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Is Google’s Ranking Algorithm Really That Complicated?

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Nate Nead


It’s easy to take Google for granted. Most of us conduct searches every day, and some of us conduct dozens or hundreds of searches every day. Whenever we conduct a search, we’re instantly greeted with hundreds to thousands of relevant results that all offer information or destinations we need. It’s so simple, so intuitive, and so easily available that we don’t much think about what’s going on behind the scenes.

But if you’re in the search engine optimization (SEO) industry, you know that there’s a lot going on behind the scenes. And if you want to take advantage of Google’s algorithm to rank higher and generate more organic traffic, you need to have at least some understanding of how Google’s ranking algorithm works.

Therein lies the problem, and a massive challenge for most SEO newcomers. As almost any SEO expert will tell you, Google’s ranking algorithm is extremely complicated. But is it really as complicated as they say? And either way, how can you understand it better?

Google Search Algorithm Transparency

Google wants to build the best technology in the world. There’s no hiding it or denying it. But Google isn’t necessarily interested in making sure that everyone understands exactly how that technology works.

The company is notorious for keeping its core search algorithm shrouded in secrecy. It does not officially publish the algorithm, but it does give hints about how it works. Why the secrecy? There are a few good explanations. For starters, their search algorithm is proprietary, and they don’t want other people copying exactly what they’re doing. This is a basic business fundamental that shouldn’t be surprising to anyone reading this.

But it’s also important because search optimizers often look for the quickest path to rise in rankings and generate more traffic, sometimes at the expense of their users. Because Google wants a reliable user experience, with consistently authentic and trustworthy results, it doesn’t want the full information on how its ranking algorithm works officially disclosed.

Because of this, it’s almost impossible to say exactly how complicated Google’s ranking algorithm is – because we’re probably never going to have eyes on it.

Google Ranking Factors and What We Know

Let’s focus on what we do know. Because of Google’s lack of transparency, we can’t say for absolute certain how Google’s search algorithm is coded or how it works. But by running our own experiments and gathering data, we can put together a list of Google ranking factors.  

The process goes something like this. Using a variety of tools, we can figure out which websites and which pages are ranking for which keywords and queries. We can study correlations, rule out certain possibilities, and eventually narrow down a list of factors that are likely responsible for allowing a website to rank highly.

There are some issues with this. Most notably, it’s hard to separate correlation from causation. For example, we know that web pages that have high rates of user engagement, as indicated by factors like time spent on page, are more likely to rank highly – but is this because Google preferentially ranks pages with that factor? Or do people naturally spend more time on page because the page ranks as highly as it does?

The safe play is to optimize for all correlative or causational factors we can find, ultimately positioning your website and your pages to rank as highly as possible. The problem is, there are literally hundreds of Google ranking factors. Some of these are more important than others, and some of them are trivially easy to accomplish – but this is still a massive list that’s difficult to parse, especially if you have limited experience in this field.

Starting With the Basics

That said, many of the ranking factors we understand can be consolidated. For example, there are individual factors for the presence of keywords in different header tags and in different places throughout your body copy – but this can be effectively summarized by saying it’s important to include relevant keywords throughout your content, especially in areas that users are likely to notice.

If we zoom out far enough, we can effectively boil down Google’s ranking algorithm to two main factors:

  • Relevance. Relevance is simply a measure of how appropriate a web page is for a user’s query. Is there content on this page that answers the user’s question? Is the keyword or phrase used by the user present on this webpage?
  • Authority. Authority is a measure of how trustworthy or how competent the source is. If Google finds thousands of results that are hypothetically relevant, it wants to preferentially select the results that are most likely to provide reliable, trustworthy information.

You can achieve more relevance and more authority by focusing on the following:

  • Onsite technical optimization. How well is your site technically optimized? In other words, how is your website built, coded, and arranged? Technical optimization means making sure your website is easy to crawl and discover, while also making sure it loads quickly and efficiently for users, providing the best possible user experience. It means making sure your website is secure for users. It means optimizing your website for mobile devices. It means optimizing your site for loading speed and dozens of other variables.
  • Onsite content. What kind of content do you have on your site and what is the quality of that content? Websites with in-depth, trustworthy content consistently ranked better than websites with no content or bad content. The relevance of your content also matters; onsite content is your best opportunity to optimize for specific keywords and phrases.
  • Offsite content and links. You’ll also need to think about your offset content and links. This is another opportunity to optimize for relevance, but links themselves are indispensable for building your trustworthiness and authority, as sites with more inbound links tend to be more authoritative than others in a predictable, measurable way.

Additional Google Ranking Factors for Complexity

After reading this simplistic breakdown, you might breathe a sigh of relief that you have Google’s ranking algorithm figured out. But remember, there’s a lot more complexity lurking beneath the surface, and it goes beyond even the most comprehensive lists of ranking factors.

