Jun 11 2025
Google AI Mode: First Thoughts & Survival Strategies

The new AI Mode tab in Google’s results, currently only active in the U.S., enables users to get an AI-generated answer to their query.

You can ask a detailed question in AI Mode, and Google will provide a summarized answer.

Google AI Mode answer for the question “what are the best ways to grow your calf muscles”Google AI Mode answer for the question [what are the best ways to grow your calf muscles], providing a detailed summary of exercises and tips (Image Credit: Barry Adams)

Google explains how it generates these answers in some recently published documentation.

The critical process is what Google calls a “query fan-out” technique, where many related queries are performed in the background.

The results from these related queries are collected, summarized, and integrated into the AI-generated response to provide more detail, accuracy, and usefulness.

Having played with AI Mode since its launch, I have to admit it’s pretty good. I get useful answers, often with detailed explanations that give me the information I am looking for. It also means I have less need to click through to cited source websites.

I have to admit that, in many cases, I find myself reluctant to click on a source webpage, even when I want additional information. It’s simpler to ask AI Mode a follow-up question rather than click to a webpage.

Much of the web has become quite challenging to navigate. Clicking on an unknown website for the first time means having to brave a potential gauntlet of cookie-consent forms, email signup pop-ups, app install overlays, autoplay videos, and a barrage of intrusive ads.

The content you came to the page for is frequently hidden behind several barriers-to-entry that the average user will only persist with if they really want to read that content.

And then in many cases, the content isn’t actually there, or is incomplete and not quite what the user was looking for.

AI Mode removes that friction. You get most of the content directly in the AI-generated answer.

You can still click to a webpage, but often it’s easier to simply ask the AI a more specific follow-up question. No need to brave unusable website experiences and risk incomplete content after all.

AI Mode & News

Contrary to AI Overviews, AI Mode will provide summaries for almost any query, including news-specific queries:

AI Mode answer for the ‘latest news’ queryAI Mode answer for the [latest news] query (Image Credit: Barry Adams)

Playing with AI Mode, I’ve seen some answers to news-specific queries that don’t even cite news sources, but link only to Wikipedia.

For contrast, the regular Google SERP for the same query features a rich Top Stories box with seven news stories.

With these types of results in AI Mode, the shelf life of news is reduced even further.

Where in search, you can rely on a Top Stories news box to persist for a few days after a major news event, in AI Mode, news sources can be rapidly replaced by Wikipedia links. This further reduces the traffic potential to news publishers.

A Google SERP for ‘who won roland garros 2025’ with a rich Top Stories box vs the AI Mode answer linking only to Wikipedia A Google SERP for [who won roland garros 2025] with a rich Top Stories box vs. the AI Mode answer linking only to Wikipedia (Image Credit: Barry Adams)

There is some uncertainty about AI Mode’s traffic impact. I’ve seen examples of AI Mode answers that provide direct links to webpages in-line with the response, which could help drive clicks.

Google is certainly not done experimenting with AI Mode. We haven’t seen the final product yet, and because it’s an experimental feature that most users aren’t engaged with (see below), there’s not much data on CTR.

As an educated guess, the click-through rate from AI Mode answers to their cited sources is expected to be at least as low, and probably lower, as the CTR from AI Overviews.

This means publishers could potentially see their traffic from Google search decline by 50% or more.

AI Mode User Adoption

The good news is that user adoption of AI Mode appears to be low.

The latest data from Similarweb shows that after an initial growth, usage of the AI Mode tab on Google.com in the U.S. has slightly dipped and now sits at just over 1%.

This makes it about half as popular as the News tab, which is not a particularly popular tab within Google’s search results to begin with.

It could be that Google’s users are satisfied with AI Overviews and don’t need expanded answers in AI Mode, or that Google hasn’t given enough visual emphasis to AI Mode to drive a lot of usage.

I suspect that Google may try to make AI Mode more prominent, with perhaps allowing users to click from an AI Overview into AI Mode (the same way you can click from a Top Stories box to the News tab), or integrate it more prominently into their default SERP.

When user adoption of AI Mode increases, the impact will be keenly felt by publishers. Google’s CEO has reiterated their commitment to sending traffic to the web, but the reality appears to contradict that.

In some of their newest documentation about AI, Google strongly hints at diminished traffic and encourages publishers to “[c]onsider looking at various indicators of conversion on your site, be it sales, signups, a more engaged audience, or information lookups about your business.”.

AI Mode Survival Strategies

Broad adoption of AI Mode, whatever form that may take, can have several impactful consequences for web publishers.

Worst case scenario, most Google search traffic to websites will disappear. If AI Mode becomes the new default Google result, expect to see a collapse of clicks from search results to websites.

Focusing heavily on optimizing for visibility in AI answers will not save your traffic, as the CTR for cited sources is likely to be very low.

In my view, publishers have roughly three strategies for survival:

1. Google Discover

Google’s Discover feed may soften the blow somewhat, especially with the rollout onto desktop Chrome browsers.

Expanded presence of Discover on all devices with a Chrome browser gives more opportunities for publishers to be visible and drive traffic.

However, a reliance on Discover as a traffic source can encourage bad habits. Disregarding Discover’s inherent volatility, the unfortunate truth is that clickbait headlines and cheap churnalism do well in the Discover feed.

Reducing reliance on search in favor of Discover is not a strategy that lends itself well to quality journalism.

There’s a real risk that, in order to survive a search apocalypse, publishers will chase after Discover clicks at any cost. I doubt this will result in a victory for content quality.

2. Traffic & Revenue Diversification

Publishers need to grow traffic and income from more channels than just search. Due to Google’s enormous monopoly in search, diversified traffic acquisition has been a challenge.

Google is the gatekeeper of most of the web’s traffic, so of course we’ve been focused on maximising that channel.

With the risk of a greatly diminished traffic potential from Google search, other channels need to pick up the slack.

We already mentioned Discover and its risks, but there are more opportunities for publishing brands to drive readers and growth.

Paywalls seem inevitable for many publishers. While I’m a fan of freemium models, publishers will have to decide for themselves what kind of subscription model they want to implement.

A key consideration is whether your output is objectively worth paying for. This is a question few publishers can honestly answer, so unbiased external opinions will be required to make the right business decision.

Podcasts have become a cornerstone of many publishers’ audience strategies, and for good reason. They’re easy to produce, and you don’t need that many subscribers to make a podcast economically feasible.

Another content format that can drive meaningful growth is video, especially short-form video that has multiplatform potential (YouTube, TikTok, Instagram, Discover).

Email newsletters are a popular channel, and I suspect this will only grow. The way many journalists have managed to grow loyal audiences on Substack is testament to this channel’s potential.

And while social media hasn’t been a key traffic driver for many years, it can still send significant visitor numbers. Don’t sleep on those Facebook open graph headlines (also valuable for Discover).

3. Direct Brand Visits

The third strategy, and probably the most important one, is to build a strong publishing brand that is actively sought out by your audience.

No matter the features that Google or any other tech intermediary rolls out, when someone wants to visit your website, they will come to you directly. Not even Google’s AI Mode would prevent you from visiting a site you specifically ask for.

A brand search for ‘daily mail’ in Google AI Mode provides a link to the site’s homepage at the top of the response (Image credit: Barry AdamA brand search for [daily mail] in Google AI Mode provides a link to the site’s homepage at the top of the response (Image credit: Barry Adams)

Brand strength translates into audience loyalty.

A recognizable publisher will find it easier to convince its readers to install their dedicated app, subscribe to their newsletters, watch their videos, and listen to their podcasts.

A strong brand presence on the web is also, ironically, a cornerstone of AI visibility optimization.

LLMs are, after all, regurgitators of the web’s content, so if your brand is mentioned frequently on the web (i.e., in LLMs’ training data), you are more likely to be cited as a source in LLM-generated answers.

Exactly how to build a strong online publishing brand is the real question. Without going into specifics, I’ll repeat what I’ve said many times before: You need to have something that people are willing to actively seek out.

If you’re just another publisher writing the same news that others are also writing, without anything that makes you unique and worthwhile, you’re going to have a very bad time. The worst thing you can be as a publisher is forgettable.

There is a risk here, too. In an effort to cater to a specific target segment, a publisher could fall victim to “audience capture“: Feeding your audience what they want to hear rather than what’s true. We already see many examples of this, to the detriment of factual journalism.

It’s a dangerous pitfall that even the biggest news brands find difficult to navigate.

Optimizing For AI

In my previous article, I wrote a bit about how to optimize for AI Overviews.

I’ll expand on this in future articles with more tips, both technical and editorial, for optimizing for AI visibility.

