Google Ecommerce SERP Features 2025 Vs. 2024 via @sejournal, @Kevin_Indig

In 2024, Google turned the SERP into a storefront.

In 2025, it turned it into a marketplace with an AI-based mind of its own.

Over the past 12 months, Google has layered AI into nearly every inch of the shopping search experience by merging organic results with product listings, rolling out AI Overviews that replace traditional product grids, and introducing a full-screen “AI Mode.”

Meanwhile, ChatGPT is inching closer to becoming a personalized shopping assistant, but for now, the most dramatic shifts for SEOs are still happening inside Google.

To understand the impact, I revisited a set of 35,000+ U.S. shopping queries I first analyzed in July 2024.

In today’s Memo, I’m breaking down the state of Google Shopping SERPs in 2025. A year later, the landscape looks … different:

• AI Overviews have started to displace classic ecommerce SERP features.
• Image packs dominate the page.
• Discussion forums are on the decline.

Plus, an exclusive comparison of 2024 vs. 2025 ecommerce SERP features and a full, detailed checklist of optimizations for the SERP features that matter most today (available for premium subscribers. I show you exactly how I do this).

This memo breaks down exactly what’s changed in Google’s shopping SERPs over the past year. Let’s goooooo.

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In the last 12 months, Google hasn’t just transformed itself into a publisher that serves up content to answer queries right in the SERP (via AI Overviews and AI Mode). It’s also built out an extensive marketplace for shopping queries.

However, Google now provides a whole slew of SERP features and AI features for ecommerce queries that are at least as impactful as AIOs and AI Mode.

Meanwhile, ChatGPT & Co. are starting to include product recommendations with links, reviews, buy buttons, and recommendations directly in the chat. (But this analysis focuses on Google results only.)

To better understand the key trends for Google shopping queries, in July 2024, I analyzed 35,305 keywords across product categories like fashion, beds, plants, and automotive in the U.S. over the last five months using seoClarity.

We’re revisiting that data today, examining those same keywords and categories for July 2025.

The results:

1. AI Overviews have started to replace product grids.
2. Ecommerce SERPs are increasingly visual.
3. There are more question-related SERP features (like People Also Ask), less UGC.
4. Fewer videos are appearing across the SERPs for product-related searches.

About the data:

• This data specifically covers Google search results and features. It doesn’t include ChatGPT, Perplexity, etc. However, we’ll touch on this briefly below.
• Over 35,000 search queries were analyzed, and the same group was examined in both July 2024 and July 2025.
• The search queries analyzed include product-related queries across a broad spectrum, from brand terms (like Walmart) to individual products (iPads) and categories (e-bikes).
• If you’re curious about the exact list of Google shopping SERP features included in this analysis, they’re included at the bottom of this memo.

Before we dig into the findings…

In Google’s shift from search engine to ecommerce marketplace (and from search engine to publisher), Google has merged as much as possible into the SERP page.

Web results and the shopping tab for shopping searches were combined as a response to Amazon’s long-standing dominance.

The shopping tab still exists, sure.

But for product-related searches, the main search page and the Google shopping experience look incredibly similar, with the Shopping tab streamlined to a product-grid experience only.

In June 2024, I reported in Critical SERP Features of Google’s shopping marketplace:

• Google has fully transitioned into a shopping marketplace by adding product filters to search result pages and implementing a direct checkout option.
• These new features create an ecommerce search experience within Google Search and may significantly impact the organic traffic merchants and retailers rely on.
• Google has quietly introduced a direct checkout feature that allows merchants to link free listings directly to their checkout pages.
• Google’s move to a shopping marketplace was likely driven by the need to compete with Amazon’s successful advertising business.
• Google faces the challenge of balancing its role as a search engine with the need to generate revenue through its shopping marketplace, especially considering its dependence on partners for logistics.

And now?

Google’s layered AI and personalized SERP features into the shopping experience as well.

Below are the Google SERP features I’ll be examining in this year-over-year (YoY) analysis, specifically, with a quick synopsis if you’re not familiar.

