SGE Archives - iPullRank https://ipullrank.com/category/sge Digital Marketing Agency in NYC Fri, 01 Aug 2025 13:30:34 +0000 en-US hourly 1 https://wordpress.org/?v=6.8.3 https://ipullrank.com/wp-content/uploads/2025/07/cropped-favicon-1-32x32.png SGE Archives - iPullRank https://ipullrank.com/category/sge 32 32 The Ultimate AI Overviews (SGE) Guide: How to Navigate Its Impact https://ipullrank.com/ai-overviews https://ipullrank.com/ai-overviews#respond Wed, 03 Apr 2024 15:00:21 +0000 https://ipullrank.com/?p=17428 Watch the FREE AI Overviews Optimization webinar replay with me and Mike King. Google Search is fundamentally changing. Marketers need to reframe the way they approach content strategy and search engine optimization. Generative AI is being incorporated into the search results now and nobody knows what that will mean for the future of search.  Will […]

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Watch the FREE AI Overviews Optimization webinar replay with me and Mike King.

Google Search is fundamentally changing. Marketers need to reframe the way they approach content strategy and search engine optimization. Generative AI is being incorporated into the search results now and nobody knows what that will mean for the future of search. 

  • Will we see a 90% decrease in organic traffic? 
  • Will AI completely replace traditional search results eventually?
  • Is SEO dead? (I couldn’t help myself).

Search results have been evolving for decades, nothing surprising there. It’s morphed from 10 blue organic links into a completely bloated smattering of ads, rich snippets, navigation elements, verticalization features, personalization features, and a few organic results.

AI Overviews (previously SGE) is Google’s crescendo, the next phase, and its biggest gamble.

We’ve got so many questions:

  • What’s Google’s vision for the future of search?
  • How will AI change search behavior?
  • What can marketers do to prepare?
  • What other bigger implications need to be considered?

Google’s never fully transparent. So let’s read between the lines, tease out some possibilities, and leave you with some things to consider.

Table of Contents

What is the AI Overview (SGE)?

The AI Overview is Google’s AI-powered snapshot that appears at the top of some search results. It consists of an AI Overview, generated text with sourced citations that provide a direct answer to the query. It sometimes includes additional context. 

Below the AI Overview are follow-up questions. We also have some different layouts depending on the verticals (specifically ecommerce and local searches).

In February, I explained my personal take on AI Overviews on the Crowd Content webinar:

Take a look at what a ‘current’ version of AI Overviews looks like. This example includes a variety of output formats:

An Example of Google's Search Generative Experience

Depending on the query, the layout potentially includes a variety of formats:

  • Paragraphs (sometimes with images)
  • Bulleted lists 
  • Breakouts (A series of sub-headers and short paragraphs, sometimes accompanied by images.)
  • Images
  • Videos

Despite the additional context, you will typically see the most relevant part of the answer highlighted:

Every text element has citations of its sources. 

Most recently, the citation dropdown has been populated by favicons from the sources.

A carousel of sources appears to the right of the AI Overview that typically includes an image, article title, date, and website name (which is a clickable link), though the carousel has been removed.

Below the AI Overview, we sees 2-3 follow-up questions that are relevant to the original query. Those follow-ups are typically different from Google’s People Also Ask feature further down the Search Results Page.

And when you click on the follow up, AI Overviews keep the conversation contextual.

Note: The design, labels, elements, and even the name of AI Overviews are constantly changing. A few tests include:

  • The aforementioned citation favicons.
  • Recently, the carousel has been completely removed.
  • The label at the top of the AI Overview previously only stated that ‘Generative AI is experimental.’
  • Citations were originally hidden in a collapsed section, revealed by a bear claw option.

SGE also includes two different layouts unique to shopping queries and local queries.

For a commercial search, you’re likely to see a list of products that are sourced from product review articles or Google’s Shopping Graph in a few different formats:

Standard Product List

eCommerce Grids

Categorized Compilation Packs

The eCommerce results have significant implications. If searchers use AI Overviews for product selections, then it’s essential that brands make sure that they have invested resources in optimizing their products in Google’s Shopping Graph.

Local searches that are based on your geography have a unique layout as well. They typically include the map pack, citations, and a carousel above the map pack. They also include labels for business attributes.

The biggest unanswered question for AI Overviews is what role will Ads play. Up to this point, we’ve seen ads appear directly above and below the snapshot, but not inside of it. Considering the importance of ad revenue, it feels inevitable that ads would be integrated into AI Overviews in some capacity. Google has indicated that they will continue to experiment with ads. According to CEO Sundar Pichai during Alphabet’s Q4 earnings call:

“As we shared last quarter, ads will continue to play an important role in the new search experience and will continue to experiment with new formats native to SGE. SGE is creating new opportunities for us to improve commercial journeys for people by showing relevant ads alongside search results. We’ve also found that people are finding ads either above or below the AI-powered overview helpful as they provide useful options for people to take action and connect with businesses.”

As important as the content, visual design, and inclusion of ads are to the success of SGE, its visibility and timing in search results are just as critical for SEOs.

UPDATE (Hours later): The paint has yet to dry on this article and hours later, news leaks that Google is considering a paid GenAI (and potentially AI Overviews) product.

It is unclear how exactly the company would seek to integrate AI-powered search into these paid-for services, which offer different pricing tiers, or when the AI-powered search offering would be ready to launch. Google could still decide to launch certain elements of its experimental AI-powered service into its main, free search engine over time, according to people familiar with its thinking. Google said the company was “not working on or considering” an ad-free search experience but that it would “continue to build new premium capabilities and services to enhance our subscription offerings across Google”.

Source

While this article doesn’t explicitly claim that Google will only use AI Overviews as a paid feature, it will be an interesting situation monitor considering that Google has rarely added paid features like these to its free products.

One reasonable concern for this business move would be the cost to run AI Overviews along with the risk to Google’s ad model.

If searchers are less likely to click on ads due to AI Overviews’ ability to satisfy user intent without leaving the search results, then that would require another way to monetize AI Overviews.

When Does AI Overviews Trigger?

The AI Overview does not appear for every search. There are five scenarios for any given query:

  • Automatically generated at the top of the search results page.
  • A truncated automatically generated at the top of the search results page that needs to be expanded.
  • A message to “Get an AI-powered overview for this search?” with a Generate button to click.
  • A message that “An AI-powered overview is not available for this search.”
  • No indication of an AI-powered presence.

Update 4/28: Google has softened the copy for your average user with “Get an AI Answer for this search?”

Further down in my coverage of AI Overviews research, you’ll see the frequency of AI Overviews’ appearance along with which industries are likely to see the feature most. That’s an important consideration since Google’s been adamant about responsibly bringing AI to search.

The Philosophy Behind AI Overviews

Google’s AI Overviews team published a 16-page SGE Overview back in May 2023 that reads like a manifesto. 

Everyone working in SEO should read the overview. It’s chock full of goals, philosophies, and intentions for the future of AI Overviews. 

Fundamentally, “SGE is an early step in transforming the Search experience with generative AI.”  

An early step, but the first of many. 

The blueprint paints a picture of the responsible implementation of generative AI search:  

“When using SGE, people will notice their search results page with familiar web results, organized in a new way to help them get more from a single search. With generative AI in Search, people are able to: 

  • Ask new kinds of questions that are more complex and more descriptive
  • Get the gist of a topic faster, with links to relevant results to explore further
  • Get started on something you need to do quickly, like writing drafts or generating imagery right from where you’re searching
  • Make progress easily, by asking conversational follow-ups or trying suggested next steps”

Naturally, Google wants AI Overviews to be a better search experience. They want you to find more helpful information without needing to pogo-stick between websites and back to search. 

And AI Overviews will play a big role in the future of search. Recently promoted Head of Search Liz Reid shared her excitement for the experiment on LinkedIn in March.

Considering Liz’s role in bringing AI to search, Google is telegraphing their plans here.

And the move makes sense from Google’s perspective. It’s not the first time they’ve aimed to keep searchers on Google. Despite all of their talk about building a healthy web, the more time spent on the search results, the more ad impressions, the more ad revenue.

Featured snippets have been around since 2014. For the most simple definitions or simple answers, pulling a word-for-word passage from a web page saved searchers a ton of time.

Truthfully, most people want the cliff notes most of the time. We don’t want the story behind the recipe (unless we do). We don’t care about the history of your nomadic life to learn about the best things to do in Jamaica (unless we do.) 

For informational searches with one answer, we don’t always need the *best* info. Information can be commodified. 

We need info that’s good enough (or Goog enough). We expect it to be accurate. AI Overviews aims to reduce the work to discover answers and solutions. Reduce the cognitive load. We’re satisficing our search results. We want the TL;DR. If we like the person, brand, or writing, we’ll put in the work to read more. We have the option. 

But the LLMs have their known limitations:

  • Hallucinations: They still make things up.
  • Misinterpretations: Despite significant gains in Natural Language Processing, it still doesn’t understand the nuance of every query.
  • Bias output: The outputs are still trained on biased content and the internet is full of biased content.
  • Persona corruption: Sometimes the output will have an opinion despite Google’s engineer’s guidelines to reflect a neutral and objective tone.

The team at Google claimed that they were going to limit the use of AI Overviews for critical queries that are financial-related or health-related(Your Money Your Life or YMYL). But early on in experimenting, YMYL searches frequently had results.

It’s in Google’s best interest for AI Overviews to produce helpful and safe results. If the AI Overview consistently creates snapshots that are deceptive, unsafe, or worse than the traditional search results, searchers will begin to look elsewhere for their information. Fewer searches mean less ad revenue: Alphabet’s primary source of revenue.

To that end, the overview claims that they have a few key processes in place to monitor, review, and evaluate performance of the feature:

  • Raters: We work with independent Search Quality Raters to help us measure the quality of outputs and the results displayed. These ratings do not directly impact SGE’s output, but are used to train the LLMs and improve the experience overall.
  • Focused analysis: Following our process for significant launches in Search today, we analyze results across multiple broad, representative query sets, as well as conduct more focused studies to confirm responses meet our quality thresholds. In particular, we focus on topic areas that may be more susceptible to known quality risks, or that are more complex and nuanced. This includes classes of queries that may be at higher risk for safety or inclusion issues and aims to ensure our protections and responses are effective across those domains.
  • Red-teaming: We conduct adversarial testing of these systems to identify areas where the systems aren’t performing as intended. This helps identify bias issues, safety concerns and other areas where we can improve the product.

With both CEO Sundar Pichai and Head of Search Liz Reid claiming that people ‘love’ SGE, it’s difficult to determine whether this is heavy-lifting PR or based on testing, feedback, and results.

“We are incorporating SGE in the product, the early feedback is positive…And we’ve been iterating on it, and it clearly works for certain types of queries very well. We are expanding the set of queries where it works very well. It definitely is answering a certain category of queries for the first time in a better way. So that gives us direction to proceed as well.”

The next phase will be seeing these results in public. As SEOs, it can be very difficult to step outside of our niche, our bubble, our industry, and see the search experience from the perspective of everyday users.

But the quality of the results depends on the technology in place.

So what’s the actual (virtual) machinery behind AI Overviews?

How Do AI Overviews Work?

Powered by a variety of Large Language Models, including an advanced version of MUM and PaLM2. it generates text from relevant content across Google’s Index.

You cannot confidently claim to know the exact tech behind what makes AI Overviews (or any of Google’s Search products run). But you can turn on your inner detective and sus out the patents, frameworks, processes, costs, and resources, that would theoretically drive the feature. Unfortunately, you can’t always trust Google employees or press releases to truthfully reveal their IP.

