Adapting to Google's Search Generative Engines (SGEs) - Part 1

Ready to embrace the new world of search?

SGEs (Search Generative Engines) are designed to understand your intent and deliver clear, concise, and close-to-human answers. Still, in its limited access Beta phase,Google SGE gives a good indication of what the future holds for us. As such, it is important to be prepared and understand the ins and outs of this soon-to-come revolution.

Let's start with a brief history of the evolution of search engines and move on to the look and feel of Google SGE Beta search results and a basic understanding of the SGE inner workings.

A Brief History of Search Engines

From Chaos to Order: The Directory Era (Early 1990s) Think of an enormous internet phone directory, organized down to the last T by a human editor. That's basically what early search engines like Yahoo! and AltaVista were. Though revolutionary for their time, the model of manually organized information was scalable only until the web finally reached a point of explosive growth.

Enter the Crawlers: The Indexing Age (Late 1990s-2000s) With the arrival of search engines like Google and AltaVista, robots known as "crawlers" took over, automatically scouring the web and indexing websites based on keywords. This marked a significant leap, offering more dynamic and comprehensive search results.

Beyond Keywords: The Rise of Semantics (2000s-2010s) As keyword limitations became apparent, search engines started incorporating natural language processing (NLP) to understand the meaning behind keywords, paving the way for more intuitive and relevant results. Think "best pizza near me" instead of just "pizza."

The Age of Personalization and Voice Search: (2010s-Present) The introduction of personalized search and voice search assistants like Siri and Alexa marked a shift towards understanding user intent and context. Search engines began delivering results tailored to individual preferences and voice queries, mimicking natural conversation.

The Paradigm Shift: Enter SGE (2020s-Present) And finally, we arrive at the dawn of Search Generative Engines. Building upon the advancements of its predecessors, SGE leverages sophisticated AI and machine learning to not just index and understand, but to generate responses tailored to individual needs. As shown in the example below, it goes beyond providing links; it synthesizes information, extracts key insights, and organizes them to best fit what the user wants. At least, according to the data stored by Google to profile the user.

What Do Google SGE Search Results Look Like

Google SGE is still in Beta mode, so it is tempting to lift the hood and get advanced knowledge of the inner workings of what the future of search engines holds.

With SGE, gone is the time when documents were written with keywords and strict algorithms. Where traditional search is, in a nutshell, more keyword-based than conceptual, the SGE integrates NLP, AI, and ML to understand the meaning and context behind the searcher's query.

For example, saying, "What is the best laptop for a writer?" returns a synthesized answer with a description of the characteristics to consider.

The list of recommended laptops that follows is based on such characteristics and displays each one next to the recommended product. This accelerates the selection time as it saves the time needed to click on each site and enables a quick comparative evaluation at a glance.

Next to the list of recommended products are suggested "Best of" websites that, presumably, would provide an alternative perspective to the one offered by Google SGE.

The list of recommended products is quite long, which means the user has to scroll for quite a while before getting to the regular Google search results. And even then, before getting to just the listing, you must go through the FAQ section.

This highlights the different options Google SGE Beta is experimenting with, making it challenging to anticipate the format it will have when Google SGE is released to all users.

For non-commercial searches, the results are quite different. That makes sense, as general knowledge, though valuable, has little market value. So, let's ask a random question unlikely to have commercial implications. For example, "How did troglodytes live?"

As you can see, the road to the “People also ask” section is much shorter than for commercial products. What is interesting is that went clicking on the “read more” arrow of the opening section, GSE defaults back to a combination of the  Bard-like interface - with suggested follow-up questions and the invitation to create your follow-up question – and access to additional sources of information next to its snippet definition.

So, for both formats like the recent BARD, the ‘People also ask’, and indexed sections are still available, they are now stacked in reverse chronological order.

Given the previous surprising difference between the two search result formats at a day’s interval, I tried a new search with the same search query. Though the format of the result is the same as for the previous result, its content has changed. Further thoughts will have to be invested in that specific aspect of SGEs.

At this stage, the exact future of search engines we are used to today is anyone's guess. Even Google is revisiting this version of SGE and using Beta users’ feedback, scrolling, and clicking patterns and comparing them to the previous version to fine-tune their SGE model.

For now, the best we can do to guess is to attempt to take a peek at the engine behind the process.

Peeking at the of Inner Workings Google SGE

There are three underlying components to SGE, each building on the previous one capabilities.

• Natural Language Processing (NLP): This equips SGE with the ability to understand your queries' natural language, with word nuances, grammar, and sentiment. In so doing, it "reads" your question just as any human would, by extracting the real meaning and intention behind your search.

  
  

• Machine Learning: SGE continually learns and adapts from the analysis of an enormous amount of text and data to extract patterns and relationships. This enables the model to predict the information you would want to be looking for even when the query is not properly posed.

  
  

• Artificial Intelligence: AI takes the insights from NLP and ML to author not just on-point answers but also informative and tailored to you. SGE is meant to be your personal research assistant with the latest technology and a constantly growing knowledge base. It does far more than just retrieving information. It understands your needs, delivers insights that answer your questions, and helps you further explore, learn, and make informed decisions.

Beyond Text: A Multimodal Experience

As we can see in the examples above, SGEs are not limited to text formats. They can generate various response formats, An SGE might present an interactive map highlighting recommended destinations, or curate a personalized video itinerary based on your interests. There are unconfirmed rumors about a soon-to-be-released voice narration that could, for example, guide you through the must-see locations.

How Could SGEs Impact SEO in the Future?

SGEs promise to herald a user experience revolution. As seen above, this revolution's main characteristics are:

• Boosting Accuracy and Relevance

• Personalization on Steroids

• Conversational capabilities

  
  

But for all its undeniable advantages, SGEs will need to be scrutinized for:

• Bias and Fairness: How can we ensure that SGEs are free from bias and deliver fair and inclusive results to all users?

• Transparency and Explainability: How can users understand how SGEs arrive at their answers and ensure the information they receive is accurate and reliable?

• Privacy and Data Protection: How can we safeguard user privacy and ensure that the vast amount of data collected by SGEs is used responsibly and ethically, and without relying on copyrighted sources?

  
  

This being said, SEO is bound to be the first field to have to adapt fast to the new search landscape.

Optimizing SEO for SGEs

Today, optimizing SEO for discoverability is a fundamental requirement for businesses’ success or for any project that depends on traffic to a website. SEO professionals are already familiar with moving from keywords to longtail keywords, maximizing the attractivity of their titles to incite clicks (all the while avoiding clickbait titles with low engagement rates), structuring content with H titles, etc., obtaining backlinks, and optimizing the website for fast loading, responsiveness and so forth.

All of this is to place their content on the coveted search results’ first page. Adapting to the new era of SGEs will require adapting fast. My previous article on SGEs is dedicated exclusively toSEO in the LLM-Powered Search Engine age.  At the time of writing that article, Google SGE Beta was not yet available, but the main principles remain.

The article goes into more detail on what to do to adapt fast by shifting from keywords to context.

My next article should focus on how to leverage SGEs for strategic business advantage.

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