Helping a client recover from a bad redesign or site migration is probably one of the most critical jobs you can face as an SEO.
The traditional approach of conducting a full forensic SEO audit works well most of the time, but what if there was a way to speed things up? You could potentially save your client a lot of money in opportunity cost.
Last November, I spoke at TechSEO Boost[1] and presented a technique my team and I regularly use to analyze traffic drops. It allows us to pinpoint this painful problem quickly and with surgical precision. As far as I know, there are no tools that currently implement this technique. I coded[2] this solution using Python.
This is the first part of a three-part series. In part two, we will manually group the pages using regular expressions and in part three we will group them automatically using machine learning techniques. Let’s walk over part one and have some fun!
Winners vs losers
Last June we signed up a client that moved from Ecommerce V3 to Shopify and the SEO traffic took a big hit. The owner set up 301 redirects between the old and new sites but made a number of unwise changes like merging a large number of categories and rewriting titles during the move.
When traffic drops, some parts of the site underperform while others don’t. I like to isolate them in order to 1) focus all efforts on the underperforming parts, and 2) learn from the parts that are doing well.
I call this analysis the “Winners vs Losers” analysis. Here, winners are the parts that do well, and losers the