So what can we infer?

Juan Gonzales Villa identified the likely Google Patent (granted in September 2023) for an AI Overviews process. What’s interesting is that it shows how AI Overviews could use one or multiple LLMs for the generated text. It also can use additional systems or skip them depending on the needs of the query.

Instead of being trained purely on a static dataset, AI Overviews uses a process called Retrieval Augmented Generation (RAG) to use current content in its output. Fresh, up-to-date information from websites.

Google takes the query, generates an AI summary from relevant content on the web, evaluates the summary, rewrites it, and then annotates it with a graded level of confidence.

Here’s a simplified visualization of the process for Google’s Index, Knowledge Graph, and LLM.

For a comprehensive technical deep dive, read How Search Generative Experience works and why retrieval-augmented generation is our future by Michael King.

Mike reveals how AI Overviews produces generated output from chunks of relevant content that can be quantitatively calculated. 

You can then use a tool like MarketBrew’s SGE Visualizer to evaluate whether your content has a high enough similarity score that it would potentially be included in AI Overviews.

I broke this down in my AI Overviews optimization article.

Read: How to Intentionally show up in Google’s AI Overviews

Watch: [Webinar Replay] How to Optimize for AI Overviews: Protect and Perfect your AI Snapshot

The technology continues to evolve and much of it is hidden in the Google blackbox where nobody can tell you exactly how it works. 

According to the AI Overview referenced earlier:

“While SGE also applies LLMs, it has been purposefully trained to carry out tasks specific to Search, including identifying high-quality web results that corroborate the information presented in the output. These models are used in tandem with our core ranking systems to deliver helpful and reliable results.”

Personally, I’m skeptical that the algorithms and systems used by AI Overviews are the same used for core search. We’ll address this a bit more in the analysis section below, but the sources for AI Overviews output rarely primarily pull from the top few organic search results.

Cindy Krum has proposed some interesting theories around how Google uses systems (Spam and Helpful Content Systems) to filter out bad data and keep the generated output clean.

This would support Google’s desire to create AI-enabled products responsibly.

The Timeline Behind the Search Generative Experience and AI Overviews

A quick timeline of AI Overviews for the uninitiated. Google is always evolving its search results. If you want to dive into everything Google has implemented over the past couple of decades, consume How Search Works. Collecting and organizing the world’s information and making it accessible to everyone is an impossible responsibility, but they’re the best at it.

That said, Google Search has earned deserved criticism. 

In January of 2022, we started to see a series of thought pieces bemoaning that Google Search Results were disappointing. Michael Seibel, Managing Director at YCombinator tweeted a scathing Twitter thread,

The replies echoed his experience. More people were turning to Reddit and TikTok for searches and that became a common theme throughout 2022.

In late 2022, ChatGPT enters the chat. All of a sudden generative AI appears to be a search competitor. However, many SEOs and Large Language Model experts caution that the tool has critical limitations for search. 

  • GPT-4 was originally trained on content from before 2021, it’s since been updated on its training data as recently as April 2023.
  • LLMs are biased by training data providing harmful results.
  • It often produces factually inaccurate results (sophisticated predictive text).

Despite these elements that make ChatGPT problematic as a search engine, in January 2023, Microsoft announced a multi-billion dollar investment in OpenAI, paving the way for an integration with the tech giant’s own search engine Bing. The rollout was quick and not without glitches and hitches.

Google had been building toward a generative AI product all along. According to the New York Times, in April it was revealed Microsoft was on the verge of a potential business deal with Samsung to replace Google Search with Bing as the homepage of Samsung’s devices.

Murmurs in the media indicated that something was coming, but we initially didn’t know if it would be a stand-alone chat (which turned out to be Gemini) or an integrated generative AI search that turned out to be the Search Generative Experience (announced in early May at Google I/O).

Weeks later, the AI Overviews Experiment was opened up and SEOs began playing with the new AI Snapshot interface.

When the December 2023 experiment ended, SEOs were skeptical that AI Overviews would ever make it into production.

In March, the 2024 Core Update and Spam Update stole headlines by integrating the Helpful Content System, devastating a range of niche sites, while independently, manual penalties were being levied on high-profile generative AI content sites.

Meanwhile, in late March, Google began to test AI Overviews in the wild. 

To see AI Overviews publicly tested despite still generating some questionable (even problematic) results was an unexpected move by Google. The same company that has maintained a culture of extremely high standards and AI responsibility. 

And if you laugh at that sentiment, watch their 2020 documentary Trillions of Questions, No Easy Answers. They have a rigorous production process that appears more cutthroat than a Ph.D. defending her dissertation.

Update, on May 14th, 2024 at Google I/O, the team announced the AI Overviews rebranding and a variety of new features including:

The Search Generative Experience has been rebranded as AI Overviews and will launch immediately.

AI Overviews uses:

• Real-time info (knowledge graph)
• Algorithmic Systems
• Customize Gemini for Search

AI Overviews can understand:

Multi-step reasoning in Google search: Ask multiple questions at the same time and conduct planning. (Meal Planning, Itineraries, Events)

Ex. Search for location with multiple criteria all at the same time.

A new AI-Organized Search Results Page:

• Clusters of results.
• Contextual Factors.
• Rolling out when you look for inspiration (dining, recipes, books, etc.)

WHOA. Ask with video. That’s intriguing.

Gemini and Search figures out the video frame by frame. Stitches it together in AI-organized result.

Check out the video.

So with Google definitively moving forward with AI Overviews, what’s the big motivation behind AI in search?

Current AI Overviews Research and Analysis

As SEOs, we’re desperate to get a handle on the implications of AI Overviews. The implications of AI Overviews across search results could have an even greater impact than featured snippets. We need to prepare for a decline in organic search traffic that comes from our traditional blue links and strategize ways that our content can appear in the AI overview and drive either organic traffic or brand awareness. 

There have already been a series of studies that have investigated a few pertinent questions for AI Overviews:

  • When do AI Overviews trigger?
  • Where do AI Overviews get its sources from?
  • How are different verticals impacted?
  • How long does it take for AI Overviews to load?

To get a custom report on the potential impact of AI Overviews on your organic search traffic, sign up for the SGE Threat Report.

Get Your Custom SGE Threat Report

Keeping in mind that AI Overviews constantly experiments and evolves, here are the current reports on AI Overviews’ impact in chronological order:

How SGE will affect your SEO and business - Bartosz Góralewicz

Report Link

Date: 8/9/23

Queries Analyzed: Undisclosed

Key Takeaways:

  • SGE results were available for 81% of queries in our database.
  • 85% of sources quoted in SGE results rank within the top 10 positions
  • eCommerce queries seem to be Google’s core focus, with a staggering 92% coverage as of July 30th, 2023
  • Finance-related queries from our database are only covered in 24% of cases
  • Google’s SGE targets 87% of medical/health-related queries we track in our database

My Take: Bartosz provides insights into specific verticals with his study. Despite the undisclosed number of keywords used for his analysis, it’s still shocking to see that Google targeted 87% of health-related searches. Considering that at Google I/O in 2023, they hedged that they would take a responsible approach to SGE and that SGE still hallucinates, you’d think we wouldn’t see so many snapshots for important YMYL searches.

How Search Generative Experience works and why retrieval-augmented generation is our future - Mike King - iPullrank & Search Engine Land

Report Link

Date: 10/19/23

Queries Analyzed: 91,000

Key Takeaways:

  • AI snapshots originally took an average of 6.08 seconds to generate. Over the past few weeks, we’ve seen the average SGE result generated in less than a second.
  • 60.34% of queries did not feature an AI snapshot.
  • Positions 1, 2, and 9 get cited the most in the AI snapshot’s carousel. 
  • 9.48% of the time, SGE does not use any of the top 10 results in the AI snapshot.
  • SGE is auto-triggered on 58% of News/Media Queries, 35% of eCommerce clothing queries, and 78% of banking queries.

My Take: Mike’s research includes one of the largest sets of keywords. The fascinating part of his focus was on the impact of SGE’s load times. If the feature takes too long to load, people are going to ignore it. Google seems to be improving the speed of SGE, but that comes with a cost. One of the underlying subplots with SGE’s implementation involves the potential cost to run it for most queries. It’s expensive. More expensive than a normal search result.

Research Study - The Impact of Google's Search Generative Experience on organic rankings - Laurence O’Toole - Authoritas

Report Link

Date: 1/4/24

Queries Analyzed: 1000 Keywords

Key Takeaways:

  • Google displays a Search Generative element for 86.8% of all search queries.
  • 65.9% of the time a small generate button appears but 34.1% of the time a pre-populated SGE element appears with a ‘Show More’ link.
  • When a user clicks on the SGE button or the ‘Show More’ link, then the No.1 organic listing shifts down the page by 1,562 pixels and 1,630 pixels respectively.  This is ~1.5 times the height of the viewport and therefore it’s inconceivable that this won’t impact click-thru rates and organic traffic.
  • On average there are 10.2 links in the SGE content post-click from 4 unique domains.
  • The largest generative content introduced 37 new visible URLs above the organic rankings!
  • 93.8% of generative links (in this dataset at least) came from sources outside the top-ranking organic domains.  With only 4.5% of generative URLs directly matching a page 1 organic URL and only 1.6% showing a different URL from the same organic ranking domain.

My take: In this first study by Laurence, I love the specific look at the pixel depth of SGE. It’s been a consistent callout among SEOs that organic search results are pushed further and further down the viewport as Google increases the number of ads and other features. Things are even worse on mobile. While we’ve seen a truncated version of SGE recently, if people click on it, there’s a good chance they’ll never get to the traditional organic results.

A Searchquake is coming! - BrightEdge

Report Link

Date: 1/18/24

Queries Analyzed:1 Billion keywords

Key Takeaways:

  • Google’s Search Generative Experience is poised to affect over $40 billion per year in ad revenue on Google.
  • 84% of search queries will include Gen AI.
  • Healthcare: 76% of queries analyzed were impacted by SGE.
  • Ecommerce: 49% of queries analyzed were impacted by SGE.
  • B2B Tech: 48% of queries analyzed were impacted by SGE.
  • Insurance: 45% of queries analyzed were impacted by SGE.
  • Education: 44% of queries analyzed were impacted by SGE.
  • Restaurants: 36% of queries analyzed were impacted by SGE.
  • Entertainment: 36%  of queries analyzed were impacted by SGE.
  • Travel: 30% of queries analyzed were impacted by SGE.
  • Finance: 17% of queries analyzed were impacted by SGE.

My Take: BrightEdge’s research is hard to swallow, but for such a massive enterprise, looking at 1 billion keywords can’t be dismissed offhand. The $40 billion impact is eye-popping despite there not being any methodology to back up such a claim. Regardless, while skeptical, it’s clear that SGE could disrupt the ROI of organic search. One consideration to keep in mind is that a great deal of that impact could be a ‘reshuffling’ of the cards. For every change to the eCommerce search results, consumers might just end up choosing a different organic path to purchase.

SGE Research Study - The Impact of Google Search Generative Experience on Brand and Product Terms - Laurence O'Toole - Authoritas

Report Link

Date: 3/21/24

Queries Analyzed: 2,900 keywords

Key Takeaways:

  • Google displays a Search Generative element for 91.4% of all search queries (this was 86.8% when we looked at eCommerce terms in January).
  • The lowest SGE penetration scores were in YMYL (Your Money – Your Life) verticals like Insurance (52%), Finance (71%), Professional Services (83%) and Health & Wellness (87%).
  • Quora.com finished in the top 20 performing generative domains in 11 of 15 categories.  The only categories where it failed to make the top 20 were Automotive and YMYL categories such as Legal, Insurance and Finance.
  • When a user clicks on the SGE button or the ‘Show More’ link, the No.1 organic listing drops down the page by an average 1,255 pixels.
  • The No.1 Organic drop varies from a high of 1,723 pixels in Office Supplies and 1,718 in Fashion & Apparel to a low of 572 in Insurance – our Wave 1 eCommerce research showed a drop of ~1,500 pixels and both studies reflect a huge change in the SERP for product related queries.
  • On average there are 10.75 links in the SGE content post-click from an average of 4.3 unique domains.
  • 62% of generative links (in this dataset at least) came from sources outside the top 10 ranking organic domains.  With only 20.1% of generative URLs directly matching a page 1 organic URL and only 17.9% showing a different URL from the same organic ranking domain.  This is much better than the 93.8% reported in our eCommerce study.

My take: Laurence found the strongest evidence that organic search traffic will be threatened by SGE. With 91.4% of queries resulting in a snapshot, no websites are safe. The big question will be whether actual users find these answers helpful enough. Commercial intent queries will still drive traffic since you can’t buy products through Google (yet), but simple informational content is at risk.

SGE Snippet Research: We Analyzed 100K Keywords to See Google’s AI in Action - Yevheniia Khromova - SE Rankings

Report Link

Date: 2/26/24

Queries Analyzed: 100,000 Keywords

Key Takeaways:

  • 64% of the analyzed keywords either have an SGE answer or a Generate button.
  • Average text length of the SGE snippet: 3,485 characters.
  • Sources: SGE most frequently has 8 links.
  • 85.5% of SGE snippets link to one or more domains from the top 10 organic results.
  • Most linked domains in SGE:
    • quora.com,
    • local.google.com (Google Maps), 
    • en.wikipedia.org
  • Keywords from the Food and Beverage niche trigger SGE snippets most often, while the News and Politics niche gets SGE responses less often.
  • SGE snippets containing text appear in SERPs alongside featured snippets approximately 25% of the time.
  • Ads were present above or below SGE 73% of the time. 27% No ads.
  • Ads at the top of the SERP accompany SGE snippets more often than shopping ads, but shopping ads tend to appear above the SGE snippet.

Industries most likely to get a snippet:

My Take: Yevheniia conducted the most recent and comprehensive research to date. She analyzes everything from vertical coverage to ad placement and frequency around SGE. One of the risks around the sources of SGE that she reveals is the overwhelming reliance of forums like Quora and Reddit for results. Considering that much of this content is unvetted experiences from anonymous users, it’s surprising that Google would allow SGE to frequently intermingle it with the Generative output. 

When searching for the best movies of all time, SGE told me that a Reddit user thinks Godfather is the best movie. That’s great. Thanks, SGE.

All of these research reports show that SGE is impactful, inevitable, and will require a reframing of how SEOs strategize. 

What do SEOs need to consider as SGE appears among the keywords they’re targeting?

What is AI Overviews' impact on SEO?

With Google releasing AI Overviews publicly. Time is of the essence. SEOs need to consider how AI Overviews might steal traffic from their content and what they can do to proactively mitigate that outcome.

Assuming that AI Overviews appears on your keyword, the biggest question remains whether searchers will interact with it. If they do, will it serve as a novelty that they’ll eventually become cognitively blind towards or will it become the primary way they engage with search?

In their first test, users didn’t appreciate the AI Overview interrupting their search.

Most likely, it will depend on your audience. For a simple query, it will eventually serve as a stronger satisfier of search intent than the featured snippet.

For more complex queries, searchers will most likely continue on to websites to either conduct more research about products and services or to find more personalized expertise and experienced contextual information that they trust.

Trust is a big factor.

Right now, for most queries: AI Overviews are not a better alternative to intuitive human research.

How quickly will that change?

Assuming that searchers lock into AI Overviews, then your brand needs to appear in the generative output for visibility and remain top of mind. 

Different types of queries will have different levels of risk. Aleyda Solis hypothesizes that there are 3 SGE risk levels to consider:

  • Low risk: When SGE duplicates what’s already in the SERPs (some Local Map Packs).
  • Low-Medium risk: When SGE summarizes an article, but still provides a clear link to it.
  • Medium-High risk: eCommerce affiliate publishers that might lose traffic to product selections in the result.

Additionally, there are major considerations for brand reputation. Traditionally, a result for a brand would have organic links that a PR team could help mitigate. We have much less control over the AI Overviews output. If anything, that would give you a quick glance at your brand’s sentiment. 

For different industries beyond eCommerce, there are other considerations:

  • Google continues to threaten the travel industry with AI Overviews tips ripped from bloggers and now AI itineraries are being included in results.
  • The News Industry has the threat of pulling hard-earned journalistic sources from their content and into AI Overviews. They can’t really afford to block Googlebot from using their content if they depend on organic search for traffic as their primary business model. I wrote about what News Organizations can do to prepare for AI Overviews.
  • We’ll keep our eyes on the backlash for AI Overviews output for YMYL searches. All it takes is a few consequential results and Google might significantly roll back on the types of queries that trigger AI Overviews.

Regardless of how you feel about AI Overviews. It’s coming. The best thing you can do right now is proactively get in front of it as best you can. Besides, businesses should have a holistic, multi-channel marketing strategy in place.

In the meantime, for a step-by-step guide on how you can begin your own experimentation with optimization for AI Overviews, check out my article.

For additional quick-hit tactics, read Malte Landwehr’s recommendations on LinkedIn.

Watch the replay of our FREE webinar on AI Overviews Optimization and get a head start on your AI Overviews prep.

A final consideration for SEOs trying to wrangle AI Overviews. At this point, there’s no reporting on traffic from AI Overviews. We don’t have anything appearing in Google Search Console (like we do for Google Discover). It’s unknown if we would see that type of reporting in a reasonable amount of time. While Google has invested more resources into Google Search Console recently, it hasn’t always been historically dependable.

Besides, if AI Overviews does get adopted by searchers, it’s likely to evolve how we search. Instead of shorter and simpler searches, people will start to get more nuanced in their searches. It’s data that will be hard to do much with at scale.

The Future of AI Overviews and SEO

AI Overviews has the potential to completely disrupt the way that everyday users actually search. At this point, I ignore it unless I’m performing SEO research. I find myself glossing over the click to generate button or the truncated click to expand button on desktop and mobile.

And that’s not to say that the normal search results are always satisfying. They’re just better than AI Overviews results.

It still takes work most of the time to get to the answers and information we need unless it’s painfully obvious. And I make a living in Search.

The dream future scenario of search is that Google can more effectively pick up context and deliver exactly the concise, key pieces of information in the right format (text, images, video, data), that we’re searching for. I’d gladly trade certain segments of my data for a personalized experience on Google. If you can use my searches, device usage, and location to provide a perfect search experience, go for it! (I might be in the minority, but what other choice do we have for a better search experience?)

The Impact of AI Overviews' Novelty

So the question remains whether AI Overviews will be a pure novelty for the average person. Will your grandma, daughter, emo punk friend from high school, or local small business owner use it?

Unless they’re forced to deal with and forced to use it, they might be opposed. Humans hate change. We love the status quo. And many of us are not likely to work hard to make our situations easier. How many people read the directions? Oh. Most people? Depends on what we’re putting together or using, right?

Brands Need to Be Prepared

There are two major considerations for brands for AI Overviews.

  1. Does your content and links to your website appear in AI Overviews?
  2. Do you get mentioned in AI Overviews because of your Brand and Authority in your space?

Industry leaders have a baked-in advantage. If you’re already considered the top brand among competitors and have been for a period of time, all of the training data will be biased toward your brand. New fresh info that’s included via RAG will influence the SERPs, but you’ll have less difficulty entering new searches.

Digital PR will continue to be a core part of your holistic SEO strategy. Whether we call it ‘Barnacle SEO’ or ‘Surround Sound SEO’ doesn’t matter. Appearing in best-of lists, partnerships, social media– will all drive visibility in AI Overviews beyond pure relevance to the searches.

Will Content Creation and Publishing Change?

If we see AI Overviews siphoning off organic search traffic in greater amounts, will we see a smaller investment in original content? There’s a legitimate risk for smaller publishers to be unable to monetize their content if traffic dwindles. That might impact strategy and the number of competitors in a given field. Independent of AI Overviews, we’re seeing Google’s recent Helpful Content System, Spam Update, and Core update decimate affiliate content sites and many niche bloggers. There are a tremendous amount of false positives, but the impact has been pretty loud in the SEO community.

Will that reduce a generation of content creators? If the focus moves to different social media platforms, you might see a different format of search where Google starts to incorporate content from those channels in the same way that it depends more on Reddit and Quora. I wrote about Reddit and Google and their recent partnership.

Google needs to find ways to support content creators, media publishers, and the like. Unless they have a plan in place, this whole movement is a risk to their ad revenue model.

Our Roles as SEOs in an AI Overviews Future

While developing a stronger presence among marketing teams, SEO still doesn’t have the seat at the table that Advertising professionals do. Like Millennials in the generation wars, SEOs risk Social Media marketing stealing their seat at the table. However, that analogy probably won’t bear fruit. SEO is changing.

Adapting will require a reframing of content production for search that works within the value that AI Overviews will deliver. We still need to appear across the entire buyer’s journey, but we’ll most likely need to:

  • Focus more on the bottom of the funnel. Be discoverable where people are more likely to make a buying decision.
  • Use structured data and unique information (information gain) to stand out. Original ideas and proprietary information will be the USP.
  • Invest heavily in brand building. Google will continue to connect the real world and the online world. Perception and visibility are becoming more tied to Brand every day.
  • Do it all with less reporting. We don’t know if AI Overviews will provide attributable reporting in Google Search Console. People will start searching with longer, more context-dependent, and nuanced queries. We want scale and volume, but we’ll need to effectively report on topics, categories, and subcategories in place of individual keywords.

What do think the future holds for search, AI Overviews, and SEO?

Next Steps

Here are three ways iPullRank can help you combine SEO and content to earn visibility for your business and drive revenue:

  1. Schedule a 30-Minute Strategy Session: Share your biggest SEO and content challenges so we can put together a custom discovery deck after looking through your digital presence. No one-size-fits-all solutions, only tailored advice to grow your business. Schedule your consultation session now.
  2. Get Our Newsletter: AI is reshaping search. The Rank Report gives you signal through the noise, so your brand doesn’t just keep up, it leads. Subscribe to the Rank Report.
  3. Enhance Your Content’s Relevance with Relevance Doctor: Not sure if your content is mathematically relevant? Use Relevance Doctor to test and improve your content’s relevancy, ensuring it ranks for your targeted keywords. Test your content relevance today.

Want more? Visit our blog for access to past webinars, exclusive guides, and insightful blogs crafted by our team of experts. 

The post The Ultimate AI Overviews (SGE) Guide: How to Navigate Its Impact appeared first on iPullRank.

]]> https://ipullrank.com/ai-overviews/feed 0 How To Optimize for AI Overviews https://ipullrank.com/optimize-content-for-sge https://ipullrank.com/optimize-content-for-sge#respond Tue, 06 Feb 2024 14:28:03 +0000 https://ipullrank.com/?p=17165 With AI technology threatening the time-space continuum of organic search, SEOs are on the verge of an existential crisis.  Should we panic? Yes and no. Appearing in a static Large Language Model (LLM), like ChatGPT-3.5, might be valuable for brand visibility, but it won’t send traffic to your website. Instead, this post will focus on […]

The post How To Optimize for AI Overviews appeared first on iPullRank.

