by 57% with 
(You are reading part 2 of a three post series on Dimension Widening USE-CASE. We also recommend you to read part 1 & part 3 in the series)
Data Integration has been one of the key challenges for analysts. We absolutely love Google Analytics for it capabilities for displaying clickstream data but we seldom wish it had the ability to import external data. Why you may ask that integration is required?
Well, say for example if you had the ability to combine your site’s SEO data with Google Analytics Page Level Metrics, a lot of interesting opportunities would open up. We decided to give this a shot by leveraging the newly released Dimension Widening Feature in Universal Analytics.
In this blog post we bring you the integration of SEO data into your Google Analytics data. (Read our
previous post to understand why Dimension Widening is important and how to use the same)
Objective of USE-CASE
SEO data is categorized into on-page and off-page data. On-page optimization refer to factors that affect your Website or Web page listing in natural search results. These factors are either controlled by you( for e.g. lengths of Title tag, H1 tag etc) or by changing the code on your page.
These on-page data points affect the visibility of your website on the search engines and also the click through rate, making them important data points.
Important factors taken into consideration for this use-case are:
- Page Title Length: The character length of the Page Title
- Meta Description Length: The character length of the Meta description
- H1 tag: The first H1 (heading) tag on page
- H1 tag length: The character length of the H1 tag
- H1 tag occurrences: Number of H1 tags on the page
- Internal links: Number of internal links
- External links: Number of external links
Now let us see how to finally widen the dimension for SEO data.
Dimension widening Steps
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Extracting SEO data
To get the SEO data, you can use any SEO tool that can provide all the above mentioned on-page data points. We have used seoClarity to get the SEO data. Now create a csv with all these data points. -
Create Custom dimensions
All the above mentioned on-page factors are not available in Google Analytics, so we need to create custom dimensions for each of them.
The above image shows two onpage data points. We need to create such custom dimensions for all of the onpage data-points.
Once the custom dimensions are created, use the same naming conventions which GA provides in the CSV prepared in the first step. For example “ga:dimension1” for “Page title length”.
Now you might be wondering that why these data points are to be entered as custom dimensions rather that custom metrics. It so happens that all these data-points are in the form of integer values which means they should be set as custom metrics in Google Analytics. But, when we were in process of implementing this use-case, we found some calculations happened on custom metrics with the hit level data. This leads to incorrect numbers of the on-page factors. To solve this problem, we have used custom dimensions instead of custom metrics for all these data points.
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Create and upload the data set
The steps for creating and uploading this data in Google Analytics and creating data set are explained in my previous blog.
While creating the data set, make sure you make ga:pagePath as the dimension key and all the other factors as custom dimensions . Below image shows the custom dimensions created for this use case.
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View data in custom reports
This report shows the length of Title Tag on the page. Based on this data, you can make the change in the title length of the pages to improve their performance in search engine.
This report shows the H1 tag occurrences in the page
You can also see the full on-page report using data feed query explorer
Insights from the USE-CASE
The above use-case gives us the following insights (which inspite of being known are not taken care of many times). There are few points that need to be taken care for onpage data:
1. Page title length: If the title is under 55 characters, you can expect at least 95% of your titles to display properly.
2. Meta description Length: Meta description tags are not important to search engine ranking but are extremely important in gaining user click-through rate of the page.The optimal length for it is 155 characters. When we further analyze the data, we see relationship between length of meta description and click
through rate for a given page. Crisp and concise meta description naturally yield more click through rate.3. H1 tag: H1 Tags containing keywords rank fairly well compared to the less optimized tags.
Stay tuned for the next use case for Off-page data in our next blogpost. Also if you have any USE-CASE in your mind that you want us to implement, feel free to Contact US
Check out our Third USE-CASE (Part 3) on Dimension Widening and learn how we integrated Off-Page SEO Data like Backlinks, PR, Page Authority, Citation Flow & Trust Flow into Google Analytics and generated awesome insights. If you want to learn more about basics of Dimension Widening Check out our Part 1
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Yaman Patel
Yaman Patel is a Technical Analyst along with being an enthusiastic Customer Success Manager at Tatvic. He is one of the proud pioneers of Tatvic's dataLayer automation tool(DLAT). He is keen on learning business intelligence technologies.HE loves to travel and explore new places.
4 Comments. Leave new
Great use case for dimension widening Bhoomika! Dimension widening can be certainly used in many interesting ways and I loved your example here. I have used dimension widening to associate the development costs for several landing pages we manage for different segments.
Thank you Mohit
Cheers!
great post….. I want to subscribe to the blog but the top RSS button doesn’t work
Hi Julian, Thank you for your comment. The RSS button is now fixed.
Cheers! 🙂