Apr 20 2026
Winning Google Ads Campaign Structures For DTC Ecommerce via @sejournal, @MenachemAni

You’ve got a whole library of winning ads from Meta to run on Google, but you don’t want to spend a ton of time setting up campaigns or becoming a Google guru. So, you take your existing creatives and pop them into Performance Max, spin up some ad copy, and let Google do its thing.

One campaign, one budget, and your entire product line targeting a broad audience – just like Meta taught you. When we audit ecommerce brands expanding to Google, this is the thinking we often see reflected in a highly consolidated account setup.

The logic makes sense if you think in Meta terms. Consolidate spend, let the algorithm find buyers, and scale what converts. It works on Meta because the platform is built on interest-based targeting. You define a pool, feed it plenty of creatives, and the system shows it to the right people.

Except … Google doesn’t work that way. Targeting is driven by active search intent, so a consolidated, broad structure doesn’t give the algorithm better signal – just noise. So, your account ends up burning through your $20,000/month budget without the architecture needed to distinguish between demand that was on its way to being captured and truly net new revenue.

If you live in the world of direct-to-consumer (DTC) and ecommerce brands and operate this way, you aren’t being careless. You’ve mastered one of the most competitive paid channels available and are simply applying that expertise to a platform that operates on entirely different principles.

Let me fix it.

Why Account Structure Is Vital To Success

Every search query in Google is a person telling you something – not a demographic or an interest category inferred from content they’ve engaged with. Explicit, real-time signal that someone is looking for what you offer right now.

That signal is the foundation of everything Google Ads is. Smart Bidding reads it, query matching acts on it, the auction gives it weight, and your campaign structure puts you in a position to capitalize on it.

This is why structure in Google Ads carries more consequence than it does on many other paid channels. Campaigns without clear segmentation and defined boundaries prevent the algorithm from learning efficiently. This spreads budget across queries that don’t reflect the same intent and makes you compete against yourself, leading to outcomes that don’t map to your actual business goals.

The other dimension is economics. Different products carry different margins, average order values, and conversion rates. A structure that treats all of them the same can’t divert spend toward products where it actually makes sense. You end up with an account that converts but doesn’t necessarily generate optimal returns.

And here’s a secret: Sometimes, I never run PMax at all. And if I do, I set it up in a way where it’s not going to just recycle Meta traffic but focus on as much net new as possible (even blocking brand, retargeting, and existing customers can’t get you to 100% net new). But if you have a very heavy Meta presence and PMax looks like it will over-index on recycling traffic, I’d move towards Shopping so we can move the needle.

3 Mistakes That Erode Efficiency For Google Ecommerce

1. Launching Every Campaign Type At Once

The instinct to go broad from day one is understandable. You have products to sell with multiple campaign types available to you and a budget ready to deploy. So you build out brand Search, Shopping, Performance Max, and YouTube, and wait for the data to come in.

The problem is that each of those campaigns needs impressions, clicks, and conversions to learn. When you split a less-than-astronomical budget across five campaign types, none of them gets enough volume to learn efficiently. Visibility is low across the board, and data is slow to compound, and Google’s machine learning systems are starved of the information they need to do better for your account.

Your account is running, but it isn’t moving. At the end of the quarter, you’ll still have no meaningful insights and won’t be able to optimize with confidence.

A smarter approach could be to start with just a couple of campaigns, like Search plus Shopping. This lets you get wider product visibility without being constrained by budget. Once those campaigns have data behind them and are generating returns, you layer in PMax, YouTube, and other formats one by one.

This way, each new move has a foundation to build on rather than competing for scraps.

2. Putting The Same Products In Multiple Campaigns

When your flagship product lives across multiple campaigns, they compete against each other in the same auction. That means a split budget, divided impressions, and not enough conversion momentum for any campaign to become meaningfully better.

Reporting is just as damaging. Sales come through, but you can’t tell which campaign was responsible. Attribution, which is already murky when two platforms are involved, gets harder. And optimization decisions get made with incomplete data.

Clean product segmentation across your account solves all three problems. Each product has a home, which makes performance readable. And when something isn’t working, you know exactly where to look.

3. Segmenting Performance Max Asset Groups By Audience Signal

Performance Max gives you audience signals as an input – customer lists, past purchasers, site visitors. The temptation is to use those signals as the basis for how you divide your asset groups. One group for past buyers, one for prospecting, one for lapsed customers.

The problem is that audience membership has nothing to do with the economics of what you’re selling. A past buyer and a new visitor can both be in the market for your highest-margin product. Structuring asset groups around who they are rather than what you’re selling means your budget isn’t organized around the products that actually matter most to your business.

A more effective approach is to build asset groups around shared product themes – bestsellers, new releases, bundles, seasonal offers. This way, the creative, the budget, and the optimization signal are all pointed at a coherent set of products with similar business value. Performance Max can still find the right audience. Your job is to give it the right product context to work with.

3 Proven Examples Of Google Ads Account Structure For Ecommerce

Example 1: Single-Product DTC Brand

A brand selling one hero product with a few variants (sizes, colors, or bundles) doesn’t need a complex account structure, just a disciplined one.

Start with two campaigns:

  • Branded search captures anyone searching for you by name (high intent), protects your brand equity, and tends to convert at a lower cost – so remember not to use automated bidding.
  • Either Performance Max or Shopping to drive product discovery.
  • If you choose PMax, divide asset groups by variant type rather than audience: one for the core product, one for bundles, one for any subscription or multi-unit offers. This keeps creative and budget in line with how the product is actually sold rather than who you think is buying it.

Adding both retail campaigns or YouTube before the first two layers capture enough conversion data only splinters your budget and stops the algorithm from learning anything meaningful to optimize against.

Example 2: Multi-Product DTC Brand With Bestsellers

Brands with larger catalogs make a common structural mistake: treating all SKUs equally. A single PMax campaign with one asset group covering 40 items gives Google no basis for prioritization and will spend where it finds the path of least resistance, which isn’t always where your margins are.

The better approach is to build asset groups around product tiers.

  • Bestsellers – products with the strongest sales velocity and healthiest margins – get their own asset group with dedicated creative and the largest share of budget.
  • New releases get a separate asset group because they need impression volume to gather data and shouldn’t compete directly with proven performers.
  • Include lower-margin, specialty, or slow-moving SKUs but cap their spend, or exclude from PMax entirely and handle them through a Shopping campaign where you have more direct control.

This structure makes performance readable by economic impact level. When a bestseller starts to slip, you see it immediately. And when a new release gains traction, you can promote it without disrupting the rest of the account.

Example 3: Seasonal DTC Brands

For brands with strong seasonal demand, like gifting or back to school, the structural challenge is running seasonal campaigns without damaging the learning of evergreen ones. The approach here is to treat seasonal pushes as additions to the account, not replacements.

  • Evergreen PMax stays live and funded at a baseline level throughout the year.
  • When a seasonal moment approaches, a separate PMax campaign is layered on with its own budget, asset groups built around the seasonal offer, and a defined run window.
  • Seasonal spend is then contained so that when it ends, the evergreen campaign’s learning history is unaffected.
  • When the seasonal campaign winds down, asset groups are paused rather than deleted. Conversion data accumulated during each period is preserved and available when the next seasonal cycle begins, which shortens the relearning period significantly compared to building a new campaign from scratch each time.

Make This Read Worthwhile: Product Segmentation Exercise

Meta finds customers by matching your offer to people’s interests. Google finds customers who are actively looking. What both platforms share is that the systems are increasingly in charge of the operational side: Smart Bidding, Advantage+, Performance Max. These tools make decisions about who sees your ads, when, and at what cost. The advertiser’s job has shifted from button pusher to signal architect.

On Google, that starts with how your campaigns and product/asset groups are organized.

Your Next Step To Value

Before you change any settings or adjust any budgets, try this product segmentation exercise.

  • Pull your catalog and group SKUs by shared characteristics: bestsellers, new releases, bundles, seasonal offers, margin tiers. The goal is to understand which products belong together and which need their own dedicated focus.
  • Once you have that, look at whether retargeting is siloed or folded into your broader activity. It should be a standalone campaign as blending it with prospecting dilutes performance data and makes it harder to read what’s actually driving new customer acquisition.

These two steps alone will give you a clearer foundation than many DTC brands have as they start layering in Google Ads as a channel.

More Resources:

Featured Image: Summit Art Creations/Shutterstock

Ecommerce MGMT 0 Comments
Apr 19 2026
Selling To AI: The Complete Guide To Agentic Commerce via @sejournal, @slobodanmanic

For 30 years, checkout has been a page. A form with fields for name, address, credit card number. Whether it was Amazon’s one-click patent or Apple Pay’s fingerprint, the innovation was always about making that form faster to get through.

The form itself never went away. Now it is.

This is the final article in a five-part series on optimizing websites for the agentic web. Part 1 covered the evolution from SEO to AAIO. Part 2 explained how to get your content cited in AI responses. Part 3 mapped the protocols forming the infrastructure layer. Part 4 got technical with how AI agents perceive your website. This article covers the commerce layer: how AI agents find products, complete purchases, and handle payments without ever loading a checkout page.

In September 2025, Stripe and OpenAI launched Instant Checkout inside ChatGPT. In January 2026, Google and Shopify unveiled the Universal Commerce Protocol at the National Retail Federation conference. Two open standards. Two competing visions for the same shift: checkout becoming a protocol, not a page.

Throughout this article, we draw exclusively from official documentation, research papers, and announcements from the companies building this infrastructure.

How We Got Here

Every generation of commerce technology has solved the same problem: reducing the friction between “I want something” and “I have it.” Agentic commerce is not a break from this pattern. It’s the pattern’s logical conclusion.

1994: The first online purchase. On Aug. 11, 1994, Phil Brandenberger used his credit card to buy Sting’s Ten Summoner’s Tales CD for $12.48 from a website called NetMarket. The New York Times covered it the next day. NetMarket’s 21-year-old CEO, Daniel Kohn, told the paper: “Even if the N.S.A. was listening in, they couldn’t get his credit card number.” Netscape’s SSL protocol, released that same year, made it possible.

The friction removed: You no longer had to go to a physical store.

Late 1990s: Comparison shopping. Within a few years, websites like BizRate (1996), mySimon (1998), and PriceGrabber (1999) let buyers see prices across multiple merchants instantly. Google entered the space in 2002 with Froogle, later renamed Google Product Search in 2007, then Google Shopping in 2012.

