Filtering Google Web Search Console data to identify blog content ideas-An automation primer

Content ideation needs a dedicated role because it’s not easy to come up with unique and catchy ideas at scale on a frequent enough basis. Strategists have to burn midnight’s oil to come up with engaging ideas that can keep both the prospects and search engines happy. After so much brainstorming and input, the math of content marketing gets severely skewed.  Kept unchecked, chances are that your content might actually cost more than your paid campaigns to actually generate leads.

In this article, we highlight this argument using the example of manually mining Google Web Search Console data to come up with blog content ideas. The focus here is not so much on how to do this data mining itself. We are more interested in highlighting the issues with the extra time and effort that this takes.

We then contrast this with an automated, alert based keyword data mining and selection technique to highlight how much more effective and efficient it can be to implement automation for coming up with the right keywords for blog content ideas.

Using Google Search Console data to identify keyword-based blog content ideas

Google Web Search Console is a free tool used by most SEOs to get insights into their search performance data. While there are more than a few tools on the market for this kind of task, we take the example of Search Console as most users would already be familiar with the various screens and reports. 

Here is a four-step method to use this tool to discover new keywords for blog content ideas. 

Let us explore each of these one-by-one.

1-Selecting the right criteria

Consider just a few examples of criteria that you could use to identify the right keywords

• Select queries that already rank high for certain pages. The logic is that it is much faster to gain incremental rankings by creating content around such keywords.
• Select queries that have a high click-to-impressions ratio. This implies that people search for these queries generate a proportionately higher number of clicks.
• Add the trend factor-You could argue that selecting queries based on just snapshot data for the above metrics might lead to false alarms and hence you might want to only select queries that show a definite trend on any of the metrics above.
• Add country/mobile dimensions-If your target market is the USA, you would be far more interested in queries that perform on your selected metrics for specific geos rather than overall aggregates.
• Exclude queries that contain brand keywords
• Identify sub-markets (e.g. Western Europe, Eastern Europe, South Asia etc.) where the metrics above perform better than global averages. This might lead to developing a customized content plan for specific audience in those countries/regions.

As you can see, the options are truly diverse and could easily become computational nightmares for large accounts with multiple properties that all employ a common keyword portfolio.

Yet, getting the metrics right should really be the first step to identifying keyword-based blog content ideas.

2-Running the reports in Search Console

You know what data you want. Now you need to actually run the various reports. A basic performance report would look something like this-

Depending upon the criteria you selected in Step 1, you would select the metrics and dimensions. For example, if you want to look at the trend of queries over a period of time and for the web channel, you would include the date dimension along with web as the channel in your search.

The example below shows results broken down by page, but for our purpose, you would be interested in looking at the queries tab.

Select the appropriate date range and click on Export. Select CSV as the download option.

3-Run calculations for your custom metrics

In the example below, you can see that the first row gives a lower CTR but higher volume than the second one. Which query do you select? The answer depends upon whether you are looking for traffic or engagement. Of course, if you are doing this manually, you may be able to make inference using judgment but what if you wish to repeat this for hundreds of keywords on a regular basis?

A ‘formula’ for this filter could look something like this

Data-savvy Marketers would immediately realize the complexity this condition introduces but we will come back to it a bit later. The point to note here is that these ‘formulae’ could quickly become complex and if you work with multiple properties (e.g. http/https versions) then chances are that doing this kind of filtering manually is simply going to be a major resource hog. Not smart!

4-Filter the matching queries and import them into your keyword management tool

Filtered queries are little use sitting in a spreadsheet. Suppose you identify 10 queries based on raw data. Can you use them all for brainstorming blog content ideas? Chances are a fat no. Why?

Because there are things called prioritization, ensuring there are the right skills for potential content ideas, getting search volumes for additional data overall, and a number of other such factors. Sending around Google Sheets or CSV files to your colleagues as part of these workflows is a primitive practice and highly unscalable. Ideally, the filtered queries should automatically get added to a shared repository where team members can discuss individual query eligibility for actual content.

The name of the game here is automation.

Using Syptus app for creating alerts and automatically importing matching keywords

Most Marketers are likely already familiar with the concept of lead scoring in B2b marketing. A software ‘scores’ a lead based on a number of pre-set rules and then gives each one a score. High-score ones typically get routed straight to the sales rep while lower scores are pushed into a nurturing funnel.

These concepts can also be applied to keyword filtering. For a moment, keep the tech side apart. Assume it can be implemented technically.

As a user, here is what your business requirements  should look like when considering an automated solution

1. Ability to set up custom rules for query selection. These should include ability to filter by dimension (e.g. URL/property, date, page, device, country)
2. Automatically route matching queries to specific topics. This could be based on things like regex matches or just plain keyword-based match. So if a query contains a phrase/exact match for a certain keyword that you are tracking, then import it.
3. Ability to automatically overlay search volume data from Ad Planner.

Once the configuration is complete, the tool should do regular imports of GWSC data and provide filtered recommendations. It should be possible to tweak the strictness of these filters so as to cut out the noise. 

At Syptus, this is exactly what we specialize in. Simply connect your Web Search Console account with Syptus and then use configurable rules to decide which search queries get added to your keyword portfolio. New additions can be set for manual review or auto-add. Data can be combined from multiple properties as part of the same Syptus account. A soon-to-be-released integration will also allow you to directly query Search volumes for filtered queries using Google Ads Planner but for now, you can use other tools that provide this data.

Conclusion

Google Search Console can provide a goldmine of data on search performance, index issues, broken links, and a lot more. However, just like Google Analytics, there is little you can do within the tool itself and at some stage, pulling your data out into an automated solution makes a lot more sense. The use case described above was for identifying keywords for blog content ideas but can equally well be used for identifying keywords for paid search campaigns.