• Images: A horizontal carousel of image results related to the query pulled from product pages or image-rich content; usually appear at the top or mid-page and link to Google Images or directly to source pages.
• Products: Displays a visual grid or carousel of products with titles, images, prices, reviews, and merchants. This includes free product listings (organic) and Product Listing Ads (PLAs) (paid).
• People Also Ask (PAA): Related questions users frequently ask. Clicking a question reveals a source link. (These often inform Google’s understanding of search intent and user curiosity.)
• Things To Know: An AI-driven feature that breaks a topic into subtopics and frequently misunderstood concepts. Found mostly on broad, educational, or commercial-intent queries, this is Google’s way of guiding users deeper into a topic and understanding deeper search intent.
• Discussion and Forums: Highlights relevant threads from platforms like Reddit, Quora, and niche forums. Answers are often community-generated and authentic. Replaced some traditional “People Also Ask” real estate for shopping or reviews queries.
• Knowledge Graph: Displays structured facts about a person, brand, product, or topic-sourced from trusted databases. Appears in a right-hand sidebar or embedded box.
• Buying Guide: A feature that explains what to consider when shopping for a product, e.g., “What to look for in a DSLR camera.” Usually placed mid-page for commerce-intent queries. It mimics a human assistant or product expert’s advice. Contains snippets and links to sources.
• Local Listing: Shows local business listings with map, ratings, hours, and quick call/location links. Prominent in searches with local intent like “shoe store near me” or “coffee shops in Detroit.”
• AI Overview: Generative AI summary at the top of the SERP that answers the query using information synthesized from multiple sources. For shopping queries, it often includes product summaries.
• Video: A carousel or block of video content, mostly from YouTube, but also from other video-hosting platforms. May include timestamps, captions, or “key moments” for long videos.
• Answer Box (a.k.a. Featured Snippet): A direct answer to a query extracted from a single web page, shown at the top of the SERP in a stylized box. Often used for factual or how-to queries. Includes the source link.
• Free Product Listings: Organic product results submitted via Google Merchant Center feeds. These listings show in the Shopping tab and occasionally in the main SERP product grid (distinct from paid Shopping ads).
• From sources across the web: A content block showing opinions or quotes on a product or topic from a variety of sites. Often used in AI Overviews or product reviews to surface aggregated user sentiment or editorial input.
• FAQ: An expandable schema-driven block showing common questions and answers sourced from a specific page. Typically appears under a site’s organic result when FAQ schema is properly implemented.
• PPC: Sponsored links shown at the top or bottom of the SERP, marked “Sponsored” or “Ad.” These can show up as text, product images/grids, etc.

In addition to the standard SERP features tracked in this analysis via the above list, here’s a look at the current Google shopping marketplace SERP features and/or elements (like toggle filters) that we’re dealing with at the halfway point of 2025.

• AI Mode (Full-Screen): Interactive, immersive full-page AI shopping experience with filters and buy links.
• Shopping filters inline: Dynamic filters (brand, color, price) within AI Mode and Shopping grids.
• Virtual try-on: This feature was recently released. It’s a generative AI module showing clothes on diverse body types (expanding by category).
• Price tracking/alerts: Users can track price drops and get alerts via Gmail or Chrome. Honestly, a pretty great tool.
• Popular stores/top stores: Scrollable carousel of prominent retailers for the product category.
• Product sites (EU market): Organic feature that shows prominent ecommerce domains (due to regulatory changes in the EU).
• Trending products/popular products: Highlights products rising in popularity based on recent search activity.
• Merchant star ratings: Display review scores and counts in summaries or tiles.
• Free shipping/returns labels: Highlighted callouts in product tiles.
• “Verified by Google” merchant badges: Google-trusted seller icon in some listings.
• Quick comparison panels: Side-by-side spec or feature comparisons (this is an early-stage rollout, similar to Amazon’s product comparison panel or module).

To illustrate with an example, let’s say you are looking for kayaks (summertime!).

On desktop (logged-in), Google will now show you product filters on the left sidebar and “Popular products” carousels in the middle on top of classic organic results, but under ads, of course.

Image Credit: Kevin Indig

Directly under the shopping product grids, you have traditional organic results along with an on-SERP Buying Guide, similar to People Also Ask questions (which is also included further down the page).

Both the Buying Guide and People Also Ask features deliver answers with links to original content.

Image Credit: Kevin Indig

On mobile, you get product filters at the top, ads above organic results, and product carousels in the form of Popular products or “Products for you.”

Image Credit: Kevin Indig

This experience doesn’t look very different from Amazon … which is the whole point.