More Resources:  

This post was originally published on SEO For Google News.

Featured Image: BestForBest/Shutterstock

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Jun 11 2025
Google Offers Voluntary Buyouts To Core U.S. Teams Amid AI Push via @sejournal, @MattGSouthern

Google is offering voluntary buyouts to employees across several of its core U.S.-based teams, including Search, Ads, engineering, marketing, and research.

The offer provides eligible employees with at least 14 weeks of severance and is available through July 1, according to reporting from The Verge and The Information.

The buyouts are limited to employees in the U.S. who report into Google’s Core Systems division, and exclude staff at DeepMind, Google Cloud, YouTube, and central ad sales.

An Exit Path, Not a Layoff

While Google has conducted layoffs in other departments earlier this year, the current program is being positioned differently.

It’s entirely voluntary and framed as an opportunity for employees to step away if their goals or performance no longer align with Google’s direction.

In a memo obtained by Business Insider, Jen Fitzpatrick, the Senior Vice President of Core Systems, explained the reasoning behind the move:

“The Voluntary Exit Program may be a fit for Core Googlers who aren’t feeling excited about and aligned with Core’s mission and goals, or those who are having difficulty meeting the demands of their role.”

Fitzpatrick added:

“This isn’t about reducing the number of people in Core. We will use this opportunity to create internal mobility and fresh growth opportunities.”

While the message downplays the idea of forced exits, this move bears a resemblance to earlier reorganizations.

In January, Google began with internal reshuffling in its Platforms and Devices division, which later led to confirmed layoffs affecting Pixel, Nest, Android, and Assistant teams. Whether the current buyouts will lead to further cuts remains to be seen.

New Return-to-Office Rules

Alongside the exit program, Google is updating its hybrid work policy.

All U.S.-based Core employees who live within 50 miles of an approved return site are being asked to transfer back to an office and follow the standard three-day in-office schedule.

Fitzpatrick noted that while remote flexibility is still supported, in-person presence is viewed as critical to collaboration and innovation.

Fitzpatrick wrote:

“When it comes to connection, collaboration, and moving quickly to innovate together, there’s just no substitute for coming together in person.”

These changes are positioned as part of a cultural shift toward spending more time in the office and aligning around shared goals.

Tied to Google’s Broader AI Push

This move comes as Google deploys its AI strategy across multiple business units. Over the past year, the company has:

This shows AI is driving changes both internally and externally.

Fitzpatrick’s memo opens by framing the current moment as a “transformational” shift for Google:

“AI is reshaping everything—our products, our tools, the way we work, how we innovate, and so on.”

Looking Ahead

While Google insists this isn’t about cutting jobs, the voluntary exit program and mandatory RTO policies make a couple of things clear. Google is fine-tuning who builds its products and how they do it.

Google wants its teams engaged, in-office, and ready to build the next generation of AI-driven tools.

For marketers and SEO professionals, this restructuring could foreshadow faster product rollouts, rapidly evolving search experiences, and continued automation in advertising tools.

Featured Image: Roman Samborskyi/Shutterstock

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Jun 10 2025
Google Shows Why Rankings Collapsed After Domain Migration via @sejournal, @martinibuster

Google’s John Mueller used a clever technique to show the publisher of an educational site how to diagnose their search performance issues, which were apparently triggered by a domain migration but were actually caused by the content.

Site With Ranking Issues

Someone posted a plea for help on the Bluesky social network to help their site recover from a site migration gone wrong. The person associated with the website attributed the de-indexing directly to the site migration because there was a direct correlation between the two events.

SEO Insight
An interesting point to highlight is that the site migration preceded the de-indexing by Google but it’s not the cause. The site migration is not the cause for the de-indexing. The migration is what set a chain of events into action that led to the real cause, which as you’ll see later on is low quality content. A common error that SEOs and publishers make is to stop investigating upon discovering the most obvious reason for why something is happening. But the most obvious reason is not always the actual reason, as you’ll see further on.

This is what was posted on social media:

“Hello SEO Community,

Sudden Deindexing & Traffic Drop after Domain Migration (from javatpoint.com to tpointtech.com) – Need Help”

Google’s John Mueller answered their plea and suggested they do a site search on Bing with their new domain, like this:

site:tpointtech.com sexy

And when you do that Bing shows “top ten list” articles about various Indian celebrities.

Google’s John Mueller also suggested doing a site search for “watch online” and “top ten list” which revealed that the site is host to scores of low quality web pages that are irrelevant to their topic.

A screenshot of one of the pages shows how abundant the off-topic web pages are on that website:

Where Did Irrelevant Pages Come From?

The irrelevant pages originated from the original domain, Javatpoint.com, from which they migrated. When they migrated to Tpointtech they also brought along all of that low quality irrelevant content as well.

Here’s a screenshot of the original domain, demonstrating that the off-topic content originated on the old domain:

Google’s John Mueller posted:

“One of the things I noticed is that there’s a lot of totally unrelated content on the site. Is that by design? If you go to Bing and use [site:tpointtech.com watch online], [site:tpointtech.com sexy], [site:tpointtech.com top 10] , similarly probably in your Search Console, it looks really weird.”

Takeaways

Bing Is Useful For Site Searches
Google’s John Mueller showed that Bing can be useful for identifying pages that Google is not indexing which could then indicate a content problem.

SEO Insight
The fact that Bing continues to index the off topic content may highlight a difference between Google and Bing. The domain migration might be showing one of the ways that Google identifies the motivation for content, whether the intent is to rank and monetize rather than create something useful to site visitors. An argument could be made that the wildly off-topic nature of the content betrays the “made-for-search-engines” motivation that Google cautions against.

Irrelevant Content
A site generally has a main topic, with branching related subtopics. But in general the main topic and subtopics relate to each other in a way that makes sense for the user. Adding wildly off-topic content low quality content betrays an intent to create content for traffic, something that Google explicitly prohibits.

Past Performance Doesn’t Predict Future Performance
There’s a tendency on the part of site publishers to shrug about their content quality because it seems to them that Google likes it just fine. But that doesn’t mean the content is fine, it means that it hasn’t become an issue yet. Some problems are dormant and when I see this in site reviews and generally say that this may not be a problem now but it could become a problem later so it’s best to be proactive about it now.

Given that the search performance issues occurred after the site migration but the irrelevant content was pre-existing it appears that the effects of the irrelevant content were muted by the standing the original content had. Nevertheless the irrelevant content was still an issue, it just hadn’t hatched into an issue yet. Migrating the site to a new domain forced Google to re-evaluate the entire site and that’s when the low quality content became an issue.

Content Quality Versus Content Intent
It’s possible for someone to make a case that the content, although irrelevant, was high quality and shouldn’t have made a difference. What calls attention to me is that the topics appear to signal an intent to create content for ranking and monetization purposes. It’s hard to argue that the content is useful for site visitors to an educational site.

Expansion Of Content Topics
Lastly there’s the issue of whether it’s a good idea to expand the range of topics that a site is relevant for. A television review site can expand to include reviews of other electronics like headphones and keyboards and it’s especially smoother if the domain name doesn’t set up the wrong expectation. That’s why domains with the product types in them are so limiting because they presume the publisher will never achieve so much success that they’ll have to expand the range of topics.

Featured Image by Shutterstock/Ollyy

Ecommerce MGMT 0 Comments
Jun 10 2025
3 Examples Of Product-Led SEO via @sejournal, @Kevin_Indig

Last week, I sent out an update on my marketplace SEO issue, and it would be a complete miss if I didn’t do the same for the topic of product-led SEO, because they’re directly related.

In this issue, you’ll get:

  • A thorough look at what product-led SEO is, what it isn’t, and where it’s valuable.
  • Three primary modalities of product-led SEO.
  • Three real-world current examples, with notes about why they’re working in the current search landscape.
  • The top watch-outs for product-led SEO programs based on modality.

And premium subscribers will get access to:

  1. My guiding checklist for product-led SEO and
  2. An interactive assessment landing in your inbox this week that will guide you in creating a high-level plan to refine your product-led approach. Sign up for full access so you don’t miss out.

Also, a quick thanks to Amanda Johnson, who partnered with me on this one. Boost your skills with Growth Memo’s weekly expert insights. Subscribe for free!

Some companies, not all, can take a hyperscalable approach to organic growth: product-led SEO.

While most sites drive SEO traffic through company-generated content (i.e., content libraries), product-led SEO allows certain sites to scale landing pages with content that comes out of the product.

This product-forward strategy can lessen the burden on your team to generate pages for a content library, and it opens the gates to SEO A/B testing, scaled internal linking strategies, and building growth loops.

In this post, I highlight five different examples and types of product-led SEO.

