I see very few articles about why you should warehouse your Google Search Console (GSC) data, which is a massive pain for many SEOs. So, I thought I'd write a very timely 2023 edition on the limitations of GSC and how warehousing is a lifesaver.
If you're like us, you rely on GSC data to improve your website's performance, understand what's important to your target audience, and how they're landing on your content. You should evaluate your SEO efforts on clicks, impressions, and keyword rank position metrics.
The problem: When your website is small with a limited number of pages, you can get to the bottom of everything that's happening on your website by tracking it on Google Sheets/Excel or, at best, by simply connecting some of your data sources into an ETL tool (Extract, Transform, and Load) like Google DataStudio. But if you're a fast-growing organization or aspire to grow fast with content as a core growth lever, you can't rely on GSC for long.
As your website grows and becomes more complex, relying solely on Google Search Console (GSC) to track website data and performance may become insufficient. GSC only provides a limited amount of data and tools for analysis, which may not be enough for fast-growing organizations or those using content as a core growth lever. More advanced tools and strategies may be necessary to track and analyze website data and performance.
Through this post, I'll share a few must-have insights that GSC either limits you or makes it really hard to get, and also show how to get those insights in one place.
A warehouse is a cloud-based destination to store and analyze data across channels. Using a warehouse, you can store and structure data to visualize it based on what you want to measure, thus improving your SEO reporting structure vastly.
Google Search Console data warehousing refers to storing and analyzing the data collected by the Google Search Console tool. By warehousing this data, website owners can gain insights into how their website performs over time and identify trends that can help them improve their SEO strategies.
Imagine this scenario: You want to improve your market share for a set of keywords compared to your competition, who is doing better on SERPs.
You run a marketing team that has a strong performance marketing setup. You advertise in Google Ads and compete for the same keywords in SEO to achieve higher click share.
Wouldn’t it make more sense to have a reporting that clearly gives you an insight into the total number of clicks in search, not just by paid or organic separately?
You might be able to do it if you’re a true spreadsheet junkie. Still, you won’t be able to have daily dashboards and share them with your colleagues easily.
In such a scenario, warehousing would help you store data from both Google Ads and Google Search Console, combine the data, and visualize it based on what you want to track.
Google Search Console data warehousing is important for several reasons:
1. It provides a centralized repository for all website data collected by Google Search Console.
2. It can help identify trends and patterns in website traffic and user behavior.
3. It enables website owners to optimize their site for search engines and improve their ranking.
4. It can be used to monitor website health and identify any technical issues that need to be addressed.
5. It can track important website metrics such as click-through rates, impressions, and average position in search results.
Overall, Google Search Console data warehousing is an important tool for website owners and digital marketers who want to improve the performance of their websites and drive more traffic.
Knowing how much traffic for your website comes from branded and non-branded keywords is at the core of taking your SEO strategy forward. For instance, if you’re too reliant on branded keywords, you’re obviously missing out on the larger volume of searches that don’t include your brand keyword.
Google Search Console doesn’t make it easy to segment your traffic into branded and non-branded. While you can add filters, it doesn’t work well at scale. Let’s say you decide to export your GSC data and then work on it; the catch is that GSC lets you extract only up to 1,000 rows.
And suppose your website is relatively large and uses multiple variations of branded and non-branded keywords. In that case, filtering them out for meaningful analysis is almost impossible.
If that wasn’t enough, GSC doesn’t let you combine the simplest conditions. Let’s say you identified an attribute that is skewing your downloads trend. Sadly, GSC doesn’t let you combine AND / OR conditions so that you can separate out the ‘total downloads’ and ‘free downloads.’ 😞
Another common task you often perform using GSC is comparing data from two different periods to analyze how impressions, clicks, click-through rate (CTR), or average position have improved over time. Though GSC offers default period filters, you often have to manually enter the dates four times while ensuring both periods are equal in length. If that isn’t pain enough, you cannot save the filters for the future. 🤷
Oftentimes, you tend to move a set of URLs between subfolders or subdomains to streamline your content experience.
The only good way to understand the overall trend of such pages is to combine the metrics of the sub-domains or top-level domains you have verified separately in your GSC. To analyze them, you have no choice but to export the data and analyze the performance of those URLs in a spreadsheet.
