Reporting on Ranking Changes with STAT’s Google Data Studio Connectors

Posted by ipfister

Since Moz exhausted the brand-new Google Data Studio Connectors for STAT, you might be wondering how to best implement them for your reporting strategy. My peers at Path Interactive and I perfectly love how granular you can get with your reports in STAT, and finally having the ability to cleanly and efficiently pull those reports into Data Studio( appropriate tools we use for our own reporting) is a godsend.

While the Historical Keyword Rankings connector reports on rank over season, it may not be as obvious how to report on rank change over time. In this post, I’ll give you step-by-step guidance on how to report on rank change — as well as a duet other filtering and reporting tips-off — while using the connectors within Google Data Studio.

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Connecting your data source

Before you begin, you need to identify a few things to set up the connector: your STAT Keyword API Key, the Project ID, and your Site ID. If you don’t once know how to identify these via the STAT API, you can head over to STAT’s documentation here to learn more. After you’ve linked these, it’s time to connect your data source.

We’re going to be doing something a little out of the ordinary now, but stay with me — you’ll accompany why in just a second!

For this step, we’ll be connecting two instances of the same source. Because our goal is to compare rank change over time, we’ll use the same source twice to identify those deltas.

When setting up your connector, be sure to name the source something that you’ll easily recognize πŸ˜› TAGEND

In my suit, I often go with something simple such as “[ patron word] STAT Keyword Connector.” When this is complete, recur the step above, but figure it something different, e.g. “[ buyer refer] STAT Keyword Connector 2. ”

Finally, make sure the metrics you plan on comparing have unique identifies for each connector. To do so, go into your data source. Click on the metric’s name so that you can rename it, and then rename it something unique. For this case we’ll be doing it for “Google Base Rank, ” since we’re comparing grades, but it can also be done for “Google Rank, ” if we wanted to compare that. Again, I are happy to just keep it simple: for the first data source call it “Google Base Rank 1, ” and then for the second data source call it “Google Base Rank 2. ” When all is said and done, it should look something like this πŸ˜› TAGEND Building your table and mixing data

Now we’ll start to get a bit more technical. Blending the data of the two connectors gives you equate two the case of an rankings against one another. Your final result will induce a table appearance the ranks of two contributed appointments, as well as their rank conversion. The five-step process will look like this:

Blend data of keyword connectors one and twoAdd in your common metrics for the two roots( keyword at the minimum, but you are eligible to computed in spot, machine, market, and search work) Add in the metric you’d like reported( Google base rank and/ or Google rank) Set date rangeApply “No Null” filter 1. Blend data of keyword connectors one and two

The first step here is to blend the two connectors so that you can compare two the case of an grades against one another.

First, you need to create a new report, or go into a report that’s already set up. Next, hand-picked your data source. Here you’ll select the first instance of the source that you set up earlier( if you’re starting on a fresh report, it’ll ask you to add a data source immediately ). Once selected, click on “Blend Data” underneath the data source on the right hand side of Google Data Studio, seen here πŸ˜› TAGEND

This will bring you to the Blend Data source tool. From now you select to add another data source, being your second speciman of the connector.

2. Add in your common metrics

Once you’ve chosen to blend both connectors, you need to set your metrics. Towards the top, you’ll view “Join keys.” This is in reference to what’s going to be the same for both instances, so here at the minimum, you want to include “keyword.” Feel free to play around here with supplementing different metrics.

Note: We’ll go over this later, but if you plan on having different diagrams filtered by a certain tag or place, make sure to add these in here.

3. Add in the type of rank you miss reported

After setting your metrics under “Join keys, ” now select the metrics that will be unique for each year. Depending on what you want to compare, under “Metrics” you’ll pick “Google Base Rank, ” “Google Rank, ” or both. You may also include “Date” here too if you’d like. Once done, click on “SUM” next to the metric name, and change this to “MIN.” You’ll experience why in just a moment.

At this part, your melded data should look something like this:

4. Set year series

Now you need to set the two date strays you’re equating to one another.

To do this, for the purposes of the firstly contact, named your first year: Under “Date Range, ” click on “Custom, ” then click on the field to select your year. Now you might see that there’s an option for two years, but for this solution, we’re using the same date for each connector.

In the end, it’ll be something like “Connector 1” selected for the “start date” and “end date” as the first of the month, and for “Connector 2, ” the “start date” and “end date” is likely to be the last of the month. This is essentially attracting in the grade for the first instance as well as the second instance, so you can compare the two.

5. Set “No Null” filter

The last step in setting up your coalesced data is creating a “No Null” filter. When the keyword connector reports on grades that your website is not ranking for, it will return as “null.” To eschewed inundating your data with flub, you need to create a filter removing instances of “null.”