Guidance here has also been fully updated to reflect the recent changes in search, including the impact of AIOs, AI Mode, and LLMs, as well as how these changes affect a product-led SEO approach.

The term product-led SEO (or PLSEO for short) was first coined by Eli Schwartz in his book of the same name.

PLSEO is an organic growth strategy where your SEO practices are focused on improving the discoverability, adoption, and user experience of your product itself within search results, instead of focusing on growing organic visibility through traditional content marketing efforts.

In plain terms, the content comes from your product instead of writers.

A few examples:

  • TripAdvisor: millions of programmatic pages supported by reviews (UGC).
  • Uber Eats: millions of programmatic pages supported by restaurants (inventory).
  • Zillow: millions of programmatic pages supported by properties (inventory).

The key distinction from marketing-led SEO is that a product or growth team considers SEO in the development of the product itself, surfacing user-generated content or other inventory directly into (Google) Search.

Unlike company-generated content, product-led SEO leverages user interactions, integrations, or data to create content.

It’s an aggregator strategy, meaning it only works for companies that “aggregate” (think: collect and group) goods like reviews, suppliers, locations, and more.

Product-led SEO has been quite the buzz, especially amongst SaaS companies, but it often gets misunderstood.

Product-led SEO is not:

  • Dependent on manually crafting every landing or content page: Unlike traditional SEO approaches, where each landing page is carefully crafted by content teams, product-led SEO often uses programmatic or automated methods to generate large volumes of pages directly from product, inventory, or user data.
  • Solely focused on targeting queries for marketing angles: While typical SEO practices might start with creating content around keyword research or audience-based query discovery, product-led SEO begins with in-product signals (e.g., what users build or interact with) and surfaces that as content.
  • Relying on fixed pages: Traditional SEO often involves creating a finite set of cornerstone assets or topic clusters, expanding content from there. Product-led SEO, however, continually scales as the product (or user base) grows – each new UGC, integration, or product addition automatically adds indexable pages.

Some companies carry out a product-led SEO strategy with user-generated content (UGC), while others might use integrations or apps.

Here, I’m going to provide a look into three primary modalities of product-led SEO – and with three real-world, current examples.

  • UGC-driven PLSEO (like Figma, Traveladvisor, or Cameo): Community members create new assets – design files, shout-out profiles, wikis, etc. – and each submission spawns its own landing page. Over time, these pages accumulate long-tail keyword coverage without editorial teams writing each one.
  • Supply-driven PLSEO (like Zapier, IMDb): In this model, the product itself “supplies” data – integrations, API endpoints, or statistical datasets – that automatically translate into SEO pages.
  • Locale-driven PLSEO (like Doordash, Booking.com, or Zillow): As listings go live or update (a new restaurant, a hotel’s availability), a corresponding city- or neighborhood-specific page is generated. These pages capture “near me” and other local keywords.

A site might choose to employ multiple modalities, depending on its offerings, but I’ll also dive into what approach may work best based on your business type or goals.

Important note before you dive in: All marketplaces are product-led SEO plays, but not all product-led SEO plays are marketplaces. For a deep dive into marketplace SEO practices, check out Effective Marketplace SEO is more like Product Growth.

With UGC-based PLSEO, user contributions (templates, profiles, reviews) become the primary SEO fuel.

Design tool Figma is an archetypal example of an SEO aggregator that drives product-led SEO through user-generated content.

The scaling mechanism for Figma is the community, where users can upload and sell templates for all sorts of use cases, from mobile app design to GUI templates.

As you can see in the screenshot below, Figma’s organic traffic is exploding.

Image Credit: Kevin Indig

If you do a quick check of Figma in your preferred SEO tool, you’ll notice the following:

  • The main URL shows that, globally, Figma’s organic rankings and traffic have held or grown slightly since the intense changes across the search landscape.
  • The organic rankings and traffic of the /community/ and /templates/ subdirectories have either held or increased, depending on the particular country.
  • The number of total pages on the site has stayed about the same.

What this likely means:

  • For Figma, its UGC product-led SEO approach is holding strong after the increase of LLM chat use for search, along with Google’s AI Overviews and AI Mode.
  • This SEO approach is difficult to replicate, which creates a growth loop for Figma that is hard to compete with.
  • A UGC-driven approach helps overcome current search challenges. Figma has a countless number of helpful kits and templates that AI-driven search and LLMs can rely on in their sourcing and recommendations. (A quick search of “what are the best free UI kits?” in ChatGPT gave me a list of 10 recommendations, and seven were from Figma. In Google’s AI Mode, I received two to three “best of” lists that were just Figma free kits.)

Notion or Typeshare follow the same approach:

  • With knowledge management software Notion, users can create their own wikis and allow Google to index specific pages, or whole workspaces.
  • Typeshare is a social posting tool that automatically adds social content to a mini blog that users can decide to index in Search.

Top Use Cases For UGC Product-Led SEO

This type of SEO excels for sites and businesses that can continuously scale content based on what users contribute or interact with, including:

  • SaaS companies where users can create their own templates, designs, or workflows to share and sell.
  • Code snippet sharing sites or platforms.
  • Knowledge-sharing forums or wiki platforms (like the HubSpot Community).
  • Review and recommendation aggregators – marketplaces like G2 and TripAdvisor.

For supply-driven product-led SEO, remember: The product itself “supplies” data. That’s the content that produces pages for optimization.

An excellent B2C example of this (and a site that you’re likely familiar with already) is IMDb.

IMDb’s massive repository of movie and TV metadata – cast lists, release dates, ratings, and filming locations – produces SEO pages that rank for film enthusiasts’ long-tail queries.

Whenever new data (e.g., “new Netflix release 2025”) is ingested via AWS Data Exchange or partner feeds, IMDb’s platform auto-generates or updates the corresponding title page, ensuring fresh content for searches like “when is [Movie Title] coming out on streaming?”

Plus, IMDb benefits from a boost with a side of UGC from user ratings and commentary.

This data-supply-driven approach turns product updates into continuous SEO signals.

Image Credit: Kevin Indig

If you do a quick check of IMDb in your preferred SEO tool, you’ll notice:

  • The main URL shows that, globally, IMDb organic rankings and traffic have held or grown slightly since the intense changes across the search landscape.
  • The organic rankings and traffic of the /boxoffice/ and /calendar/ subdirectories have either held, increased, or even skyrocketed, depending on the particular country.
  • The number of total pages on the site has decreased slightly in the last 12 months, by about 23%.

What this likely means:

  • For IMDb, its supply-driven product-led SEO approach is holding strong after the increase of LLM chat use for search, along with Google’s AI Overviews and AI Mode.
  • This SEO approach is difficult for other sites to replicate, which creates a growth loop for IMDb that is hard to compete with. IMDb’s global data supply is robust and hard to beat.
  • A supply-driven approach helps overcome current search challenges. Because IMDb is responsive to date-driven, rating-driven data changes, it provides an excellent source of updated, live information for traditional searching and LLMs to surface in conversation-based searches.

Top Use Cases For Supply-Driven Product-Led SEO

This type of PLSEO excels when you have unique, defensible datasets and a templating system to publish pages at scale, capturing long-tail and high-intent queries without manual content creation.

Examples of orgs that could benefit from this modality include:

  • Security and vulnerability databases that can auto-publish advisory pages for each newly discovered vulnerability (like Snyk).
  • Real-time pricing and compensation sites (think Glassdoor or fintech rate comparison sites).
  • SaaS products that collect user behavior or performance metrics (e.g., “Average page load times for Shopify stores”).

The locale-driven PLSEO modality leverages hyperlocal or geo-specific inventory – restaurants, homes, hotels – to create SEO pages for every location or zip code.

Food delivery service Doordash scales organic traffic by aggregating restaurants and types of food, similar to Uber Eats or Instacart.

Image Credit: Kevin Indig

Since food delivery has a strong local intent, near me queries are essential. Doordash addresses that with an extensive list of city pages.

The right page layout and content are key for sites that scale through product inventory.

  • Doordash’s city pages contain restaurants, text, and FAQ.
  • Restaurant pages themselves follow a similar pattern: They cover meals to order, reviews, and FAQ.
  • Another important factor? Internal linking. City pages link to nearby cities; restaurant pages to restaurants in the same city.

Doordash has also created pages for schools (order near a campus), hotels (order near a hotel), and zip codes to cover all possible user intentions.

Other examples of product inventory-driven sites are real estate site Zillow or coupon code site Retailmenot.

If you do a quick check of Doordash in your preferred SEO tool, you’ll notice:

  • The main URL shows Doordash’s organic rankings and traffic have declined significantly and then held in the U.S. market, but have grown slightly since January 2025 in some global markets.
  • It has reduced its total number of pages by ~30%.