Let’s look at a few scenarios:
1. You want to measure long-term trends for certain pages over a given period.
2. You want to analyze the sudden drop in the number of clicks for specific pages.
3. You want a list of unique keywords you rank for on the first page or overall.
Though these are fundamental leading indicators for search optimization, you won’t be able to track them within the GSC interface.
Now, let’s say your website ranks for thousands of keywords. You can neither group your related keywords to track specific clusters nor easily include a simple ‘buy%’ or ‘shop%’ to measure the increase or decrease in transactional traffic trends.
While you can measure the impression trends via the page experience report, it’s basic. It isn’t something that an SEO or content marketer would find actionable.
It doesn’t tell you what percentage of your traffic is getting good on-page experience or the growth differential between your faster and slower pages, so you know which pages are holding your growth back due to slower loading time.
You probably use multilingual content if you have a presence in multiple countries or cater to customers across different countries. And for the search engine, you specify the website versions in foreign languages in the HREFLANG attribute so that those website versions aren’t seen as duplicates.
With GSC, measuring the performance of even a single foreign language page is hard. Measuring the cannibalization between the right and wrong HREFLANG variations of URLs at a folder or global level is almost impossible. And if your website has subdomains, you probably will give up on GSC much earlier. 😅
All said and done, GSC offers unique data that you own and provides the most accurate view of what your website's visitors are looking for and how your existing content is performing for those searches.
GSC data provides a comprehensive range of keywords your brand's website currently ranks for. It provides data for keywords that generate even one impression in 16 months across any country, device, or search type, even if it didn't generate a click to the website.
The set of keywords in your GSC is a measure of your existing website content's proven relevance. These keywords help you define your initial market for demand capture.
The search impressions data for these keywords can be mapped to search behavior in each country, language, device, and search type. Then you can segment the keywords by mapping them to search intents using natural language processing (NLP) techniques.
Subsequently, the total market can be built from the ground up, from the micro-segments of the market defined by statistical sampling of keywords within each micro-segment. Each micro-segment can be considered a unique combination of search intent, language, country, device, and search type.
Click-through rate models can be applied to SERP position data for all the URLs your website ranks for to arrive at the current percentage of the market your brand already owns.
The above total market view can be supplemented with additional keyword lists. First, the above technique is used to identify the top overlapping competitors and URLs. Subsequently, keyword gaps are discovered through keyword research of competitor URLs.
This can greatly help extend the total market size view into dimensions in which brands may have aspirations or have competitive keywords or content gaps based on competitor URLs.
All this can be challenging for most brands unless you have a tool like Quattr that works on top of your Google Search Console and brings them all in one place.
Typically, you would use an ETL tool like Stitch Data or Fivetran to load the data into a warehouse. Common warehouses include Snowflake, Google BigQuery, Microsoft Azure, and Databricks. While Snowflake and BigQuery can be expensive, they offer better performance and data-sharing capabilities than the rest.
What makes Quattr unique and reliable is—it’s not just an ETL tool. It is a unified website and SEO platform that warehouses search console data, besides other website analytics, paid marketing campaigns, and competitive intelligence data, into a Snowflake-based high-performance warehouse.
Quattr combines your multi-year GSC trends across all brand domains with rank-tracking data, market share trends, Lighthouse performance metrics, Google Analytics metrics, Google Ads trends, Crawl logs, and more.
Here’s how Quattr works:
When the search console data arrives, the NLP algorithms mine it for search intent, detect search query language and classify all URLs into a customizable multi-level taxonomy of business categories. Then it offers powerful drill-down capabilities to explain trends, save them, share via Slack, or even capture JIRA tickets.
To set up Google Search Console Data Warehousing, connect your Google Search Console account with a data warehouses tool like BigQuery, Snowflake, or Redshift. Once connected, you can configure the data pipeline to extract, transform, and load the data into your data warehouse.
Using Google Search Console Data Warehousing provides several benefits, including better data accuracy, improved data integration with other sources, and the ability to perform more complex analysis and reporting on Google Search Console data. Quattr's SEO platform already follows these steps & presents your warehouse data in easy-to-understand data points you can leverage in your SEO growth strategy.
Yes, Google Search Console data can be combined with other data sources in a data warehouse. By integrating Google Search Console data with other sources like website analytics, CRM, advertising, and social media data, you can better understand your online activities and how they impact your business.
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