Image Credit: Kevin Indig

Google’s shopping experience lets users explore products on a variety of marketplaces, like Amazon, Walmart, eBay, Etsy, & Co.

From an SEO perspective, the prominent position of product grid (listings) and filters likely significantly impacts CTR, organic visibility, and ultimately, revenue.

But let’s take a look at the same search via AI Mode.

Below is the desktop experience via Chrome.

I’ve zoomed out here so you get the whole view, but it takes the user two to three scrolls to get to the product grid when in a standard view.

Image Credit: Kevin Indig

Here on mobile, getting to product recommendations takes several scrolls. In one instance, I received a result that included a list of places near me in my city where I could get a kayak.

Image Credit: Kevin Indig

Keeping the current Google shopping SERP experience in mind, here’s what the data shows.

This is the most noteworthy shift found in the data, as you can probably guess.

Since March 2025, when Google began rolling out AI Overviews more aggressively, they’ve also started replacing (organic) product grids.

Image Credit: Kevin Indig

The graph above might look like it represents minimal changes when you examine it in a timeline view, but you can see the trend even better when moving AIOs to a second y-axis (below).

Image Credit: Kevin Indig

I expect AI Overviews to still show the product grids searchers have become accustomed to, although they might take a different form.

When searching for [which camera tripod should I buy?], for example, we find an AI Overview at the top with specific product recommendations.

Image Credit: Kevin Indig

Of course, AI Mode takes that a step further with richer product recommendations and buying guides.

(Shoutout to The New York Times and the other five sources for this AI Mode answer … which now don’t see an ad impression or affiliate click.)

Image Credit: Kevin Indig

As a result of this shift, which I predict will only increase over time, tracking your brand mentions and product links in AI Overviews becomes critical. Skip this at your own risk.

Here, you’ll see the increase in image packs over time, with a big shift in March 2025.

Image packs for ecommerce-related queries grew from ~60% in 2024 to a new baseline of over 90% of keywords in 2025.

Image Credit: Kevin Indig

Also, notice how Google systematically tests SERP layouts between core updates (e.g., the dip in the graph above happens between the March and June 2025 Core Updates).

Having strong product images, which are properly optimized, continues to be crucial for ecommerce search.

Since January 2025, Google has shown more People Also Asked (PAA) features at the cost of Discussions & Forums.

Even though Reddit is the second most visible site on the web, I’m surprised to see more PAA – two years after Google removed FAQ rich snippets from the SERPs.

Image Credit: Kevin Indig

This is something you want to consider tracking for queries that are directly related to your products, if you’re not doing so already. (You can do this in classic SEO tools like Semrush or Ahrefs, for example.)

Since August 2024, Google has systematically reduced the number of videos in the ecommerce search results.

Image Credit: Kevin Indig

It seems that images have taken a lot of the real estate videos that used to own.

Image Credit: Kevin Indig

As a result, videos are less important in ecommerce search, while images are increasingly more important.

If you’ve been creating and optimizing videos and haven’t seen the SEO results you wanted for your products/site, this could be your signal to invest in other types of content.

While this analysis covers Google SERP data specifically, it’d be a miss to not discuss the new shopping features in ChatGPT.

However, we don’t yet have months and months of data on LLM-based conversational product recommendations to give us good, clear information, so I anticipate there will be more analysis ahead once more time passes.

ChatGPT’s shopping experience is starting to look a lot like Google’s  – but with a twist: Instead of viewing lists of blue links or multiple product grids, it curates a conversational shortlist with minimal product listings included.

No affiliate links and no paid ads (yet).

Image Credit: Kevin Indig

OpenAI integrates real-time product data from tools like Klarna and Shopify, allowing ChatGPT to surface up-to-date prices, availability, reviews, and product details in a shoppable card-style format.

ChatGPT also offers a “Why you might like this” and “What people are saying” generative summary when a specific product is clicked.

Image Credit: Kevin Indig

OpenAI offers the following guidance about how these products are selected [source]:

A product appears in the visual carousel when ChatGPT perceives it’s relevant to the user’s intent. ChatGPT assesses intent based on the user’s query and other available context, such as memories or custom instructions….

When determining which products to surface, ChatGPT considers:

• Structured metadata from third-party providers (e.g., price, product description) and other third-party content (e.g., reviews).