What this likely means:

  • For Doordash, its product inventory, product-led SEO approach is holding strong after the increase of LLM chat use for search, along with Google’s AI Overviews and AI Mode.
  • Part of this sustained presence (despite a challenging SEO landscape) is likely due to investment in the brand, expanding globally, and reducing unimportant pages and topics to their business.

I predict that search engines and LLMs will continue to give favor to hyperlocal content, which is hard to match.

These product inventory sites that are centered on location (like Doordash, Zillow) or millions of products have the right infrastructure to do it well.

This particular approach to product-led SEO can work well for businesses that can programmatically generate search-ready pages from their product or listing inventory, including:

  • Food delivery & local ordering platforms.
  • Real estate marketplaces.
  • Ecommerce retailers with expansive catalogs.
  • Travel & accommodation aggregators.
  • Automotive listing portals.

While product-led SEO can drive the creation of SEO growth loops around your business – ones that are difficult for your competitors to replicate – this approach doesn’t come without some big challenges.

Keep the following in mind:

1. Sites using PLSEO approaches need to watch out for SEO hygiene, spam, and site maintenance issues.

Inventory changes (menus, listings, hours, availability) on the site can keep content fresh – an advantage for both classic SEO and potential LLM training inputs.

However, the hygiene and maintenance required to keep these pages functioning and accurate are significant. Don’t employ this practice without the proper infrastructure in place to maintain it over time.

And if you rely on UGC? It’s mission-critical to have smart QA processes and spam filters in place to ensure content quality.

2. SEO aggregators, especially marketplaces, have been significantly impacted by the rollouts of AI-based search.

PLSEO is not exempt from the impact of Google’s AIOs, AI Mode, and LLM-based search. In actuality, many aggregator marketplaces have been disproportionally affected.

One of the biggest challenges, especially for product-led UGC SEO plays, is that all your hard work may go unclicked.

Creating systems to do this kind of SEO at scale is labor-intensive.

It’s highly likely that AIOs, AI Mode, and LLMs will reference the user generated content without you earning the organic traffic for it.

However, building a strong, trusted brand through community, publication mentions, and shared links can earn more mentions in LLMs.

Because I recently reworked my in-depth guide to marketplace SEO, I’m going to save you some extra scrolling here.

If you’re interested in the best use cases and how to approach marketplace SEO from a product growth mindset, take a leap over here for some great examples and a full framework: Effective Marketplace SEO is more like Product Growth.

3. Don’t cut corners on the depth of information provided in favor of scaling.

For many sites, the key to scaling product-led SEO is deploying a programmatic approach.

But programmatic landing pages should still contain a depth of information, have strong technical SEO, and engaging content with sufficient user value.

If you don’t have these resources and practices in place, along with the proper processes to maintain pages over time, then it’s likely programmatic SEO isn’t for your org.

With the rise of AI-based search, LLMs like ChatGPT, as well as Google’s AI Overviews and AI Mode, are moving toward understanding and presenting information in more conversational and context-rich formats, which programmatic pages often lack.

Another watch-out? If these programmatic pages are highly templated with lots of elements, they’re often a lot for a human reader to take in at once. And that can lead to poor UX if not done correctly.

4. Future challenge: A web surfed by AI agents.

While it’s likely we don’t need to be worried about this today, we need to start brainstorming how to adapt our content creation for what the web could look like tomorrow.

In what ways would your product-led SEO approach need to change to adapt to AI agent traffic, while also prioritizing human UX?

If users start using queries and commands like “order my favorite dish from the Indian food restaurant I went to last month and have it delivered,” or “give me 3 for-sale listings of 2 bed, 2 bath condos in my area that I didn’t review last week,” to send an AI agent to your site, how would your PLSEO practices need to adapt?

What about PLSEO practices that surface unique integrations, templates, and workflows?

If AI agents become users of products and software themselves – and therefore also have the ability to generate their own apps, integrations, and product workflows as needed – humans, and even their AI counterparts, then skip the need for this search entirely. (Brands that solely rely on these types of searches could say goodbye to organic traffic and visibility.)

I don’t have the answers here – I’d argue no one does right now. So, no need for immediate alarm or dramatic changes.

But it’s important to start investing time and testing to consider what your brand may need to change for an AI agent future.

Adopting a product-led SEO strategy can unlock substantial growth – and growth that holds and is sustained despite the increase in AI-based search – but it’s not a one-size-fits-all solution.

When executed well, PLSEO turns your product (or product data) into an ever-expanding library of SEO assets.

Instead of relying solely on a content team to crank out new blog posts or landing pages, you leverage in-product signals – user contributions, integrations, inventory feeds – to automatically spawn indexable pages.

But before starting or reworking your product-led SEO program, you need to have the right motions in place. For this SEO approach, there are many essential moving parts – and each one is important.

Featured Image: Paulo Bobita/Search Engine Journal

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Jun 9 2025
Google Responds To Site That Lost Ranks After Googlebot DDoS Crawl via @sejournal, @martinibuster

Google’s John Mueller answered a question about a site that received millions of Googlebot requests for pages that don’t exist, with one non-existent URL receiving over two million hits, essentially DDoS-level page requests. The publisher’s concerns about crawl budget and rankings seemingly were realized, as the site subsequently experienced a drop in search visibility.

NoIndex Pages Removed And Converted To 410

The 410 Gone server response code belongs to the family 400 response codes that indicate a page is not available. The 404 response means that a page is not available and makes no claims as to whether the URL will return in the future, it simply says the page is not available.

The 410 Gone status code means that the page is gone and likely will never return. Unlike the 404 status code, the 410 signals the browser or crawler that the missing status of the resource is intentional and that any links to the resource should be removed.

The person asking the question was following up on a question they posted three weeks ago on Reddit where they noted that they had about 11 million URLs that should not have been discoverable that they removed entirely and began serving a 410 response code. After a month and a half Googlebot continued to return looking for the missing pages. They shared their concern about crawl budget and subsequent impacts to their rankings as a result.

Mueller at the time forwarded them to a Google support page.

Rankings Loss As Google Continues To Hit Site At DDOS Levels

Three weeks later things have not improved and they posted a follow-up question noting they’ve received over five millions requests for pages that don’t exist. They posted an actual URL in their question but I anonymized it, otherwise it’s verbatim.

The person asked:

“Googlebot continues to aggressively crawl a single URL (with query strings), even though it’s been returning a 410 (Gone) status for about two months now.

In just the past 30 days, we’ve seen approximately 5.4 million requests from Googlebot. Of those, around 2.4 million were directed at this one URL:
https://example.net/software/virtual-dj/ with the ?feature query string.

We’ve also seen a significant drop in our visibility on Google during this period, and I can’t help but wonder if there’s a connection — something just feels off. The affected page is:
https://example.net/software/virtual-dj/?feature=…

The reason Google discovered all these URLs in the first place is that we unintentionally exposed them in a JSON payload generated by Next.js — they weren’t actual links on the site.

We have changed how our “multiple features” works (using ?mf querystring and that querystring is in robots.txt)

Would it be problematic to add something like this to our robots.txt?

Disallow: /software/virtual-dj/?feature=*

Main goal: to stop this excessive crawling from flooding our logs and potentially triggering unintended side effects.”

Google’s John Mueller confirmed that it’s Google’s normal behavior to keep returning to check if a page that is missing has returned. This is Google’s default behavior based on the experience that publishers can make mistakes and so they will periodically return to verify whether the page has been restored. This is meant to be a helpful feature for publishers who might unintentionally remove a web page.

Mueller responded:

“Google attempts to recrawl pages that once existed for a really long time, and if you have a lot of them, you’ll probably see more of them. This isn’t a problem – it’s fine to have pages be gone, even if it’s tons of them. That said, disallowing crawling with robots.txt is also fine, if the requests annoy you.”

Caution: Technical SEO Ahead

This next part is where the SEO gets technical. Mueller cautions that the proposed solution of adding a robots.txt could inadvertently break rendering for pages that aren’t supposed to be missing.

He’s basically advising the person asking the question to:

  • Double-check that the ?feature= URLs are not being used at all in any frontend code or JSON payloads that power important pages.
  • Use Chrome DevTools to simulate what happens if those URLs are blocked — to catch breakage early.
  • Monitor Search Console for Soft 404s to spot any unintended impact on pages that should be indexed.

John Mueller continued:

“The main thing I’d watch out for is that these are really all returning 404/410, and not that some of them are used by something like JavaScript on pages that you want to have indexed (since you mentioned JSON payload).

