What is python?
Python is an object-oriented open source programming language.
According to Python.org, its simple and easy-to-learn syntax emphasizes readability and, therefore, reduces the cost of program maintenance.
It is used in natural language processing (NLP), search / tracking data analysis and automation of SEO tools.
I am not a Python developer, so this article is not about how to build Python scripts.
Instead, it is a list of the six SEO tasks that you can automate with Python-based on my experience of executing repetitive and tedious tasks that took me and my team a long time to do:
Response Code Analysis
One of the most common frustrations experienced by SEO agencies and consultants is that customers do not implement their recommendations, even if they are critical to improving organic performance.
The reasons vary by customer, but a common cause is that they simply do not have the experience or resources to implement those recommendations.
And that is especially true if they have a challenging content management system.
Fortunately, there are solutions to help, such as the SEO automation company RankSense, which allows users to implement up to three priority recommendations such as title tags or robots.txt and daily or weekly descriptions in the Cloudflare content delivery network (CDN).
(Although RankSense currently only works with Cloudflare, they are working to add 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 big impact on SEO, such as blocking the entire site because they pushed a new provisional site into production without changing the robots.txt file.
However, RankSense alerts users to errors like this and instantly corrects them so that they do not affect organic traffic.
2. Visibility benchmarking
The comparative visibility assessment reviews the current visibility of a site against the competition and identifies the gaps in the current coverage of keywords/content.
It also identifies where competitors have visibility that your site does not have.
You can usually extract data with SEMrush, BrightEdge Data Cube, and other data sources.
To do this, enter the data in Excel and organize it by keywords with and without branding and indifferent visibility zones.
This is quite challenging if you have many unbranded keywords, commercial lines, and competitors, and if you have several categories and subcategories.
However, using Python scripts, you can automate the process and analyze traffic between sites with overlapping keywords to capture untapped audiences and find content gaps.
This is much faster and may take only a few hours.
3. Intention Categorization
Part of the comparative visibility assessment process is the categorization of intentions, an exhausting process that used to be done manually.
For a large site with thousands or even millions of keywords, ranking the keywords by intention (see, think, do) could be your worst nightmare and take weeks.
Now, however, it is possible to make an automated classification of intentions through deep learning.
Deep learning is based on sophisticated neural networks.
python is the most common language used behind the scenes due to extensive library & adoption within the academic community
4. XML Sitemaps
XML site maps are like real maps of your website, which allow Google to know the most important pages, as well as the pages to track.
If you have a dynamic site with thousands or millions of pages, it can be difficult to see which pages are indexed, especially if all URLs are in a massive XML file.
Now, suppose you have pages of critical importance on your site that must be crawled and indexed at all costs.
For example, the best sellers on an e-commerce site, or the most popular destinations on a travel site.
If you combine your most important pages with less important pages in your XML site maps (which is the default behavior in most CMS-generated site maps), you may not know when some of your best pages have crawling problems or indexing.
However, with Python scripts, you can easily create custom XML site maps that include only the pages you are interested in closely monitoring to implement on your server and send it to Google Search Console.
5. Response Code Analysis
Google and other search engines continue to use links as a signal and remain important to improve organic visibility.
It’s about quality, not quantity.
Links should be obtained for the excellent content on your site and how that content helps people solve problems, or how it offers products that can help solve problems.
Now imagine that you had a critical page on your site, one that has many links and rankings for thousands of keywords, and it breaks or has a 302 redirect and I didn’t know it until you looked at your analysis and saw a drop in traffic and income.
Fortunately, there is a Python script called Pylinkvalidator that can verify all your URL status codes to make sure you don’t have broken pages or pages that redirect to another URL.
6. SEO analysis
We all love SEO tools that provide a quick analysis of a page to see any SEO problem, such as:
Does the page have a good title tag or does it have the title tag?
Is the meta description missing or is it convincing enough to get a click?
Does the page have adequate structured data?
How many words does this page have?
What are the most common phrases used on this page?
This Python SEO analyzer can easily identify problems on each page that you can solve and prioritize to increase your organic performance.
Automation is helping SEO professionals save time and be more efficient so we can focus on the strategy to improve the organic performance of our customers.
Python is a very promising programming language that can help automate time-consuming tasks to be done in minutes, and with no programming or limited experience.
As Google becomes more sophisticated with advances in machine learning over time, more and more elements will be automated.
That is why it is important that SEO professionals become familiar with programming languages such as Python that can help them gain an advantage in time and efficiency.