• Model responses generated by ChatGPT before it considers any new search results. Learn more.

• OpenAI safety standards.

Depending on the user’s needs, some of these factors will be more relevant than others. For example, if the user specifies a budget of $30, ChatGPT will focus more on price, whereas if price isn’t important, it may focus on other aspects instead.

OpenAI also explains how merchants are selected for products [source]:

When a user clicks on a product, we may show a list of merchants offering it. This list is generated based on merchant and product metadata we receive from third-party providers. Currently, the order in which we display merchants is predominantly determined by these providers….

To that end, we’re exploring ways for merchants to provide us their product feeds directly, which will help ensure more accurate and current listings. If you’re interested in participating, complete the interest form here, and we’ll notify you once submissions open.

That being said, it takes some trial and error to trigger product recommendations directly in the chat.

For instance, the prompt [can you help me find the best kayaks for beginners] results in an output that includes product recommendations, while the query [what are the best kayaks for beginners] results in a list without shopping results, features, or links.

Prompts with action-oriented language like “can you help me” and “will you find” may have a higher likelihood of offering shopping results directly in the chat, while queries like “what is the best” and “what are the best” and “compare the features of” may result in a variety of recommendations.

Image Credit: Kevin Indig

Featured Image: Paulo Bobita/Search Engine Journal

Next Steps for AI Shopping

For two decades, search has driven ecommerce. Whether through Google, Amazon, or on‑site, the sequence was simple: a shopper types a query, compares results, and makes a purchase. Search engine marketing, organic and paid, enhanced performance.

That model is evolving.

Generative AI “agents” can now help shoppers compare products, prices, and options, often in a single conversational step. Shoppers initiate the interactions; agents then search relevant sites and respond based on the query, past preferences, and purchase history.

What Is Agentic Shopping?

“Agentic” refers to autonomous systems that can independently act and decide. Agentic shopping uses AI agents —  ChatGPT, Gemini, Perplexity, Claude — to guide the buying journey for a user. Think of it as a personal shopping assistant that interprets a request, searches multiple retailers, and surfaces relevant product and price options.

Retailgentic, the Substack publication of ReFiBuy, a soon-to-launch agentic tool provider, recently published its test of Comet, Perplexity’s AI-driven browser:

Shopper: This bed set is pretty expensive, but I like it. Can you find it cheaper?

Comet: I searched for a better price on the Allied Home Down Alternative XLT Dorm Kit (comforter, pillow, and mattress pad, Twin XL) and it is still listed at $84.99 at Target — the standard price for this exact bundled set. I didn’t find this specific 3‑piece kit for less elsewhere.

However, other retail sites (like Kohl’s and Macy’s) offer Twin XL bedding pieces or comforter sets individually.

Instead of hopping from site to site, the shopper gets an answer in one dialog.

Why It Matters

Shoppers are warming to AI shopping, though unevenly by age. A February 2025 New Consumer survey (PDF) of approximately 3,000 U.S. residents found that 64% of Gen Zs (ages late 20s to early 40s) and Millennials (mid-teens to late 20s) are “very” or “somewhat” comfortable interacting with an AI shopping advisor, versus 40% for Gen Xs (mid 40s to early 60s).

AI platforms are capitalizing:

• ChatGPT now embeds Shop Pay, Shopify’s hosted checkout and payment tool. Shoppers can discover, evaluate, and purchase goods from Shopify-powered merchants without leaving the chat, turning conversational AI into a sales channel.

• Perplexity’s agent‑led checkout, in partnership with PayPal, enables purchases, travel bookings, and event ticket sales directly in chat.

• Structured product feeds in Perplexity can ingest clean, up‑to‑date product data, such as from beauty brand Ulta (powered by Rithum, my employer), for accurate pricing, attributes, and real‑time recommendations.

Next Steps

There’s no definitive AI playbook, but merchants can still prepare.

Audit product data

Universal standards for AI product feeds don’t (yet) exist, but you’re likely in good shape if you already maintain a product feed, such as for Google Shopping. Make sure it includes all key attributes: size, color, material, weight, and use cases.

Track AI visibility

Test how your products appear in genAI platforms. Brands and manufacturers can prompt their name to see how it surfaces. Even better, try prompts that shoppers might use. See how AI ranks or references your products compared with competitors. For example, “Find me the best backpack that fits two days of clothes and fits under an airplane seat” or “List the highest-rated cordless drills from DeWalt under $200.”