It’s really hard to recognize when you’re disallowing crawling of an embedded resource (be it directly embedded in the page, or loaded on demand) – sometimes the page that references it stops rendering and can’t be indexed at all.

If you have JavaScript client-side-rendered pages, I’d try to find out where the URLs used to be referenced (if you can) and block the URLs in Chrome dev tools to see what happens when you load the page.

If you can’t figure out where they were, I’d disallow a part of them, and monitor the Soft-404 errors in Search Console to see if anything visibly happens there.

If you’re not using JavaScript client-side-rendering, you can probably ignore this paragraph :-).”

The Difference Between The Obvious Reason And The Actual Cause

Google’s John Mueller is right to suggest a deeper diagnostic to rule out errors on the part of the publisher. A publisher error started the chain of events that led to the indexing of pages against the publisher’s wishes. So it’s reasonable to ask the publisher to check if there may be a more plausible reason to account for a loss of search visibility. This is a classic situation where an obvious reason is not necessarily the correct reason. There’s a difference between being an obvious reason and being the actual cause. So Mueller’s suggestion to not give up on finding the cause is good advice.

Read the original discussion here.

Featured Image by Shutterstock/PlutusART

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Jun 9 2025
Google’s Local Job Type Algorithm Detailed In Research Paper via @sejournal, @martinibuster

Google published a research paper describing how it extracts “services offered” information from local business sites to add it to business profiles in Google Maps and Search. The algorithm describes specific relevance factors and confirms that the system has been successfully in use for a year.

What makes this research paper especially notable is that one of the authors is Marc Najork, a distinguished research scientist at Google who is associated with many milestones in information retrieval, natural language processing, and artificial intelligence.

The purpose of this system is to make it easier for users to find local businesses that provide the services they are looking for. The paper was published in 2024 (according to the Internet Archive) and is dated 2023.

The research paper explains:

“…to reduce user effort, we developed and deployed a pipeline to automatically extract the job types from business websites. For example, if a web page owned by a plumbing business states: “we provide toilet installation and faucet repair service”, our pipeline outputs toilet installation and faucet repair as the job types for this business.”

Developing A Local Search System

The first step for creating a system for crawling and extracting job type information was to create training data from scratch. They selected billions of home pages that are listed in Google business profiles and extracted job type information from tables and formatted lists on home pages or pages that were one click away from the home pages. This job type data became the seed set of job types.

The extracted job type data was used as search queries, augmented with query expansion (synonyms) to expand the list of job types to include all possible variations of job type keyword phrases.

Second Step: Fixing A Relevance Problem

Google’s researchers applied their system on the billions of pages and it didn’t work as intended because many pages had job type phrases that were not describing services offered.

The research paper explains:

“We found that many pages mention job type names for other purposes like giving life tips. For example, a web page that teaches readers to deal with bed bugs might contain a sentence like a solution is to call home cleaning services if you find bed bugs in your home. They usually provide services like bed bug control. Though this page mentions multiple job type names, the page is not provided by a home cleaning business.”

Limiting the crawling and indexing to identifying job type keyword phrases resulted in false positives. The solution was to incorporate sentences that surrounded the keyword phrases so that they could better understand the context of the job type keyword phrases.

The success of using surrounding text is explained:

“As shown in Table 2, JobModelSurround performs significantly better than JobModel, which suggests that the surrounding words could indeed explain the intent of the seed job type mentions. This successfully improves the semantic understanding without processing the entire text of each page, keeping our models efficient.”

SEO Insight
The described local search algorithm is purposely excluding all information on the page and zeroing in on job type keyword phrases and surrounding words and phrases around those keywords. This shows the importance of how the words around important keyword phrases can provide context for the keyword phrases and make it easier for Google’s crawlers to understand what the page is about without having to process the entire web page.

SEO Insight
Another insight is that Google is not indexing the entire web page for the limited purpose of identifying job type keyword phrases. The algorithm is hunting for the keyword phrase and surrounding keyword phrases.

SEO Insight
The concept of analyzing only a part of a page is similar to Google’s Centerpiece Annotation where a section of content is identified as the main topic of the page. I’m not saying these are related. I’m just pointing out one feature out of many where a Google algorithm zeroes in on just a section of a page.

The System Uses BERT

Google used the BERT language model to classify whether phrases extracted from business websites describe actual job types. BERT was fine-tuned on labeled examples and given additional context such as website structure, URL patterns, and business category to improve precision without sacrificing scalability.

The Extraction System Can Be Generalized To Other Contexts

An interesting finding detailed by the research paper is that the system they developed can be used in areas (domains) other than local businesses, such as “expertise finding, legal and medical information extraction.”

They write:

“The lessons we shared in developing the largescale extraction pipeline from scratch can generalize to other information extraction or machine learning tasks. They have direct applications to domain-specific extraction tasks, exemplified by expertise finding, legal and medical information extraction.

Three most important lessons are:

(1) utilizing the data properties such as structured content could alleviate the cold start problem of data annotation;

(2) formulating the task as a retrieval problem could help researchers and practitioners deal with a large dataset;

(3) the context information could improve the model quality without sacrificing its scalability.”

Job Type Extract Is A Success

The research paper says that their system is a success, it has a high level of precision (accuracy) and that it is scalable. The research paper says that it has already been in use for a year. The research is dated 2023 but according to the Internet Archive (Wayback Machine), it was published sometime in July 2024.

The researchers write:

“Our pipeline is executed periodically to keep the extracted content up-to-date. It is currently deployed in production, and the output job types are surfaced to millions of Google Search and Maps users.”

Takeaways

  • Google’s Algorithm That Extracts Job Types from Webpages
    Google developed an algorithm that extracts “job types” (i.e., services offered) from business websites to display in Google Maps and Search.
  • Pipeline Extracts From Unstructured Content
    Instead of relying on structured HTML elements, the algorithm reads free-text content, making it effective even when services are buried in paragraphs.
  • Contextual Relevance Is Important
    The system evaluates surrounding words to confirm that service-related terms are actually relevant to the business, improving accuracy.
  • Model Generalization Potential
    The approach can be applied to other fields like legal or medical information extraction, showing how it can be applied to other kinds of knowledge.
  • High Accuracy and Scalability
    The system has been deployed for over a year and delivers scalable, high-precision results across billions of webpages.

Google published a research paper about an algorithm that automatically extracts service descriptions from local business websites by analyzing keyword phrases and their surrounding context, enabling more accurate and up-to-date listings in Google Maps and Search. This technique avoids dependence on HTML structure and can be adapted for use in other industries where extracting information from unstructured text is needed.

Read the research paper abstract and download the PDF version here:

Job Type Extraction for Service Businesses

Featured Image by Shutterstock/ViDI Studio

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Jun 6 2025
Google’s Update To Recipe Structured Data Confirms A Ranking Criteria via @sejournal, @martinibuster

Google updated the Recipe Schema.org structured data documentation to reflect more precise guidance on what the image structured data property affects and where to find additional information about ranking recipe images in the regular organic search results.

Schema.org Structured Data And Rich Results

The SEO and publisher community refers to the text results as the organic search results or the ten blue links. Google refers to them as the text results.

Structured data helps a site’s content become eligible to rank in Google’s rich results but it generally doesn’t help content rank better in the text results.

That’s the concept underlying Google’s update to the Recipe structured data guidance with the addition of two sentences:

“Specifying the image property in Recipe markup has no impact on the image chosen for a text result image. To optimize for a text result image, follow the image SEO best practices.”

Recipe structured data influences the images shown in the Recipe Rich Results. The structured data does not influence the image rankings in the regular text results (aka the organic search results).

Ranking Images In Text Results

Google offers documentation for image best practices which specify normal HTML like the and elements. Google also recommends using an image sitemap, a sitemap that’s specifically for images.

Something to pay particular attention to is to not use images that have blurry qualities to them. Always use sharp images to give your images the best chance for showing up in the search results.

I know that some images may contain slight purposeful blurring for optimization purposes (blurring decreases image size) and to enhance the perspective of foreground and background. But Google recommends using sharp images and to avoid blurring in images. Google doesn’t say it’s an image ranking factor, but it does make that recommendation.

Here’s what Google’s image optimization guidance recommends:

“High-quality photos appeal to users more than blurry, unclear images. Also, sharp images are more appealing to users in the result thumbnail and can increase the likelihood of getting traffic from users.”

In my opinion I think it’s best to avoid excessive use of blurring. I only have my own anecdotal experience with purposely blurred images not showing up in the search results. So, to me it’s interesting to see my experience confirmed that Google treats blurred images as a negative quality and sharp images as a positive quality.