The friction removed: You no longer had to visit each store to compare.

1998: The store adapts to you. Amazon deployed item-to-item collaborative filtering at scale, the algorithm behind “customers who bought this also bought.” Greg Linden, Brent Smith, and Jeremy York published the underlying research in IEEE Internet Computing in 2003. In 2017, the journal named it the best paper in its 20-year history.

The friction removed: You no longer had to know exactly what you wanted.

2015: Commerce moves into conversations. Chris Messina, then Developer Experience Lead at Uber, coined the term “conversational commerce” in a January 2015 Medium post, describing “delivering convenience, personalization, and decision support while people are on the go.” In April 2016, Mark Zuckerberg launched the Facebook Messenger Platform, declaring: “I’ve never met anyone who likes calling a business.” Meanwhile, in China, WeChat had already proved the model. Its Mini Programs, launched January 2017, generated 800 billion yuan (~$115 billion) in transactions by 2019.

The friction removed: You no longer had to open a store’s website.

2014-2023: Voice and social commerce. Amazon Echo launched in November 2014, promising you could buy things without a screen. The promise was mostly unfulfilled. Social commerce had better luck: TikTok Shop, launched in the U.S. in September 2023, reached $33.2 billion in global sales by 2024. Content became the storefront.

The friction removed: Purchase intent was created inside the feed, not searched for.

2024: AI starts shopping for you. Within months, every major platform launched AI shopping features. Amazon introduced Rufus in February, a conversational assistant trained on its product catalog. Google rebuilt Shopping with AI in October, drawing on 50 billion product listings. Perplexity launched “Buy with Pro” in November, turning a search engine into a store.

The friction removed: AI did the research, comparison, and recommendation for you.

2025: The buyer disappears. In January, OpenAI launched Operator, an agent that navigated websites, filled forms, and completed purchases autonomously. In May, Google announced “Buy for Me” at I/O 2025. In September, Instant Checkout went live in ChatGPT.

The friction removed: The last one. The human no longer needs to be there for the transaction to happen.

Each of these shifts was about the same thing: removing one more step between wanting and having. Agentic commerce removes the final step: doing it yourself.

Checkout Is No Longer A Page

Here’s the shift in one sentence: In traditional commerce, the seller builds the checkout experience. In agentic commerce, the agent does.

When you buy something on a website today, you interact with the merchant’s checkout page. They designed the form, they chose the layout, they control the flow. You fill in your details, click “Buy,” and the payment processes.

In agentic commerce, the AI agent presents the checkout information within its own interface. ChatGPT shows you the product, the price, the shipping options, within the chat. You confirm. The agent handles the rest. The merchant never renders a page. They receive an API call.

Stripe’s agentic commerce guide puts it directly: “The parts of commerce that used to be user experience problems are becoming protocol problems.” Instead of optimizing button colors and form layouts, merchants are defining API endpoints and product feeds. Discovery, comparison, and checkout are all handled by the agent. The merchant’s job shifts to supplying structured product data and processing the order.

Emily Glassberg Sands, Stripe’s Head of Information and Data Science, framed the broader implications: “Agents don’t just change who’s at the checkout. They change who’s doing the searching, the deciding, the trusting. All of it.”

I discussed this with Jes Scholz, who ran digital across 140+ ecommerce brands at Ringier, on the podcast. Her experience was clear: Agents browse in text mode, and if they can’t parse your site cleanly, they leave. No second chances.

This isn’t theoretical. As of February 2026, several agentic commerce implementations are live. ChatGPT Instant Checkout is available to U.S. users on Free, Plus, and Pro plans. Etsy, Instacart, and Walmart are among the merchants processing orders through it. Shopify’s Agentic Storefronts are active by default for eligible merchants, syndicating products to ChatGPT, Google AI Mode, Microsoft Copilot, and Perplexity simultaneously. Perplexity launched Instant Buy with PayPal in November 2025, allowing purchases directly within the chat interface with merchants like Wayfair, Abercrombie & Fitch, and thousands more via BigCommerce and Wix.

Every major AI company is moving in this direction. Anthropic, the company behind Claude, has been equally explicit about its commerce plans. In February 2026, Anthropic confirmed it is building features for “agentic commerce, where Claude acts on a user’s behalf to handle a purchase or booking end to end,” while committing to keeping the experience ad-free with no sponsored links or third-party product placements. Claude already connects to Stripe, PayPal, and Square via MCP integrations. And in June 2025, Anthropic published Project Vend, a research experiment where Claude autonomously operated a physical retail store for a month, managing inventory, pricing, supplier relations, and customer interactions. The results were instructive: The agent performed well at supplier discovery and customer service, but sold items at a loss and hallucinated payment details. A useful preview of both the potential and the current limitations.

Two open protocols are making this possible. Both launched within four months of each other.

The Agentic Commerce Protocol

The Agentic Commerce Protocol (ACP) is an open standard co-developed by OpenAI and Stripe, announced Sept. 29, 2025. Licensed under Apache 2.0, it defines how AI agents complete purchases on behalf of users.

ACP uses a four-party model: the buyer (discovers and approves), the AI agent (presents products and handles checkout UI), the merchant (processes the order and payment), and the payment service provider (handles payment credentials securely). The merchant remains the merchant of record. They process the payment, handle fulfillment, manage returns. The agent is an intermediary, not a marketplace.

The protocol defines four API endpoints:

Endpoint Purpose
Create Checkout Agent sends a product SKU; merchant generates a cart with pricing, shipping, and payment options
Update Checkout Modifies quantities, shipping method, or customer details mid-flow
Complete Checkout Agent sends a payment token; merchant processes payment and returns order confirmation
Cancel Checkout Signals cancellation; merchant releases reserved inventory

The responsibility shift is worth spelling out:

Responsibility Traditional Checkout ACP Checkout
Checkout UI Seller Agent
Payment credential collection Seller Agent
Cart and data model Seller Seller
Payment processing Seller Seller

The agent handles what the buyer sees. The seller handles what happens after they click “Buy.” ACP can be implemented as either a REST API or an MCP server, connecting naturally to the protocol ecosystem covered in Part 3.

Stripe’s Agentic Commerce Suite, launched Dec. 11, 2025, makes ACP adoption practical. Ahmed Gharib, Stripe’s Product Lead for Agentic Commerce, described it as “a low-code solution enabling businesses to sell across multiple AI agents via a single integration.” Without it, connecting to each AI agent individually would take up to six months of bespoke engineering per platform.

The Suite has three components: product discovery (sync your catalog and Stripe distributes it to AI agents), checkout (powered by Stripe’s Checkout Sessions API, handling taxes and shipping), and payments (using Shared Payment Tokens and Stripe Radar for fraud detection). Merchants connect their existing product catalog or upload directly to Stripe, then select which AI agents to sell through from the Stripe Dashboard.

The ecosystem is growing quickly. Beyond OpenAI, Stripe lists Microsoft Copilot, Anthropic, Perplexity, Vercel, and Replit as AI platform partners. On the ecommerce side, Squarespace, Wix, WooCommerce, BigCommerce, and commercetools have integrated. Salesforce announced ACP support in October 2025. Shopify’s 1 million+ US merchants are coming soon.

The Universal Commerce Protocol

Four months after ACP launched, a different coalition unveiled a second standard.

The Universal Commerce Protocol (UCP) was co-developed by Shopify and Google, announced Jan. 11, 2026 at the National Retail Federation conference in New York. Google CEO Sundar Pichai presented it. The co-developers include Etsy, Wayfair, Target, and Walmart. Over 20 companies endorsed it at launch, including Mastercard, Visa, Best Buy, Home Depot, Macy’s, American Express, and Stripe. I broke down UCP and its strategic implications the week it launched on the podcast.

Where ACP is tightly focused on the checkout flow, UCP is designed as a full commerce standard covering discovery through post-purchase. Its architecture is modeled after TCP/IP, with three layers:

Layer Purpose
Shopping Service Core primitives: checkout sessions, line items, totals, messages, status
Capabilities Major functional areas (Checkout, Orders, Catalog), each independently versioned
Extensions Domain-specific schemas, added via composition without a central registry

UCP is protocol-agnostic. It supports REST, MCP, A2A, and AP2 (Agent Payments Protocol, Google’s standard for agent-initiated payments). ACP currently supports REST and MCP.

Discovery works through a published profile at /.well-known/ucp, similar to how A2A agents publish their capabilities at /.well-known/agent-card.json (covered in Part 3). Both agents and merchants declare their capabilities, and on each request, the system computes the intersection of what they can do together. Ashish Gupta, VP/GM of Merchant Shopping at Google, described the logic: “The shift to agentic commerce will require a shared language across the ecosystem.”

The two protocols reflect different strategic positions. ACP, built by the company running the AI agent (OpenAI) and the company processing the payment (Stripe), is optimized for getting transactions through ChatGPT quickly. UCP, built by the company hosting the merchants (Shopify) and the company running search (Google), is designed for a multi-agent future where many AI platforms compete for the same shoppers.

Dimension ACP (Stripe + OpenAI) UCP (Shopify + Google)
Launched Sept. 29, 2025 Jan. 11, 2026
Focus Checkout flow Full commerce journey
Transport REST, MCP REST, MCP, A2A, AP2
Payment Shared Payment Tokens (Stripe) AP2 with cryptographic Mandates
Discovery Structured product feeds /.well-known/ucp endpoint
Integration effort Days (existing Stripe merchants) Weeks to months
Coalition OpenAI, Stripe, Salesforce Google, Shopify, Mastercard, Visa

The good news for merchants: These aren’t mutually exclusive. Shopify merchants can serve both simultaneously. The same products appear in ChatGPT via ACP and in Google AI Mode via UCP. Shopify’s Agentic Storefronts handle the multi-protocol complexity, syndicating catalog data across ChatGPT, Google AI Mode, Microsoft Copilot, and Perplexity from a single admin panel.

Vanessa Lee, Shopify’s VP of Product, framed the company’s position: “Agentic commerce has so much potential to redefine shopping and we want to make sure it can scale.”

The Trust Problem: Payments Without People

Both protocols face the same foundational challenge: How do you process a payment when the person with the credit card isn’t the one at the checkout?

Traditional commerce treats credential possession as a trust signal. If you have the card number, the expiry date, and the CVV, you’re probably the cardholder. Agentic commerce breaks this assumption. The agent has been authorized to act on the buyer’s behalf, but it’s not the buyer. As Stripe’s Kevin Miller wrote in his October 2025 blog post: “Trust can’t be inferred. It has to be explicitly granted, scoped, and enforced in code.”

Javelin Strategy & Research, cited by Visa, describes this as the shift from “card-not-present” to “person-not-present” transactions. It’s a useful framing. Card-not-present fraud was the defining challenge of ecommerce. Person-not-present fraud is the defining challenge of agentic commerce.

Shared Payment Tokens

Stripe’s solution is the Shared Payment Token (SPT), a new payment primitive designed specifically for agent transactions. Here’s how it works:

  1. The buyer saves a payment method with the AI platform (e.g., ChatGPT).
  2. When approving a purchase, the AI platform issues an SPT scoped to the specific merchant, capped at the checkout amount, with a time limit.
  3. The AI platform sends the SPT to the merchant via ACP.
  4. The merchant creates a Stripe PaymentIntent using the token.
  5. Stripe processes the payment, applying fraud detection in real time.

The buyer’s actual card details are never shared with the merchant or the agent. Each token is programmable (scoped by merchant, time, and amount), reusable across platforms, and revocable at any time. For existing Stripe merchants, enabling SPTs requires “as little as one line of code.”

The Payment Networks Respond

The card networks have launched their own standards. Visa introduced the Trusted Agent Protocol in October 2025, an open framework built on HTTP Message Signatures that helps merchants distinguish legitimate AI agents from malicious bots. Developed in collaboration with Cloudflare, it has feedback from Adyen, Checkout.com, Microsoft, Shopify, Stripe, and Worldpay, among others.

Mastercard launched Agent Pay in April 2025, introducing “Agentic Tokens” that build on its existing tokenization infrastructure. Each agent action uses permissions and limits defined by the consumer. Mastercard CEO Michael Miebach described agent-led payments as a “significant paradigm shift” for the industry. U.S. issuers were enabled in November 2025, with global rollout in early 2026.

PayPal joined the ACP ecosystem on October 28, 2025, enabling PayPal wallets for ChatGPT checkout and building an ACP server that connects its global merchant catalog without requiring individual merchant integrations.

Google launched its own payment standard in parallel. The Agent Payments Protocol (AP2), announced September 2025 with 60+ industry partners, uses Verifiable Digital Credentials and a cryptographic Mandate system to create tamper-evident proof of user consent at every step of the transaction. AP2 is payment-agnostic, supporting credit and debit cards, real-time bank transfers, and even stablecoins via a Coinbase x402 extension. It’s integrated directly into UCP.

Fraud Without Fingerprints

Traditional fraud detection relies on human behavioral signals: mouse movements, typing patterns, browsing behavior, session duration. AI agents have none of these. A legitimate agent transaction can look indistinguishable from a sophisticated bot attack.

Stripe addressed this by building what they describe as “the world’s first AI foundation model for payments,” a transformer-based model trained on tens of billions of transactions. The model treats each charge as a token and behavior sequences as context, ingesting signals including IPs, payment methods, geography, device characteristics, and merchant traits. When SPTs are used, Stripe Radar relays risk signals including dispute likelihood, card testing detection, and stolen card indicators to help “differentiate between high-intent agents and low-trust automated bots.”

The attack surface is also novel. Researchers demonstrated in a June 2025 study that ecommerce agents are susceptible to visual prompt injection: malicious content embedded in product listings can hijack agent behavior during shopping tasks. All agents tested were vulnerable. A separate study accepted to IEEE S&P 2026 found that 13% of randomly selected ecommerce websites had already exposed their chatbot plugins to indirect prompt injection via third-party content like product reviews. And a January 2025 paper on authenticated delegation argues that for agentic commerce to function at scale, the industry needs standardized mechanisms to “explicitly delegate authority to agents, transparently identify those agents as AI, and enforce human-centered choices around security and permissions.” SPTs, the Trusted Agent Protocol, and Agent Pay are all early answers to that challenge.

The concern is real on the consumer side, too. 88% of consumers surveyed by Javelin are concerned that AI will be used for identity fraud, according to Visa’s analysis. Building trust infrastructure that works for agent transactions is the prerequisite for agentic commerce scaling beyond early adopters.

→ Read More: Trust In AI Shopping Is Limited As Shoppers Verify On Websites

Who’s Already Selling to AI

Despite the infrastructure still being built, adoption is moving fast.

AI platforms with commerce capabilities:

Merchants and brands on board:

The early adopter list reads like a mall directory. URBN (parent of Anthropologie, Free People, and Urban Outfitters), Etsy, Coach, Kate Spade, Glossier, Vuori, Spanx, SKIMS, Ashley Furniture, Revolve, and Halara are among those onboarding to Stripe’s Agentic Commerce Suite. Walmart and Instacart are live on ChatGPT. Gymshark, Everlane, and Monos are live on Google AI Mode via UCP.

Ecommerce platforms enabling it:

Shopify’s 1 million+ U.S. merchants are eligible for ChatGPT integration. BigCommerce, Wix, Squarespace, WooCommerce, and commercetools have integrated with Stripe’s Suite. Salesforce Commerce Cloud announced ACP support in October 2025, with new Agentforce agents for merchant, buyer, and personal shopper workflows.

The Market

The market projections vary widely, which tells you how early we are. McKinsey projects $1 trillion in U.S. retail revenue orchestrated by agents by 2030, scaling to $3-5 trillion globally. Gartner predicts 90% of B2B purchases will be handled by AI agents within three years, intermediating $15 trillion in spending by 2028. Forrester predicts that by 2026, one-third of retail marketplace projects will be deserted as answer engines steal traffic.

The consumer side is more cautious. A Contentsquare survey of 1,300 U.S. consumers found 30% willing to let an AI agent complete a purchase on their behalf. A YouGov survey of 1,287 U.S. adults found 65% trust AI to compare prices, but only 14% trust it to actually place an order. Among Gen Z, that number rises to 20%. The gap between “I’ll let AI help me shop” and “I’ll let AI buy for me” is where we are right now.

But the traffic is already there. AI-driven traffic to U.S. retail websites grew 4,700% year-over-year by mid-2025, according to Adobe Analytics. Shopify reported that orders attributed to AI searches grew 11x since January 2025. OpenAI estimates approximately 2% of all ChatGPT queries are shopping-related, roughly 50 million shopping queries daily across a user base of 700 million weekly users.

Academic research is starting to reveal what happens when agents do the buying. A Columbia Business School and Yale study (August 2025) introduced ACES, the first agentic ecommerce simulator, and tested six frontier models, including Claude and GPT-4. They found that AI shopping agents exhibit “choice homogeneity,” concentrating demand on a small number of products and showing strong position biases in how listings are ranked. The researchers warn of winner-take-all dynamics and the emergence of “AI-SEO,” where sellers optimize listings specifically for agent behavior rather than human preferences. A February 2026 study on personalized product curation found that current agentic systems remain “largely insufficient” for tailored product recommendations in open-web settings. The agents are getting better at buying. They’re not yet great at buying the right thing for a specific person.

The infrastructure is being built regardless of whether consumers are fully ready. When they are, the businesses that are prepared will be the ones the agents can find.

How To Get Started

The good news: For most businesses, the entry point is simpler than you’d expect.

If you’re on Shopify, you may already be selling to AI. Agentic Storefronts are active by default for eligible U.S. merchants. Your products are syndicated to ChatGPT, Google AI Mode, Microsoft Copilot, and Perplexity from your existing Shopify admin. Check your dashboard for the agentic channel settings and ensure your product data (descriptions, images, categories) is clean and complete.

If you’re on Stripe, enabling Shared Payment Tokens for ACP requires as little as one line of code. The Agentic Commerce Suite handles catalog syndication, checkout, and fraud detection. Connect your product catalog, select which AI agents to sell through, and you’re live.

If you’re on BigCommerce, Wix, Squarespace, or WooCommerce, integrations with Stripe’s Suite are available. BigCommerce described the shift from “months of bespoke engineering work” per AI platform to “a single, configurable integration.”

Regardless of platform, the protocol integrations get you connected. But agents still need to find and understand your products. This is where the work from Part 2 (getting cited) and Part 4 (being agent-readable) converges with commerce.

Audit your product data. Agents parse your catalog programmatically. Every product needs:

  • A descriptive, specific title (“Men’s Organic Cotton Crew Neck T-Shirt, Navy,” not “Blue Shirt”).
  • A complete description including materials, dimensions, care instructions, and use cases.
  • Accurate, real-time pricing and stock availability.
  • High-quality images with descriptive alt text.
  • Consistent categorization across your catalog.

Add structured markup. At minimum, every product page should include Product schema with name, description, image, sku, and brand, plus nested Offer schema with price, priceCurrency, availability, and seller. If you have reviews, add AggregateRating. This is the machine-readable layer that agents parse when direct protocol integrations aren’t available. I talked about this with Duane Forrester, who co-launched Schema.org while at Bing, on the podcast. His argument: consistent structured data builds what he calls “machine comfort bias,” where AI systems develop a preference for sources that have proven reliable over time.

Test your agent visibility. Open ChatGPT, Perplexity, and Google AI Mode, and ask them to recommend products in your category. If yours don’t appear, agents can’t sell them. View your product pages in reader mode or a text-based browser to see what agents see when they visit your site directly (covered in Part 4).

Track agent-driven traffic. ChatGPT appends utm_source=chatgpt.com to referral links. Perplexity and other AI platforms leave similar referral signatures. Set up segments in your analytics to isolate AI-referred visits and monitor conversion rates separately from human traffic. The numbers are small now, but the 4,700% year-over-year growth in AI traffic to retail means they won’t stay small.

Walmart CEO Doug McMillon put it directly: “For many years now, ecommerce shopping experiences have consisted of a search bar and a long list of item responses. That is about to change.”

Whether it changes next quarter or next year for your business depends on whether your product data is ready when the agents come looking.

Key Takeaways

  • Checkout is becoming a protocol, not a page. In agentic commerce, the AI agent handles the interface; the merchant processes the order. Two open standards, ACP (Stripe + OpenAI) and UCP (Shopify + Google), define how this works.
  • Both protocols are open and growing fast. ACP launched in September 2025 and powers Instant Checkout in ChatGPT. UCP launched in January 2026 with endorsements from Mastercard, Visa, Walmart, and Target. They’re complementary, not mutually exclusive. Shopify merchants can serve both simultaneously.
  • Shared Payment Tokens solve the “person-not-present” problem. When the buyer isn’t at the checkout, traditional trust signals break down. SPTs are programmable, scoped, time-limited, and revocable, letting agents initiate payments without ever seeing the buyer’s card details.
  • Payment networks are building their own standards. Visa’s Trusted Agent Protocol and Mastercard’s Agent Pay provide authentication and fraud frameworks specific to agent transactions. PayPal joined the ACP ecosystem. The payments infrastructure for agentic commerce is taking shape across the industry.
  • Major brands are already live. Etsy, Walmart, Instacart, Glossier, SKIMS, Coach, and dozens more are selling through AI agents today. Ecommerce platforms, including Shopify, BigCommerce, Wix, Squarespace, and WooCommerce, have integrations available.
  • Consumer trust is lagging behind infrastructure. Only 14% of consumers currently trust AI to place orders on their behalf. But AI-driven traffic to retail grew 4,700% in a year. The infrastructure is being built for the adoption curve that follows.

This is the final article in a five-part series on the agentic web. Part 1 framed the shift from SEO to AAIO. Part 2 covered how to get cited by AI. Part 3 mapped the protocols. Part 4 explained how agents perceive your website. This article covered where it all leads: transactions.

The thread connecting all five parts is straightforward. Structured data helps AI find you. Clean content helps AI cite you. Accessible HTML helps AI navigate you. Structured commerce protocols help AI buy from you. It’s the same principle at every layer: Make your business machine-readable, and the machines will do business with you.

Kevin Miller, Stripe’s Head of Payments, captured the moment: “Stripe spent the last 15 years optimizing commerce for human shoppers. Now, we’re starting to do the same with agents.”

The agents are already shopping. The question is whether they can find your store.

More Resources:

This post was originally published on No Hacks.

Featured Image: showcake/Shutterstock

Ecommerce MGMT 0 Comments
Apr 8 2026
Why Product Feeds Shouldn’t Be The Most Ignored SEO System In Ecommerce

Most ecommerce brands obsess over category pages and backlinks or product optimizations, while their product feeds remain auto-generated and underoptimized. Product feeds act as the backbone of ecommerce site catalogs and have long been the sole remit of PPC teams, but in the new era of AI Search, this is changing.

Back in 2023, Search Console added enhancements to the Shopping tab Listings report to help brands to get a better understanding of how their products were being seen in the Merchant Center.

We’ve also seen the emergence of OpenAI’s Product Feed specification as a specific requirement to allow ChatGPT to accurately index and display products. Although more recently, we’ve seen announcements that OpenAI has ended Instant Checkout and considering new directions.

These changes are pulling product feed visibility directly into the SEO performance ecosystem and aligning it as general “search infrastructure,” not just “ads infrastructure.”

In this article, we’ll be talking you through the value that product feeds can bring to businesses and how SEO aligns with this.

SEO’s Role In Product Feeds

In ecommerce, product feeds are often seen as “set it and forget it” assets, but treating these feeds as simply raw data is an immediate missed opportunity to boost visibility across organic search, shopping, and agentic commerce in the future.

While a standard product feed provides basic data to search bots, an optimized feed enhances attribute accuracy to ensure your products appear for high-intent search queries. By refining your product data, you bridge the gap between technical specs and consumer needs, increasing both visibility and click-through rates.

SEO can help to optimize feeds across four main pillars:

1. Semantic Query Mapping

SEOs don’t just use basic product names. They use consumer language built out of query mapping and intent-matching.

By front-loading titles with high-intent keywords and “long-tail” descriptions that include attributes like color, material, or use-case, products are more likely to appear where the user’s intent is highest.

Example:

Instead of “Men’s Waterproof Jacket Black”

SEO Driven Product Feed: “Brand X Men’s Waterproof Running Jacket – Black Lightweight Performance Shell”

2. Taxonomy Logic

Taxonomy is important to stop your products from being lost in the void. A misplaced product can quickly become a lost sale.

By refining categorization and product grouping, general terms like “tactical hiking boots” won’t get buried under generalized categories like “general footwear.”

Building a logical hierarchy allows algorithms to crawl and understand the catalog with higher confidence of exactly who the product is targeting. All products within your feed will be automatically assigned a product category.

Ensuring your taxonomy, as well as the titles, descriptions, and GTIN information, will help to ensure that products are correctly categorized according to [google_product_category] attribute.

3. Structured Data

In Google Shopping, structured data acts as the anchor of “truth” that connects your website to your Merchant Center feed.

Structured data allows Google and other bots to directly pull product data from your HTML, creating a form of automated data validation. If, for example, your feed says a product is $50, but your schema says $60, Google will likely disapprove the listing.

In many cases, high-performing feeds rely on structured data to update price and availability in real-time. If you run a flash sale, Google’s crawler can detect the change via schema and updates your Shopping Ads, preventing “out of stock” clicks.

When it comes to agentic commerce, agents will query schema properties to see if your product fits the user’s specific constraints.

Structured data provides hard facts and allows agents to see if a product is “agent-ready” for checkout.

4. Analytical Review

Having a highly analytical mind that is always looking for opportunity, SEOs can help to identify any “ghost products” and diagnose whether the issues are down to attributes, images, or descriptions, providing ongoing optimization recommendations.

As we move into an era of AI-driven discovery, the quality of a brand’s feed data can quickly become a reflection of a brand’s reputation.

By providing more context within the feed, you are more likely to see your brand get recommended in conversational search and show up in organic shopping.

What Ecommerce Brands Get Wrong With Product Feed Optimization

The majority of issues that we see in product feeds come from inconsistencies and a lack of depth within the feed.

From conversations with brand managers, this seems to stem from a lack of ownership within a channel and a lack of understanding of the impact of what these inconsistencies can have.

In some cases, feeds can be disapproved due to having inaccurate price status due to inconsistency between the feed and a landing page.

Other common issues include:

  • Auto-generated Shopify titles.
  • No keyword layering.
  • Inconsistent variants.
  • Missing GTIN/MPN.
  • Thin descriptions.
  • Feed data not aligned with on-page SEO.

This is where having the eyes of an SEO who is used to ongoing technical auditing and hygiene maintenance, and understands the value of structured data and content for context, can be vital in product feed performance.

How Product Feeds Directly Impact Organic & AI Visibility

Quite simply, the more context you can provide in your product feed, the more chances you have of being shown or cited in traditional search and in AI engines.

If a product feed is missing critical attributes like size, color, material, compatibility, or use case, the product won’t just rank lower; it will become ineligible for more specific, high-intent queries.

As search queries grow longer and intent becomes more nuanced, i.e., searchers looking for “men’s waterproof trail running jacket black medium” rather than just “men’s trail running jacket,” feeds need to evolve past being simple descriptors.

They need to properly layer structured attributes that mirror how real customers search and filter online. The more complete the product feed, the more opportunities there will be for your products to appear online across Shopping to AI-generated citations.

What Product Feed Optimization Actually Looks Like

There are a few stages of product feed optimization that SEOs need to be both aware of and able to deliver.

Keyword & Intent Architecture

SEOs should approach product feeds the same way they approach category and content strategy.

Keyword research should be conducted at a product level, identifying high-intent modifiers such as size, material, compatibility, and demographic, and layer those attributes both into product titles and feed data.

Rather than relying on generic exports from Shopify or another ecommerce platform, product titles should reflect real organic search behavior around how customers actually query products.

Structured Data Alignment

SEOs should also make sure that feed attributes match on-page schema.

Keeping a close eye on Merchant Center for any potential issues, such as missing GTINs or prices not matching, and making any necessary adjustments to schema/structured data, will help to ensure that the feed is consistent and context is fully delivered to bots.

Variant Consolidation Strategy

This leans heavily into faceted navigation – which ecommerce SEOs have been battling for years.

By determining when product variations should be grouped under a single parent entity versus a standalone URL, SEOs can have more control over any unnecessary duplication and cannibalization.

This can also help to protect crawl efficiencies across large product catalogs and declutter product feeds.

Feed Health Monitoring

Similar to how SEOs regularly run technical crawls of websites to maintain hygiene and pick up any issues, SEOs should also treat feed governance as part of their regular checks.

This includes actively monitoring feed errors and addressing any Merchant Center issues that might limit visibility.

Prioritizing AI Search Readiness

A large opportunity for the future of search comes with agentic commerce, and product feeds are going to align directly with this.

By ensuring feeds are clearly structured and contain complete and accurate attributes, SEOs can reinforce strong product entity signals and provide clarity, which AI systems rely on to determine what to display in comparisons and recommendations.

Final Thoughts

Product feeds are no longer just paid media assets; they are core search infrastructure that directly impacts organic shopping visibility and AI-driven discovery.

Even the strongest category pages can’t compensate for inconsistent or poorly structured data at scale.

As search becomes more conversational and comparative, structured product clarity is going to be the difference between brands that are cited and brands that are not.

More Resources:

Featured Image: Roman Samborskyi/Shutterstock

Ecommerce MGMT 0 Comments
Mar 19 2026
Google Expands UCP With Cart, Catalog, Onboarding via @sejournal, @MattGSouthern

Google updated the Universal Commerce Protocol with new Cart and Catalog capabilities, highlighted Identity Linking as an available option, and announced a simpler onboarding process through Merchant Center.

The update is UCP’s first since Google launched the protocol at NRF in January. Cart and Catalog are published as draft specifications. Identity Linking is in the latest stable version of the spec.

What The New Capabilities Do

The additions expand what AI agents can do within UCP-powered shopping experiences.

Cart lets agents save or add multiple items to a shopping basket from a single store. According to the UCP spec, Cart is designed for pre-purchase exploration, allowing agents to build baskets before a shopper commits to a purchase. Carts can then convert to checkout sessions when the shopper is ready.

Catalog enables agents to retrieve real-time product details from a retailer’s inventory. That includes variants, pricing, and stock availability. The Catalog spec supports both search and direct product lookups.

This is relevant to the product discovery question raised in earlier UCP coverage. Agents can now query live catalog data rather than relying solely on product feeds.

Identity Linking allows shoppers to connect their retailer accounts to UCP-integrated platforms using OAuth 2.0. That means loyalty pricing, member discounts, or free shipping offers can carry over when a shopper buys through AI Mode or Gemini instead of the retailer’s own site.

Identity Linking was part of the UCP spec at launch. Google’s blog post groups it with Cart and Catalog as a newly available option for adopters.

All three capabilities are optional. Retailers choose which ones to support.

Merchant Center Onboarding

Separately, Google said it is simplifying UCP onboarding through Merchant Center. The company described the process as designed to bring in “more retailers of all sizes” and said it would roll out over the coming months.

Google’s Merchant Center help page still lists the checkout feature as available to selected merchants, with an interest form for those who want to participate. The page specifies that only product listings using the native_commerce product attribute will display the checkout button.

Three platform partners announced plans to implement UCP. Commerce Inc, Salesforce, and Stripe each published separate announcements. Google said its implementations will arrive “in the near future,” with others to follow.

For retailers not building a direct UCP integration, platform support from these providers could lower the technical barrier to participation.

Why This Matters

Simplified Merchant Center onboarding and third-party platform support open the door for retailers without engineering teams building custom integrations.

Cart and Catalog also change the scope of what UCP handles. At launch, UCP could process a single-item checkout. Now agents can build multi-item baskets and pull live product data. That moves UCP closer to replicating a full shopping experience inside Google’s AI surfaces.

The tradeoffs for retailers are the same ones identified in January. Sales happen on Google’s surfaces instead of owned sites. Identity Linking adds loyalty benefits to that equation, which may make the tradeoff more palatable for some retailers and more concerning for others who see loyalty programs as a reason shoppers come to their sites directly.

Looking Ahead

Cart and Catalog are draft specifications, meaning their status may change as community contributors provide feedback in the open-source project.

Google said it plans to bring UCP capabilities to AI Mode in Search, the Gemini app, and beyond. The company has not provided a more specific timeline for the Merchant Center onboarding rollout beyond “coming months.”

Ecommerce MGMT 0 Comments
Feb 23 2026
Agentic Commerce Optimization: A Technical Guide To Prepare For Google’s UCP via @sejournal, @alexmoss

In January, I wrote about the birth of agentic commerce through both Agentic Commerce Protocol (ACP) and Universal Commerce Protocol (UCP), and how this could impact us all as consumers, business owners, and SEOs. As we still sit on waitlists for both, this doesn’t mean that we can’t prepare for it.

UCP fixes a real-life problem for many, minimizing the fragmented commerce journey. Instead of building separate integrations for every agent platform as we have been mostly doing in the past, you can now [theoretically] integrate once and will integrate seamlessly with other tools and platforms.

But note here that, as opposed to ACP which focuses more so on the checkout → fulfillment → payment journey, UCP goes beyond this with six capabilities covering the entire commerce lifecycle.

This, of course, will impact an SEO’s ambit. As we shift from optimizing for clicks to optimizing for selection, we also need to ensure that it’s you/your client that is selected through data integrity, product signals, and AI-readable commerce capabilities. Structured data has always served an important role for the internet as a whole and will continue to be the driving force on how you can serve agents, crawlers, and humans in the best way possible.

I allude to a possible new acronym “ACO” – Agentic Commerce Optimization – and the following could be considered the closest we can get to guidelines on how we undertake it.

UCP Isn’t Coming, It’s Here

UCP was only announced in January, but there’s already confirmation that its capabilities are rolling out. On Feb. 11, 2026, Vidhya Srinivasan (VP/GM of Advertising & Commerce at Google) announced that Wayfair and Etsy now use UCP so that you can purchase directly within AI Mode, and was observed the next day by Brodie Clark.

UCP’s Six Layered Capabilities

On the day UCP was released, Google explained its methodology.

From this, I defined six core capabilities:

  1. Product Discovery – how agents find and surface your inventory during research.
  2. Cart Management – multi-item baskets, dynamic pricing, complex basket rules.
  3. Identity Linking – OAuth 2.0 authorization for personalized experiences and loyalty.
  4. Checkout – session creation, tax calculation, payment handling.
  5. Order Management – webhook-based lifecycle and logistical updates.
  6. Vertical Capabilities – extensible modules for specialized use cases like travel booking windows or subscription schedules.

UCP’s schema authoring guide shows how capabilities are defined through versioned JSON schemas, which act as the foundation of the protocol. When it comes to considering this as an SEO, properties such as offers, aggregateRating, and shippingDetails aren’t just for surfacing rich snippets, etc., for product discovery, they’re now what agents query during the entire process.

Schema Is, And Will Continue To Be, Essential

UCP’s technical specification uses its own JSON schema-based vocabulary. Whilst it doesn’t build on schema.org directly, it remains critical in the broader ecosystem. As Pascal Fleury Fleury said at Google Search Central Live in December, “schema is the glue that binds all these ontologies together”. UCP handles the transaction; schema.org helps agents decide who to transact with.

Ensure you’re on top of and populate product schema as much as you can. It may seem like SEO 101. Regardless, audit all of this now to ensure you’re not missing anything when UCP really rolls out.

This includes checks on:

  • Product schema (with complete coverage): All core fields: name, description, SKU, GTIN, brand, related images, and offers.
  • Offers must include: Price, priceCurrency, availability, URL, seller. Add aggregateRating and review to ensure you have positive third-party perspective.
  • Ensure all product variants output correctly.
  • Include shippingDetails with delivery estimates.
  • Organization and Brand: Assists with “Merchant of Record” verification. If you’re not an Organization, then fallback to Person.
  • Designated FAQPage: Ensure you have an FAQpage as these can be incorporated alongside product-level FAQs and used as part of its decision-making.

Prepare Your Merchant Center Feed

UCP will utilize your existing Merchant Center feed as the discovery layer. This means that beyond the normal on-site schema you provide, Merchant Center itself requires more details that you can populate within its platform.

  • Return policies (required to be a Merchant of Record): Complete all return costs, return windows, and policy links. These will be used not just within the checkout and transactional areas, but again a consideration for selection at all. Advanced accounts need policies at each sub-account level.
  • Customer support information: Not only would initial information be offered to the customer, but there may be ways in which entry-level customer support queries can be completely managed, thus increasing customer satisfaction while minimizing customer support agent capacity.
  • Agentic checkout eligibility: Add the native_commerce attribute to your feed, as products are only eligible here if this is set up.
  • Product identifiers: Each product must have an ID, and correlate to the product ID when using the checkout API.
  • Product consumer warnings: Any product warning should assert the consumer_notice attribute.

Google recommends that this be done through a supplemental data source in Merchant Center rather than modifying your primary feed, which would prevent incorrect formatting or other invalidation.

Lastly, double-check if the products you’re selling aren’t included within its product restrictions list, as there are several that, if you do offer those things, you should consider how to manage alongside the abilities of UCP.

Optimizing Conversational Commerce Attributes

Within the UCP blog post announcement, Srinivasan introduced a way for more clarity with conversational commerce attributes:

“…we’re announcing dozens of new data attributes in Merchant Center designed for easy discovery in the conversational commerce era, on surfaces like AI Mode, Gemini and Business Agent. These new attributes complement retailers’ existing data feeds and go beyond traditional keywords to include things like answers to common product questions, compatible accessories or substitutes.”

These provide further clarity (and therefore minimize hallucinations) during the discovery process in order to be selected or ruled out.

Not only would this incorporate product and brand-related FAQs, but take this a step further to also consider:

  • Compatibility: Potential up-sell opportunities.
  • Substitution: An opportunity for dealing with out-of-stock items.
  • Related products: Great for cross-sell opportunities.

Furthermore, this can be used to become even more specific, moving beyond basic attributes to agent-parseable details. Now, if a product is “purple” on a basic level, “dark purple” or even something unobvious, such as “Wolf” (real example below), may be more appropriate for finer detail while still falling under “purple.” The same can be considered for sizes, materials (or a mixture of materials), etc.

Multi-Modal Fan-Out Selection

When executed well, optimizing for conversational commerce attributes will increase the possibility of selection within fan-out query results. When considering some of these attributes, it is worth looking at tools, such as WordLift’s Visual Fan-Out simulator, which illustrates how a single image decomposes into multiple search intents, revealing which attributes agents may prioritize when performing query fan-out. But how would this look?

As an example, I used one product image and browsed downward three horizons. Using On’s Cloudsurfer Max as an example (used with permission):

Cloudsurfer Max in the colour “Wolf”
Image credit: On

Using just one product image, this is what is presented on the surface:

Screenshot from WordLift’s Visual Fan-Out simulator, February 2026

It immediately noticed that the product was On, and specifically from the Cloudsurfer range. Great start! Now let’s see what it sees over the horizon:

Screenshot from WordLift’s Visual Fan-Out simulator, February 2026
Screenshot from WordLift’s Visual Fan-Out simulator, February 2026
Screenshot from WordLift’s Visual Fan-Out simulator, February 2026

Here, you can draw inspiration or direction on how best to place yourself for potential and likely fan-out queries. With this example, I found it interesting that Horizon 2 mentions performance running gear as a large category, then when performing fan-out on that showed the related products around gear in general. This shows how wide LLMs consider selection and how you can present attributes to attract selection.

UCP’s Roadmap Is Expanding Into Multi-Verticals

UCP is already planning to go beyond one single purchase but expands beyond retail into travel, services, and other verticals. Its roadmap details several priorities over the coming year, including:

  • Multi‑item carts and complex baskets: Moving beyond single‑item checkout to native multi‑item carts, bundling, promotions, tax/shipping logic, and more realistic fulfillment handling.
  • Loyalty and account linking: Standardized loyalty program management and account linking so agents can apply points, member pricing, and benefits across merchants.
  • Post‑purchase support: Support for order tracking, returns, and customer‑service handoff so agents can manage customer support post-sale.
  • Personalization signals: Richer signals for cross‑sell/upsell, wishlists, history, and context‑based recommendations.
  • New verticals: Expansion beyond retail into travel, services, digital goods, and food/restaurant use cases via extensions to the protocol.

Each of the points above is worth further reading and consideration if this is something your brand may offer. Furthermore, its plans to expand beyond retail into travel, services, digital goods, and hospitality mean that, if you’re working within any of these verticals, you need to be even more prepared to ensure eligibility.

Social Proof And Third-Party Perspective

Regardless of how well you may optimize on-site to prepare for UCP, all this data integrity still needs to be validated by trusted third-party sources.

Third-party platforms, such as Trustpilot and G2, appear to be frequently cited and trusted among most of the LLMs, so I’d still advise that you continue to collect those positive brand and product reviews in order to satisfy consensus, resulting in more opportunities to be selected during product discovery.

TL;DR – Prepare Now

If you own or manage any form of ecommerce site, now is the time to ensure you’re preparing for UCP’s rollout as soon as possible. It’s only a matter of time, and with AI Mode spreading into default experiences, getting ahead of the rollout is essential.

  1. Join the UCP waitlist.
  2. Prepare Merchant Center: return policies, native_commerce attribute.
  3. Ensure your developers research and understand the UCP documentation.
  4. Populate conversational attributes: question-answers, compatibility, substitutes.
  5. Audit and improve any schema where applicable.

This is moving faster than most previous commerce shifts, and brands that wait for full rollout signals will already be behind. This isn’t a short-term LLM gimmick but is part of the largest change in the ecommerce space.

More Resources:

Featured Image: Roman Samborskyi/Shutterstock

Ecommerce MGMT 0 Comments
Jan 22 2026
Shopify Shares More Details On Universal Commerce Protocol (UCP) via @sejournal, @martinibuster

Harvey Finkelstein, the president of Shopify, was recently interviewed about their open source Universal Commerce Protocol (UCP), which enables agentic AI shopping. Co-developed with Google, he explains how UCP enables brands to be discovered by customers based on personalized recommendations, as opposed to advertising and classic search paradigms that are less personalized.

Finkelstein said that the Universal Commerce Protocol (UCP) is designed to enable AI agents to surface products in a manner that merchants can control, show consumers personalized recommendations based on users’ preferences, and deliver a shopping experience that’s as good as any ecommerce store platform.

Shopify is also opening agentic commerce access to brands that are not Shopify customers through their Agentic plan, which he briefly mentions. This plan is designed for enterprise brands and merchants who do not use Shopify to upload their product data to Shopify’s infrastructure so it can be discovered and purchased directly by AI agents.

This positions Shopify as infrastructure for agentic commerce, not just a hosted commerce platform. This makes it easier for brands to gain immediate access to agentic shopping channels without having to migrate platforms.

Finkelstein also points out that agentic commerce only works if consumers can access all brands, not just those on Shopify.

Shopify’s Finkelstein said that UCP will enable merchants to more effectively control how their products are shown. He also discussed their strategy of bringing agentic shopping to all brands, regardless of whether they are on Shopify or not.

He explained:

“We created this protocol called Universal Commerce Protocol which effectively is this universal language is open sourced so that all merchants can speak directly to every single one of the agents.

And the best way to explain it is up until now, it was really just about like a single transaction.

So I can buy something on ChatGPT or Gemini or Microsoft. there’s no concept of loyalty or subscription or bundling or, you know, if it’s furniture, for example, please don’t ship it to me on Thursday. I’m not home Thursday. Send it Friday.

So this idea of creating this universal protocol that we co-developed with Google means that now merchants can actually tell these agents exactly how to show their products on these agentic tools. And it should be as good as it is on the online store. So that was a really, really big one.

The second thing we announced also with Google is that now we’re actually expanding. You can sell everywhere commerce is happening from an agentic perspective.

So we’re going beyond the agentic storefronts of just ChatGPT, which is what we said, you know, in Q3. Now it’s also, we’re going to be working with Gemini, with AI mode in Google Search, and also with copilot.

And maybe the last one is that we’re actually bringing agentic commerce to every brand, whether or not they’re on Shopify.

So if you’re not on Shopify, but you want to have your product syndicated and indexed, you can do so with our agentic plan.”

Access To Many Brands Is Key

Finkelstein stressed that the key to the success of agentic AI is to be able to show the widest possible selection of brands. He said it’s a big opportunity.

He explained:

“I think if Agentic is going to do what a lot of us think it’s going to do from a commerce perspective, you have to give consumers all the brands.

We obviously want them all on Shopify, but there’s some brands that want to participate now, but it may take some time for them to migrate over.

So this idea of opening up to anyone, we think is a big opportunity.”

Who Will Be The Early Adopters?

Finkelstein was asked about who the early adopters will be. His answer was cautious, seemingly acknowledging that it’s likely not going to immediately be a big crush of people turning to AI to buy things.

He answered:

“I think it’ll likely be something that like most people use some of the time and some people use most of the time. I don’t think it’s going to cross the threshold of most most, the way e-commerce does now. It’s just going to take time. It’s going to take some time.”

AI Chat Reduces Friction

Finkelstein said that Universal Commerce Protocol (UCP) enables better shopping experiences, reducing the “friction” that AI shopping may have produced. He believes that once people start having good experiences shopping with an agent, they will start to get into the habit of using it for other kinds of shopping and begin relying on it.

Finkelstein explained:

“Once you have a good experience, I think the actual friction reduces. You’ll keep having it over and over again.

But the thing that we felt was missing, and this is the reason why I think this UCP protocol is so important, is it was very difficult to do merchandising inside of these applications.

And this protocol allows you to do a lot more… Well, up until UCP happened, you couldn’t actually do subscriptions. Now you can.

Or this idea of bundling, you know, for Gymshark, it’s a huge part of their business is if you buy these, you’ll also buy these as well. You can do that as well.

So I think all of these things are sort of in line with creating a much more delightful experience in the chat.”

Merit Based Shopping Versus SEO?

Finkelstein brought up the topic of merit-based shopping where products are recommended to a user because it is what they are looking for. He used the phrase “merit-based shopping” as a contrast to today’s online advertising ecosystems that prioritize products that pay to be shown as a recommendation. The main point is that shopping recommendations are made based on personalization.

Finkelstein explained:

“And I think ultimately what it leads to is like, this will be merit-based shopping, which will be different than I think some of the traditional retailers who were kind of leaning on their balance sheets to spend money on ads. You can’t really game the system in that that way.

You actually have to be, from a context perspective, the right product for the right consumer.”

What Happens To Creative Assets And SEO

One of the podcast hosts asked about what happens to creative assets like photos, saying that he noticed that shopping AI uses images. He asked how that was going to evolve. Finkelstein’s answer touched on SEO in the context of how agentic AI shopping is about showing products based on user preferences, a tighter form of relevance than in the advertising and classic search ecosystems.

Finkelstein explained:

“I think …the idea of SEO won’t exist in Agentic because again, it’s merit-based and it’s mostly based on the context history you’ve had.

But I do think though, you’re going to have… these brands are going to have people at their companies who are thinking a lot about like consistent updates to UCP, consistent updates to the catalog.

So they may pull something off the catalog and say, we don’t want to sell it anymore this way. So I think there’s going to be, I don’t know if they’re going to be actual jobs, but there’s going to be people inside of the company, potentially in the merchandising department, who say, actually, the way that we want to sell all this, the way we want to describe this to these agents is a particular way.

And then because of UCP and because of Shopify catalog, it gets easily disseminated across every single one of these agentic applications. So the experience just gets better and better.

I think you have to be a little bit of a techno optimist… as I am, to believe that even if the experience is not incredible right now, it’s likely just going to get better at this ridiculous pace.”

Cutting Out Incentivized Recommendations

When asked what’s the most exciting thing about Agentic AI, he returned to the concept of merit-based shopping, where LLMs have the ability to personalize responses by learning user preferences and therefore recommend a product that fits within that person’s requirements. He contrasted that with what happens in the real world, where a salesperson’s recommendations are influenced by commissions.

So what he is excited about is the idea of the playing field being leveled. He mentioned the possibility of lesser-known brands, like True Classic Tees, being surfaced in AI shopping because that kind of brand is a match for a specific consumer.

He responded:

“Most of the excitement is actually around this idea of like, is there a potential for this to level the playing field? Meaning, you know, if I’ve done a bunch of research historically on an agentic application …about the stuff that I love, the brands that I love. …It probably should not show me a generic pair of boots.

So the excitement actually is around like, is this going to introduce more brands that otherwise are unknown to more people or, you know, True Classic Tee, for example, which, you know, if you’re looking for a black t-shirt, I suspect on a search engine, you’re not going to see True Classic Tee come up that much, but it’s an incredible product and ultimately it can be found on these agentic tools in a way that it probably couldn’t historically.”

Agentic AI Will Accelerate Online Shopping

The other thing that Finkelstein is excited about is that he believes Agentic AI shopping will accelerate the amount of shopping that is done online. He compared using Agentic AI to the COVID moment, where people changed their work and shopping behavior in a major way that became permanent.

He then circled back to the idea that Agentic AI is less biased:

“I think it’s actually a better version of that because it’s an unbiased discussion, an unbiased conversation.”

Watch the video podcast interview at a few minutes after the 3 hour mark:

Featured Image by Shutterstock/Julien Tromeur

Ecommerce MGMT 0 Comments
Jan 21 2026
The Smart Way To Take Back Control Of Google’s Performance Max [A Step-By-Step Guide]

This post was sponsored by Channable. The opinions expressed in this article are the sponsor’s own.

If you’ve ever watched your best-selling product devour your entire ad budget while dozens of promising SKUs sit in the dark, you’re not alone.

Google’s Performance Max (PMax) campaigns have transformed ecommerce advertising since launching in 2021.

For many advertisers, PMax introduced a significant challenge: a lack of transparency in budget allocation. Without clear insights into which placements, audiences, or assets are driving performance, it’s easy to feel like you’re flying blind.

The good news? You don’t have to stay there.

This guide walks you through a practical framework for reclaiming control over your Performance Max campaigns, allowing you to segment products by actual performance and make data-driven decisions rather than hope AI figures it out for you.

Make Sure Your Product Feeds Are Ready For PMax

The Budget Black Hole: Where Your Performance Max Ad Spend Actually Goes

Most ecommerce brands start by organizing PMax campaigns around categories. Shoes in one campaign. Accessories in another. That seems logical and clean but can completely ignore how products actually perform.

Here’s what typically happens:

  • Top sellers monopolize budget. Google’s algorithm prioritizes products with strong historical performance, which means your star items keep getting the spotlight while everything else struggles for visibility.
  • New arrivals never get traction. Without performance history, fresh products can’t compete, so they never build the data they need to succeed.
  • “Zombie” products stay invisible. Some items might perform well if given the chance, but static segmentation never gives them that opportunity.
  • Manual adjustments eat your time. Every tweak requires you to dig through data, make changes, and hope for the best.

The result? Wasted potential, uneven budget distribution, and marketing teams stuck reacting instead of strategizing. You’re already doing the hard work; this framework helps that effort go further and helps you set and manage your PPC budget efficiently and effectively.

