3 Examples Of Product-Led SEO

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.

What Product-Led SEO Is – And What It’s Not

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.

What Product-Led SEO Isn’t

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.

3 Examples Of Product-Led SEO

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.

Figma: UGC-Driven Product-Led SEO

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.

IMDb: Supply-Driven PLSEO

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”).

Doordash: Locale-Driven Product-Led SEO

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.

Top Use Cases For Locale-Driven PLSEO

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.

Top Challenges For Product-Led SEO Implementation

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.

The Value In A Product-Led SEO Approach

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