One of the best and most often used programmes for monitoring and analysing the traffic to your website is Google Analytics. However, did you know that you may also export your Google Analytics data to Google cloud-based data repository, BigQuery?
BigQuery is a scalable and adaptable platform that lets you store enormous datasets and perform SQL queries on them. Your ability to gain fresh perspectives and prospects for enhancing your SEO approach depends on the ability to export your Google Analytics data to BigQuery.
We'll demonstrate how to use Google Analytics BigQuery export to improve your SEO performance in this blog post. We'll talk about the following subjects:
- What is Google Analytics BigQuery export and why should you use it?
- How to set up Google Analytics BigQuery export for your website
- How to query and analyze your Google Analytics data in BigQuery
- How to use BigQuery data to optimize your SEO strategy
What is Google Analytics BigQuery export and why should you use it?
Why does this help with SEO? The use of Google Analytics BigQuery export has the following advantages:
- To gain a more complete picture of your SEO success, you may integrate your Google Analytics data with information from other data sources like Google Search Console, Google Ads, or third-party tools.
- To find hidden patterns and trends in your Google Analytics data, you can run intricate and customised queries like cohort analysis, funnel analysis, attribution modelling, or segmentation analysis.
- To visualise and communicate your SEO insights with your team or clients, you may develop personalised dashboards and reports using software like Data Studio, Tableau, or Power BI.
How to set up Google Analytics BigQuery export for your website
- Create a GA4 property for your website by following this guide.
- Create a BigQuery project by following this guide.
- Link your GA4 property to your BigQuery project by following this guide.
How to query and analyze your Google Analytics data in BigQuery
- Go to the [BigQuery console] and select your project and dataset.
- Click on the “Compose new query” button and enter your SQL query in the editor.
- Click on the “Run” button and view the results in the table below.
For instance, you can use the following query to show the top 10 landing sites based on organic traffic over the previous 30 days:
SELECT
page.page_location AS landing_page,
SUM(CASE WHEN traffic_source.medium = 'organic' THEN 1 ELSE 0 END) AS organic_sessions
FROM
`your-project.your-dataset.ga_sessions_*`
CROSS JOIN
UNNEST(hits) AS hit
CROSS JOIN
UNNEST(hit.page) AS page
WHERE
_TABLE_SUFFIX BETWEEN FORMAT_DATE('%Y%m%d', DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY))
AND FORMAT_DATE('%Y%m%d', CURRENT_DATE())
AND hit.hit_number = 1
GROUP BY
landing_page
ORDER BY
organic_sessions DESC
LIMIT
10;
The result will look something like this:
| landing_page | organic_sessions |
|---|---|
| https://www.example.com/blog/how-to-use-google-analytics-bigquery-export-to-boost-your-seo-strategy | 1234 |
| https://www.example.com/blog/seo-writing-tips-for-blog-posts | 567 |
| https://www.example.com/blog/best-seo-tools-for-2023 | 345 |
You can use different queries to answer different SEO questions, such as:
- What are the most popular keywords that drive organic traffic to your website?
- How does organic traffic vary by device, location, or time of day?
- What is the conversion rate and revenue of organic traffic compared to other channels?
- How does organic traffic affect user behavior, such as bounce rate, session duration, or pages per session?
How to use BigQuery data to optimize your SEO strategy
- Find and resolve any technical problems that are harming your SEO, such as duplicate content, slow-loading pages, or broken links.
- By selecting keywords that are relevant to the queries of your audience and by offering information that will help them with their problems, you can optimise your content for user intent and relevancy.
- By assigning logical and clear categories, subcategories, and tags to your pages and using internal links to point people to similar information, you may improve the structure and navigation of your website.
- Use appealing and responsive design, persuasive headlines and calls to action, as well as interactive elements like photographs, videos, and quizzes, to improve your user experience and engagement.
- By producing high-quality, original material, citing reliable sources, and obtaining backlinks from respected websites, you can increase your authority and trustworthiness.
Conclusion:
FAQs
How to set up Google Analytics BigQuery export for my website?
You need both a BigQuery project and a Google Analytics 4 (GA4) property in order to use the Google Analytics BigQuery export feature. If you don't already have them, you can make them for nothing by doing the following:
Use this guide to create a GA4 property for your website.
Use this instructions to create a BigQuery project. By following this guide, you can connect your GA4 property to your BigQuery project.
How to query and analyze my Google Analytics data in BigQuery?
To query and analyze your Google Analytics data in BigQuery, you need to use SQL. SQL is a standard language for manipulating and retrieving data from databases. If you are not familiar with SQL, you can learn the basics by following this tutorial. To access your Google Analytics data in BigQuery, you need to use the web UI or the command-line tool. You can also use other tools that connect to BigQuery, such as Data Studio, Tableau, or Power BI.
How to use BigQuery data to optimize my SEO strategy?
Analyzing your Google Analytics data in BigQuery can provide valuable insights into your SEO performance and areas for improvement. Here are some ways to optimize your SEO strategy using BigQuery data:
1. Identify and address technical issues impacting SEO, like broken links, slow loading pages, or duplicate content.
2. Refine your content for user intent and relevance by incorporating keywords aligned with your audience's search queries and delivering informative answers.
3. Enhance site structure and navigation by establishing clear categories, subcategories, and tags, and implementing internal links to direct users to related content.
4. Elevate the user experience and engagement with an appealing and responsive design, compelling headlines, effective calls to action, and interactive elements like images, videos, or quizzes.
5. Boost authority and trustworthiness by producing high-quality, original content, referencing reputable sources, and earning backlinks from credible websites.
How much does it cost to use Google Analytics BigQuery export?
Up to 10 GB of data per month can be exported for free from Google Analytics BigQuery. The BigQuery pricing model, which is dependent on the volume of data saved and processed, will be applied after that. To determine your costs, you can also utilise the BigQuery pricing calculator.
What are the best practices for using Google Analytics BigQuery export?
Here are some best practices for using Google Analytics BigQuery export:
For each GA4 property that you want to export data from, create a separate BigQuery project.
For cost- and query-savings, use partitions and clustering. Reuse similar logic by using views and functions to make your searches simpler.
Your data processing and reporting processes can be automated by using scheduled queries and scripts.
To secure your data and adhere to privacy laws, use access control and encryption.
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