Read Google’s updated Recipe structured data documentation about images here:

https://developers.google.com/search/docs/appearance/structured-data/recipe#image

Read more about images in Google’s text results here.

Read about blurry and sharp images here:

https://developers.google.com/search/docs/appearance/google-images#good-quality-photos%20optimize-for-speed

Featured Image by Shutterstock/Dean Drobot

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Jun 6 2025
Newly Released Data Shows Desktop AI Search Referrals Dominate via @sejournal, @martinibuster

BrightEdge Enterprise SEO platform released new data showing distinctive patterns across major AI search and chatbot platforms and also called attention to potential disruption from Apple if it breaks with Google as the default search engine in Safari.

Desktop AI Traffic Dominance

One of the key findings in the BrightEdge data is that traffic to websites from AI chatbots and search engines is highest from desktop users. The exception is Google Search which is reported to send more traffic from mobile devices over desktop.

The report notes that 94% of the traffic from ChatGPT originates from desktop apps with just 6% of referrals coming from mobile apps. BrightEdge speculates that the reason why there’s less mobile traffic is because ChatGPT’s mobile app shows an in-app preview, requiring a user to execute a second click to navigate to an external site. This creates a referral bottleneck that doesn’t exist on the desktop.

But that doesn’t explain why Perplexity, Bing, and Google Gemini also show similar levels of desktop traffic dominance. Could it be a contextual difference where users on desktop are using AI for business and mobile use is less casual? The fact that Google Search sends more mobile referral traffic than desktop could suggest a contextual reason for the disparity in mobile traffic from AI search and chatbots.

BrightEdge shared their insights:

“While Google maintains an overwhelming market share in overall search (89%) and an even stronger position on mobile (93%), its dominance is particularly crucial in mobile web search. BrightEdge data indicates that Apple phones alone account for 57% of Google’s mobile traffic to US and European brand websites. But with Safari being the default for around a billion users, any change to that default could reallocate countless search queries overnight.

Apple’s vendor-agnostic Apple Intelligence also suggests opportunities for seismic shifts in web search. While generative AI tools have surged in popularity through apps on IOS, mobile web search—where the majority of search still occurs—remains largely controlled by Google via Safari defaults. This makes Apple’s control of Safari the most valuable real estate in the mobile search landscape.”

Here are the traffic referral statistics provided by BrightEdge:

  • Google Search: Only major AI search with mobile majority traffic referrals (53% mobile vs 44% desktop)
  • ChatGPT: 94% desktop, just 6% mobile referrals
  • Perplexity: 96.5% desktop, 3.4% mobile
  • Bing: 94% desktop, 4% mobile
  • Google Gemini: 91% desktop, 5% mobile

Apple May Play The Kingmaker?

With Apple’s Worldwide Developers Conference (WWDC) nearing, one of the changes that many will be alert to is any announcement relative to the company’s Safari browser which controls the default search settings on nearly a billion devices. A change in search provider in Safari could initiate dramatic changes to who the new winners and losers are in web search.

Perplexity asserts that the outcome of changes to Safari browser defaults may impact search marketing calculations for the following reasons:

“58% of Google’s mobile traffic to brand websites comes from iPhones

Safari remains the default browser for nearly a billion users

Apple has not yet embedded AI-powered search into its mobile web stack”

Takeaways

  • Desktop Users Of AI Search Account For The Majority Of Referral Traffic
    Most AI-generated search traffic from from ChatGPT, Perplexity, Bing, and Gemini comes from desktop usage, not mobile.
  • Google Search Is The Traffic Referral Outlier
    Unlike other AI search tools, Google Search still delivers a majority of its traffic via mobile devices.
  • In-App Previews May Limit ChatGPT Mobile AI Referrals
    ChatGPT’s mobile app requires an extra click to visit external sites, possibly explaining low mobile referral numbers.
  • Apple’s Position Is Pivotal To Search Marketing
    Apple devices account for over half of Google’s mobile traffic to brand websites, giving Apple an outsized impact on mobile search traffic.
  • Safari Default And Greater Market Share
    With Safari set as the default browser for nearly a billion users, Apple effectively controls the gate to mobile web search.
  • Perplexity Stands To Gain Market Share
    If Apple switches Safari’s default search to Perplexity, the resulting shift in traffic could remake the competitive balance in search marketing.
  • Search Marketers Should Watch WWDC
    Any change announced at Apple’s WWDC regarding Safari’s search engine could have large-scale impact on search marketing.

BrightEdge data shows that desktop usage is the dominant source of traffic referrals from AI-powered search tools like ChatGPT, Perplexity, Bing, and Gemini, with Google Search as the only major platform that sends more traffic via mobile.

This pattern could suggest a behavioral split between desktop users, who may be performing work-related or research-heavy tasks, and mobile users, who may be browsing more casually. BrightEdge also points to a bottleneck built into the ChatGPT app that creates a one-click barrier to mobile traffic referrals.

BrightEdge’s data further cites Apple’s control over Safari, which is installed on nearly a billion devices, as a potential disruptor due to a possible change in the default search engine away from Google. Such a shift could significantly alter mobile search traffic patterns.

Read more at BrightEdge

The Open Frontier of Mobile AI Search

Featured Image by Shutterstock/Tada Images

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Jun 6 2025
Respected SEO Rockstar Deconstructs SEO For Google’s AI Search via @sejournal, @martinibuster

One of the SEO industry’s SEO Rockstars recently shared his opinion about SEO for generative AI, calling attention to facts about Google and how the new AI search really works.

Greg Boser is a search marketing pioneer with a deep level of experience that few in the industry can match or even begin to imagine.

Digital Marketers And The History Of SEO

His post was in response to a tweet by someone else that in his opinion overstated that SEO is losing dominance. Greg began his SEO rant by pointing out how some search marketer’s conception of SEO is outdated but they’re so new to SEO that they don’t realize it.

For example, the practice of buying links is one of the oldest tactics in SEO, so old that newcomers to SEO gave it a new name, PBN (private blog network), as if giving link buying a new name changes it somehow. And by the way, I’ve never seen a PBN that was private. The moment you put anything out on the web Google knows about it. If an automated spambot can find it in literally five minutes, Google probably already knows about it, too.

Greg wrote:

“If anyone out there wants to write their own “Everything you think you know is wrong. GEO is the way” article, just follow these simple steps:

1. Frame “SEO” as everything that was a thing between 2000 – 2006. Make sure to mention buying backlinks and stuffing keywords. And try and convince people the only KPI was rankings.”

Google’s Organic Links

The second part of his post calls attention to the fact that Google has not been a ten organic links search engine for a long time. Google providing answers isn’t new.

He posted:

“2. Frame the current state of things as if it all happened in the last 2 weeks. Do not under any circumstances mention any of the following things from the past 15 years:

2009 – Rich Snippets
2011 – Knowledge Graph (things not strings)
2013 – Hummingbird (Semantic understanding of conversational queries)
2014 – Featured Snippets – (direct answers at position “Zero”)
2015 – PPA Boxes (related questions anticipating follow-up questions)
2015 – RankBrain (machine learning to interpret ambiguous queries)
2019 – BERT (NLP to better understand context)
2021 – MUM (BERT on Steroids)
2023 – SGE (The birth of AIO)”

Overstate The Problem

The next part is a reaction to the naive marketing schtick that tries to stir up fear about AI search in order to present themselves as the answer.

He wrote:

“3. Overstate the complexity to create a sense of fear and anxiety and then close with “Your only hope is to hire a GEO expert”

Is AI Search Complex And Does It Change Everything?

I think it’s reasonable to say that AI Search is complex because Google’s AI Mode and to a lesser extent AI Overviews, is showing links to a wider range of search intents than regular searches used to show. Even Google’s Rich Snippets were aligned to the search intent of the original search query.

That’s no longer the case with AIO and AI Mode search results. That’s the whole point about Query Fan-out (read about a patent that describes what Query Fan-out might be), that the original query is broken out into follow-up questions.

Greg Boser has a point though in a follow-up post where he said that the query fan-out technique is pretty similar to People Also Ask (PAA), Google’s just sticking it into the AI Mode results.

He wrote in a follow-up post about Query fan-out:

“Yeah the query fan thing is the rage of the day. It’s like PAA is getting memory holed.”

AI Mode Is A Serious Threat To SEO?

I agree with Greg to a certain extent that AI Mode is not a threat to SEO. The same principles about promoting your site, technical SEO and so on still apply. The big difference is that AI Mode is not directly answering the query but providing answers to the entire information journey. You can dismiss it as just PAA above the fold but that’s still a big deal because it complicates what you’re going to try to rank for.