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With AI technology threatening the time-space continuum of organic search, SEOs are on the verge of an existential crisis. 

Should we panic?

Yes and no.

Appearing in a static Large Language Model (LLM), like ChatGPT-3.5, might be valuable for brand visibility, but it won’t send traffic to your website.

Instead, this post will focus on how to feature in Google’s AI Overviews ( previously SGE), which not only boosts visibility but also drives traffic to your site through source links. While our primary focus is on AI Overviews, the principles discussed are applicable across various platforms, including Microsoft’s Copilot, Bard, Perplexity, and Claude.

Internet-connected platforms enhance LLM output by retrieving current, relevant content from search engine indices. This process is called Retrieval Augmented Generation (RAG).

I won’t go into extensive details as to how it works, but in essence, it’s combining the power of the LLM with a selection of highly relevant content from the internet that allows the chatbot to produce a more up-to-date and factually accurate output.

RAG-with-a-knowledge-graph

For a comprehensive explanation of RAG read: How Search Generative Experience works and why retrieval-augmented generation is our future

Generative-AI-powered search engines (RAG LLMs) are essentially a moving target for content creators. The complexity of SEO just became even more challenging. They use context from someone’s previous searches, may offer varied outputs over time, and currently, there is limited reporting available from these platforms.

To optimize your content for RAG LLMs, you need to reverse engineer the existing generative content for the keywords that you want to target. 

To do that, you need to document your progress and results, develop a comprehensive keyword list, an approach to analyzing generative content effectively, and a content optimization and refinement process.

Can you optimize for RAG LLMs?

Watch:
[Webinar Replay] How to Optimize for AI Overviews: Protect and Perfect Your AI Snapshot

Optimizing for AI Overviews is akin to targeting featured snippets, though with more variables and less predictability. Success hinges on matching the format and relevance of the existing top-performing content.

The relevance of the content is not ambiguous. It’s quantitatively based on mathematical formulas. Tools like Orbitwise, MarketMuse, SurferSEO, and MarketBrew’s AI Overviews Visualiser tool can help you assess the relevance of your content for a targeted keyword. 

RAG LLM outputs are based on probabilities, meaning your primary challenge in appearing in AI Overviews for a targeted query lies in understanding and adapting to these probabilistic models.

The fundamental function of RAG is the pretty predictable retrieval aspect. Your primary objective is to have a chunk of your content that is most relevant to the query. 

What do you need to know? 

  • How big is the chunk used for the output?
  • How relevant is your chunk versus the chunk from your competing pages?

Another major consideration:

The more saturated the topic or vague the query, the more volatility we’ll see in the results day to day.

If you find a very specific query that your indexed content answers, chances are, you already appear in the AI Overviews output:

SGE Specific example robots txt

AI Overview searches often fluctuate with different sources and formats daily. While producing relevant content, it does not currently align with traditional SEO factors like authority backlinks or user behavior.

For instance, when searching ‘best movies of all time‘, I encountered a result quoting an anonymous Redditor, demonstrating the unpredictable nature of AI Overviews’ source selection. Hardly, an example of reliable trustworthiness.

A study on AI Overviews by Authoritas (despite the small sample size of 1000 queries) showed that 93.8% of citations were not in the top 10 organic results. 

This directly contradicted the study performed by Mike King months earlier across 90K queries, which showed links were most likely sourced from organic positions 1,2, and 9. 

The reason we shouldn’t discount the Authoritas study stems from the rapid improvement of AI Overviews.

We should also anticipate more changes going forward as Google incorporates its most recent AI model – Gemini.

Optimizing for AI Overviews requires a process of informed experimentation.

Documenting your experiments and tracking your performance comes next.

UPDATE: AI Overviews is changing constantly. On May 14th, 2024, Google rebranded SGE as AI Overviews at their annual Google I/O event.

Additionally, Since starting this article, the cisation carousel has completely disappeared from the snapshot for me. Is that going to be the final output? To be determined. But it highlights the evolving nature of versions of this experiment.

Document your Generative AI Search Engine experiments

The fundamental truth about RAG LLMs and generative AI is that they’re always changing. They’re machine learners that depend on a dynamic index of content depending on the search engine.

Google will access a different indexed internet than Bing. 

While there’s some semblance of stability, content drops in and out of the index daily. LLM output is based on probabilities. Platforms like AI Overviews are likely to incorporate more personalized factors like searcher location into the output, though imperfect. 

It’s a fool’s errand to expect the same result for every person every day.

Our goal should be to increase the likelihood that our content is sourced for a set of searches across our targeted topics and subtopics.

That caveat can inform our documentation, strategy, and experimentation.

When we track the performance of our content in RAG-powered platforms, we’ll want to keep track of the following data points:

  • Target Keyword
  • Target URL
  • Topic/Subtopic
  • Platform
  • Search Intent
  • Starting Date
  • Number of Citations/Products/Listings
  • Citation/Products/Listings Ranking (dependent on the UI of your platform)
  • Content Relevance Score
  • Content Formats
  • Number of Fraggles Per Result
  • Follow Up / Related Searches
  • Notes on content update experiments

It would help to also create a secondary table to track query improvements across dates. You’d be able to annotate those updates with content improvements.

You can make a copy and use this AI Overviews Performance Tracker spreadsheet that I’ve created for you.

With your spreadsheet in hand, you can start mapping out your keyword topic clusters.

Create a map of search journey keywords

Searches on chatbots quickly become longer and more nuanced due to the natural language interactions with the platforms.

Your goal is subtopic coverage across variations of long-tail searchers.

Before engaging in keyword research, you may already have an existing keyword portfolio in place. For this process, select a specific topic or subtopic cluster of existing content that you want to focus on.

But keep in mind that traditional keyword research exists as a guide for RAG LLMs. You need to supplement it with market research and audience insights, people-also-asked queries, and follow-up searches from your targeted platform.

Your keyword list should start with an entry point in your audience’s search journey. That might be sourced from content already on your site or you may want to create content specifically designed for your AI Overviews tests. 

Once you’ve settled on your initial keyword, you can develop your audience-driven keyword list. Our search process is rarely linear, search queries for chatbots are exponential, and your focus should begin with the most valuable and high-volume beginning query.

Imagine you’re the content marketing lead for an online children’s education center. You sell courses, books, and educational tools. Your core audience consists of parents and educators. 

For anyone who has rambunctious toddlers running around, coming up with activities is exhausting. Thankfully, you already have a great blog post that has a list of potential toddler activities. That’s a great starting query for our exercise.

toddler activities

Traditional keyword research: Relevance and volume-driven

SEO keyword tools can help us quickly generate a ton of relevant searches backed by keyword volume. Keyword research tools lack transparency in their data sources. Your mileage may vary. 

But the following tools have become relatively dependable in the industry:

First, we’ll jump over to Ahrefs and use the keyword explorer for our keyword – toddler activities:

Turns out to be a great starting point. We see about 7.5K monthly searches and a medium keyword difficulty. For term match, we do see local intent queries, so we’ll want to keep that in mind when we look at the SERP.

Now we can start to build our list of keywords that expand from this search journey.

First stop:

Looking solid! 47 keywords around my topic. While that’s a great starting point, we want to go next level and source directly from Google for related queries. 

People Also Ask

Google provides a rich snippet for most queries called People Also Ask.

There’s a dynamite tool that can collect the searches that appear. Guess what it’s called? AlsoAsked.

Respect to simple, brilliant marketing. You can get a visualization 3 levels deep and on the paid plan, you can export a CSV of the keywords.

Let’s pop in our target keyword and see what we get.

43 more relevant keywords. The cool thing with AlsoAsked is that you get more of the natural language queries that a normal person might ask in an organic conversation.

Follow Up Keywords

Each LLM platform has its preferred method of introducing related searches. Similar to AI Overviews, follow-ups aren’t static either. You could collect searches for a few days or weeks to build out your list. Look at the slight differences between AI Overviews, Copilot, and Perplexity.

AI Overviews Follow Ups

Bing Related Queries

Perplexity Related Questions

With these platforms, you can see how quickly you can develop a massive portfolio of keywords. Choosing a topic and subset of keywords that are mapped directly to your business goals (revenue, conversions, and traffic) will be the most important use of your time and resources.

Before moving on to analyzing the content generated for your keywords, add your list of keywords to your spreadsheet.

Analyze the output from your RAG LLM platform

Getting your content in the generative text for your targeted query is part logic and part art. Without purely depending on trial and error, you can design a rubric that you can use to evaluate the content, identify patterns, and stitch together for your content updates, creation, and optimization.

First, familiarize yourself with the expected generative output of your targeted platform. 

Google’s AI Overviews – the AI snapshot comes in three flavors depending on search intent. 

Informational snapshots can include paragraphs, breakout text sections, bulleted lists,  images, videos, citations, and mentions (drop-down source links for text).

Local snapshots can include everything in an informational snapshot plus a map and Google Business Profile Listings.

Shopping snapshots can include everything in an informational snapshot plus product listings, product cards, and product packs.

Revisiting our toddler activity keyword list, let’s look at one of the informational keywords for analysis:

activities for toddlers at home

To highlight the dynamic changes of AI Overviews. Within a few hours, the output remained the same. But across 3 days, we already see a change in output.

1/26/24

1/29/24

Search intent: Informational

Content Types: 

  • Breakout Text – the order has changed
  • Bulleted Lists – one of the lists no longer exists
  • Image (that expands to an image search)

Citations: 9 -> 8

Mentions: 3 -> 2

The changes are subtle, but highlight that tracking over time is a worthwhile effort.

Analyze the types of websites that are sourced in the GenAI output

We know that AI Overviews is sourcing content based on the relevance of the content. What’s fascinating about the cited content is that it’s not automatically information from the highest-ranking pages. We can’t pinpoint the exact factors that are used to select content for the generative text. 

iPullRank Founder, Michael King identified that AI Overviews sometimes creates generative output based on individual passages called Fraggles (coined by Cindy Krum). Fraggles include the actual text used by the platform’s RAG mechanisms in the linked URL. That text is used to generate the AI output.

Here’s an AI Overviews result for “Taylor Swift” and the Fraggle from Investopedia that it uses:

Reverse engineering those Fraggles and improving on the relevancy, simplicity, and clarity of the content are the best ways to earn your place in the AI Overviews and replace the Fraggle incumbent.

To analyze these Fraggles based on the mathematical values they hold using vector embeddings (see Mike’s article on content relevance), we can use MarketBrew’s free AI Overviews Visualizer tool which analyzes the relevance of the page to the targeted keyword in the context of AI Overviews. 

Why did Market Brew founder Scott Stouffer and his team build the AI Overviews Visualizer? 

“As a search engineer spanning over 20 years, what I am seeing is that AI Overviews is ushering in the 4th version of semantic algorithms. First, we had TF-IDF, then we had keywords, then it was entities, and now we have embeddings. It was an inevitable progression. During the entity movement, for a long time, Stanford NLP tried really hard to push NLP as the primary way to define and use structure within the latest semantic algorithms, but embeddings implicitly include the structure along with so many additional relationships. 

For example, if you embed the HTML tags with the content, you actually bring along all of the styling/heading vectors without having to analyze any of them. The only issue with embeddings is that it’s hard to visualize what is happening, and that’s why Market Brew launched the AI Overviews Visualizer.”

The AI Overviews Visualizer works by comprehending the intricacies of embedding-based searches and how your content reacts to different searches, the free AI Overviews (Search Generative Experience) Visualizer aims to bring users closer to understanding AI Overviews optimization strategies.