Multiple Channels

Widespread use of AI shopping is far from certain.

Adoption varies. Younger shoppers are more comfortable, older shoppers less so.

Accuracy is uneven. AI can show outdated prices, inventory, and product details, as many are scraping product data, which is prone to errors, instead of using product feeds. In ChatGPT, products unrelated to a query sometimes appear in comparison carousels.

AI shopping agents could become an important revenue channel, but they’re not a replacement for direct customer relationships, traditional search, or advertising. Make your product data AI‑ready while continuing to diversify your sales mix.

Invest in multiple channels, customer engagement, and building a brand that can thrive regardless of how shoppers discover products.

Merging SEO And Content Using Your Knowledge Graph to AI-Proof Content via @sejournal, @marthavanberkel

New AI platforms, powered by generative technologies like Google’s Gemini, Microsoft’s Copilot, Grok, and countless specialized chatbots, are rapidly becoming the front door for digital discovery.

We’ve entered an era of machine-led discovery, where AI systems aggregate, summarize, and contextualize content across multiple platforms.

Users today no longer follow a linear journey from keyword to website. Instead, they engage in conversations and move fluidly between channels and experiences.

These shifts are being driven by new types of digital engagement, including:

• AI-generated overviews, such as AI Overviews in Google, that pull data from many sources.
• Conversational search, such as ChatGPT and Gemini, where follow-up questions replace traditional browsing.
• Social engagement, with platforms like TikTok equipped with their own generative search features, engaging entire generations in interactive journeys of discovery.

The result is a new definition of discoverability and a need to rethink how you manage your brand across these experiences.

It’s not enough to optimize your brand’s website for search engines. You must ensure your website content is machine-consumable and semantically connected to appear in AI-generated results.

This is why forward-thinking organizations are turning to schema markup (structured data) and building content knowledge graphs to manage the data layer that powers both traditional search and emerging AI platforms.

Semantic structured data transforms your content into a machine-readable network of information, enabling your brand to be recognized, connected, and potentially included in AI-driven experiences across channels.

In this article, we’ll explore how SEO and content teams can partner to build a content knowledge graph that fuels discoverability in the age of AI, and why this approach is critical for enterprise brands aiming to future-proof their digital presence.

Why Schema Markup Is Your Strategic Data Layer

You may be asking, “Schema markup – is that not just for rich results (visual changes in SERP)?”

Schema markup is no longer just a technical SEO tactic for achieving rich results; it can also be used to define the content on your website and its relationship to other entities within your brand.

When you apply markup in a connected way, AI and search can do more accurate inferencing, resulting in more accurate targeting to user queries or prompts.

In May 2025, Google and Microsoft both reiterated that the use of structured data does make your content “machine-readable” that makes you eligible for certain features. [Editor’s note: Although, Gary Illyes recently said to avoid excessive use and that Schema is not a ranking factor.]

Schema markup can be a strategic foundation for creating a data layer that feeds AI systems. While schema markup is a technical SEO approach, it all starts with content.

When You Implement Schema Markup, You’re:

Defining Entities

Schema markup clarifies the “things” your content is about, such as products, services, people, locations, and more.

It provides precise tags that help machines recognize and categorize your content accurately.

Establishing Relationships

Beyond defining individual entities (a.k.a. topics), schema markup describes how those entities connect to each other and to broader topics across the web.

This creates a web of meaning that mirrors how humans understand context and relationships.

Providing Machine-Readable Context

Schema markup assists your content to be machine-readable.

It enables search engines and AI tools to confidently identify, interpret, and surface your content in relevant contexts, which can help your brand appear where it is most relevant.

Enterprise SEO and content teams can work together to implement schema markup to create a content knowledge graph, a structured representation of your brand’s expertise, offerings, and topic authority.

When you do this, the data you put into search and AI platforms is ready for large language models (LLMs) to make accurate inferences, which can help with consumer visibility.

What Is A Content Knowledge Graph?

A content knowledge graph organizes your website’s data into a network of interconnected entities and topics, all defined by implementing schema markup based on the Schema.org vocabulary. This graph serves as a digital map of your brand’s expertise and topical authority.

