The final two SQL patterns posts focus on the core components of search: content and keywords. Today we’ll analyze how content (URLs) attracts traffic from search, and we’ll wrap up the series by analyzing how users search for that content. The URL dimension is perhaps the most important dimension in SEO because it represents the […]
SEO SQL Pattern #3: Grouping and filtering URLs for content analysis Read More »
The time dimension is often overlooked in digital analytics. We take it for granted because the whole point of digital marketing is to grow—traffic, customers, and revenue—over time. Performance is inherently a factor of time! This post follows from the post on Pattern #1, which is all about ranking data and does not take time
SEO SQL Pattern #2: Analyzing performance over time Read More »
Google Search Console only provides a few metrics: impressions, clicks, CTR, and average position. The first three are easy to understand, but the average position can trip up even the savviest analyst if they’re not careful. That’s why I started this series on SQL Patterns for SEO with a (perhaps overly) thorough explanation of how
SEO SQL Pattern #1: Aggregating Position Metrics and CTR Curves Read More »
At the beginning of every month, Google Search Console sends out its Monthly Search Performance email to site owners. Each month I receive this email, I’m reminded of two things: 1) My site’s performance doesn’t really change much, and 2) These statistics are a great way to demonstrate how to write SQL for the Google
Google Search Console Monthly Performance Emails in SQL Tutorial Read More »
A few days ago, Cyrus Shepard posted an example about using Levenshtein distance to compute the similarity or dissimilarity (aka the edit distance) between two strings. His example showed how to apply Levenshtein distance in Google Sheets in the context of written content, but it inspired me to demonstrate another common application: query similarity. Since
Longtail Query Analysis in BigQuery using Levenshtein Distance Read More »
Discover the differences between the searchdata_url_impression and searchdata_site_impression tables, and learn how they can help you analyze your site’s performance at different levels of granularity.
A Primer on Google Search Console Tables in BigQuery Read More »
You may have heard the hype about integrating Google Search Console and Google BigQuery using the new Bulk Export feature. If you’re like me, you recognize the power of analyzing SEO data with SQL or the BI tool of your choice (you’ve probably heard of Looker and Looker Studio). Or you might be thinking to
Google Search Console to BigQuery: The Complete Guide to GSC Bulk Export Read More »
I’ve had a few SEO consulting projects lately that have given me a chance to work with many more keywords than anybody should be working within a spreadsheet. When I come across these types of projects, I use the same workflow: bring all the data into a database and run the analyses in SQL. I
SQL for SEO: A step-by-step keyword analytics tutorial Read More »
Go search Google for “free tiktok followers” and you will see some brilliant, although dirty #blackhat #SEO. Most of the top ten results belong to a spammer who has hacked into dozens of sites and created fake profiles and uploaded .pdf files to public directories with spun content. That scam is surprising, but what’s particularly
There’s something to learn from black hat SEO tactics for PDF files Read More »
This is a quick post to provide context for the SEO A/B test-curious people out there. I was prompted by a thread in Measure Slack and figured long-form would make more sense. I didn’t want to make this another hideously long SEO-ified post but rather get to the point quickly. Here’s the post and then
Read This Before You Run Your First SEO A/B Test Read More »
