7 Search Ranking Factors Analyzed: A Follow-Up Study

Back it on up...

A quick refresher from last time: I pulled data from 50 keyword-targeted articles written on Brafton’s blog between January and June of 2018.
We used a technique of writing these articles published earlier on Moz that generates some seriously awesome results (we’re talking more than doubling our organic traffic in the last six months, but we will get to that in another publication).
We pulled this data again… Only I updated and reran all the data manually, doubling the dataset. No APIs. My brain is Swiss cheese.
We wanted to see how newly written, original content performs over time, and which factors may have impacted that performance.

Why do this the hard way, dude?

“Why not just pull hundreds (or thousands!) of data points from search results to broaden your dataset?”, you might be thinking. It’s been done successfully quite a few times!
Trust me, I was thinking the same thing while weeping tears into my keyboard.
The answer was simple: I wanted to do something different from the massive aggregate studies. I wanted a level of control over as many potentially influential variables as possible.
By using our own data, the study benefited from:
  • The same root Domain Authority across all content.
  • Similar individual URL link profiles (some laughs on that later).
  • Known original publish dates and without reoptimization efforts or tinkering.
  • Known original keyword targets for each blog (rather than guessing).
  • Known and consistent content depth/quality scores (MarketMuse).
  • Similar content writing techniques for targeting specific keywords for each blog.
You will never eliminate the possibility of misinterpreting correlation as causation. But controlling some of the variables can help.
As Rand once said in a Whiteboard Friday, “Correlation does not imply causation (but it sure is a hint).
Caveat:
What we gained in control, we lost in sample size. A sample size of 96 is much less useful than ten thousand, or a hundred thousand. So look at the data carefully and use discretion when considering the ranking factors you find most likely to be true.
This resource can help gauge the confidence you should put into each Pearson Correlation value. Generally, the stronger the relationship, the smaller sample size needed to be be confident in the results.

So what exactly have you done here?

We have generated hints at what may influence the organic performance of newly created content. No more, and no less. But they are indeed interesting hints and maybe worth further discussion or research.

What have you not done?

We have not published sweeping generalizations about Google’s algorithm. This post should not be read as a definitive guide to Google’s algorithm, nor should you assume that your site will demonstrate the same correlations.

So what should I do with this data?

The best way to read this article, is to observe the potential correlations we observed with our data and consider the possibility of how those correlations may or may not apply to your content and strategy.
I’m hoping that this study takes a new approach to studying individual URLs and stimulates constructive debate and conversation.
Your constructive criticism is welcome, and hopefully pushes these conversations forward!

The stat sheet

So quit jabbering and show me the goods, you say? Alright, let’s start with our stats sheet, formatted like a baseball card, because why not?:
*Note: Only blogs with complete ranking data were used in the study. We threw out blogs with missing data rather than adding arbitrary numbers.
And as always, here is the original data set if you care to reproduce my results.
So now the part you have been waiting for...

The analysis

To start, please use a refresher on the Pearson Correlation Coefficient from my last blog post, or Rand’s.

1. Time and performance

I started with a question: “Do blogs age like a Macallan 18 served up neat on a warm summer Friday afternoon, or like tepid milk on a hot summer Tuesday?
Does the time indexed play a role in how a piece of content performs?

Correlation 1: Time and target keyword position

First we will map the target keyword ranking positions against the number of days its corresponding blog has been indexed. Visually, if there is any correlation we will see some sort of negative or positive linear relationship.
There is a clear negative relationship between the two variables, which means the two variables may be related. But we need to go beyond visuals and use the PCC.

sumber : https://moz.com

No comments for "7 Search Ranking Factors Analyzed: A Follow-Up Study"