How To Get 34% Increase In Traffic By Optimizing Your Existing Content

Enter Google Search Console API.

Its API comes in handy in these situations, because you can pull all the data and use it somewhere else. In addition, you have no limit to what you can pull and you can export queries with the page that ranks for it and the metrics related. Not only is this quite crucial for the success in SERP, but it also makes a good solution to common problems such as not provided keywords in Google Analytics.

The bigger your website gets, the harder it is to manage your website's presence in the SERP. And, let's be honest, the majority of website owners don't go back to review the existing content and see its performance, but rather checks it in bulk and as a main tool for driving more traffic uses new content. While logical and a valid solution, often, we'll realize that there are hidden gems in the old content, too.

Now, not all tactics are so logical either, sometimes when you look back, you can see that the majority of your existing content doesn't really drive any traffic and, to reduce the number of pages link juice gets spread to, you might actually choose to delete it and, opposite of common sense, increase the organic traffic.

However, we don't go that far, and this approach is all about getting on top of what you have when it comes to the existing content, which keywords are you ranking for and which keywords could you rank for if minimum targeted effort is invested.

There are few ways to approach this and one of them would be to use a programming language to query the Google Search Console API for which I've chosen R. And, another would be to use a Google Sheets add-on called 'Search Analytics for Sheets'. If you're less technical, the obvious choice would be the add-on, but if you like to get your hands a little bit dirty you go with R.

The reason why I started doing this process with R was because the add-on didn't work for quite a long time due to changes in GSC API, but prior to writing this, I've checked and, surprisingly enough, it worked. However, it often has authorization issues for which I haven't found a solution except for using something else for pulling the data. Therefore, the guide itself will include both ways.

While R might not be the first choice for the majority of people, I find it pretty comfortable for working with APIs and manipulating data on a scale. For the purpose of optimizing content and this small tutorial we'll use R just to pull the data from GSC and the rest we'll do in Google Sheets. The package we're using is developed by IIH Nordic's very own Mark Edmondson and it has saved many SEOs and Web Analysts.

To use it you'll have to obtain Client ID and Secret which you can do using the Google API Console.

The purpose of the last function is to provide you with a path to the CSV file you have created, but as an alternative, you can use setwd() with a path where you'd like it to create a file.

After you have done this, import the CSV file or paste the data into the Google Sheets and continue reading this article from the next section.

Note: at this stage, you won't have the option "Search Analytics for Sheets".

Once you click "Get add-ons", you should search for "Search Analytics for Sheets" and install it.

Go again to Add-ons, but now, since you have the option, go to Search Analytics for Sheets and then 'Open Sidebar'

From the options, at this stage, you only need "Query" and "Page" and then just press "Request Data" at the end. However, if you are targeting a specific country with a language other than English you should also use the "Filter By" option, choose "Country" and write down the country you're targeting.

From here, you'll have a table that should look like this:

Press Cmd + A (or Ctrl + A on Windows) to select the whole table and then, Data > Pivot Table.

As for the settings, the pivot table should contain the following:

Rows:

• Page, Order: Ascending
• Query, Order: Descending, Sort By: SUM of Impressions (can be added after adding impressions as values)

Columns: None

Values:

• Clicks, Summarize By: SUM
• Impressions, Summarize By: SUM
• CTR, Summarize By: AVERAGE
• POSITION, Summarize By: AVERAGE

Filters: None

To make it easier to read and work with, you can select the entire column where CTR is and format it as a percentage and the entire Position column and reduce the number of decimals to one or two.

Now it's time to select the pages we're going to work with. We're looking for pages that have keywords with a significant search volume (read: impressions) while ranking roughly between 8th and 12th position. To do that, first we'll identify the pages with enough impressions:

From there, we'll open to see which keywords are there for each of those to find keywords we want to target better with the page. From this particular example, after opening up the queries, I have identified 3 keywords I'd like to target better:

Now that I have the keywords my page ranks on a position where if it moves up by few positions, significant results can be achieved, I would do some of the following to get there:

• Adjust the title to better address the keyword (of course, you don't want to kill your rankings for keywords with higher search volume if you have that already)

• Address the keyword I'd like to target in the first paragraph

• Create another paragraph to address the keywords

• Build links towards this page.
  

After an initial smaller drop in the traffic, two weeks after, Google started picking up the changes and we started seeing significant, positive change in the organic traffic. In total, the changes accounted for an increase by 34.17% in Users and 34.15% in Sessions which can be seen on the screenshot below.

As for the individual articles, here are the 3 most important ones when it comes to the effort put: