How often have you planned, created and implemented content, but failed to check whether it delivered on your original objectives?
Recently I wrote about using website data to discover content ideas that work, today I want to follow up on this by showing you the results of using data in this way.
Apollo Insights has been used as the main focus for demonstrating results as this gives me the ‘at a glance’ view and aggregation of the many data sets that I need.
For identifying some of these gains you could use Google Webmaster Tools (GWT), Google Analytics (GA) and a host of other third party tools. However, it is the access to everything in a single place that has made these results so meaningful.
Where useful, and as direct ‘like for like’ comparisons, I have also used GWT ‘before and after’ screen shots.
In my last post I said it was important to identify and fill gaps within GWT’s Content Keywords feature. The goal of this is to positively impact the relevancy, hierarchy and volume of commercial intent keywords being associated with your website through your content.
The top of this content hierarchy previously looked like this:
Now it looks like this:
Some of the most important gains seen from implementing the right content include:
Seeing that the new content you’ve created has positively impacted Google Content Keywords is great, but what does it actually mean?
Here is where Apollo Insights comes to the fore – below is a snapshot of a deep data grid. I’ve highlighted a few of the results items that I would be looking at:
Using our deep data platform and combining this with specialist expertise we are able to see:
The important message at this stage is not to see content creation as a single phase approach. Improving visibility and traffic from creating content that works is great, but the next stage is to continue perfecting that content for the changing needs of the visitors digesting it.
GWT is great for seeing how, over a 90 day period, content changes you made have positively impacted total site visibility or even impacted refined trends and topics over that same timeframe.
But what happens when you want to see how content you created 12 months ago has performed cumulatively since its inception?
What about the visibility of a single topic and how it’s changed and reacted to multiple revisions, additions and data driven changes?
This is where you need more comprehensive access to deeper data, which we can see this practically below.
The above looks like a finished job and a pat on the back right?
The initial wins are good; over a year we can see that content changes and additions have contributed towards more than a 100 percent increase in visibility.
OK, the impact has tailed off after 12 months, but can we still say that the content has done its job?
What happens when we decide to make another phase of content changes, refresh information and include actionable insights from looking at the bigger picture?
In the previous section we looked at a pretty comprehensive example of data driven content more than doubling impressions over a 12 month period.
Let’s look at what happens next, once we use trends data and associated Apollo Insights to boost the existing content on this topic further.
It’s important to note at this stage that trend analysis and data creation from trends is not possible on 90 day rolling period (via GWT) without many manual data tasks (or through Apollo Insights).
Looking at the above content example we can see:
I started this post looking back over content created, reminding you of the value of checking whether content met or exceeded initial objectives.
My intention with ending this post was to show you how next phase content wins are almost always available. These wins become even more actionable when you have the direct access to and insight from deep data, combined with the expertise to make the most of that information.
To leave you with one tip on creating content that delivers on your objectives: stop using disperse data sources and start using deep data; the best results come from seeing the bigger picture.
Lee is Head of Services at Vertical Leap.
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Categories: Machine Learning, Martech, SEO
Categories: Data & Analytics
Categories: Data & Analytics, Data Science