. Unfortunately, a great deal of content created for print was never structured or tagged to support reliable retrieval.
How readers engage with your content
A simple thought experiment: make yourself a reader for a moment. Pick a topic of interest, something you might want to find in a content archive. Now decide: how likely is it that you’ll search by format or issue date?
Unless you are conducting research, the answer is almost certainly “not likely at all.” We search by topic, often using natural language. The average content consumer isn’t going to know an ISBN or the newsletter in which a particular article appeared.
Because a great deal of content created for print was never structured or tagged to support reliable retrieval, publishers have had to decide how much time, energy and resources they want to invest in creating that structure. The answers vary by content provider, but there’s a common theme for successful efforts.
Begin with the end in mind
In structuring (and restructuring) content to support effective retrieval, it’s important to begin with the end in mind. Clearly articulate the problem you trying to solve.
Perhaps you’re sitting on a gold mine of useful content, but you don’t have a reliable way of finding what you need. Here, the measures of success are internal: less time spent looking for a particular article; widespread access to themes or ideas that are important to your writers and editors; or easy integration of past and current content into a new offering. Think about the data that matters before you start a project.
Alternately, you may be trying to serve an audience that can’t yet see how much rich content you offer. Perhaps those great “do’s and don’ts” lists that you run every month are buried inside an article whose headline and content hides the list itself. Maybe there’s a great graphic – a timeline or a chart your staff spent weeks fine-tuning – that can’t be found in a text-only search.
In such cases, you’re going to have to make choices about the things that matter most. Restructuring content to provide the results readers want can be an expensive investment.
Data analytics can play a key role in shaping the investments you make. Look at what people search when they come to your site. Study the time they spend with your content and how often they stay to look at other articles or resources. And then match those analytics to what you know or should know about your own content.
In these ways, data provides you with a feedback mechanism. Understanding the content you have and how readers engage with it are two ways to direct your content strategy on an ongoing basis.