How To Fix It: Segment Campaigns By What’s Actually Working

Instead of organizing campaigns by category, segment by how products actually perform.

This approach creates dynamic groupings that automatically shift as performance data changes with no manual reshuffling.

Step 1: Classify Your Products into Three Groups

Start by categorizing your catalogue based on real performance metrics: ROAS, clicks, conversions, and visibility.

Image created by Channable, January 2026

Star Products

These are your proven winners, with high ROAS, strong click-through rates, and consistent conversions. Your goal with stars is to maximize their potential while protecting margins.

  • Set higher ROAS targets (3x–5x or above based on your margins).
  • Allocate budget confidently.
  • Monitor to ensure profitability stays intact.

Zombie Products

These are the “invisible” items that haven’t had enough exposure to prove themselves. They might be underperformers, or they might be hidden gems waiting for their moment.

  • Set lower ROAS targets (0.5x–2x) to prioritize visibility.
  • Give them a dedicated budget to gather performance data.
  • Review regularly and promote graduates to the star category.

New Arrivals

Fresh products need their own ramp-up period before being judged against established items. Without historical data, they can’t compete fairly in a mixed campaign.

  • Create a separate campaign specifically for new launches.
  • Use dynamic date fields to automatically include recently added items.
  • Set goals focused on awareness and data collection rather than immediate ROAS.

Step 2: Define Your Performance Thresholds

Decide what metrics determine which bucket a product falls into. For example:

  • Stars: ROAS above 3x–5x, strong click volume, goal is maximizing profitability.
  • Zombies: ROAS below 2x or insufficient data, low click volume, goal is testing and learning.
  • New Arrivals: Date-based (for example, added within last 30 days), goal is building visibility.

Your thresholds will depend on your margins, industry, and historical benchmarks. The key is defining clear criteria so products can move between segments automatically as their performance changes.

Step 3: Shorten Your Analysis Window

Many advertisers’ default to 30-day lookback windows for performance analysis. For fast-moving catalogues, that’s too slow.

Consider shifting to a 14-day rolling window for better analysis. You’ll get:

  • Faster reactions to performance shifts
  • More accurate data for seasonal or trending items
  • Less wasted spend on products that peaked two weeks ago

This is especially important for fashion, home goods, and any category where trends move quickly.

Step 4: Apply Segmentation Across All Channels

Your segmentation logic shouldn’t stop at Google. The same star/zombie/new arrival framework can (and should) apply to:

  • Meta Ads
  • Pinterest
  • TikTok
  • Criteo
  • Amazon

Cross-channel consistency compounds your optimization efforts. A product that’s a “zombie” on Google might be a star on TikTok, or vice versa. Unified segmentation helps you connect products to the right audiences on the right channels and distribute budget accordingly.

Step 5: Build Rules That Move Products Automatically

Here’s where the real efficiency gains come in. Instead of manually reviewing every SKU, create rules that automatically shift products between campaigns based on performance.

For example:

  • If ROAS exceeds 3x–5x over your analysis window – Move to Stars campaign
  • If ROAS falls below 2x or clicks drop below your average (for example, 20 clicks in 14 days) – Move to Zombies campaign
  • If product was added within a set time limit (for example, the last 30 days) -Include in New Arrivals campaign

This dynamic automation ensures your campaigns stay optimized without requiring constant manual intervention.

Get Smart: Let Intelligent Automation Do the Heavy Lifting

Image created by Channable, January 2026

The steps above work—but implementing them manually across thousands of SKUs and multiple channels is time-consuming. Product-level performance data lives in different dashboards. Calculating ROAS at the SKU level requires combining data from multiple sources. And building automation rules from scratch takes technical resources most teams don’t have.

This is where the right use of feed management and the right use of PPC automation really helps. For example, it can merge product-level performance data into a single view and let you build rules that automatically segment products based on criteria you define.

To see what this looks like in practice, Canadian fashion retailer La Maison Simons offers a useful reference point. They faced the same challenges-category-based campaigns where top sellers consumed the budget while newer items never gained traction.

After shifting to performance-based segmentation, they saw measurable improvements without increasing ad spend:

  • ROAS nearly doubled over a three-year period
  • Cost-per-click decreased while click-through rates improved
  • Average order value increased by 14%
  • Their dedicated new arrivals campaigns consistently outperformed expectations
  • Perhaps most notably, their previously “invisible” products became some of their strongest performers once they received dedicated visibility

The takeaway isn’t about any single tool, it’s that performance-driven segmentation works. When you stop letting one popular item take all the budget and start giving every product a fair shot based on data, the results tend to follow.

Learn more about the success story and the full details of their approach here.

Quick Principles to Keep in Mind

Image created by Channable, January 2026
  • Segment by performance, not category: Budget flows to what works, not what’s familiar
  • Use 14-day windows for fast-moving catalogues: Capture fresher signals, reduce wasted spend
  • Give new products their own campaign: Build data before judging against established items
  • Automate product movement between segments: Save time and stay responsive without manual work
  • Apply logic across all paid channels: Compounding optimization across Google, Meta, TikTok, and more

Your Next Step

Performance Max doesn’t have to feel like handing Google your wallet and hoping for the best. With the right segmentation strategy, you can restore control, surface overlooked opportunities and make smarter decisions about where your budget goes.

Curious whether your product data is ready for this kind of optimization? A free feed and segmentation audit can help you find gaps and opportunities, no commitment, just clarity.

Because better data leads to better decisions. And better decisions lead to results you can actually control.

GET MY FREE FEED AUDIT

Image Credits

Featured Image: Image by Channable Used with permission.

In-Post Images: Images by Channable. Used with permission.

Ecommerce MGMT 0 Comments
Jan 14 2026
Google: AI Mode Checkout Can’t Raise Prices via @sejournal, @MattGSouthern

Google is disputing claims that its new AI-powered shopping checkout work could enable what critics describe as “surveillance pricing” or other forms of overcharging.

The back-and-forth started after Lindsay Owens, executive director of consumer economics think tank Groundwork Collaborative, criticized Google’s newly announced Universal Commerce Protocol and pointed to language in its public roadmap about “cross-sell and upsell modules.”

U.S. Sen. Elizabeth Warren amplified the criticism, saying Google is “using troves of your data to help retailers trick you into spending more money.”

Google’s corporate account News from Google replied that the claims “around pricing are inaccurate,” adding that merchants are prohibited from showing higher prices on Google than what appears on their own sites.

What Triggered The Back-And-Forth

Owens wrote on X that Google’s announcement about integrating shopping into AI Mode and Gemini included “personalized upselling,” which she described as “analyzing your chat data and using it to overcharge you.”

Warren then reposted Owens’ thread and echoed the allegation in stronger terms, calling it “plain wrong” that Google would use user data to help retailers “trick you into spending more money.”

Google responded publicly on X with a thread disputing the premise.

News from Google wrote on X:

“These claims around pricing are inaccurate. We strictly prohibit merchants from showing prices on Google that are higher than what is reflected on their site, period.”

Google also addressed the “upselling” term directly:

“The term ‘upselling’ is not about overcharging. It’s a standard way for retailers to show additional premium product options that people might be interested in.”

And it added that “Direct Offers” can only move in one direction:

“‘Direct Offers’ is a pilot that enables merchants to offer a lower priced deal or add extra services like free shipping … it cannot be used to raise prices.”

Where “Upsell Modules” Shows Up

The language critics are pointing to is in the Universal Commerce Protocol roadmap, which lists “Native cross-sell and upsell modules” as an upcoming initiative, described as enabling “personalized recommendations and upsells based on user context.”

Separately, Google’s technical write-up on UCP says AI shopping experiences need support for things like “real-time inventory checks, dynamic pricing, and instant transactions” within a conversational context. The “dynamic pricing” phrasing is broad, but it is part of what critics are interpreting through a consumer protection lens.

Google’s Ads & Commerce blog post presents UCP as covering the entire shopping journey, linking it to AI Mode and Gemini, while emphasizing that retailers stay the seller of record.

Why This Matters

I have covered Google’s price accuracy enforcement going back years, including Merchant Center policies meant to prevent situations where a shopper sees one price and gets a higher one at checkout. That history is why the “prices on Google versus prices on your site” line is doing so much work in Google’s response.

The bigger picture is that Google is trying to turn AI Mode and Gemini into places where product discovery can end with a transaction. When that happens, the conversation stops being purely about relevance and starts being about pricing rules, disclosures, and what “personalization” means in practice.

Looking Ahead

If this becomes another layer of feed requirements and policy edge cases, retailers will feel it immediately. If it reduces drop-off between product discovery and checkout, Google will likely push harder to make it a default part of AI Mode shopping.

Featured Image: zikg/Shutterstock

Ecommerce MGMT 0 Comments
Jan 12 2026
Google’s UCP Checkout Brings New Tradeoffs For Retailers via @sejournal, @MattGSouthern

When Google announced that shoppers could complete purchases directly in AI Mode, the focus was on convenience and technical capability. A retailer who emailed Search Engine Journal raised different questions about what gets lost when the transaction moves to Google’s surfaces.

The retailer cited concerns that customers never visit the store, see accessory recommendations from other sellers, and lose brand connection when making purchases on Google.

The concern shows a tradeoff in Google’s Universal Commerce Protocol. Retailers gain potential access to customers at the moment of purchase intent. However, they may lose some of the brand environment, discovery patterns, and relationship-building that occur when shoppers visit owned sites.

What Changes When Checkout Leaves Your Site

The change affects several parts of how retailers interact with customers.

Cross-selling

Cross-selling may change shape. A customer buying a camera on your site might see lens recommendations, memory cards, or cases based on your merchandising strategy.

Google says it plans to add capabilities like discovering related products, applying loyalty rewards, and powering custom shopping experiences on Google, but it hasn’t detailed reporting, fees, or data-sharing for AI Mode checkout.

If loyalty rewards, saved preferences, and checkout work more smoothly on Google surfaces, some shoppers may prefer that experience even if retailers have less control over it. Whether that tradeoff benefits retailers depends on details Google hasn’t disclosed yet.

Brand Connection

Brand storytelling can get compressed into whatever product data feeds into Google’s systems. Retailers invest in site design, content, and navigation to communicate what makes them different. That investment may not fully transfer when the interaction happens in AI Mode’s standardized interface.

The customer relationship dynamics change. Retailers traditionally owned the full transaction flow: discovery, consideration, purchase, and post-purchase communication. For orders completed inside AI Mode, Google would host more of the discovery and checkout experience on its own surfaces, while retailers remain the seller of record.

