5 practical data blending examples for smarter SEO insights

Here's a step-by-step guide to blending data tables from different tools to uncover valuable new insights using Python (or SQL). Sometimes we face questions that are hard to answer with the information from isolated tools. One powerful technique we can use is to combine data from different tools to discover valuable new insights. You can use Google Data Studio to perform data blending, but note that it’s limited to only one type of blending technique: a left outer join (discussed below). I will cover a more comprehensive list of data blending techniques that you can do in Python (or SQL if you prefer it). Let’s explore some practical SEO applications. Overall approach: In order to blend separate data tables (think spreadsheets in Excel), you need one or more columns that they need to have in common. For example, we could match the column ga:landingPagePath in a Google Analytics table with the page column in a Google Search Console table. When we combine data tables this way, we have several options to compute the resulting table. The Venn diagrams above illustrate standard set theory used to represent the membership of elements in the resulting set. Let’s discuss each example: Full Outer Join: The elements in the resulting set include the union of all the elements in the source sets. All elements from both sides of the join are included, with joined information if they share a key, and blanks otherwise.