Let’s try it for the example above:

You can see that the chunk has a high similarity (relevance) score of 82%. While it’s not the highest chunk in the article, it must hit a certain threshold for AI Overviews to include it in the snapshot. At the time, the combination of the general relevance of the article and the specific Fraggle chunk earned it inclusion in the AI Overviews output.

So returning to our example, let’s identify the citations, where they exist in the top 100 results, and whether they are a fraggle or general content.

The citations from our example on the 26th are:

  1. 10 Chinese New Year Montessori-Inspired Tray Activities for Toddlers (Not in Top 100 – Fraggle – Similar Score – 82%)
  2. 15 Secrets to Entertaining a Toddler at Home  (Not in Top 100 – Headings)
  3. 15 Water Play Activities for Toddlers (Not in Top 100 – Fraggle)
  4. How to keep an active toddler occupied indoors (Not in Top 100 – Fraggle)
  5. Ball Painting: Sensory Process Art for Kids (Not in Top 100 – Fraggle)
  6. How To Occupy An Extremely Active Toddler At Home? (Not in Top 100 – Fraggle)
  7. 12 Fun Halloween Activities for Kids You Can Do at Home (Not in Top 100 – Fraggle)
  8. 9 Sensory Activities for Curious Babies and Toddlers (Not in Top 100 – Fraggle)

In this one anecdotal example, none of the results appeared in the top 100 organic rankings. This highlights an opportunity for sites that want to leapfrog the top 10 results, but also a frustration that quality, relevant results do not automatically appear in the AI Overviews output.

1. 10 Chinese New Year Montessori-Inspired Tray Activities for Toddlers 

  • Ranking: Not in Top 100 
  • Publish Date: 1-16-2020
  • Fraggle: Yes 
  • Relevance Score: 82%
  • Content Type: Breakout

Now let’s look at how MarketBrew’s free AI Overviews Visualizer tool analyzes the relevance to the AI Overviews output:

2. 15 Secrets to Entertaining a Toddler at Home  

  • Ranking: Not in Top 100 
  • Publish Date: 5-10-2021
  • Fraggle: No (Headers)
  • Relevance Score: 87%
  • Content Type: Bulleted List

Analysis: The content in this chunk to generate the bulleted list was a series of headings similar to what you’d expect from a normal featured snippet.

3. 15 Water Play Activities for Toddlers

  • Ranking: Not in Top 100 
  • Publish Date: 6-15-2021
  • Fraggle: Yes
  • Relevance Score: 69%
  • Content Type: Breakout Content

Analysis: It surprised me that this isn’t even the top similarity score chunk from the content.

4. How to keep an active toddler occupied indoors

  • Ranking: Not in Top 100 
  • Publish Date: 3-27-2020
  • Fraggle: Yes
  • Relevance Score: 78%
  • Content Type: Breakout Content

Analysis: Again, not the top similarity score chunk from the content.

5. Ball Painting: Sensory Process Art for Kids

  • Ranking: Not in Top 100 
  • Publish Date: 6-16-2022
  • Fraggle: Yes
  • Relevance Score: 85%
  • Content Type: Breakout Content

Analysis: It makes you begin to wonder what other factors beyond similarity score are at play here.

6. How To Occupy An Extremely Active Toddler At Home?

  • Ranking: Not in Top 100 
  • Publish Date: Unknown
  • Fraggle: Yes
  • Relevance Score: 68%
  • Content Type: Breakout Content

Analysis: This article had 4 different broken images across the content. 

7. 12 Fun Halloween Activities for Kids You Can Do at Home

  • Ranking: Not in Top 100 
  • Publish Date: 6-16-2022
  • Fraggle: Yes
  • Relevance Score: 80%
  • Content Type: Breakout Content

8. 9 Sensory Activities for Curious Babies and Toddlers

  • Ranking: Not in Top 100 
  • Publish Date: Unknown
  • Fraggle: Yes
  • Relevance Score: 86%
  • Content Type: Breakout Content

What’s fascinating about these fraggles are that they don’t come from many popular brands. They’re not the most original or unique suggestions. Some of them include exact matches of words from the keyword, but for the most part, they are topically relevant. The UX of the websites they’re sourced from have poor designs and are sometimes littered with ads. The content is old. The content is sometimes buried within the article, not immediately at the top of the page.

And when we use the free relevance score tool Orbitwise to look at the top AI Overviews result from miraculove.com, it has a lower relevance score (68) compared to 2 of the top 3 organically ranked articles that are not included in the AI Overviews result: busytoddler.com (72) and pampers.com (78)

When we look at the highest Fraggle ‘chunk’ from the Pampers article, it hits an 89% similarity score, but the content is not ‘unique’ compared to all of the other content out there. Arts and Crafts feel generic compared to the other ideas.

Optimizing Products in AI Overviews

eCommerce queries have a much bigger impact on business value than informational queries for many brands. It’s not exclusive to eCommerce brands. Affiliate marketing for media publishers can still be a source of revenue. 

So what do you need to consider for a commercial query in AI Overviews? 

When a shopping result is triggered on AI Overviews, the AI snapshot will provide those direct product listings in the form of the pack, grid, or individual listing.

Each of the listings has a direct citation. Typically it’s either an affiliate-incentivized listicle with a series or product or it’s taken directly from Google’s Shopping graph.

Product Listing Example Sourced from an Article and the Google Shopping Graph

Product Listings sourced only from the Google Shopping Graph

In February 2023, Google published a blog post explaining how the Google Shopping Graph would work to serve customers.

The Shopping Graph is an “ML-powered, real-time data set of the world’s products and sellers. It stores billions of global product listings (more on that next), plus specific information about those products — like availability, reviews from other shoppers, pros and cons, materials, colors and sizes.” 

It’s so much more than the specs on the products. Whenever you click on a product listing in AI Overviews, you can see the extensive information and content that informs the result.

The future of eCommerce still exists as a content ecosystem. Affiliate content still matters for AI Overviews. Whether niche publishers produce the same type of content is negotiable. It’s probable that affiliate commission from content might drop dramatically. But when one content producer drops, another may take its place. We could also see a bigger move to video affiliate content that monetizes on other channels of search. Maybe RAG LLMs will more effectively incorporate video content in the future. Right now, written content and product listings dominate AI Overviews commercial query results.

Going back to our example, it’s clear why the first product was listed based on the description from the sourced product review website.

The blurb directly speaks to the query.

“A budget-friendly trainer that is a fantastic pick for individuals looking to get back into the sport or who are new to it.”

Translation: affordable running shoes for beginners.

Content relevance clearly matters in product listings for AI Overviews.

Beyond the obvious content relevance factors, the rankings of listings in AI Overviews does not directly map to the listings in Google’s Shopping Tab grid.

Here’s the AI Overviews Grid:

Here’s the shopping tab grid for the same query:

It’s a completely different list of products. SEO Consultant Brodie Clark recently published a comprehensive list of factors that determine why certain products rank in organic search product carousels and grids.

His top factors include:

  1. Valid items and eligibility
  2. Exact product match
  3. An extensive set of information provided
  4. Maintained various product titles
  5. Sourced product reviews
  6. A distinct, competitive edge

These recommendations support many best practices around organic search best practices for eCommerce. 

Your products need to be properly implemented in the Shopping Graph. You need social proof. You need as much info as possible for a buyer to make an informed decision. It all leads to a good search experience (Google’s ultimate goal).

It logically makes sense from the product panel and the explicit mention of products pulled from the Shopping Graph that you should approach your product visibility the same way for RAG LLMs.

Tactics To Incorporate Based on Recent Studies

Content relevance has proven to be a core factor for inclusion in generative output. But what other proactive steps can we take to increase the likelihood that our content is preferable as a citation or brand mention in RAG LLMs?

In addition to the analysis of large data sets, SEOs, data scientists, and AI engineers are beginning to conduct academic experiments that test theories.

A group of researchers conducted an experiment that proposes the concept of Generative Engine Optimization (GEO).

The academics set performance benchmarks, tested content formats and types, and analyzed the results in comparison to existing LLMs.

They identified a few techniques that could offer an advantage for SEOs targeting RAG LLMs.

These models still operate as a black box without clear reasons as to what content contributes to the output. It’s logical to conclude that current SEO principles supporting quality content would improve your chances of appearing in the results.

Data and authority signals would help produce stronger fact-based results that support the goals of the creators of these models. The output needs to be useful and trustworthy. Statistics, citations, and authoritative language help serve that purpose.

While there’s yet to be conclusive evidence that other techniques provide a definitive advantage, it makes sense that other elements that support the output goals of these models would eventually be rewarded.

Here are a few specific tactics that your team should consider.

Improve Your Written Content Chunks (The Featured Snippet Approach)

Much has been written about writing content that targets featured snippets. Intentionally improve the clarity, usefulness, and authority of specific chunks of your writing that serve a specific intent for your targeted query. 

Accurate, concise, and structured responses elevate your chance of being highlighted and likely improve visibility.

Add FAQs Chunks

Frequently Asked Questions (FAQs) are a standardized format that specifically mirrors that chunk format that matters for RAG LLMs. FAQs address common user queries and offer direct, valuable information. This approach not only enhances user experience but also aligns with search engines’ preference for content that directly answers specific questions. As RAG LLM searches become more specific and detailed, FAQs become more valuable.

Information Gain

Integrating high-value, information not readily available elsewhere into your content can give you an edge. This concept, known as information gain, involves providing unique insights or data, making your content more valuable and likely to be ranked higher by LLM-driven search algorithms. Expert insights can be an excellent source for this unique information. These chunks can be especially valuable for informational queries.

Quotes from Subject Matter Experts (SMEs)

Speaking of expertise, adding directly attributed quotes from subject matter experts lends credibility and authority to your content. Expert insights offer depth and perspective, boosting the quality of your information. This enhances trustworthiness, a factor that’s increasingly important in search engine algorithms.

Relevant and Fresh Statistics

Incorporate recent and relevant statistics. It keeps your content current and fact-based. Updated stats ensure your content remains informative and relevant, which is critical for maintaining high search rankings and user engagement.

Structured Data (JSON, HTML Structure, On-Page SEO, Technical SEO)

Implement structured data, such as JSON-LD, and optimize HTML structures to improve how search engines understand and index your content. On-page and technical SEO, including aspects like rendering and crawling, are fundamental for ensuring your content is accessible and understandable to search engines. The same goes for RAG LLMs.

Structured data continues to grow in importance for search engines, yet only a slight majority of websites implement it:

Source: https://almanac.httparchive.org/en/2022/structured-data#a-year-in-review

As LLMs depend more on Knowledge Graphs to understand attributes of websites, brands, and people, your ability to provide ‘relationship clues’ with semantically structured data can emerge as a major competitive advantage.

Shopping Graph (eCommerce)

Speaking of advantageous graphs, for eCommerce brands, integrating with Google’s Shopping Graph allows real-time inventory and pricing updates, enhancing product visibility. This tool connects your products directly with potential buyers, optimizing for transactional search queries.

As we mentioned above, AI Overviews pulls heavily from the Google Shopping Graph for commercial queries. Anything that includes products in the shopping snapshot would benefit from comprehensive and up-to-date shopping graph data.

Testing and Experimenting with AI Overview Content Placement

What about in real time? Can we actually put any of this into play and see our content appear in the AI snapshot?

For these experiments, we’re going to attempt to adjust the similarity score (cosine similarity) for our target keyword. We will add new content chunks that match the search intent of keyword, add value, and have a higher similarity score than the incumbents using the AI Overview Visualizer.