Michael Bonfils, another old timer SEO recently observed that AI search is eliminating the beginning and middle part of the sales funnel, observing about AI search:

“This is, you know, we have a funnel, we all know which is the awareness consideration phase and the whole center and then finally the purchase stage. The consideration stage is the critical side of our funnel. We’re not getting the data. How are we going to get the data?”

So yeah, AI Search is different than anything we’ve seen before but, as Greg points out, it’s still SEO and adapting to change is has always been a part of it.

Read Greg Boser’s post on X:

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Jun 5 2025
Effective Marketplace SEO Is More Like Product Growth via @sejournal, @Kevin_Indig

Below, I’ve got an update on my marketplace SEO issue – and this edition is more robust, taking into account learnings from the UX study of AIOs as well as the latest shifts in the search landscape.

(Shifts that, I’d argue, have a disproportionate impact on online marketplaces.)

I’ll cover:

  • What marketplace SEO is and why it’s different.
  • The top 3 things marketplace SEO practitioners need to keep in mind about AI + LLMs.
  • How to do marketplace SEO from a product growth approach.
  • An incredible real-world example from Tripadvisor (they’re killing it over there).

Plus, premium subscribers will get my five-phase framework to ensure you’re approaching marketplace SEO from an overall product growth perspective … and my top considerations for marketplaces to stay ahead of competitors and LLMs. (That’s all at the end of this issue. You can subscribe to get full access here.)

Also, a quick thanks to Amanda Johnson, who partnered with me to bring this marketplace SEO issue into 2025.

Boost your skills with Growth Memo’s weekly expert insights. Subscribe for free!

In my opinion, there are two major types of SEO: product-led and marketing-led.

Marketplace SEO (as I call it) is a product-led SEO function of your organization. Without it, companies like Tripadvisor, Zillow, Meta, or Glassdoor wouldn’t be where they are today.

The key lessons about marketplaces from my time at G2 and my work with Nextdoor, Bounce, and others: Good SEO is the result of product growth, not just website optimization.

It’s a different way to think about and execute SEO because marketplaces have massive scale advantages over other types of businesses.

But AI threatens this moat.

And this is mission critical: 80% of new users coming from SEO is not uncommon for marketplaces. Yes, even in the current SEO landscape.

In fact, I called this out in March 2025 in CheggMate – that information sites, especially marketplaces, are being disproportionately affected in search by AIOs and LLMs.

Why understanding marketplace SEO matters: Most sites on the web are not marketplaces. The approach to marketplace SEO is very different from non-marketplace sites. Applying the wrong approach severely limits the impact on company growth.

If you’re running a marketplace site (which I also sometimes call an SEO aggregator), your goal is to become a trusted source that retrieval layers (RAG), AI Overviews, and chatbots pull from directly.

This requires opening your site to AI crawlers, baking in rich schema, exposing structured APIs or data feeds for RAG pipelines, and continually fueling fresh, authoritative UGC.

Meanwhile, AI tools are churning out optimized titles, descriptions, and rich snippets across millions of pages faster than any human team could.

Essentially, marketplace SEO has evolved into a true product-growth partnership, where UX, community incentives, and AI visibility all work together to keep up visibility with our audiences.

Let’s talk about how to do that.

But first, we have to get on the same page about what marketplace SEO entails (because you know how our industry likes to throw around new buzzphrases every day).

What Is Marketplace SEO?

Marketplace SEO is the practice of optimizing a site based on its inventory (supply) so that potential buyers (demand) land directly on your marketplace when they’re about to make a decision, like a software purchase, booking a flight, scheduling a medical appointment, etc.

Marketplaces often rely on user-generated reviews and commentary (plus the right technical signals) to build trust and relevance.

Think of marketplace SEO as the art – and science – of making two-sided platforms (like G2, Tripadvisor, ZocDoc, or Nextdoor) impossible to miss.

Their unique advantage comes from having a large number of pages on their site across a few different templates, which leads to multiplier effects for internal linking, testing, and target audience size.

Marketplaces orchestrate thousands (even millions) of user-generated listings, reviews, and seller storefronts so that search engines point back to the service you provide: connecting consumers to solutions they need.

In short, marketplace SEO is about optimizing the entire ecosystem – beyond the homepage or blog – to turbocharge discoverability for every seller, every listing, every time.

Is Marketplace SEO Product-Led SEO?

They definitely share DNA, but marketplace SEO stands on its own.

Like product-led SEO, we embed optimization into the product experience itself, using user actions (reviews, ratings, uploads) as our content engine.

But on a marketplace site, we also juggle multi-vendor dynamics, inventory churn, and network effects that a standalone SaaS app doesn’t face.

At G2, for example, we saw real SEO gains from optimizing the review submission process (by encouraging longer reviews to create more content), which wouldn’t necessarily fall into product-led SEO.

So yes, it’s product-led in spirit because we grow through the product and improvements to the marketplace, but it demands marketplace-specific plays to keep the flywheel spinning.

With marketplace SEO, you’re playing in the same sandbox as product-led SEO, but perhaps with a few extra toys.

Marketplace SEO Is Inherently Different Than Other SEO Programs – And It Deserves A Deeper Understanding Of The Impact Of AI

If the overwhelming majority of new users heading to marketplaces are coming from organic search, understanding the differences between regular old SEO and marketplace SEO is absolutely crucial.

  • Marketplaces have a low per-user revenue: Low ARPU, or Average Revenue Per User, often makes advertising or outbound sales too expensive for buyer/seller marketplaces.
  • Scaling visibility looks different for marketplaces by industry: Retail marketplaces can scale on advertising but lean on SEO to diversify growth channels and make marketing spend sustainable.
  • The majority of marketplaces are UGC-based: In the era of AI-generated consensus content, UGC-based marketplaces have an edge, especially ones with high trust signals that cull out fake user content and reviews.

In addition to these core differences, marketplaces are aggregators. (You can read more about my thoughts on SEO integrators vs. aggregators here.)

What does that mean exactly?

  • Aggregators “collect and group” the supply side of a market and offer it to the demand side through a streamlined user experience.
  • They are often either retail marketplaces or connect buyers with sellers in a market:
    • G2 connects software buyers with sellers.
    • Uber Eats connects hungry people with restaurants (and drivers).
    • Amazon connects buyers with third-party retailers.
    • Instacart connects shoppers with supermarkets.
  • What sets marketplace aggregators apart from integrators is content generation: New content is generated either by users or products, but not by the company itself.
  • Aggregators and integrators scale SEO differently: Aggregator SEO is closer to product-led growth (PLG), while Integrator SEO is closer to marketing.

I show examples of different aggregator types here: SEO Strategy Archetypes.

When thinking about marketplace SEO, most marketers jump straight into solving technical SEO problems, like title/content/internal link optimization.

While doing those things is not wrong (I mean, they’ve got to be done), focusing only on these practices will limit the scale of SEO impact you can have.

Marketplaces And LLMs: Here’s What Marketplace SEO Practitioners Need To Keep In Mind About AI

To earn that sweet, sweet organic visibility, you must think about architecting your marketplace like an AI-friendly product. This isn’t something you can skip.

To compete in an AI-first world, your platform must be:

  1. Fast: Aim for sub-200 ms load times (suggested by Google1) for both pages and APIs so AI crawlers (like GPTBot or Bingbot) don’t drop you and real users stick around.
  2. Structured: Make sure to use comprehensive schema markup for Products, Reviews, FAQs, and Organizations. Use clear heading hierarchies and semantic HTML so retrieval-augmented generation (RAG) layers can pull precise Q&A snippets and data points. Quick callout here: There are differing opinions on whether schema markup and proper hierarchies impact LLM visibility or not. Google advises it in their AI “Features” guidance2, but it’s controversial whether or not it’s helpful for other answer engines. My take? If your competitors are using it robustly, and they have better LLM or Google visibility than you do, you likely need to use it, too.
  3. Intent-rich: Frame each listing page as a mini conversational answer – implement bullet-list specs, FAQ accordions, and “compare-to” tables so LLMs find exactly what they need in one query. (I’ve got a great Tripadvisor example of this below.)
Google’s SEO best practices for “AI Features” (Image Credit: Kevin Indig)

For marketplaces, SEO is product design.

When you treat search as a core feature – designing facets, filters, and dynamic landing pages around user intent – you’re not just optimizing for discovery, you’re crafting the entire search experience.

Finally, what happens after the click is just as critical as earning it:

  • Can users refine results with intuitive facets and AI-powered autocomplete?
  • Do your review widgets, “similar listings,” and “ask a question” prompts keep people moving through your funnel?
  • Are your core flows – signup, review creation, checkout – so frictionless that AI-agent driven traffic could convert as reliably as human traffic?

