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 industries – and the associated increase in workplace productivity and employee/customer experience we’ve witnessed – would have been impossible without data. In addition, tools that help professionals process data have been equally essential to support these developments.

And the world of data is just beginning. IDC reports that by 2025, global data will reach 175 zettabytes. That’s a lot of data. In fact, that’s more than double the amount of global data produced in 2022. This huge growth suggests that the processing tools currently used by professionals will be more critical than ever in the years to come. As such, pros should take the time to evaluate their tech stacks now and predict which features will prove most successful in the long run.

For marketers, the correct codification and application of data insights will be more crucial than ever in the years to come. Despite the vast amount of marketing data 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 research found that when marketers perform A/B testing to assess the effectiveness of different web pages, they often stop processing the data after reaching a certain level of significance. When this happens, marketers neglect to consider marginal factors that seriously alter results. Ergo, they operate on an incomplete conception of consumer behavior.

As the amount of data in the world skyrockets, technical SEO becomes more complex, and consumer expectations shift, people who work in marketing need to reassess their relationship with data. Namely, they need to revisit the close connection between SEO and data science and take advantage of new insights that this connection can provide.


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Why infrastructure is essential for a consistent and 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 deal with rapid modernization. Beyond that, the logic behind consumer purchasing decisions has changed and new search trends have emerged suggesting that the whole consumer-business relationship has changed.

Combine these phenomena with the fact that technical SEO has become more complicated since 2020, and it’s clear that marketing departments have a challenge ahead. The days of simple URL indexing are over, replaced by a need for UI and page format optimization driven by Google’s Core Web Vitals. Google, which still holds more than 80% of the search engine market, now assesses the readability, format and load times of web pages, regularly punishing web pages with a frustrating user interface.

But what does “frustrating user interface” entail? 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 get a clearer picture of search engine performance and of consumer behavior. Using the data also allows SEO marketers to predict future trends using business insights; research and identify opportunities in emerging markets; understand, extract and automate insights from complex data; and create visual representations of data using unified dashboards.

Indeed, marketers are increasingly relying on data processing to assess the health of web pages and SEO statistics. Indeed, SEO marketers who rely on data-driven insights and processing evolve alongside consumer expectations, new search trends, and the development of technical standards, thereby “winning” the search game.

We see this trend unfold in real time at BrightEdge. Over 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 a mastery of machine learning (ML), artificial intelligence (AI), and data science. But don’t expect marketers to become experts in a new field overnight. That’s where an updated tech stack comes in.

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

Modern SEO marketers have a daunting amount of data to decipher. Everyday tasks such as research, on-site analytics, 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 sufficient data science degrees or experience to perform these siled tasks.

Thus, SEO marketers should prioritize AI and ML-enabled tools. The right SEO solution will put data software at the heart of its technology stack and enable non-data scientists to successfully extract insights from noise.

Let’s see 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 browsing a website in search of surface content and information – a marketer’s primary audience. The second type of visitor is a search engine spider or bot. These virtual audiences scour the technical content of a page for links and code that indicate a page’s relevance to specific search queries.

But what if website errors such as broken links or unintended permission issues trip up a bot and omit crucial content from its crawling? Obstacles like this cause web pages to consistently rank lower in search results. To circumvent this game-changing SEO flaw, marketers can analyze blogs that track every single interaction on a web page. But the amount of interactions compiled by weblogs leads to complex data that SEO marketers cannot manually analyze without hours of downtime.

This is where the AI ​​comes in. Log file analyzers sift through millions of files to automatically identify bot interactions that may impact search engine rankings. Once a log file analyzer identifies an issue, SEO marketers can apply a fix and instantly improve web performance.

This example alludes to the usefulness of AI and ML for analyzing complex SEO data. However, the implications of AI and ML-based tools go far beyond blogging. For another example, take the research from the Wharton School of Business at the University of Pennsylvania 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 close the gap between 90% and 99% significance without requiring weeks of data analysis. This illustrates how a modern technology stack can increase a marketer’s data trust and simultaneously improve their SEO.

Even more promising, modern SEO tools present a new level of industry-specific insights that provide tailored best practices. Retail marketers, for example, could use smart SEO tools to determine that retail web experiences commonly suffer from duplicate content. Or banking industry marketers might identify that concise content performs best among its customers. This information covers a wide range of dimensions – including average word count and load times by industry and various other industry-specific issues – and can shed light on where, specifically, SEO marketers can gain an edge over competition.

Discover crucial data and transform the future of SEO marketing

Today, many SEO marketers miss the intent-based insights that exist in their data, but remain obscured by the sheer complexity of information proliferation. Optimizing AI and ML tools allow SEO marketers to uncover and understand this latent insight before applying it across all digital channels to optimize content at scale, regardless of scale. technology and consumer behavior.

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

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


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