We’ll then check in on the keywords daily with the hope that we see our content sourced in the snapshot.

While we do not expect the results to be sustainable (you may search for the same query on your browser and not see our citation appear), we believe we can scientifically reverse-engineer our content and leverage RAG processes to improve our AI Overviews visibility.

Experiment 1: Keyword - Enterprise SEO

For this experiment, our website is ranking 46th. Despite falling down the SERPs, we might have an opportunity to capture placement in AI Overviews by improving the similarity score of our competing content chunks.

The top output includes 4 chunks that rank at 1 (HubSpot), 3 (Backlinko), 5 (SearchEngine Journal), and not in the top 100 (LinkedIn Guest Post). So we can earn a space in that top-definition output.

Here are the 4 competing definitions, their contributing content chunks, and the similarity scores.

Enterprise SEO: Everything You Need to Know - HubSpot - (Similarity Score 87%)

Enterprise SEO - Backlinko (Similarity Score 73%)

Enterprise SEO Guide: Strategies, Tools, & More - Search Engine Journal (Similarity Score 82%)

Enterprise SEO vs. Traditional SEO: What's the difference?-  LinkedIn (Similarity Score 88%)

Enterprise Services for Growing Organizations - iPullRank (Similarity Score 66%)

Analyzing the definitions that were pulled into the generated AI Overviews output that defines Enterprise SEO using the AI Overviews Visualizer, iPullRank clearly doesn’t match the similarity score when comparing chunks. 

So our first step is to write a better definition that has a score that matches the existing contributors.

Enterprise SEO is a strategic approach to search engine optimization tailored for large organizations or websites. For enterprises with larger websites and vast volumes of content, it addresses complexities in optimizing technical aspects, crafting strategic content, and ensuring scalability to align with broader corporate goals. This practice emphasizes collaboration across departments, leveraging data for decision-making, and managing compliance to drive long-term search engine success.

We published an updated definition with a competitive similarity score of 86% on February 1st, 2024, we’ll see if this can earn us placement.

Experiment Outcome: February 5th, 2024

Our updated snippet has not appeared in the AI Overviews snapshot for Enterprise SEO.

Experiment 2: Keyword - Robots.txt

For the next experiment, we’re going to try to win a placements by creating a bulleted list that might be important enough and have a strong enough similarity score that it would be included.

Here’s a bulleted list we’ve added to the page.

“Things that are commonly included in a robots.txt file:

  • User-agent: Specifies the web crawler to which the rule applies. A * can be used to apply the rules to all web crawlers.
  • Disallow: Indicates which URLs or directories the specified web crawlers are not allowed to access.
  • Allow: Specifies exceptions to the Disallow directives, indicating which URLs or directories the specified web crawlers can access. This is particularly useful for allowing access to a specific file within a disallowed directory.
  • Sitemap: Provides the URL to the website’s sitemap, which helps crawlers find and index content more efficiently.
  • Crawl-delay: Sets the number of seconds a crawler should wait between making requests to the server. This can help manage server load.
  • Noindex: Directs crawlers not to index specific URLs. However, it’s important to note that the Noindex directive in a robots.txt file is not officially supported by all search engines, and using meta tags on the page itself is a more reliable method.”

We published an updated definition with a competitive similarity score of 82% on February 1st, 2024. 

Experiment Outcome: February 5th, 2024

Our updated snippet has not appeared in the originally targeted AI Overviews snapshot for Robots.txt, but it did show up for the Follow Up search “What are the different types of robots.txt directives?”

Experiment 3: Keyword - Media Organization Examples

For this final experiment, our article is ranked number 1 for the query Media Organization Examples, but we’re not appearing in the AI Overviews breakout. 

What’s odd about this AI Overviews output is that the content doesn’t make any sense. 

How is Social Media or Billboards a type of media organization? In this case, AI Overviews selects the wrong entity type for ‘media organization.’

Our content has a list of specific examples, but not generalized examples. So the expectation here is that we can create a short list of generalized examples, add specifics to them, and see if that will earn us placement.

“Here are some examples of media organizations:

  • News Media: Entities dedicated to the timely reporting of events and issues across a broad spectrum of topics.
  • Trade Publications: Magazines and websites aimed at professionals in specific industries, offering news, updates, and insights pertinent to their trade.
  • Niche Media: Platforms catering to specialized interests or audiences, offering content tailored to specific hobbies, professions, or demographic groups.
  • Sports Media: Outlets focused on sports news, commentary, and analysis, covering everything from local teams to international competitions.
  • Broadcast Media: Includes television and radio networks and stations that deliver content ranging from news and entertainment to educational programming.”

We published an updated definition with a competitive similarity score of 85% on February 1st, 2024. 

Experiment Outcome: February 5th, 2024

Our updated snippet appears in its own section! We have successfully improved the similarity score and matched the content type to appear in the AI Overviews snapshot.

Whether this citation stays in this result remains to be seen, but it’s achievable like a featured snippet. This process can inform your SEO content strategy going forward for AI Overviews.

Evaluate your article against the targeted keyword for cosine similarity, update it to match the content format output, monitor and track progress.

RAG LLM Optimization: Uncharted Waters, Hard to Track, Essential to Consider

Being prescriptive on RAG LLM optimization isn’t irresponsible. It’s a smart way to future-proof your digital presence because it depends on the fundamental SEO and content quality concepts that we stand by. You see talking heads argue and discuss the ethics and commercial risks of AI-powered chatbots. It’s easy to ignore the developments of search and online visibility and fight to protect your IPs by any means necessary. That’s the wrong move. 

Recently, the New York Times submitted a massive lawsuit against OpenAI for copyright infringement. Guess who is currently hiring AI engineers for generative AI journalism initiatives?

It’s a BlockBuster moment for many brands. Nobody is arguing that it’s fair or easy. But creating high-quality content, reevaluating your business models, and allowing your brand to participate in and even dominate the conversations is an important choice and roadmap moving forward.

Next Steps

Here are three ways iPullRank can help you combine SEO and content to earn visibility for your business and drive revenue:

  1. Schedule a 30-Minute Strategy Session: Share your biggest SEO and content challenges so we can put together a custom discovery deck after looking through your digital presence. No one-size-fits-all solutions, only tailored advice to grow your business. Schedule your consultation session now.
  2. Get Our Newsletter: AI is reshaping search. The Rank Report gives you signal through the noise, so your brand doesn’t just keep up, it leads. Subscribe to the Rank Report.
  3. Enhance Your Content’s Relevance with Relevance Doctor: Not sure if your content is mathematically relevant? Use Relevance Doctor to test and improve your content’s relevancy, ensuring it ranks for your targeted keywords. Test your content relevance today.

Want more? Visit our blog for access to past webinars, exclusive guides, and insightful blogs crafted by our team of experts. 

The post How To Optimize for AI Overviews appeared first on iPullRank.

]]> https://ipullrank.com/optimize-content-for-sge/feed 0 News Outlets Can’t Avoid Google’s AI – But Here’s How You Can Prepare For It https://ipullrank.com/news-seo-and-sge https://ipullrank.com/news-seo-and-sge#respond Thu, 14 Dec 2023 15:52:26 +0000 https://ipullrank.com/?p=17017 News publishers aren’t immune to Google’s AI Overviews. When Google rolls out the AI snapshot (a featured snippet on steroids), every industry will see organic search traffic disrupted in some capacity. But newsrooms might be better positioned to adapt to the new version of search than most organizations. Let’s not ignore the fact that most […]

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News publishers aren’t immune to Google’s AI Overviews. When Google rolls out the AI snapshot (a featured snippet on steroids), every industry will see organic search traffic disrupted in some capacity. But newsrooms might be better positioned to adapt to the new version of search than most organizations.

Let’s not ignore the fact that most media publications have been dealing with a depreciating business model for decades. Journalists have already experienced disruption moving from the analog to the digital. Revenue comes from ads, subscriptions, sponsorships, affiliate marketing, and events. 

The largest news outlets are dealing with different challenges from upstarts. 

Brand affinity and authority are massively advantageous, but reputation is still fragile. Look at the politicization of newsrooms across the spectrum. You cannot say that revenue hasn’t been impacted.

To complicate matters further, larger conglomerations are purchasing media outlets across industries and niches installing their roadmaps, revenue goals, and agendas.

The digital leaders of news outlets have an impossible challenge to grow, sustain, and adapt.

News media and Google have had a precarious relationship over the years. While Google has documented how they provide publishers with tools to support journalists and show up in Google News, Top Stories, and other features that drive traffic to their sites, it’s also lacked reliability and fairness (poor canonical reliability, AMP, and generative AI plagiarism). 

Now, there’s an even bigger conversation already being discussed by SEOs and news outlet marketing executives. Generative AI. 

  • What role does it play? 
  • How will Google insert its large language models into search? 
  • How will that impact organic search traffic?

One big and important distinction before we go deeper. As of now, the Search Generative Experience only applies to normal Organic Search. Google News remains unscathed. That massive source of traffic from Google should remain untouched. While Google is capable of anything, to destroy that would be to completely destroy their relationship with the news media. 

But aside from that small (potentially) silver lining, there’s a bigger question looming.

Do we block Google from using our content in its generative AI text?

The current (and harsh) reality? You can’t. 

As of October, you’d need to block Googlebot to protect yourself from Google’s AI Overviews (SGE) completely.

For what it’s worth, it might not even matter. Your content will likely end up in sources like the Common Crawl which are also used in training data sets.

We don’t expect any of the largest news organizations to do block Googlebot any time soon.

Update: Barry Adams who writes the SEO for Google News Newsletter pointed out that “several huge publications have blocked ccbot in their robots.txt recently, including the NY Times and The Guardian. It’s a bit late now, as the LLMs have already been trained, but it’s still a significant symbolic step.” 

Press Gazette biggest news sites

Source: Press Gazette

There’s also an inevitable copyright battle on the horizon between publishers and Google in the context of AI Overviews, but those legal suits can take years. So you need a plan regardless.

Established outlets have benefited from the recognizable and trustworthy brands they’ve built over time. A recognizable brand generates search traffic.

Brand Trust according to Reuter’s Digital News Report 2023

Why do news outlets need to address their approach to Google’s new AI experiment? 

The AI Snapshot appears above Top Stories and organic search results. 

If you’re missing from those results, you’re losing out on important visibility opportunities. See anyone missing for ‘News’ according to Google’s AI Snapshot?

Results from Google’s AI Snapshot for “News”

Keep in mind that many search queries that news publishers target across informational, local, and eCommerce queries also are susceptible to AI Overviews.

What You Need to Know About AI Overviews

Let’s investigate what AI Overviews currently look like, the potential impact of AI Overviews results on news outlets, and what media publishers can do to prepare their sites and strategies so they don’t lose even more traffic to Google in this new iteration of search.

According to Srinivasan Venkatachary, VP of Engineering and AI Product Expansion at Google, via a 16-page internal document of the product (updated in October), the AI Overview is “an early step in transforming the Search experience with generative AI. When using [SGE], people will notice their search results page with familiar web results, organized in a new way to help them get more from a single search.”

The experience includes:

  • The AI Snapshot: A summary of generated text and links that serves to answer the query at a general level.
  • Conversational Mode: An ability to add context and follow-up queries that can advance the journey with greater depth and breadth. 
  • Vertical Experiences: Goal-driven journeys specifically designed to surface a better shopping or local search experience.

Each of these elements of the product has significant implications for SEO that we’ll explore further in this post.

