How data science and technical SEO can boost SEO marketing

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Our world thrives on data. The scale of digital transformation we’ve seen across all industries—and the associated increases in workplace productivity and employee/customer experience we’ve seen—would have been impossible without data. In addition, tools that help professionals process data have been equally essential in supporting these developments.

And the world of data is just getting started. IDC reports global data will reach by 2025 175 zettabytes. That’s a lot of data. In fact, it is more than double the amount of global data produced in 2022. This massive growth suggests that the processing tools professionals currently use will be more critical than ever in the coming years. As such, professionals should take the time to assess their tech stacks now and predict which functionalities will be most fruitful in the long run.

For marketers, the right codification and application of data insights will be more important than ever in the coming years. Despite the vast amount of marketing data that is processed each year, research from the University of Pennsylvania’s Wharton School of Business suggests that: 57% of marketers misinterpret their data, leading to costly mistakes. This study found that when marketers perform A/B testing to assess the effectiveness of different web pages, they often abort data processing after reaching a certain threshold of significance. When this happens, marketers neglect to consider fringe factors that seriously alter results. Ergo, they operate on an incomplete understanding of consumer behaviour.

As the amount of global data skyrockets, technical SEO becomes more complex and consumer expectations change, people working in marketing need to reassess their relationship with data. They need to rethink the close connection between SEO and data science and take advantage of the new insights this connection can provide.

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Why infrastructure is critical to a cohesive, modern SEO strategy

The COVID-19 pandemic accelerated digital transformation by about 10 years. As pandemic-era consumers sought digital platforms to meet needs previously met by in-person experiences, many organizations were forced to grapple with rapid modernization. Beyond that, the rationale behind consumer purchasing decisions shifted and new search trends emergedsuggesting that the entire relationship between consumer and business had changed.

Combine these phenomena with the fact that technical SEO has get more complicated since 2020, and it is clear that marketing departments are facing a challenge. Gone are the days of simple URL indexing, replaced by a need for UI optimization and page layout, powered by Google’s Core Web Vitals. Google, which still captures more than 80% of the search engine market, now rates web pages for readability, size and load times, routinely punishing web pages with a frustrating user interface.

But what does a “frustrating user interface” mean? SEO marketers must rely on data science and data-driven tools to answer this question. For example, through statistical analysis, full API access to datasets and data processing algorithms designed for Big Data, marketers can gain a clearer picture of search engine performance and consumer behavior. Using data, SEO marketers can also predict future trends using business insights; research and identify emerging market opportunities; understand, extract and automate insights from complex data; and build visual representations of data using unified dashboards.

Marketers increasingly rely on data processing to assess web page health and SEO metrics. That’s because SEO marketers who rely on data-based insights and processing are evolving with consumer expectations, new search trends, and developing technical standards, helping them “win” the search game.

We see this trend playing out in real time at BrightEdge. In the past 18 months, our customers have generated 11 times the volume of site processing data.

Of course, this level of data processing is difficult without proficiency in machine learning (ML), artificial intelligence (AI), and data science. But marketers shouldn’t be expected to become experts in a new area overnight. That’s where an updated tech stack comes in.

How to Extract SEO Data (Without Being a Data Scientist)

Modern SEO marketers have an intimidating amount of data to decipher. Everyday tasks such as research, on-site analysis, and user intent modeling generate huge amounts of information. Ideally, marketers should also experiment with their data to find industry or organization-specific insights. But most marketers don’t have a degree or enough data science experience to perform these tasks in a silo.

That’s why SEO marketers should prioritize AI and ML-enabled tools. The right SEO solution puts data software at the core of the tech stack and empowers non-data scientists to successfully extract insights from the noise.

Let’s take a look at how AI and ML tools can have a significant impact on SEO marketing. As SEO marketers know, all web pages have two types of visitors. The traditional visitor is a human who surfs a website in search of content and information on the surface – a marketer’s primary target audience. The second type of visitor is a search engine spider or bot. These virtual audiences comb through a page’s technical content for links and code that indicate a page’s relevance to specific searches.

But what happens when website errors like broken links or accidental permission issues cause a bot to stumble and omit critical content from its analysis? Such stumbling blocks cause webpages to routinely rank lower in search results. To get around this SEO-breaking mistake, marketers can analyze weblogs that track every unique web page interaction. But the sheer volume of interactions collected by weblogs leads to complex data that SEO marketers can’t manually analyze without hours of downtime.

That’s where AI comes in. Log file analyzers sort millions of files to automatically identify bot interactions that could affect search engine placement. Once a log file analyzer identifies a problem, SEO marketers can apply a solution and immediately improve web performance.

This example refers to the usefulness of AI and ML for complex SEO data analysis. However, the implications of AI and ML-driven tools go far beyond weblogs. For another example, take the Research from the University of Pennsylvania Wharton School of Business that we referenced at the top of this article. In the case of A/B testing, AI and ML processing tools would perform predictive analytics to bridge the gap between 90% and 99% significance without the need for weeks of data analysis. This illustrates how a modern tech stack can increase a marketer’s data trust while improving their SEO.

Even more promising, modern SEO tools provide a new level of industry-specific insights that provide tailored best practices. For example, retail marketers could use intelligent SEO tools to determine that retail web experiences typically suffer from duplicate content. Or marketers in the banking industry might find that concise content performs best with its customers. These insights span a wide range of dimensions — including average word count and load times by industry and several other industry-specific issues — and can pinpoint where SEO marketers, in particular, can gain a competitive edge.

Discovering Crucial Data and Transforming the Future of SEO Marketing

Today, many SEO marketers miss the intent-based insights that reside in their data, but are obscured by the sheer complexity of informational sprawl. By optimizing AI and ML tools, SEO marketers can discover and understand these latent insights before applying them across all digital channels to optimize content for scale, regardless of changing technologies and consumer behavior.

After all, the familiarity of organic search remains a constant. Marketers must act accordingly and evolve with modern trends to prioritize SEO, especially as data flows become more complex. Tomorrow’s successful SEO strategies won’t ignore this data — they’ll analyze and leverage it like data scientists do.

Lemuel Park is co-founder and chief technology officer at BrightEdge.

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