The degree to which retailers can access customer journey data that normally informs merchandising and marketing is unknown.

The Amazon Parallel

The situation resembles dynamics that already exist with Amazon marketplace sellers. Third-party sellers on Amazon get access to massive customer traffic. Marketplace sellers often accept less control over the customer experience and limited access to relationship signals compared with selling on their own sites.

Google’s protocol creates similar dynamics but extends them across the open web rather than within a single marketplace. Google positions UCP as an open standard, in contrast to Amazon’s closed marketplace model. The key difference: Amazon requires sellers to list products on its platform. UCP lets Google insert checkout capabilities into AI Mode while products technically remain on participating retailers’ inventory systems.

Whether that distinction leads to more data for retailers or a different platform dependency depends on reporting and data-sharing details Google hasn’t specified.

When It Makes Sense, When It Doesn’t

Some retail business models rely heavily on price, convenience, and fulfillment speed. For these retailers, losing the site visit may matter less if UCP delivers customers when they’re ready to buy.

Other retailers compete on curation, brand experience, and discovery. A customer visiting a specialty outdoor gear retailer expects to explore complementary products, read buying guides, and engage with brand content. Moving more of the purchase flow onto Google surfaces could reduce how much of that value proposition happens on a retailer’s site.

The calculation also depends on customer acquisition costs. For example, if you’re paying $30 to acquire a customer through Google Ads and they buy a $50 product on your site, the unit economics work when you can cross-sell or build long-term relationship value. If checkout happens on Google’s surface and you can’t cross-sell or retarget, the same acquisition cost may not be worth it.

What’s Known Versus What’s Speculation

Google said eligible U.S. retailers will be able to participate in UCP checkout through AI Mode in Search and the Gemini app. Google says retailers remain the seller of record and can customize the integration.

A separate Google Developers blog post explains that merchants remain the Merchant of Record and highlights an embedded option for a customized checkout experience. But the announcement didn’t detail the data-sharing arrangement, fee structure, or the funnel-level reporting retailers will receive for AI Mode checkout events.

The protocol is described as “open,” but adoption requirements, integration complexity, and whether non-Google AI systems can use it are unclear.

Google’s Business Agent feature demonstrates one use of the new protocol: branded AI chat appears in Search results for participating retailers, but the interaction occurs on Google’s platform.

Some analysts frame the change as existential, using terms like “extinction event” for certain retail models. That’s based on assumptions about adoption rates, customer behavior, and competitive dynamics that haven’t played out yet.

The more measured question retailers are asking: Does this create fragmentation where they need to optimize for multiple checkout flows, or consolidation where Google becomes the dominant transaction layer for product searches?

Questions Without Clear Answers

Three implementation details will likely determine how disruptive AI Mode checkout becomes for retailers:

  1. Merchant Center control: whether participation is explicitly opt-in and retailers can limit checkout to specific products or categories.
  2. Measurement: what reporting retailers get for actions on Google surfaces and whether AI Mode orders can be distinguished from standard site conversions.
  3. Customer and journey data: what signals, if any, come back to retailers to support lifecycle marketing and merchandising decisions.

Google has outlined the direction for UCP but hasn’t detailed these operational components.

Looking Ahead

Google said UCP checkout will roll out to eligible U.S. retailers soon, but hasn’t provided specific timing. Business Agent, which puts branded AI chat on Search results, went live Jan. 12.

Retailers questioning the tradeoffs between visibility and control face a pattern that’s played out before with Amazon, Google Shopping, and social commerce. Early participants gain access to new traffic sources but accept platform rules they don’t control. Late adopters may find themselves at a disadvantage.

The core question several retailers have raised is: Can they maintain the brand differentiation and relationship-building that justified creating owned channels when the transaction occurs on someone else’s platform?

The protocol is too new to know yet.

Featured Image: michnik101/Shutterstock

Ecommerce MGMT 0 Comments
Jan 12 2026
Agentic Commerce: What SEOs Need To Consider (ACP & UCP) via @sejournal, @alexmoss

In my last post, I referenced how there is now a growing split between the “human” web and the “agentic” web, where AI agents are becoming an additional audience/profile alongside the “traditional” human visitors we have been optimizing for for years.

This shift is now becoming more aggressive, especially when it comes to the transactional web in the form of agentic commerce. 2026 will see the accelerated adoption of this method, where store owners will now have to cater to and optimize for both the human and agentic visitor concurrently.

The recent launch of Universal Commerce Protocol (UCP) from Google underlines the push towards this integration of AI and ecommerce experiences.

What Is Agentic Commerce?

Agentic commerce is when agents complete purchases autonomously on behalf of users. Now, a human can engage with a large language model platform, where the agent will browse and purchase from a site on behalf (and with approval) of the human. Not only is the agent acting as the gatekeeper for information gain and influencing decisions, but they are also acting as the gatekeeper for the transaction itself.

This is a step beyond delegating an LLM to act as a recommendation agent or a method of validation, but now transfers authority to actually transact.

Enter ACP (Agentic Commerce Protocol)

On Sept. 29, 2025, OpenAI and Stripe announced their partnership and, within this, launched ACP, an open standard that defines how AI agents, merchants, and payment providers interact to complete agentic and programmatic purchases.

On the same day, OpenAI detailed platforms that were immediately able to benefit from agentic commerce, including Shopify and Etsy, with others following suit using the protocol, including Walmart and Instacart.

From a CMS point of view, Shopify hit the ground running by enabling ACP for over 1 million merchants from the day of the announcement. WooCommerce has followed suit more recently by announcing it will be part of Stripe’s launch of Agentic Commerce Suite, which will allow even more merchants the ability to sell products through various AI-based platforms.

But ACP was launched three months ago, and as we now know, things move fast…

UCP: Google’s Answer To The Immersive Agentic Commerce Experience

Google just announced the launch of Universal Commerce Protocol, which widens some boundaries applied by ACP by tackling a broader problem, providing any AI surface (like Search AI Mode or Gemini) a common language to discover merchants, understand their capabilities, and orchestrate full journeys from discovery through order management, as well as engagement beyond a purchase (also made seamless using Google Pay). This is also done by integrating with other existing standards, including APIs, Agent2Agent (A2A), and the Model Context Protocol (MCP).

Aspect ACP (OpenAI) UCP (Google)
Primary focus Agent‑led commerce in ChatGPT and ACP‑aware agents.​ Unified rail for many agents/surfaces talking to merchants.
Journey Coverage Product feed, checkout, fulfillment, delegated payment. Discovery, checkout, discounts, fulfillment, order management, payments.
Driver OpenAI + Stripe & ecosystem partners. Google + retailers/platforms (Shopify, Etsy, Walmart, etc.).

Here, Google adds to the possibilities of the commerce experience, where SEOs can adopt both ACP and UCP in order to accommodate both platforms and ecosystems.

This will only become more immersive as 2026 progresses. Google has a great advantage of knowing a lot about individual users, and features such as AI features inside Gmail illustrate Google can utilize and understand much more context about individuals in order to provide an even more frictionless experience.

Why This Matters For SEOs

As SEOs, we’ve spent over a generation optimizing for humans, albeit for various personas or ICPs. While we are still required to do this, we must now include the agent as an additional consideration. This does pose another challenge: that AI agents don’t browse pages but instead query APIs, parse product feeds, and evaluate structured data.

As such, we need to optimize for this. Maybe I can give it a name…

ACO: Agentic Commerce Optimization

I don’t want to trigger you by introducing yet another acronym to what seems to be a previous year of new acronyms, but for the sake of this post, let’s pretend that ACO is something you’ve been told to do now, as well as SEO, even though this is still SEO.

What would I need to consider and optimize for for successful ACO?

  • Crawlability: Agents still follow links, take journeys, and understand IA.
  • Format: Content needs to be concise with less fluff, but enough to ensure unique value has been added, and that it provides consistency throughout the site as a whole.
  • Structured Data: Agents will become more reliant on existing standards, especially if they’re open source.
  • Brand Authority And Sentiment: Populating your products well is, of course, paramount, but without positive brand sentiment, you have the challenge of convincing the agent to cite you as part of that discovery, then have to convince the human who will have that feedback presented to them. Third-party perspectives will become a larger contribution towards some of the agents’ grounding procedures before any agentic commerce begins.

Sounds familiar, right? While ACP is a connector between your site and the platforms that allow agents to use it, and CMSs are out there to make that connection as seamless as possible, this isn’t just a switch where, when switched on, is automatically optimized.

ACO = SEO.  

Schema.org Is The Glue

Pascal Fleury presenting structured data options at Search Central Live Zurich December 2025
Image Credit: Alex Moss, January 2026

Last month at Google Search Central Live in Zurich, Pascal Fleury went into detail about structured data for Shopping, where we can see that, while “schema.org is the glue that holds [structured data] together,” there are still other industry standards, such as GS1, that will add even more granular detail to products that will not only help inform agents on really specific details but also understand that you’re a great source of information to continue ingest from.

Product schema, pricing, availability, reviews, FAQs, shipping options, and other logistics, loyalty schemes –  all of this structured data will need close optimization. If it’s missing or incorrect, you’re invisible to agent-mediated discovery.

Test The Agents

Even before your store is ACP-enabled, test how agents perceive your products. Ask platforms about products in your category. Do they surface your brand? How do they describe your products and complementary offerings? What information are they presenting, from both first-party and third-party perspectives? And more importantly, what is missing that you expected to be present?

Then, enable. What are the differences? Compare the results.

What Can I Do About It Now?

ACP

For WooCommerce and Wix, you will unfortunately need to join Stripe’s waitlist for ACS. Shopify users also have to join their own waitlist. Until then, we will have to wait until full rollout, but expect this to accelerate in Q1 of 2026.

If you work with a site where you have to integrate ACP directly into your CMS, any early adopters will perhaps benefit from early discovery, while the other CMSs catch up and competition is lower. So here, while this will require more resources, you will be able to take advantage of what ACP has to offer while most wait for their CMS platform to create the solution for them.

UCP

This is extremely fresh information, but I suggest that some time to understand it in detail, as well as experiment where possible using their documentation and GitHub repo, I know that’s how a lot of my time will be spent in the next few weeks.

More Resources:

Featured Image: Koupei Studio/Shutterstock

Ecommerce MGMT 0 Comments