technodotacademy - Tumblr blog

technodotacademy · 4 years ago

Text

How Data Science influences SEO?

Search Engine Optimization is always an enthusiastic profession, we couldn’t predict what would happen when we added certain keywords to the content or new web pages to the website. Perhaps as a result we will get higher ranking on the SERP(Search Engine Result Page) and our organic visitors will convert into leads or vice versa.The behavior and the position of our website on the Internet will vary based in a particular intervals, on the impacts of Google algorithms, competitor’s digital marketing strategy,etc.But all these factors we couldn't predict. How can we predict the performance of our website after adding certain changes to it?Is this Possible?

The simplified answer to this question is now we have the data and tools to find the answers.

What is Data SEO

Data SEO is the advanced field that brings data science methods of analysis to Search Engine Optimisation(SEO).Data science uses scientific principles, statistics, and machine learning to bring together and analyze digital data for the purpose of decision making.

This can be include data like:

• Audit results

• Backlinks

• Traffic acquisition

• On-site user behavior

• On-site conversion or purchase behavior

• SERP intent indicators

• Top SERP domains

• Keywords

• Ranking

• App usage and interaction

• Trending queries

• Entities

• And any other online data

The primary objective is to understand these data are related, and whether those relationships are statistically significant.If so, we can use those relationships to evaluate, predict, automate, or even influence different tasks or phenomena in SEO.

But digital data comes in increasingly massive volumes. Because Data SEO can involve so much data, it’s common to apply skills related to statistical sampling, programming, or even big data algorithms.

Knowledge and skills for SEOs needs to gain

Here are some of the most impactful skills to have on your team or at your disposal.

Data analysis

Knowing how to analyze data is one of the key elements in successful Data SEO projects. Data analysis is the process of using mathematical and statistical methods to understand a set of data and to draw conclusions from it. This requires the ability to understand and apply different methods of analysis, and to be aware of biases and how they manifest in data.

Data preparation

In order to be able to analyze data correctly, it often needs to be prepared. Data preparation includes ensuring that the data is accurate and complete, and transforming it into a format that is compatible with other data and with the chosen methods of analysis.

Adapting scripts and code

You don’t need to know how to program to adopt Data SEO, but a basic understanding of how code and scripts work is extremely helpful. It will allow you to be able to adapt scripts and code created and shared by others in the SEO community to your own particular case.

Sense of community

Data SEO improves when the SEO community’s knowledge and skills improve. The SEOs already working on Data SEO share advice, code, and case studies.They ask questions and help others find solutions. Sharing your own templates,code and experiences, and asking for and providing help where you can, is without question one of the most important skills in Data SEO.

Technologies of Data SEO

APIs

APIs are built-in means for scripts and programs to access data stored in or produced by other tools. For example, instead of manually exporting elements in a crawl report, if the crawler has an API, it can be done programmatically, making it much easier to automatically integrate results in another analysis or in a data visualization. APIs can also be used to create or alter content in a tool, for example to create a new page via your website’s CMS, and add the right categories, tags,meta information, and other settings.

In short, using APIs allows you direct access to the data, whether you are providing data or obtaining it. Using APIs, you can avoid having to manually copy, export, import, or interpret SEO data. They are an important step in creating automations and processes.

Internet scraping

We often imagine scraping as an unethical practice, so let’s be clear: we’re not talking about scraping the whole of your competitor’s site for nefarious purposes, nor are we talking about scraping the search engine results pages, which is forbidden by Google.Instead, we’re talking about scraping data from your own website. Given the number of different teams that can contribute content to a website, sometimes the website itself is the easiest source of data that ends up on the website. Any information available on a rendered web page can be scraped, whether it’s provided by your CMS or injected later in the rendering process by scripts, service workers, or other means.

Data visualization platforms

With the growing amount of data available, how you view and interpret data is essential to drawing accurate conclusions and making informed decisions on how to act. Data visualization helps humans better summarize trends, report on progress, and identify elements that need to be prioritized. Preparing, formatting, and feeding data to a visualization platform is an important step in Data SEO. You do this on a smaller scale when you prepare columns in a spreadsheet before creating a chart. When working with a data visualization (like Google Data Studio) or business intelligence platform (like Tableau or Power BI), the process can sometimes be a bit more complex.

Cloud storage

As the data you use in SEO processes grows, a good place to keep it is in cloud storage, where it is protected, but remains accessible to the different tools that make up your SEO process.

Data warehouses

Within a company, data warehouses bring together many types of data from different departments across the company, and make them all available to everyone. Not only does this help you take business data into account in yourSEO strategy, it also helps other departments — from IT to paid search to product development or logistics — take SEO data into account in their own work. By integrating SEO into the larger business goals and practices in your company, it’s easier to allow others to “own” parts of your SEO strategy, and therefore to help push to fix problems and implement optimizations.Many technical SEOs have begun to acquire coding skills, particularly in languages such as Python and R, which facilitate data visualization. In both of these languages, there are an increasing number of libraries, or sets of pre-programmed functions, made available by the coding community that can be applied to SEO purposes. These range anywhere from Pandas, which allows you to use Python for the same tasks you might do in a spreadsheet, to Prophet, which can be used to make SEO predictions.

Notebooks

Coding notebooks, like Jupyter Notebooks and Google CoLab Notebooks have become a popular way to share code. They unite code, descriptions, and results in a runnable coding environment that is easy to share and use for demonstrations.

Machine learning technologies

As both websites and search engine algorithms become more complex, it becomes difficult to use pen-and-paper analysis to find patterns and predict future effects in SEO data. Machine learning works by training a model, or showing it the results for a series of inputs, and then allowing it to generate a mathematical model that best explains the relationship.

NLP models

At the heart of SEO today is how a search engine understands the goals behind a query, and how it determines which pages in its index are most pertinent to that goal. To do so, search engines rely on NLP,or natural language processing: the study of how machines interpret and use human language.

#digitalmarketing #DataSEO TechnoDotAcademy

0 notes