We live in a world where technology is truly changing almost every aspect of our lives.
In SEO, that includes making it easier to automate tasks that would otherwise take days, weeks, or months.
And that’s why more SEO professionals are using automation to speed up boring and repetitive tasks with Python.
What Is Python?
Python is an open-source, object-oriented programming language.
According to Python.org, its simple, easy-to-learn syntax emphasizes readability and therefore reduces the cost of program maintenance.
It is used in natural language processing (NLP), search/crawl data analysis, and SEO tool automation.
I’m not a Python developer, so this article is not about how to build Python scripts.
Instead, it’s a list of the six SEO tasks you can automate with Python based on my experience of running repetitive and tedious tasks that took me and my team a lot of time to do:
Response Code Analysis
Here’s a closer look.
One of the most common frustrations SEO agencies and consultants experience is clients not implementing their recommendations even if they are critical to improving organic performance.
Reasons vary by client, but one common cause is they simply don’t have the expertise or resources to implement those recommendations.
And that’s especially true if they have a challenging content management system.
Luckily there are solutions to help like SEO automation firm RankSense, which allows users to implement up to three priority recommendations like title tags or robots.txt and descriptions daily or weekly in content delivery network (CDN) Cloudflare.
(While RankSense currently only works with Cloudflare, they are working on adding new CDNs soon.)
Now SEO recommendations can be implemented in days instead of months.
In addition, developers are only human, which means they can sometimes make mistakes that have a major impact on SEO, like blocking the entire site because they pushed a new staging site into production without changing the robots.txt file.
RankSense, however, alerts users to errors like this and corrects them instantly so they don’t impact organic traffic.
2. Visibility Benchmarking
Visibility benchmarking reviews a site’s current visibility against competitors and identifies the gaps in current keyword/content coverage.
It also identifies where competitors have visibility your site does not.
Typically, you can pull data with SEMrush, BrightEdge Data Cube, and other data sources.
To do this, you enter the data into Excel and organize the data by branded and non-branded keywords and in different visibility zones.
This is quite challenging if you have a lot of non-branded keywords, business lines, and competitors – and if you have multiple categories and subcategories.
Using Python scripts, however, you can automate the process and analyze cross-site traffic with overlapping keywords to capture untapped audiences and find content gaps.
This is much faster and can take only hours to do.
3. Intent Categorization
Part of the visibility benchmarking process is intent categorization, an exhausting process that used to be done manually.
For a big site with thousands or even millions of keywords, categorizing keywords by intent – See, Think, Do – could be your worst nightmare and take weeks.
Now, however, it’s possible to do automated intent classification using deep learning.
Deep learning relies on sophisticated neural networks.
Python is the most common language used behind the scenes due to its extensive library and adoption within the academic community.
3. XML Sitemaps
XML sitemaps are like actual maps of your website, which let Google know about the most important pages, as well as which pages it should crawl.
If you have a dynamic site with thousands or millions of pages, it could be hard to see which pages are indexed – especially if all the URLs are in one massive XML file.
Now, let’s say that you have critically important pages on your site that must be crawled and indexed at all costs.
For example, the best sellers on an ecommerce site, or the most popular destinations on a travel site.
If you mix your most important pages with other less important ones in your XML sitemaps (which is the default behavior in most CMS-generated sitemaps), you won’t be able to tell when some of your best pages are having crawling or indexing issues.
Using Python scripts, however, you can easily create custom XML sitemaps that include only the pages you are interested in keeping a close eye on to deploy on your server and submit to Google Search Console.
4. Response Code Analysis
Links are still used as a signal by Google and other search engines and remain important for improving organic visibility.
It’s about quality, not quantity.
Links should be earned by great content on your site and how that content helps people solve problems – or how it offers products that can help solve problems.
Now imagine you had a critical page on your site – one that has a lot of links and ranks for thousands of keywords – and it becomes broken or has a 302 redirect and you did not know about it until you looked at your analytics and saw a drop in traffic and revenue.
Fortunately, there is a Python script called Pylinkvalidator that can check all your URL status codes to make sure you don’t have any broken pages or pages that redirect to another URL.
The only issue with this is if you have a large site, it will take time to do unless you download some optional libraries.
5. SEO Analysis
We all love SEO tools that provide a quick analysis of a page to see any SEO issue, such as:
Does the page have a good title tag or does it have a title tag at all?