Why Learn Python for SEO (By Elias Dabbas)

This post is part of the complete Guide on Python for SEO

Learning programming and data science is obviously good and gets you productive, but you have to put the effort to make it happen. As an SEO, here are some reasons why learning Python might be a good idea for you.

Multiple data sources

No tool gives you the full picture of your business. Any proper data analysis will typically contain data from multiple sources. You will always need to combine data from different sources to get a better view of your performance and ask meaningful questions about the business. The tools don’t usually “talk” to each other, so you will have to do that yourself. Consider the simple question of checking if the bounce rate of your discounted items is any different from those that are not.

Assuming the discount information is not included in the URL or the page title, then Google Analytics can’t help you.

You will need to either crawl the pages yourself or use a crawler to crawl the site. You need to ask the crawler to extract the page elements that contain the discount information.

Now you have two tables:

The crawler table showing among other things, the URLs, as well as the discount information.
The analytics table showing URLs and their bounce rates

Now you will need to merge them, making sure that URLs in the first table align perfectly with the URLs in the second, and your task becomes easy when you have a new bigger table.

New possibilities

Sticking to our example, now that you have those tables, you might even ask a more detailed question like, “How high does the discount have to be for the bounce rate to drop by X%?”. While you’re at it, you also have H1, H2, H3, meta descriptions, load times, and more. Why not check if any of those has any relation to the bounce rate, time on page, conversion rate, etc.?

Automation

To implement the above example you will need to write some code, which would be a set of instructions for the computer to execute. Once that is written, and you make sure it is running properly, running it again the following month, can be done by simply hitting “run”. Or you can build on that analysis every month until you have a mature and solid workflow for that particular case.

Do things that are impossible to do manually: handling large amounts of data, repeating tedious processes, merging tables, importing data from different sources, are very difficult, and in many cases impossible to do manually.

All this (handling multiple data sources, automation, productivity, and doing stuff you simply can’t do manually) means that you can have more time to focus on strategic issues of analysis, insights, and strategy, as opposed to worrying about the low-level details of merging two tables together or crawling a few thousand pages.

The caveat to this assumption is that it’s going to take you some time and experience to get to the level where you can use it as easily as you would use spreadsheets and presentations.

As David Allen (author of Getting Things Done) put it, “You have to think about stuff [spend time learning Python] more than you realize, but not as much as you’re afraid you might.”