Imagine your website as a library. Without a knowledge graph, AI systems trying to read your site have to sift through thousands of pages, hoping to piece together meaning from scattered words and phrases.

With a content knowledge graph:

• Entities are defined. Machines can informed precisely who, what, and where you’re talking about.
• Topics are connected. Machines can better understand and infer how subjects relate. For example, machines can infer that “cardiology” encompasses entities like heart disease, cholesterol, or specific medical procedures.
• Content becomes query-ready. your content is assisted to become structured data that AI can reference, cite, and include in responses.

When your content is organized into a knowledge graph, you’re effectively supplying AI platforms with information about your products, services, and expertise.

This becomes a powerful control point for how your brand is represented in AI search experiences.

Rather than leaving it to chance how AI systems interpret your web content, you can help to proactively shape the narrative and ensure machines have the right signals to potentially include your brand in conversations, summaries, and recommendations.

Your organization’s leaders should be aware this is now a strategic issue, not just a technical one.

A content knowledge graph gives you some influence over how your organization’s expertise and authority are recognized and distributed by AI systems, which can impact discoverability, reputation, and competitive advantage in a rapidly evolving digital landscape.

This structure can improve your chances of appearing in AI-generated answers and equips your content and SEO teams with data-driven insights to guide your content strategy and optimization efforts.

How Enterprise SEO And Content Teams Can Build A Content Knowledge Graph

Here’s how enterprise teams can operationalize a content knowledge graph to future-proof discoverability and unify SEO and content strategies:

1. Define What You Want To Be Known For

Enterprise brands should start by identifying their core topical authority areas. Ask:

• Which topics matter most to our audience and brand?
• Where do we want to be the recognized authority?
• What new topics are emerging in our industry that we should own?

These strategic priorities shape the pillars of your content knowledge graph.

2. Use Schema Markup To Define Key Entities

Next, use schema markup to:

• Identify key entities tied to your priority topics, such as products, services, people, places, or concepts.
• Connect those entities to each other through Schema.org properties, such as “about,” “mentions,” or “sameAs.”
• Ensure consistent entity definitions across your entire site so that AI systems can reliably identify and understand entities and their relationships.

This is how your content becomes machine-readable and more likely to be accurately included in AI-driven results and recommendations.

3. Audit Your Existing Content Against Your Content Knowledge Graph

Instead of just tracking keywords, enterprises should audit their content based on entity coverage:

• Are all priority entities represented on your site?
• Do you have “entity homes” (pillar pages) that serve as authoritative hubs for those priority entities?
• Where are there gaps in entity coverage that could limit your presence in search and AI responses?
• What content opportunities exist to improve coverage of priority entities where these gaps have been identified?

A thorough audit provides a clear roadmap for aligning your content strategy with how machines interpret and surface information, ensuring your brand has the potential to be discoverable in evolving AI-driven search experiences.

4. Create Pillar Pages And Fill Content Gaps

Based on your findings from Step 3, create dedicated pillar pages for high-priority entities where needed. These become the authoritative source that:

• Defines the entity.
• Links to supporting content, including case studies, blog posts, or service pages.
• Signals to search engines and AI systems on where to find reliable information about that entity.

Supporting content can then be created to expand on subtopics and related entities that link back to these pillar pages, ensuring comprehensive coverage of topics.

5. Measure Performance By Entity And Topic

Finally, enterprises should track how well their content performs at the entity and topic levels:

• Which entities drive impressions and clicks in AI-powered search results?
• Are there emerging entities gaining traction in your industry that you should cover?
• How does your topical authority compare to competitors?

This data-driven approach enables continuous optimization, helping you to stay visible as AI search evolves.

Why SEO And Content Teams Are The Heroes Of The AI Search Evolution

In this new landscape, where AI generates answers before users ever reach your website, schema markup and content knowledge graphs provide a critical control point.

They enable your brand to signal its authority to machines, support the possibility of accurate inclusion in AI results and overviews, and inform SEO and content investment based on data, not guesswork.

For enterprise organizations, this isn’t just an SEO tactic; it’s a strategic imperative that could protect visibility and brand presence in the new digital ecosystem.

So, the question remains: What does your brand want to be known for?

Your content knowledge graph is the infrastructure that ensures AI systems, and by extension, your future customers, know the answer.

More Resources:

Featured Image: Urbanscape/Shutterstock