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Our world thrives on data. The scale of digital transformation we’ve seen across all industries — and the associated boost in workplace productivity and employee/customer experience we’ve witnessed — would have been impossible without data. Furthermore, tools that help professionals process data have been equally vital in aiding these developments.
And the world of data is only getting started. IDC reports that by 2025, worldwide data will reach 175 zettabytes. That’s a lot of data. In fact, it’s more than double the amount of worldwide data produced in 2022. This enormous growth suggests that the processing tools professionals currently use will be more critical than ever in years to come. As such, professionals should take the time to assess their tech stacks now and predict which functionalities will prove most fruitful in the long run.
For marketers, the correct codification and application of data insights will be more crucial than ever in the coming years. Despite the vast amount of marketing data processed annually, research from the University of Pennsylvania’s Wharton School of Business suggests that 57% of marketers incorrectly interpret their data, leading to costly mistakes. This research found that when marketers conduct 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 account for fringe factors that seriously alter results. Ergo, they operate on an incomplete conception of consumer behavior.
As the amount of worldwide data skyrockets, technical SEO becomes increasingly complex, and consumer expectations shift, people who work in marketing must reassess their relationship with data. Namely, they must review the close link between SEO and data science and take advantage of the new insights this bond can provide.
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Why infrastructure is critical for a cohesive, modern SEO strategy
The COVID-19 pandemic accelerated digital transformation by about 10 years. As pandemic-era consumers sought digital platforms to fulfill needs previously met by in-person experiences, many organizations were forced to grapple with rapid modernization. Beyond that, the rationale behind consumers’ purchasing decisions shifted and new search trends emerged, suggesting that the whole consumer-business relationship had changed.
Combine these phenomena with the fact that technical SEO has become more complicated since 2020, and it’s clear marketing departments have a challenge on their hands. The days of simple URL indexing are gone, replaced by a need for UI and page format optimization driven by Google’s Core Web Vitals. Google, which still captures more than 80% of the search engine market, now assesses webpages for readability, format and load times, routinely punishing webpages with frustrating UI.
But what does “frustrating UI” 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 obtain a clearer picture of search engine performance and consumer behavior. Utilizing data also allows SEO marketers to forecast 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.
Indeed, marketers increasingly rely on data processing to assess webpage health and SEO statistics. That’s because SEO marketers who depend on data-based insights and processing evolve alongside consumer expectations, new search trends and developing technical standards, thereby “winning” the search game.
We see this trend playing out 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 proficiency in machine learning (ML), artificial intelligence (AI) and data science. But marketers should not be expected 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 an intimidating amount of data to decipher. Everyday tasks like research, on-site analysis and user intent modeling generate massive amounts of information. Ideally, marketers should also experiment with their data to find industry- or organization-specific insights. But most marketers don’t have degrees or sufficient data science experience to conduct these tasks in a silo.
Thus, SEO marketers must prioritize AI- and ML-enabled tools. The right SEO solution will place data software at the core of its tech stack and enable non-data scientists to extract insights from the noise successfully.
Let’s break down how AI and ML tools can significantly impact SEO marketing. As SEO marketers know, all webpages have two types of visitors. The traditional visitor is a human being browsing a website for surface-level content and information — a marketer’s primary audience. The second type of visitor is a search engine spider or bot. These virtual audiences comb a page’s technical content searching for links and code that indicate a page’s relevance to specific search queries.
But what happens if website errors like broken links or unintended permission issues cause a bot to stumble and omit crucial content from its analysis? Stumbling blocks like this cause webpages to routinely rank lower in search results. To circumvent this SEO-breaking flaw, marketers can analyze weblogs that track every unique webpage interaction. But the sheer amount of interactions compiled by weblogs leads to complex data that SEO marketers cannot manually analyze without hours of downtime.
That’s where AI comes in. Log file analyzers sort through millions of files to automatically identify bot interactions that may impact search engine placement. After a log file analyzer identifies an issue, SEO marketers can apply a fix and instantly improve web performance.
This example hints at the utility 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’s 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 conduct predictive analytics to bridge the gap between 90% and 99% significance without requiring weeks of data analysis. This exemplifies how a modern tech stack can increase a marketer’s data confidence and simultaneously improve their SEO.
Even more promisingly, modern SEO tools present a new level of industry-specific insights that provide tailored best practices. Retail marketers, for example, could use intelligent SEO tools to determine that retail web experiences typically suffer from duplicate content. Or marketers in the banking industry could identify that concise content performs highest among its clients. These insights span a breadth of dimensions — including average word counts and load times by industry and various other industry-specific problems — and can illuminate where, specifically, SEO marketers can gain an edge over the competition.
Uncovering crucial data and transforming the future of SEO marketing
Today, many SEO marketers miss out on intent-based insights that exist in their data but remain obscured by the sheer complexity of informational sprawl. Optimizing AI and ML tools allows SEO marketers to uncover and understand these latent insights before applying them across all digital channels to optimize content for scale, regardless of shifting technologies and consumer behavior.
After all, the prominence of organic search remains a constant. Marketers must act accordingly and evolve with modern trends to prioritize SEO, particularly as data streams become more complex. The successful SEO strategies of tomorrow will not ignore this data — they’ll analyze and harness it, like data scientists.
Lemuel Park is cofounder and chief technology officer at BrightEdge.
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