Posted by Jeff_Baker

Grab yourself a cup of coffee (or two) and buckle up, because
we’re doing maths today.

Again.

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.

Days live vs. target keyword position

PCC

-.343

Relationship

Moderate

The data shows a moderate relationship between how long a blog
has been indexed and the positional ranking of the target
keyword.

But before getting carried away, we shouldn’t solely trust one
statistical method and call it a day. Let’s take a look at things
another way: Let’s compare the average age of articles whose
target keywords rank in the top ten against the average age of
articles whose target keywords rank outside the top ten.

Average age of articles based on position

Target KW position ≤ 10

144.8 days

Target KW position > 10

84.1 days

Now a story is starting to become clear: Our newly written
content takes a significant amount of time to fully mature.

But for the sake of exhausting this hint, let’s look at the
data one final way. We will group the data into buckets of target
keyword positions, and days indexed, then apply them to a
heatmap.

This should show us a clear visual clustering of how articles
perform over time.

This chart, quite literally, paints a picture. According to the
data, we shouldn’t expect a new article to realize its full
potential until at least 100 days, and likely longer. As a blog
post ages, it appears to gain more favorable target keyword
positioning.

Correlation 2: Time and total ranking keywords on URL

You’ll find that when you write an article it will (hopefully)
rank for the keyword you target. But often times it will also rank
for other keywords. Some of these are variants of the target
keyword, some are tangentially related, and some are purely random
noise.

Instinct will tell you that you want your articles to rank for
as many keywords as possible (ideally variants and tangentially
related keywords).

Predictably, we have found that the relationship between the
number of keywords an article ranks for and its estimated monthly
organic traffic (per SEMrush) is strong (.447).

We want all of our articles to do things like this:

We want lots of variants each with significant search volume.
But, does an article increase the total number of keywords it ranks
for over time? Let’s take a look.

Visually this graph looks a little murky due to the existence of
two clear outliers on the far right. We will first run the analysis
with the outliers, and again without. With the outliers, we observe
the following:

Days live vs. total keywords ranking on URL
(w/outliers)

PCC

.281

Relationship

Weak/borderline moderate

There appears to be a relationship between the two variables,
but it isn’t as strong. Let’s see what happens when we remove
those two outliers:

Visually, the relationship looks stronger. Let’s look at the
PCC:

Days live vs. total keywords ranking on URL (without
outliers)

PCC

.390

Relationship

Moderate/borderline strong

The relationship appears to be much stronger with the two
outliers removed.

But again, let’s look at things another way.

Let’s look at the average age of the top 25% of articles and
compare them to the average age of the bottom 25% of articles:

Average age of top 25% of articles versus bottom
25%

Top 25%

148.9 days

Bottom 25%

73.8 days

This is exactly why we look at data multiple ways! The top 25%
of blog posts with the most ranking keywords have been indexed an
average of 149 days, while the bottom 25% have been indexed 74 days
— roughly half.

To be fully sure, let’s again cluster the data into a heatmap
to observe where performance falls on the time continuum:

We see a very similar pattern as in our previous analysis: a
clustering of top-performing blogs starting at around 100 days.

Time and performance assumptions

You still with me? Good, because we are saying something BIG
here. In our observation, it takes between 3 and 5 months for new
content to perform in organic search. Or at the very least,
mature.

To look at this one final way, I’ve created a scatterplot of
only the top 25% of highest performing blogs and compared them to
their time indexed:

There are 48 data plots on this chart, the blue plots represent
the top 25% of articles in terms of strongest target keyword
ranking position. The orange plots represent the top 25% of
articles with the highest number of keyword rankings on their URL.
(These can be, and some are, the same URL.)

Looking at the data a little more closely, we see the
following:

90% of the top 25% of highest-performing content took at least
100 days to mature, and only two articles took less than 75
days.

Time and performance conclusion

For those of you just starting a content marketing program,
remember that you may not see the full organic potential for your
first piece of content until month 3 at the earliest. And, it takes
at least a couple months of content production to make a true
impact, so you really should wait a minimum of 6 months to look for
any sort of results.

In conclusion, we expect new content to take at least 100 days
to fully mature.

2. Links

But wait, some of you may be saying. What about links, buddy?
Articles build links over time, too!

It stands to reason that, over time, a blog will gain links (and
ranking potential) over time. Links matter, and higher positioned
rankings gain links at a faster rate
. Thus, we are at risk of
misinterpreting correlation for causation if we don’t look at
this carefully.

But what none of you know, that I know, is that being the
terrible SEO that I am, I had no linking strategy with this
campaign.

And I mean zero strategy. The average article generated 1.3
links from .5 linking domains.

Nice.

Linking domains vs. target keyword position

PCC

-.022

Relationship

None

Average linking domains to top 25% of articles

.46

Average linking domains to bottom 25% of articles

.46

The one thing consistent across all the articles was a shocking
and embarrassing lack of inbound links. This is demonstrated by an
insignificant correlation coefficient of -.022. The same goes for
the total number of links per URL, with a correlation coefficient
of -.029.

These articles appear to have performed primarily on their
content rather than inbound links.

(And they certainly would have performed much better with a
strong, or any, linking strategy. Nobody is arguing the value of
links here.) But mostly…

Shame on me.

Shame. Shame. Shame.

But on a positive note, we were able to generate a more
controlled experiment on the effects of time and blog performance.
So, don’t fire me just yet?

Note: It would be interesting to pull link quality metrics into
the discussion (for the precious few links we did earn) rather than
total volume. However, after a cursory look at the data, nothing
stood out as being significant.

3. Word count

Content marketers and SEOs love talking about word count. And
for good reason. When we collectively agreed that “quality
content” was the key to rankings, it would stand to reason that
longer content would be more comprehensive, and thus do a better
job of satisfying searcher intent. So let’s test that theory.

Correlation 1: Target keyword position versus total word count

Will longer articles increase the likelihood of ranking for the
keyword you are targeting?

Not in our case. To be sure, let’s run a similar analysis as
before.

Word count vs. target keyword position

PCC

.111

Relationship

Negligible

Average word count of top 25% articles

1,774

Average word count of bottom 25% articles

1,919

The data shows no impact on rankings based on the length of our
articles.

Correlation 2: Total keywords ranking on URL versus word count

One would think that longer content would result in is
additional ranking keywords, right? Even by accident, you would
think that the more related topics you discuss in an article, the
more keywords you will rank for. Let’s see if that’s true:

Total keywords ranking on URL vs. word
count

PCC

-.074

Relationship

None

Not in this case.

Word count, speculative tangent

So how can it be that so many studies demonstrate higher word
counts result in more favorable rankings? Some reconciliation is in
order, so allow me to speculate on what I think may be happening in
these studies.

  1. Most likely: Measurement techniques. These
    studies generally look at one factor relative to rankings: average
    absolute word count based on position. (And, there actually isn’t
    much of a difference in average word count between position one and
    ten.)

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