First, click on “Add A Filter” below where you selected the time scope. Next, towards the bottom, click on “Create A Filter.” Set the parameters of the filter as “Exclude”> “Google Base Rank 1( 2) ”> “Is Null.” Be sure to reputation the filter something identifiable such as “No Null.” It should look like this:

Applying grade change to your report

Now you can create a new province that will report on the rank alter by making a calculated arena to find the difference of the two ranks.

Under aspects, select “Add Dimension, ” and click on “Create Field.” You can words it “Rank Change, ” but to create the field, start typing “Google Base Rank, ” and you’ll see your specimen from each connector come up. To prepare the calculated orbit, select your “Google Base Rank 1” and subtract it from “Google Base Rank 2, ” so it should look something like this:

Hit apply, and now your rank deepen shall be determined!

There is also an additional way to get the same result, but with a few cases flaws, such as not being able to name the header, as well as not being able to filter or sort your rank alteration. The benefit to this approach is that it’s easier to set up initially, as you don’t actually need to blend the data. However , not setting up the blended data will too surrender having the initial grade noticeable. When in your edit thought, named a custom-made time compas that you’re reporting on under “Default date range.” Here, you can then specified a analogy year: if looking back a month, you are eligible to designated this to the firstly. If you go with this option, it should look like this πŸ˜› TAGEND

Head into the “Style” tab, where you can change the comparison to “Show Absolute Change” under “Metrics.” You can also change the colorings of your positive and negative arrows to more accurately represent the movement( you can see from above that the “negative” change is a green arrow, this defaults to red ).

Using filters

Applying filters to your data set can be extremely beneficial to making sense of your data! Using filters with the connector can help you segment out standings for a particular location, or initiate maps that substantiate ranks for a specific keyword group that you’ve set up utilizing keyword tags.

Take a look at their respective reports I set up as two examples. Within STAT, I started keyword labels to target points are defined in what zip code they were. Then, I was able to create a filter for each planned targeting that keyword label πŸ˜› TAGEND

Setting filters up is extremely simple. First, go into edit mode. Next, scroll down the side until you find “Filter.” Then under Filter> Table Filter, click on “Add a Filter.” This will bring you to the filter picker. Toward the bottom, click on “Create a Filter.” Here you can determined the parameters for the filter you’d like to show.

Some of my other favourites include filtering to only show the top few pages( filters out non-relevant and high-pitched ranks ), abusing the keyword tag filter like I evidenced before, and too filtering by orientation. But you don’t have to stop there! Adding in the additional dimensions available to you in the connector, you can use the filter to show things such as desktop vs. portable or how your keyword ranking carry-on does in different markets.

Blending your Google Analytics, Google Search Console, and STAT data

One of my favorite expends for the connectors is the ability to blend the data with your Google Analytics and Google Search Console data. By blending this data together, you’re able to instantly bind keyword rankings with different metrics, such as clicks or destination completions.

You’re probably a pro at coalesced data at this item, but just for reference, the data coalesced should look like this πŸ˜› TAGEND

A few things to note: it’s important what ordering you articulated the connectors in. I’ve found that adding the STAT connector firstly works best( i.e. if you placed Google Analytics firstly, you’ll get a report with the loathsome “not found” keyword ). Additionally, to pull in Search Console data in order to match with your other connectors, exercising “Query” will have the same effect as “Keyword.”

The result would look something like this, but feel free to revise the specific characteristics how you wish!

Now you can go even further with this and match up URLs, but this will require some RegEx.

You’ll rename the “Google URL” field in STAT and “Landing Page” field in Google Search Console in order to match the URL structure convection within Google Analytics by taking out the domain portion of the URL. To do this, go into your data source for each STAT connector and Google Search Console, and click “Add A Field” in the top right.

Next, open to following RegEx for the STAT connector πŸ˜› TAGEND

REGEXP_REPLACE( Google URL, ” .*[\\.] com”, “”)

And for Google Search console:

REGEXP_REPLACE( Landing Page, ” .*[\\.] com”, “”)

Remember to specify them something to differentiate from the default field. I use “Landing Page( no land ). ”

When building a report, use these new orbits for consistency across the URL structure so that, when you select them when melding data, they’ll match.

Use this method in the same way as above to get the desired results of pluck in data from across all three connectors to match up with each other! In the end you should be able to find what keyword grades for what URL, as well as have many hearings or clinks that are brought in as well as goal endings, or any other combination.

Well there you have it! Hope this was helpful to you. If you have any other questions you can comment below or find me on Twitter @ianpfister. Happy reporting!

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