The AI Snapshot functions as a super-powered featured snippet. The display of the AI Snapshot falls into four scenarios for every search:

  • Automatically generated at the top of the search results page.
  • A message to “Get an AI-powered overview for this search?” with a Generate button to click.
  • A message that “An AI-powered overview is not available for this search”
  • No indication of an AI-powered presence.

AI Overviews is currently available in 120 countries and across 7 languages. To try AI Overviews for yourself as a consumer, you can opt-in to the feature in Google’s Search Labs.

Update: AI Overviews are now appearing publicly. 

There’s a lot that we don’t know about AI Overviews. Juan Gonzalez Villa discovered the patent behind the technology and reveals how Google takes the query, generates an AI summary from relevant content on the web, evaluates the summary, rewrites it, and then annotates it with a graded level of confidence.

Mike King explains how Retrieval Augmented Generation (RAG) powers the process behind the AI Overviews patent. We’ll return to Mike’s discoveries on AI Overviews, but you can watch how he evaluates the AI Overviews threat level in his iPullrank webinar from November.

AI Overviews will not be limited to the AI Snapshot experience on mobile and desktop. Google is also experimenting with a version to supplement your search experience while browsing.

On Chrome, you’ll be able to click an extension icon that will reveal a side panel on your browser. The can be activated for ANY website. The AI Overview while browsing allows you to:

  • Generate an AI-powered bulleted summary of the page
  • Explore content on the page
  • See related searches relevant to the page
  • Learn more about the source

When you click to learn more about the source, you’ll also see:

  • Web results about the source
  • When the site was first indexed
  • Info about the feature image
  • More about the topic (including links to related and competitor content)

There’s also a version that can be accessed on mobile devices via a Bard/Information icon at the bottom of the browser.

The implications of these features could mean that Google can steer your audience off of your website. Whether this will be another novelty that’s ignored by the masses is yet to be seen, but it’s part of the bigger picture that SEOs at news outlets need to consider.

A series of important considerations before we dive into the impact of AI Overviews on News Outlets:

  • Constant State of Change: Google is rapidly iterating the results and the way it’s triggered. Since its release in May, Google has added citations within the generated text to attributed articles.
  • Ad Impact: Because it’s in experimental mode, we do not know the precise role that ads will play in the final product. Currently, ads sometimes appear above the AI snapshot and below the snapshot, but not in the actual snapshot. 
  • Result Quality: Google does care about responsibly integrating generative AI into its products. The perceived quality of the results appears to be influenced by an abundance of caution to avoid generating factually incorrect, untrustworthy content. Originally, Google implied that important health, safety, and financial-related queries wouldn’t trigger AI Overviews snapshots, but upon rollout, that does not seem to be the case. 
  • Unknown User Impact: We don’t have any insights into whether people will click links inside the AI snapshot or scroll down to traditional organic results. We don’t know if searchers will click to trigger the snapshot in the first place. Different audiences will likely react differently. Some might be struck by the novelty of the AI-generated results. For more complex queries, searchers might still specifically look for brands they trust.

Is it a fool’s errand to predict how this will play out in the wild? Human behavior is unpredictable and this new version of search is improving rapidly, but the job of a business leader is to mitigate risk and part of that is addressing potential threats. 

We need to speculate and make an informed decision. Responsible marketing leaders and SEOs are already taking precautionary measures to protect where they might be at risk.

The Potential Impact of AI Overviews on News Outlets

As AI Overviews roll out, we can expect extreme volatility in the SERPs. 

We need to keep in mind that this will be a transitory phase. Regardless of Google’s extensive experimentation and testing, they’ve been known to be somewhat reactionary. 

If they see a massive negative impact on ad revenue, we could see a rollback in the frequency of triggered AI Snapshots. If users spend more time on Google, they might turn up the dial. It’s hard to predict Google’s perception though. Spending more time on Google could be perceived as a negative thing as it relates to search quality. 

News outlets need to be prepared with a strategy regardless of the outcome. 

When Google first announced AI Overviews, they implied at the Google I/O keynote that AI Overviews would not appear on Your Money Your Life (YMYL) queries, but that’s not always been the case. Typically these queries fall under financial, health, and political content. But we’ve seen AI Overviews on everything from financial advisor-related queries to how to treat depression. 

Google however holds back on breaking news queries that depend on authoritative brand journalism and require sensitivity. The last thing Google wants is an AI-generated snapshot that’s wrong, offensive, or deadly.

Here’s an example where it doesn’t appear for a recent series of deadly tornados in Tennessee:

But your SEO team should still manually investigate where it’s appearing. Take for instance the sensitive nature of Israel and Palestine. AI Overviews can be triggered for the query “What’s happening in Israel?”:

We don’t know whether searchers will generate the AI snapshot out of novelty or because they consider it an improved experience. But if Google decides to automatically generate the snapshot or if your audience does start to trigger it consistently, that’s a lot of traffic to risk losing to competitors.

Product Reviews, Affiliate Marketing, and the eCommerce Impact

The more dangerous and pressing concern for many news publications: the affiliate marketing play. In recent years, outlets like the New York Times have invested significant resources into product review content (Wirecutters) that drives revenue from affiliate links.

These editorial, in-depth reviews cover thousands of different products and have softened the blow of a decline in ad revenue. But AI Overviews will likely land a decisive blow on organic traffic to these product reviews. At least, that’s what much of the SEO industry is afraid of.

eCommerce commercial queries generate a completely different type of snapshot in AI Overviews. It still aggregates tips and insights about product decisions, but it includes the actual products inside the search results. 

Let’s look at the same query side-by-side—first, the AI Overviews version.

Now, we’ve got the existing organic version.

It’s a completely different set of sources. We only see one piece of affiliate-driven content in the AI Overviews version. The other two reference links are from LinkedIn (what??). Organic search is the standard murderer’s row of product reviews.

The difference is stark and terrifying.

Additionally, if you’re a news outlet, how confident are you that a random searcher (with a lack of affinity for your brand) will click on the AI Overviews result that links to your page and then choose the product from the affiliate link on your site?

That’s some significant friction between your editorial product review content and affiliate marketing revenue.

It’s a legitimate concern for news outlets and one they must resolve with creativity. Google still depends on this authoritative content to support its eCommerce AI Snapshot. So how will it play out?

One potential solution would be an adjustment to the affiliate marketing business model. Brands like Wirecutters, Tom’s, CNET, or any of these affiliate marketing sites could make sponsorship or content deals with brands but that’s unlikely to replace the revenue. This would not benefit the consumer. It’s hard enough to find impartial reviews. Online 3rd party reviews have been gamed and brands can’t write about their products objectively.

This is one of those aspects of AI Overviews that could have a massive unintended effect on consumer experience. 

This leads to the key question that news desks need to address immediately:

Should we invest in optimizing our news content for AI Overviews? 

Google does not provide the ability to block the generative AI crawlers from accessing your site, but if it does what should you do? The debate became important when OpenAI provided websites with a directive to block its chatbot via the Robots.txt file. 

Ryan Jones of Razorfish makes a compelling case that you should take a wait-and-see approach. Despite talking about ChatGPT, the same argument can be made for Google’s AI Overviews:

“OpenAI says it will cite sources when plugins pull data from third-party websites. This means there will definitely be potential to get clicks from ChatGPT if a user pulls in your content.

Blocking access only means that ChatGPT (or your user) will cite somebody else’s website.

Many people having this debate start with the flawed assumption that if people can’t get the content from ChatGPT, then they’ll have to visit the website.

I don’t think that’s true. The reality is that they’ll get the content from your competitor.”

Before exploring what your organization should do, let’s consider the pros and cons of AI Overviews.

Positive impacts:

  • Increased visibility and traffic: AI Overviews can help news outlets reach a wider audience by jumping to the top of the search results. If you typically don’t rank for top queries, due to competition from the top news brands, AI Overviews might provide a better chance at ‘getting lucky.’ 

This could lead to increased traffic and engagement, particularly for smaller outlets that struggle to compete with larger publications. It could also benefit larger publications that optimize for AI Overviews. But if we’re being honest, this is like offering ‘exposure’ to a content creator without clear compensation.

  • Improved user experience: By providing concise and easy-to-understand summaries of complex topics, AI Overviews can improve the user experience for news consumers. This could encourage users to stay on the search results page longer and engage with more content. But this also benefits Google more than your outlet.
  • Enhanced brand awareness: News outlets can leverage AI Overviews to promote specific brands and topics, potentially leading to increased brand awareness and recognition.
  • New opportunities for monetization: AI Overviews may open up new avenues for news outlets to monetize their content. For example, they could partner with Google to display targeted advertising alongside their AI Overviews summaries. This is what Google most likely is hoping for.

Negative impacts:

  • Reduced traffic: If users find the information they need directly in AI Overviews summaries, they may be less likely to click through to the original news articles. This could lead to a significant decrease in traffic for news outlets, particularly those that rely heavily on organic search referrals.
  • Loss of control over content: News outlets may lose control over how their content is presented in AI Overview summaries. This could lead to misinterpretations or inaccuracies, potentially damaging their brand reputation.
  • Increased competition: With AI Overviews providing summaries of various news sources, it could become harder for individual outlets to stand out and attract attention.

Overall, the impact of AI Overviews on news outlets will likely be mixed, but leaning negative. While it presents real opportunities for increased visibility and engagement, it also poses significant risks regarding traffic loss and reduced control over content. There are too many unknowns at this point. News outlets will need to adapt their strategies.

Here are some additional factors to consider:

  • The specific implementation of AI Overviews: The final impact of AI Overviews will depend on how Google chooses to implement the technology. It is possible that Google will address some of the concerns of news outlets, such as by providing attribution for content and allowing news outlets to opt out of AI Overview summaries.
  • The ability of news outlets to adapt: News outlets that are able to adapt their content and strategies to take advantage of AI Overviews are more likely to succeed. This may involve creating content that is specifically optimized for AI Overviews, such as short summaries and bullet points.
  • How other publishers react: The media landscape is constantly evolving, and new technologies are emerging all the time. News outlets need to be prepared to adapt their strategies to remain competitive in this dynamic environment.

Ultimately, the impact of AI Overviews on news outlets will depend on a variety of factors. However, it is clear that this technology has the potential to significantly change the way news is consumed and distributed. News outlets need to be aware of the potential risks and opportunities associated with AI Overviews and develop strategies to adapt and thrive in this new era of search.

News outlets have complex search journeys. They generate organic search traffic from:

Time Sensitive Content:

  • Breaking news stories
  • Top stories driven by journalism
  • Trending stories

Evergreen Content:

  • Consumer education stories
  • Content hubs and pieces around entities (people, businesses, places)
  • Product Reviews and Affiliate Marketing

Both categories drive the brand and revenue. 

Despite the wait-and-see approach from many of my colleagues in the field, I don’t think you can afford to ignore or even avoid the impact of AI Overviews. There’s too much at stake.

What Can News Outlets Do To Prepare for AI Overviews?

Confronting generative AI for journalists is uncomfortable. By playing the game, you’re capitulating to Google’s content theft. Every news outlet is structured differently, but your SEO, technical team, editorial, and developers should all be working together to focus on strategies that will ultimately keep the lights on.

Intentionally avoiding the AI snapshot could be considered a move to cut off your nose despite your face.

Expert Perspectives from News SEOs

I reached out to a few SEOs in the industry for their perspectives on how to move forward. Everyone has a nuanced take.

