This previous post walked through a process for selecting article topics based, in part, on Google data.
Here’s a little case study that shows how such a data-driven process can pay off.
Connected Futures is a brand journalism website aimed at CIOs and other business executives. It’s run by the giant networking company Cisco, and I worked with the site for a year-plus.
I looked at the site’s analytics and saw that most of the traffic coming from search engines went to articles on similar topics: “digital business”, “digitization”, “digital disruption”, things like that. In layman’s terms, this suggested what Google thinks the site is about. Articles closely related to digital business would stand a reasonably good chance of ranking in search. That’s where Connected Futures has the most authority, from Google’s POV.
So I started by comparing search volume for these top-level terms.
Digital transformation was not only a relatively popular term, but also rising in search volume, whereas the others are flat or declining.
So what new angle could we take on digital transformation?
The related topics at the bottom of a Google results page for “digital transformation” at that time suggested “digital transformation office”.
Perfect! I assigned the article What does a next-level digital transformation office look like? to a great freelance writer, Sharon Fisher.
That article is now the second organic result on the Google search for “digital transformation office”, ranking above material from heavyweight sites like McKinsey, Deloitte, SAS, and Microsoft.
That means a steady stream of traffic to that richly reported article (20 months later, and counting), on a topic that’s well targeted to the site’s intended audience.