The Growth Memo’s UX study of AIOs confirmed that the “second-click” or “validation click” is more important than ever … if you’re fortunate enough to get that organic click, your UX and brand trust signals have to be on point.

If you bake streamlined post-click moments into your roadmap, you can turn that initial brand visibility into real engagement and trust.

Amanda jumping in here and getting all meta with a first-person note: We cannot stress enough how important the on-page experience after that earned organic click truly is. I can’t even begin to count how many times I’ve left a marketplace because of the UX hurdles or poor site search functions, only to go back to Google, ChatGPT, or just go directly to seller websites and circumvent marketplaces as a whole – despite my strong desire to compare options and read reviews outside of the actual seller’s platform.

To scale marketplace SEO successfully, you need to optimize across the whole range of product growth: website, product, and network effects.

Think of marketplace SEO as a product-wide system, not a simple checklist or set of tactics.

  • Here’s why: Marketplace SEO lives and dies with the volume and quality of pages. As a result, SEO pros need to become product managers and work on offering incentives and reducing friction across the user journey. Think funnel optimization, but broader.
  • Example: At G2, we went deep into the review creation process to understand where we need to remove or add friction to get the right balance between not only more reviews, but better ones.
  • But: Be careful with scaling pages too aggressively and falling below Google’s line for “quality.” As I explain in SEOzempic, too many low-quality pages can be more harmful than helpful.

Here’s what you need to consider to run your marketplace SEO program from a product growth approach:

Optimize The Website

This is a no-brainer but still deserves mention here. Optimizing the website for organic search and LLM visibility is, of course, an essential part of marketplace SEO.

But the most important areas of optimization for marketplaces are:

  • Indexing and crawl management.
  • Internal linking.
  • Titles & rich snippets.
  • Robust schema markup.
  • Core Web Vitals.
  • On-page content.
  • Key pieces of information.
  • Listing optimization.
  • Visual and interactive elements like maps and UGC videos.
  • New page types.

Each of these areas provides enough depth to fill roadmaps for years.

The key to winning is doing the basics incredibly well and building an experimentation engine that surfaces new wins and levers.

Amanda jumping in here again: If possible, don’t skip video. Yes, even if UGC videos require an internal quality control program/review in place. Underestimating the power of organic and even low-fi videos showcasing the product or user’s final decision (i.e., to go on a trip to Rome or sign up for new software based on a core feature) can earn you visibility in AIOs and LLMs. A recent client of mine earned a significant video-embed AIO mention with very clear brand visibility for a core targeted query … all with a short, simple video explaining the concept and how their product helped. It was easy to do. You bet we’ll be running tests to see if we can accomplish that on repeat.

Let’s look at this Tripadvisor example below, where every element is intentional and tested.

The site didn’t start like that, but it evolved over time. TripAdvisor has SEO deeply ingrained in its DNA. You can rest assured that every element is there for a reason.

And the interface has been updated and improved with the incorporation of AI, to include:

  • An AI assistant that discreetly follows the user (without interrupting) at the top of the page.
  • AI-assisted, community-guided itineraries.
  • A more robust travel guide section with tips and FAQs.

For further reading (and another marketplace SEO example), check out Marketplace Deep Dive – Q1 (Case Study: Zillow).

Image Credit: Kevin Indig

The product experience for marketplaces spans the sign-up, content creation, and admin experience (sometimes more).

It’s vital for SEO to be involved in product optimizations and improvements because it directly impacts the number and quality of pages.

Strategic questions SEO pros should ask themselves:

  • What (incentives) and who (user profile) drives new content? It’s critical for marketplaces to find out why users create content or buy products.
  • Where do users get stuck when creating new content? Where is it too easy? Too little friction decreases content quality; too much inhibits content volume. Get the balance right.
  • What are the core growth loops in the business? Every company has inputs and outputs that perpetuate the business forward. Inputs are things you can do to incentivize or control user behavior. (For example, offering a free month when bringing a friend.) Outputs are things that happen as a result of controlled inputs, which in themselves can drive growth. (For example, the friend you brought now also brings a friend.)
  • What entities need their own page type? Marketplaces often organize around key entities – places, companies, brands, or people – because entity-focused templates help LLMs and search engines understand your site structure. That said, not every template must be built around an entity; some pages serve functional or task-oriented purposes without centering on a single entity.
  • What optimization surfaces are available? Examples: Google’s new AI Mode, AIOs, SERP snippets, LLM citations, your core landing pages, your site’s sign-up funnel.
  • How can the company build a continuous testing engine? After optimizing for the basics, most wins come from experiments. Test, observe, and record outcomes, especially where Google’s AIOs, AI Mode, and LLMs are concerned. (Pro-tip: Review the LLM’s reasoning behind the outputs where your brand has visibility.)
  • What metrics are critical? Monitoring the right metrics that reflect the user journey (and core growth loops) defines your focus. Keep in mind: Impressions and branded search are metrics you should be paying attention to more than ever before.

Amanda jumping in here one more time: Please, I beg of you on behalf of all strategists everywhere – allow your SEO and content strategists the room and resources to test … and even fail. Above, Kevin calls out the need to build a continuous testing engine, and if you want to push forward in building organic visibility and authority in this new era of search, whether you’re a marketplace or an integrator site that’s a direct seller, testing is crucial. Teams that test (albeit wisely), fail, learn, and grow are going to be the ones who come out ahead during this chaotic season in search.

Develop Network Effects

Marketplaces are able to develop powerful network effects that accelerate growth and defend themselves from challengers.

Network effects = competitive advantages that grow with the company. Network effects get better over time (like production cost) and become your organization’s edge.

They can become protective moats, but only when they’re successful and mature.

Examples of network effects can include factors like:

  • Brand: recognition and visibility.
  • Economies of scale: doing things more efficiently than your competitors.
  • Switching cost: increasing opportunity cost of switching to a competitor.
  • Deep tech: proprietary technology that solves specific problems.
  • Systems of intelligence: data, monitoring systems, and understanding of customers and the market.

SEO Integrators don’t have access to the same network effects that SEO Aggregators (like marketplaces) do. Economies of scale are an example of this.

It would be absurd to say SEO needs to own network effects – it’s a company effort.

But SEO, as the largest user acquisition channel for marketplaces, needs to be aware and work toward building network effects.

G2, for example, has developed such a prominent reputation that the G2 badge is a sign of credibility for software buyers.

That, of course, wasn’t the case when G2 (crowd) started. It developed over time and with sustained quality.

As a result, companies pay to add the badge to their sites and drive new reviews, which adds to the overall value of the marketplace.

In this example, UserGuiding not only adds them to their site in the footer, but also publishes a piece of content each year, noting their annual badge increase.3

Image Credit: Kevin Indig

Overall, the product growth approach to marketplace SEO has experimentation and funnel analysis that leads to continuous improvement at its core – that’s not what you would typically expect in classic SEO plays.

A lot is changing – and at rapid speeds – due to LLM search. This affects aggregator sites that rely on marketplace SEO practices to stay visible.

Here are a few considerations to help you stay ahead and grounded in future thinking.

1. Plan content quality for both LLMs and actual humans:

  • What parts of your site would an LLM flag as thin, redundant, or low-trust?
  • What parts of your site do humans bypass altogether?
  • Audit low-value boilerplates (e.g., duplicate category intros) and enrich with real user insights or data visualizations.

2. Study human usage patterns:

  • Which pages or features have high bounce rates or low engagement?
  • Why do people find these features, pages, or modules unengaging?
  • If users skip them, AI likely will too-identify and rework those weak spots into stronger, intent-aligned experiences.

3. Scrutinize your marketplace’s internal search:

  • Is your in-app search engine smarter than Google or an LLM at understanding your inventory? (If not, this is a big problem.)
  • Invest in embeddings-based search, synonym maps, and AI-driven recommendations so buyers find what they need faster.

4. Work toward visibility resilience, no matter what happens in search:

  • If organic SEO disappeared tomorrow, what parts of your marketplace would still attract qualified traffic?
  • What do you need to do today to mitigate reliance on classic or outdated SEO tactics and results?
  • Look to direct channels – email, social communities, partnerships – and fortify them so you’re not over-reliant on any single source.

5. Diversify your marketing channels if you haven’t already:

  • Explore app integrations and in-product suggestions to capture audiences where they already live.

Experiment with live-commerce, social-commerce, and brand collaborations to fill gaps beyond search.

1 About PageSpeed Insights 

2 AI features and your website

3 G2 Fall 2024: UserGuiding Doubled the Badges Once Again!

Featured Image: Paulo Bobita/Search Engine Journal

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