How to Apply Data Science to Your SEO Data?

The present digital economy makes the effective use of data a no-brainer. Without it, progress would be impossible, as data is fundamental to the smooth functioning of various agencies and firms. When leveraged correctly, data helps companies understand customer needs, shaping their strategy and driving recruitment decisions. Data analysis will help to understand the performance and user behavior of E-commerce websites, and with the help of the data statics we can improve website shipping functions, E-commerce conversions and user interface to get good result.

Raw data, however, is useless. Only through strategic collection and meaningful analysis does it provide businesses with a competitive edge. This is why most businesses are investing in data science, a field that combines statistics, mathematics and technical knowledge to create effective data analysis tools.

Data science provides endless opportunities, such as the availability of information, focusing on under performing areas, and renewing the organizational business culture. To avail these benefits for your business, start integrating your SEO data with data science. Here’s how:

Identify Dependent and Independent Variables

Data preprocessing is a huge step forward in developing an effective, long-term SEO strategy and a machine learning model – also called a regression model – can make the process easier. Based on the data sets used, the variables of machine learning may be classified into two categories – dependent and independent. The latter are used to figure out the former.

Keep in mind that if it’s not a plan scientific phenomenon or formula, statistical analysis will prove insufficient to tell you which variables are the dependent ones.

While all this sounds complicated on paper, it’s actually quite easy and straightforward. You just need two things – first, a thorough understanding of your business. Unless you know your business intimately, you will fail to understand the problem and choose any independent or dependent variable.

Businesses rely on regression models to figure out the impact of changes in independent variables on a dependent one. For e-commerce stores, the dependent variable is usually sales or conversion rates. Independent variables include social media expenditures and email sends along with SEO. The regression model permits business owners to measure the impact of every independent variable on sales separately.

So, with the aid of a regression model, it is possible to gauge how much a 25 percent rise in Instagram ad spends can improve sales. The business owner simply requires the cost of SEO and sales amounts to predict just how SEO spending affects revenue.

Boost Server Log Analysis with Data Science

Server log analysis ensures regulatory compliance, compliance with security policies, and compliance with audits. While most of your log messages and log data will be normal and repetitive, repeatedly inspecting the same thing wastes precious time and resources. That’s why data science should be used to power up server log analysis and identify any anomalies.

Investing in data science saves the time necessary to troubleshoot and detect an issue, decreases the churn by offering a better user experience, and improves the overall security of the system. There is a large scope of use cases for server log analysis, from handling performance issues and security head-on to improving service quality.

Server log analysis offers businesses an unrestricted view into the way search engine bots interact with different websites, supplying crucial information to better understand why bots ignore certain pages on a site and the best optimization strategies. Leveraging the power of data science should imbue business owners with the confidence necessary to identify potential problem pages on the website. Implement the bot insights to shape your SEO campaign in a more fulfilling direction later on.

Apply Data Science for SEO Visualization

Data visualization provides a bird’s eye statistical overview on your data along with a few of its attributes, such as the base distribution, helpful features, and the presence of outliers. Data visualization basically deals with presenting the data in such a way that more information is presented visually.

Most SEO professionals rely on standard line charts to explore data for client communication and insights. But line charts are extremely ineffective when it comes to displaying relative proportions or comparing data. In most instances, conveying a simple message becomes a wasted effort because the situation has to be verbally explained. On the other hand, presenting the information in the form of chart could have done the job in a matter of seconds.

The question is, how does data science benefit the process? Well, being unable to discuss the site’s performance requires a more vocal approach to speaking about the objections and confusion. And data science comes into the picture. Data visualization isn’t anything new to search marketers. What data science does is make it easier to explain line charts, pie charts, and scatter plots.

Data science makes it easier to understand when to use a particular graph or chart. Apart from that, the story gets communicated a lot clearly with the introduction of data science.

As far as SEO is concerned, visualization makes the data from Google Ads, Analytics, Keyword Planner, and other sources more accessible to businesses. Moreover, the process saves time and helps identify optimization trends.

When you boost data visualization with data science, competitive analysis and backlink analysis are a piece of cake. If that’s not all, a data science-powered data visualization process is helpful in developing a link building campaign. Use it for analyzing the anchor text distribution for backlinks, geographic location, and backlink quality.

This information is highly useful in understanding how websites are optimized. Visualization coupled with data science makes it both easier to understand and more comprehensible to others.

Decode the Data Science Approach to SEO

Copying and pasting code and running scripts is one thing. But to truly harness the potential of your SEO data, you need to focus on the entire package over time. Focus on three main avenues viz. analytics, statistics, and tools and support.

Analytics explains how Google Analytics can be used along with the general analytics platforms. On the other hand, statistics is used for understanding why all data is not created equal. Tools and support has more to do with figuring out version control and clean code works, and being able to consider the greater community who’ve been in this position before and seek advice from them.

Keep in mind that when it comes to integrating something as groundbreaking as data science, it’s best to take it one step at a time and let yourself enjoy the ride. But to use the standard data science principles effectively one needs to master the general principles over a specific period.

SEO has always been plagued by challenges like third-party metrics and the lack of clarity. Earlier, SEO was approached through a combination of linear thinking and best practice. While this was suitable for less competitive verticals, data science offers a more mature alternative.

Now, business owners are capable of combining different data sets to find out which variable probably has the greatest impact. Compare the gathered information against both low and high performers. Data models offer clarity once the opportunities pour in. Soaring confidence levels also indicate the possibility of the change and make certain improvements.

Check Out Tools That Enable Data Science Applications for SEO

Optimizing website SEO is not easy. Results from SEO campaigns take a long time to manifest. Coupled with the unpredictability of Google algorithm and the guesswork involved, it’s easy to see why many businesses rely on data science tools. Some common ones include:

  • Tableau: This specialized tool focuses on data visualization and business intelligence. Tableau can query relational spreadsheets, databases, cloud databases, and OLAP cubes. This tool converts these databases into interactive dashboards, making it easier for individuals without any programming knowledge. This drag-and-drop tool is quite speedy, being able to analyze millions of rows in a couple of rows in a few seconds.
  • CrazyEgg: Small and medium-sized businesses that run websites must monitor the traffic and figure out user interaction trends. A/B testing is the most common methodology for analyzing website performance and related traffic. CrazyEgg makes it possible to embed these features on the site to enhance its performance. The tool takes snapshots of site pages and develops reports that reflect the click behavior of the user. Moreover, its recording features helps live track users as they navigate on your site.
  • BigQuery: This big data analytics platform can process large datasets. The service is based on the Google Cloud Storage Infrastructure.

Concluding Remarks

Data science unlocks new ways to leverage data from profits to developing new business models. Using a combination of external and internal data, companies can create innovative new services and products that leverage SEO for extracting actionable insights and greater ROI.

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