  •  Semantic search. Google no longer considers keywords in isolation, or in strict, one-to-one relationships. It now uses semantic search, understanding the context and meaning of keywords and phrases. It makes it much more difficult to optimize for specific phrases using old school techniques.
  • User behavior. To what extent does your behavior play a role in search rankings? We can make estimates based on measurements, but it’s hard to determine how much of this is correlation and how much is causation.
  • Personalization. Optimization is all about helping users find what they’re looking for. That’s why Google employs many tools to personalize search results. Based on your location, your profile, and even your search history, you may end up seeing very different results than someone else searching for the same keyword phrase.
  • Ongoing changes. Google search is not a stagnant entity. It’s constantly changing, with new updates, user interface tweaks, and more. As such, the half-life of knowledge in the SEO industry is relatively short.
  •  Industry-specific variables. Not all industries are treated exactly the same by Google’s search ranking algorithm. Certain industries require additional strategic considerations – and certain industries simply have a harder time ranking than others.
  •  Machine learning and AI. In recent years, Google has attempted to automate as much of its search engine (and as many search engine updates) as possible. Its primary way of doing this is by incorporating machine learning and AI algorithms to better understand user search behavior and automatically apply updates based on what they learn. Because these updates sometimes happen in a “black box,” it’s impossible to understand all the minute details – even for the engineers that designed them.

Google’s Algorithm is Highly Complex 

So what’s the bottom line here? The truth is yes, Google’s ranking algorithm is extremely complicated if you’re judging it based on its raw sophistication. But if you’re looking at practical outcomes, it’s easy to summarize some of the “broad strokes” ways that Google operates. In the span of this short article, we’ve covered many of the basics, and with a few hours of follow-up reading, you can probably understand most of the elements of how Google’s ranking algorithm works. But thanks to automated AI updates, even Google’s top engineers probably don’t understand everything about it – and that’s perfectly okay. 

 

Nate Nead

Nate Nead is the CEO & Managing Member of Nead, LLC, a consulting company that provides strategic advisory services across multiple disciplines including finance, marketing and software development. For over a decade Nate had provided strategic guidance on M&A, capital procurement, technology and marketing solutions for some of the most well-known online brands. He and his team advise Fortune 500 and SMB clients alike. The team is based in Seattle, Washington; El Paso, Texas and West Palm Beach, Florida.

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Fintech Kennek raises $12.5M seed round to digitize lending

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Google eyed for $2 billion Anthropic deal after major Amazon play


London-based fintech startup Kennek has raised $12.5 million in seed funding to expand its lending operating system.

According to an Oct. 10 tech.eu report, the round was led by HV Capital and included participation from Dutch Founders Fund, AlbionVC, FFVC, Plug & Play Ventures, and Syndicate One. Kennek offers software-as-a-service tools to help non-bank lenders streamline their operations using open banking, open finance, and payments.

The platform aims to automate time-consuming manual tasks and consolidate fragmented data to simplify lending. Xavier De Pauw, founder of Kennek said:

“Until kennek, lenders had to devote countless hours to menial operational tasks and deal with jumbled and hard-coded data – which makes every other part of lending a headache. As former lenders ourselves, we lived and breathed these frustrations, and built kennek to make them a thing of the past.”

The company said the latest funding round was oversubscribed and closed quickly despite the challenging fundraising environment. The new capital will be used to expand Kennek’s engineering team and strengthen its market position in the UK while exploring expansion into other European markets. Barbod Namini, Partner at lead investor HV Capital, commented on the investment:

“Kennek has developed an ambitious and genuinely unique proposition which we think can be the foundation of the entire alternative lending space. […] It is a complicated market and a solution that brings together all information and stakeholders onto a single platform is highly compelling for both lenders & the ecosystem as a whole.”

The fintech lending space has grown rapidly in recent years, but many lenders still rely on legacy systems and manual processes that limit efficiency and scalability. Kennek aims to leverage open banking and data integration to provide lenders with a more streamlined, automated lending experience.

The seed funding will allow the London-based startup to continue developing its platform and expanding its team to meet demand from non-bank lenders looking to digitize operations. Kennek’s focus on the UK and Europe also comes amid rising adoption of open banking and open finance in the regions.

Featured Image Credit: Photo from Kennek.io; Thank you!

Radek Zielinski

Radek Zielinski is an experienced technology and financial journalist with a passion for cybersecurity and futurology.

