- Leverage tools. Enhance your search data analysis capabilities with Google Looker's data blending feature and BigQuery's direct data export capability.
- Explore options. Exporting search data allows for more comprehensive analysis and additional visualization options beyond native reports.
- Demonstration resource. Utilize the demonstration example provided by Google and learn syntax-related tasks to make better search strategy decisions using these tools.
Editor's note: The author, Pierre DeBois, will be speaking at next month's CMSWire Connect conference in Austin, Texas, on the subject of migrating from Universal Analytics to Google Analytics 4.
Marketers are always looking for better ways to explore their data, especially search data. Google announced two new features to enhance its Search Console platform, while also ensuring seamless interoperability between Search Console and two of Google’s major data platforms, Looker and BigQuery.
Google Looker now includes a new data blending feature, while BigQuery introduces a direct data export capability. Both features address needs at opposite ends of the analytics toolchain. Moreover, both can serve marketers well in establishing an integrated system of data analytics.
Why Export Your Search Data?
Combining Measurement and Tech Search Data
Google Search Console has been able to be integrated into Google Analytics for a few years. The reason for the integration is to combine the robust measurement capabilities of Google Analytics with the technical search data that Search Console provides.
The Search Console integrations with Google Looker and Google BigQuery are an extension of that theme. Each case is an example of providing more nuanced measurement capability that can be used for deeper exploration of search data relationships.
Related Article: Google's Winning Business Intelligence Pieces: Looker and BigQuery SQL
Another reason to export Google Search Console data is to offer additional visualization options beyond its native reports. The report settings work fine for revealing immediate search performance metrics such as identifying which search queries are most likely to display a site or app in a search results page. Marketers can use such information to understand how the query metrics influence user activity.
The Report Table
But the report table interface is designed to display only one column of metrics at a time. The interface limitation means that main table metrics — query, page, country, device, search appearance and dates — can not be filtered for two metrics concurrently. For example, you can filter to view pages or queries, but not query by page simultaneously. The same limitation also goes for sorting a data set for decreasing or increasing order under one filter rather than according to a sequence. In the queries section of the performance report, users can sort by clicks or by impressions, but not in a combined sequence.
Multiple Filter Settings
This table design is not unusual among browser-based user interfaces that display dashboards and data visualizations.
Many times the tables containing multiple filter settings in a browser page require some complexity in the underlying code to enable swift adjustments to concurrent filters.
More Comprehensive Analysis
Exporting the data allows you to take advantage of viewing your data with a sequence of two or more distinct filters. This approach facilitates a more comprehensive analysis, highlighting the relationships between search metrics and other crucial key performance indicators (KPIs) that managers need to monitor.
Related Article: The Value of Site Search to Your Customer Experience Analytics
Blending Data Within Looker Studio
Google Looker introduced data blending to complement data export needs. Blending is not just a technique. Google Looker treats blends as a unique table akin to SQL joins. In SQL, joining tables requires specific syntax.
However, Looker blends offer no-code solutions, meaning no data syntax is necessary. This feature allows you to integrate data from various sources without needing expertise in a specific programming language to create tables or extract data subsets.
Google Looker Example: Blending Data
For instance, suppose you are managing a hotel or a chain of hotels, and you have data showing the average daily rate, which represents the average daily check-in rate, along with cancellation rates. By blending the data in Google Looker, you can uncover potential correlations between these two distinct metrics. This process makes it more convenient to achieve a nuanced analysis.
How to Blend Data in Google Looker
To blend data, users should log into their Google Looker account and select a report in the standard manner. In the report, users can choose a report component — a section where they want to add data.
Next, they should click on the properties panel on the right side of the screen. A column titled "Setup" appears, displaying the +BLEND DATA button for users to click. A pop-up emerges at the bottom of the screen, providing access to the "Manage blends menu." In the bottom left of the menu, users can click "Add blend."
Although blended data incorporates sources external to it, Looker treats blends as embedded data. This means that edits to data metrics are performed within the report. This aspect also influences the workflow when deciding to share tables and visualizations.
Integrating Search Console Data With BigQuery
Earlier this year, another integration feature involving Google BigQuery was introduced. This bulk data export feature is designed for advanced data exploration needs that surpass Looker or the Search Console API. It offers the possibility of storing data for extended periods, providing convenience for situations requiring more in-depth analysis.
Marketers can schedule a daily export of Search Console performance data into a BigQuery account. They can set the export destination from within Search Console, either to a BigQuery property or to an external storage service. Once activated, the export can take up to 48 hours. From there marketers can log into their BigQuery accounts to run complex queries over all the performance data available.
No Anonymized Queries
To protect the privacy of the user making the query, Google notes that anonymized queries are excluded. Anonymized queries are ones in which users have opted out of identification while conducting a search query in any of the Google properties. From an analysis perspective, this can be significant if numerous anonymized queries appear in the data. Analysts should take note of this to account for any data set anomalies in advanced analysis.
What Should Marketers Do?
These export features are timely as marketing teams rush to get their GA4 data settings straight. Google has introduced features across Google Analytics, Search Console, Looker and BigQuery to form a Google-branded stack.
The Initial Step for Marketers
The most accessible initial step marketers can take is to utilize the demonstration example provided by Google. This allows you to assume the role of a school administrator tasked with blending school-related data. The example is designed to help you become familiar with the process.
Big Query is designed for more syntax-related tasks, such as data warehousing. Marketers can learn how to work with syntax — namely SQL, Python, and other languages — through the courses and resources suggested in my post.
Final Thoughts on Exporting Google Search Console Data
Using these tools can help managers identify responsive data segments more easily and make better search strategy decisions. This includes determining which pages to update, what phrases to strengthen — and how to tie all of it to the desired customer experience.