As is customary this time of year, we look back at the year that was. What were the top posts of the year? What caught your eye, convinced you to click, and compelled you to share?
I’m a fan of looking at our marketing through more than one lens. This week, we’ll walk through my blog from a few different points of view, and see which perspectives make the most sense. We’ll conclude by using what we’ve learned to set a strategy for 2018.
Basic Blog Analytics
We’ll begin by preparing our data. I love the Google Analytics Addon for Google Sheets. It’s a fast, easy way to extract a lot of data about a website. We’ll combine that with social media sharing data (tools like BuzzSumo can extract this) and SEO data (tools like Moz can extract this).
All these services provide APIs to connect to, as well as relatively friendly user interfaces. For the occasional ad hoc report, as this one is, putting the data together in Excel is good enough. If this was a report we wanted to generate and review more frequently than an annual wrap-up, we would definitely want to automate it.
When we blend all three data sources together, we build a comprehensive look at our site’s performance.
First, let’s use any data analysis tool like Tableau to understand the basics. What are the top 10 posts by total number of users that I published this year?
Fascinating. One post did far better in terms of readership than almost every other post.
What about total social shares?
We see much less variance in social sharing – but the top shared post is not the top read post. Interesting.
What about pages which have earned search authority?
In search, in the ways our content serves our machines, we see significantly less variance than either users or total social shares. Compared to those two human metrics, search value from page to page is not drastically different.
Summary of Basic Metrics
What do we see? We see conflicting information; we see pages that do well by one metric but not by another, and pages measured by human metrics with substantially more variance than machine metrics.
What does this mean? It means we’ve got a bit of a mystery to solve. If we know our overall goal – in my case, total users reading the blog – we need to solve for that goal. What contributes most towards it? Which metrics should we pay attention to? Which metrics should we ignore?
To solve this mystery, we’ll rely on more sophisticated mathematics in the next post in this series. Stay tuned!
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