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Politics

Fortune 500’s race for generative AI breakthroughs

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Deanna Ritchie


As excitement around generative AI grows, Fortune 500 companies, including Goldman Sachs, are carefully examining the possible applications of this technology. A recent survey of U.S. executives indicated that 60% believe generative AI will substantially impact their businesses in the long term. However, they anticipate a one to two-year timeframe before implementing their initial solutions. This optimism stems from the potential of generative AI to revolutionize various aspects of businesses, from enhancing customer experiences to optimizing internal processes. In the short term, companies will likely focus on pilot projects and experimentation, gradually integrating generative AI into their operations as they witness its positive influence on efficiency and profitability.

Goldman Sachs’ Cautious Approach to Implementing Generative AI

In a recent interview, Goldman Sachs CIO Marco Argenti revealed that the firm has not yet implemented any generative AI use cases. Instead, the company focuses on experimentation and setting high standards before adopting the technology. Argenti recognized the desire for outcomes in areas like developer and operational efficiency but emphasized ensuring precision before putting experimental AI use cases into production.

According to Argenti, striking the right balance between driving innovation and maintaining accuracy is crucial for successfully integrating generative AI within the firm. Goldman Sachs intends to continue exploring this emerging technology’s potential benefits and applications while diligently assessing risks to ensure it meets the company’s stringent quality standards.

One possible application for Goldman Sachs is in software development, where the company has observed a 20-40% productivity increase during its trials. The goal is for 1,000 developers to utilize generative AI tools by year’s end. However, Argenti emphasized that a well-defined expectation of return on investment is necessary before fully integrating generative AI into production.

To achieve this, the company plans to implement a systematic and strategic approach to adopting generative AI, ensuring that it complements and enhances the skills of its developers. Additionally, Goldman Sachs intends to evaluate the long-term impact of generative AI on their software development processes and the overall quality of the applications being developed.

Goldman Sachs’ approach to AI implementation goes beyond merely executing models. The firm has created a platform encompassing technical, legal, and compliance assessments to filter out improper content and keep track of all interactions. This comprehensive system ensures seamless integration of artificial intelligence in operations while adhering to regulatory standards and maintaining client confidentiality. Moreover, the platform continuously improves and adapts its algorithms, allowing Goldman Sachs to stay at the forefront of technology and offer its clients the most efficient and secure services.

Featured Image Credit: Photo by Google DeepMind; Pexels; Thank you!

Deanna Ritchie

Managing Editor at ReadWrite

Deanna is the Managing Editor at ReadWrite. Previously she worked as the Editor in Chief for Startup Grind and has over 20+ years of experience in content management and content development.

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UK seizes web3 opportunity simplifying crypto regulations

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Deanna Ritchie


As Web3 companies increasingly consider leaving the United States due to regulatory ambiguity, the United Kingdom must simplify its cryptocurrency regulations to attract these businesses. The conservative think tank Policy Exchange recently released a report detailing ten suggestions for improving Web3 regulation in the country. Among the recommendations are reducing liability for token holders in decentralized autonomous organizations (DAOs) and encouraging the Financial Conduct Authority (FCA) to adopt alternative Know Your Customer (KYC) methodologies, such as digital identities and blockchain analytics tools. These suggestions aim to position the UK as a hub for Web3 innovation and attract blockchain-based businesses looking for a more conducive regulatory environment.

Streamlining Cryptocurrency Regulations for Innovation

To make it easier for emerging Web3 companies to navigate existing legal frameworks and contribute to the UK’s digital economy growth, the government must streamline cryptocurrency regulations and adopt forward-looking approaches. By making the regulatory landscape clear and straightforward, the UK can create an environment that fosters innovation, growth, and competitiveness in the global fintech industry.

The Policy Exchange report also recommends not weakening self-hosted wallets or treating proof-of-stake (PoS) services as financial services. This approach aims to protect the fundamental principles of decentralization and user autonomy while strongly emphasizing security and regulatory compliance. By doing so, the UK can nurture an environment that encourages innovation and the continued growth of blockchain technology.

Despite recent strict measures by UK authorities, such as His Majesty’s Treasury and the FCA, toward the digital assets sector, the proposed changes in the Policy Exchange report strive to make the UK a more attractive location for Web3 enterprises. By adopting these suggestions, the UK can demonstrate its commitment to fostering innovation in the rapidly evolving blockchain and cryptocurrency industries while ensuring a robust and transparent regulatory environment.

The ongoing uncertainty surrounding cryptocurrency regulations in various countries has prompted Web3 companies to explore alternative jurisdictions with more precise legal frameworks. As the United States grapples with regulatory ambiguity, the United Kingdom can position itself as a hub for Web3 innovation by simplifying and streamlining its cryptocurrency regulations.

Featured Image Credit: Photo by Jonathan Borba; Pexels; Thank you!

Deanna Ritchie

Managing Editor at ReadWrite

Deanna is the Managing Editor at ReadWrite. Previously she worked as the Editor in Chief for Startup Grind and has over 20+ years of experience in content management and content development.

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