Glen Allsopp, Founder of Detailed

For the most part, media outlets fall into three camps: 

  • Genuinely seem to be concerned about the future of SEO 
  • Don’t appear to be concerned at all and are continuing as normal (or at least that’s the impression they give) 
  • Alert to what might change and working towards reducing their reliance on Google, but willing to see how things play out before doing anything drastic. 

I think the third camp is the approach I would recommend. Monitor what’s happening in search results and what appears to be working for others, but keep a focus on what’s currently working for your site right now and try not to get too caught up in any major changes you might regret. 

Continue building a brand people would seek out in search results and miss if they don’t see you ranking.  Being focused on alternative traffic & revenue sources is good (newsletters, social media, on-platform views with ads in them) but that isn’t a new thing. It has always been smart to have at least some focus on growing your reach via other channels.  

Nobody knows what things will look like a year or two from now or what will fully roll out but that goes for any platform. 

We’ve had X reducing traffic to certain major news sites. Facebook link reach diminishing and even blocking Canadian news entirely. 

New rules coming regarding email promotions and engagement rates for newsletters. I could give more examples of platform risk outside of search. 

So in summary, stay on top of what is happening and be prepared to act if needed, but don’t do anything too drastic if search is working well for you and/or close competitors right now.

Shelby Blackley, Newsroom SEO Editor at The Athletic & co-founder WTF is SEO? newsletter

Wait and see. SEO truly is the wait-and-see industry. The catapult of AI and the creation of Search Generative Experience was the dominant trend of the year — but every time we thought it was going to alter the search engine experience as we knew it, it really didn’t. Sure, traffic is fluctuating, but that’s always happened in search. That’s not to say there aren’t implications of this technology — and the current experiments of SGE show the concerns that come from using AI. A lot of the industry’s concerns right now are around the integrity of the product, and that is very fair. Integrity is the cornerstone of journalism. But publishers are beholden to the off-platform tech giants, which have very different goals. 

There’s so much we don’t know. The technology needs work. There are many legal implications for companies that are using AI without permission — which could take years to figure out. Patience and future-proofing are more important than ever, so look at how you can set your publication up for success instead of concerning yourself with protecting what you currently have. 

SGE’s emergence provides publishers a chance to turn the mirror on themselves and determine whether the content they provide is useful, trustworthy, and authoritative. Go all in on your niche. Provide more avenues for on-site engagement. Create a newsletter community that returns regularly. Experiment with content types. Honor local priorities. Deprioritize areas that take too much time, and use AI to make your mundane tasks easier. 

Google needs news. Its ecosystem relies on publishers providing information that its readers can apply to their everyday lives so they continue to come back and use the platform. As the technology continues to iterate (and Google strikes agreements with governments across the world, including Canada and Australia, about paying for news), it’s imperative that publishers continue to iterate. Change is constant. Accept change, and get good at it.

Alli Berry, SEO & Content Strategy Consultant

It feels like we’re in a bit of a wait-and-see moment with SGE. This isn’t the time for publishers to start scaling a strategy as the results are quite volatile at the moment. Most of the SGE I have seen in the SERPs has been on broad, evergreen queries, and I think this is good news for news publishers. 

It will always be harder for Google to replicate news coverage with AI. Being timely should continue to be a win for humans creating content for publications because AI-generated content is only as good as its dataset. It can’t create content about a breaking topic it hasn’t yet learned about. 

If I were a publisher, I would be de-prioritizing evergreen content as a strategy recognizing that SGE is likely coming for those more basic, broad queries. I would instead focus on ways to differentiate from AI – timely, quality reporting with quotes from real people.  I would start studying what is getting pulled into the SGE and try to reverse engineer why. There will be an opportunity to drive more shopping traffic if you can get yourself into those SGE areas of the SERP.

Kevin Indig, Growth Memo

  1. Potential impact: I just want to flag that nobody knows for sure what will happen with SGE because a lot can change. I’m not convinced we’re seeing the final version of SGE. But if it launches the way it looks right now, publishers will lose a lot of affiliate revenue. I think their Google News and Discover income is safe for now. 
  2. What should news outlets do: Get good at Google Discover and better at Google News – go all in on expertise in their content.

I think generally, publishers are in a bad spot and it will get worse. The best outcome for them would be to get bought by a tech company or find a licensing agreement with Google or LLM makers.

Jessie Willms, SEO editor and co-founder of the WTF is SEO? newsletter

Google has been expanding its footprint in search results for a long time — taking more real estate for itself around major news events (see: 2023 Golden Globes nominations. Easy content – the Winners & Nominees file – is now easily accessible in search results). With SGE, Google is likely to take even more ground for itself – but that’s nothing new. And it shouldn’t be a huge, existential concern for outlets. Instead, publishers should take SGE as an opportunity to reflect on the value they can add for readers on queries where Google is likely to serve a generative experience. How can you create content that does something or provides value to readers that Google cannot? Yes, Google will invariably encroach on your traffic – but take stock of the clicks you’re losing. Are they readers who stick around? Or are they sugar-rush page views? Can you shift focus to the queries and questions Google can’t answer? 

Once users are on-site, focus on providing the best possible reader experience. That means super-clean sites that aren’t bogged down by relentless advertising and video or newsletter popups. Not only does that build reader trust, but as Barry Adams often says: The best defense against the whims of Google’s algo updates is strong reader loyalty. Focus on what you can control: Providing a dynamic mix of content on your homepage, a strong recirculation campaign on article pages, and – obviously – whip-smart, helpful content. 

As Shelby articulated in a recent newsletter, publishers can (and perhaps should) block Google-extended, the crawler that trains Google’s Bard and Vertex AI generative APIs. Google wants to use publisher content to train its products for free. Don’t let it. 

John Shehata, CEO and founder of NewzDash (also founder of NESS News SEO Conference)

Many publishers have affiliate content on their sites that link to Amazon and other external commerce sites where they earn commission. I predict that this revenue will be impacted as when users interact with SGE and see the recommended product, they can see price comparisons for all these products on the same screen.

My recommendations to prepare for SGE:

  • Brands need to invest in building authoritative profiles (E-E-A-T) of their writers and experts.
  • Brands should also empower their writers to have an engaging presence on social media – become creators – and produce social content to promote their writing.
  • Entities (Person: schema) will become an increasingly important entry point for the Perspectives tabs, especially providing social “proof” on the authenticity of the author.
  • Brands should invest in UGC – User Generated Content – either on their sites (comments, forums), and/or having a strong presence in public forums.
  • Brands should invest in short-form videos not just for SGE, but it is a great fit for Social Media (Instagram Reels, YouTube Shorts, etc.)
  • SEOs will need to make sure that their content is well-organized and easy to read. Having proper Schema is no longer a luxury but is a must.
  • SEOs will need to make sure that their content is up-to-date. Content refreshes must be part of the workflow that publishers adapt and part of the writers’ quota.  
  • SEOs will need to make sure that their content is optimized for mobile devices.
  • SEOs need to ensure that their videos and images are well-optimized as Google is becoming more visual.

Independent of the holding pattern strategies, if you’re intent on competing for AI Overviews, Google has provided clues for you to appear in the snapshot with a link that may or may not deliver traffic to your publication.

You can begin to reverse engineer the results.

To get a sense of how Google is identifying relevant content, let’s return to Mike King’s deep dive into AI Overivews and Retrieval Augmented Generation.

He investigated a keyword set of 91,000 keywords and came away with a few important insights.

Where does AI Overviews source content?

  • Organic content from positions 1, 2, and 9 are most likely to be incorporated in the AI snapshot’s carousel.
  • 9.48% of the time, it does not use any of the top 10 results in the AI snapshot.
  • 60.34% of queries from the dataset did not feature an AI snapshot.

Mike also discovered that Google pulls chunks (passages) from relevant content and aggregates them to build the generative AI snapshot text. 

He built a tool for you to identify and investigate the chunks for your targeted query – Raggle.

But what’s interesting about the passages that AI Overviews pulls, they’re explicitly the most relevant chunks. Google is possibly pulling from a variety of related queries and generating the text. When Mike compared the included passes to the generated text, the chunks were more relevant to the AI snapshot text than the original query alone.

This is our clue for reverse engineering. Keep in mind, that this is speculation and that the slightest adjustment to the query could influence the generative AI snapshot. 

It’s not likely that optimizing for one query will work across the entire topic. But you can repeat this process for the follow-up prompts included below the snapshot.

So how does all of this work?

Let’s take an important query that will be relevant to many news outlets over the next 6 months: 2024 Olympics.

Here’s what it currently looks like in AI Overviews:

We see that Google currently pulls in: 

Before diving into the chunks, you can also see how AI Overviews is moving beyond text to include video in the results. It’s not clear if the content around the menu is from the transcript or the actual video using Google’s newest LLM, Gemini, but either way, adding a transcript is critical for accessibility. 

Important takeaway from the included video: it was published only 17 hours ago, so you can see that there’s an element of freshness considered in these results. That’s your news team’s secret weapon. You could build an entire YouTube strategy around fresh videos for targeted topics.

The AI Overviews follow-up questions for this query include:

  • Where will the 2028 Olympics be held?
  • Where are all the Olympic venues located?
  • Why are the Olympics every 4 years?

You can begin to see the journey that AI Overviews takes you on from these follow-ups and the supported sources.

Looking at the chunks, we can pinpoint the relevant content that’s pulled by AI Overviews. 

Here’s an example of the chunks identified by the Raggle tool for the keyword Olympics:

You can parse out the important facts for each of the chunks and use that to update your topic pages or relevant pages with that content. Regardless of the immediate dividends, you’ll be able to fortify those topics over time and give your outlet a better chance at showing up for more relevant queries on the topic.

As you connect your topic pages to the various articles via internal links and update them with relevant content reverse-engineered by AI Overviews to prioritize that chunked content, you’re likely to see that benefit your presence when breaking news develops.

Whether you’re refreshing your older content or covering breaking news, your SEO team should include this process within your newsroom. Check to see if there’s an AI Snapshot for the breaking news query, analyze the chunks, and get the facts or unique elements into your journalist’s hands to include in their pieces.

Your Approach to AI Overviews: Proactive or Reactive?

So what should media outlets do? Traditionally, as SEOs, we recommend taking a wait-and-see approach. But AI Overviews could be majorly disruptive for news outlets, especially those that depend on affiliate marketing for a massive portion of their revenue.

Reverse engineering AI Overviews in its current state won’t negatively impact your content. If anything, you’re proactively addressing what Google wants from content and doing your best to stay ahead of your competitors. 

In the meantime, if you want to get your AI Overviews Threat Report to assess the potential impact on your targeted keywords and organic traffic, sign up here. And if you want a custom Raggle tool or AI Overviews content strategy, contact us for a consultation.

Next Steps

Here are three ways iPullRank can help you combine SEO and content to earn visibility for your business and drive revenue:

  1. Schedule a 30-Minute Strategy Session: Share your biggest SEO and content challenges so we can put together a custom discovery deck after looking through your digital presence. No one-size-fits-all solutions, only tailored advice to grow your business. Schedule your consultation session now.
  2. Get Our Newsletter: AI is reshaping search. The Rank Report gives you signal through the noise, so your brand doesn’t just keep up, it leads. Subscribe to the Rank Report.
  3. Enhance Your Content’s Relevance with Relevance Doctor: Not sure if your content is mathematically relevant? Use Relevance Doctor to test and improve your content’s relevancy, ensuring it ranks for your targeted keywords. Test your content relevance today.

Want more? Visit our blog for access to past webinars, exclusive guides, and insightful blogs crafted by our team of experts. 

The post News Outlets Can’t Avoid Google’s AI – But Here’s How You Can Prepare For It appeared first on iPullRank.

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