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

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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

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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

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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
Jan 11 2026
Google Announces AI Mode Checkout Protocol, Business Agent via @sejournal, @MattGSouthern

Google announced tools that let shoppers complete purchases directly within AI Mode and chat with branded AI agents in Search results.

Users can purchase from eligible product listings on Google. Retailers are still the seller of record, while the checkout happens on Google surfaces instead of the retailer’s website.

Universal Commerce Protocol Powers AI Mode Checkout

Google launched the Universal Commerce Protocol, an open standard for what it calls “agentic commerce.” The protocol will power checkout on eligible Google product listings in AI Mode in Search and the Gemini app.

Google developed UCP with Shopify, Etsy, Wayfair, Target, and Walmart. More than 20 additional companies endorsed it, including Adyen, American Express, Best Buy, Mastercard, Stripe, The Home Depot, and Visa.

Shoppers will use Google Pay with payment methods and shipping info from Google Wallet. PayPal support is coming. UCP checkout starts with eligible U.S. retailers, with global expansion planned.

Business Agent Brings Branded Chat To Search

Business Agent lets shoppers chat with brands in Search results. Google describes it as a “virtual sales associate” that can answer product questions in the brand’s voice.

The feature goes live January 12 with Lowe’s, Michael’s, Poshmark, Reebok, and others. Eligible U.S. retailers can activate and customize the agent through Merchant Center.

Google plans to add capabilities for training agents on retailer data, providing product offers, and enabling purchases within the chat experience.

Direct Offers Pilot Tests Ads In AI Mode

Google also announced Direct Offers, a new ad pilot in AI Mode. It allows advertisers to offer exclusive discounts to people searching for products.

Google gave an example of a rug search where relevant retailers could feature a special 20% discount. Retailers set up offers in campaign settings, and Google determines when to display them.

Early partners include Petco, e.l.f. Cosmetics, Samsonite, Rugs USA, and Shopify merchants.

Why This Matters

Checkout in AI Mode means a user searching for a product can research, compare, and buy without ever reaching the retailer’s site.

For ecommerce sites, this changes the traffic equation. The sale still happens, but the site visit may not. Retailers participating in UCP gain access to high-intent buyers at the moment of decision. Those who don’t participate may find their products harder to surface when users expect to complete transactions without leaving Google.

Looking Ahead

Checkout in AI Mode rolls out to eligible U.S. retailers soon. Business Agent launches January 12. Direct Offers is in pilot with select advertisers.

Google said it plans to add new Merchant Center data attributes designed for discovery in AI Mode, Gemini, and Business Agent. The company will roll out the new attributes with a small group of retailers soon before expanding more broadly.

Featured Image: hafakot/Shutterstock

Ecommerce MGMT 0 Comments
Nov 20 2025
How To Manage Demand Fluctuation During Key Ecommerce Shopping Seasons via @sejournal, @brookeosmundson

Ecommerce demand doesn’t rise and fall in a straight line throughout the years.

It can build gradually, spike hard, stall, or drop with little-to-no warning. During peak shopping periods like Black Friday, Cyber Monday, Prime Day(s), Back-to-School, these swings become even more intense.

For PPC marketers, that volatility affects far more than just traffic or CPCs. It influences bidding strategies, budgets, inventory planning, campaign structures, and even internal operations.

Managing demand fluctuation isn’t just about “spending more when demand is high.” It’s also about knowing when demand is coming, preparing your accounts before the surge, staying in control while competition rises, and stabilizing performance after the peak ends.

It means understanding that marketing decisions affect logistics and profitability, not just vanity metrics like impression share.

This article will walk you through how to manage demand in a way that improves performance and protects the business across each phase of the season.

1. Understand And Anticipate Seasonal Demand

Predictable seasonal spikes are only predictable if you know what to look for.

Demand rarely appears out of nowhere. It ramps up gradually. The marketers who recognize early changes in behavior are the ones who scale at the right time instead of reacting too late.

Start with historical data from your own account. Look at when impressions and clicks began to rise last year, not just when the holiday officially started.

Compare year-over-year and week-over-week trends to identify whether demand is starting earlier. In many industries, consumers begin researching long before they’re ready to buy, which means waiting until “the big day” is too late to build momentum.

Conversion lag is another signal. If your data shows it normally takes five days from first click to purchase, and your promo begins on Friday, you need to start increasing budget earlier in the week. Otherwise, you’ll miss buyers who started the journey before the event.

Don’t ignore external factors. Shipping cutoff dates, competitor promotions, weather trends, and even economic sentiment can accelerate or delay demand. The data in the platform only shows part of the picture, while market behavior provides the context.

Forecasting is also critical. Even a simple model based on past revenue, impression share, and growth targets can help you determine expected demand and budget requirements.

This helps create a baseline so you can recognize when performance is ahead or behind expectations and adjust accordingly.

2. Align Bids And Budgets With Demand

Once demand starts building, your bidding and budgeting strategy must evolve with it. This is where many marketers either scale too slowly and miss opportunity. On the opposite side, you scale too aggressively and burn through budget prematurely.

If you’re using Smart Bidding, seasonality adjustments in Google Ads or Microsoft Ads can help the algorithm prepare for a short-term spike that differs from typical trends. These are best used for specific, limited windows (e.g., a 3-day flash sale) rather than entire multi-week seasons.

When demand returns to normal, remove the adjustment so the system doesn’t keep bidding too high in a softening market.

Target settings also matter. A tROAS (Target Return on Ad Spend) goal that works during regular pricing may be too restrictive during steep discounts. Likewise, a CPA goal may need to be relaxed slightly if conversion rates are temporarily lower but lifetime value remains strong.

In some cases, switching to a “Maximize” strategy gives the system more flexibility to capture demand efficiently, especially when intent is high and margin is acceptable.

If using “Maximize Conversions” (or Conversion Value), you could set more flexible bid limits to let the algorithm know you’re willing to pay more for conversions without letting it go haywire and have a mind of its own.

Budgets require just as much attention as bids. If campaigns are capping out early in the day, you’re likely missing high-intent shoppers later. Increasing budgets, reallocating across campaigns, or adjusting bids to stretch delivery can help you maintain visibility during peak hours. Shared budgets can also allow strong-performing categories to pull in more spend without manual intervention.

Scaling back after the surge is equally important. Abrupt budget cuts or major bid changes can disrupt algorithmic learning. Gradual reductions give the system time to recalibrate as demand normalizes.

3. Keep Product Availability And Campaign Structures Aligned

Even the best campaign strategy falls apart if product availability isn’t properly managed.

During peak shopping seasons, inventory can change rapidly. If feeds don’t update quickly, ads may continue promoting items that are low or out of stock. This leads to wasting spend and hurting customer experience.

Be sure to increase your feed update frequency during high-demand periods. This could mean multiple syncs per day if possible.

Ensure that price, availability, and shipping information are accurate. If your platform or feed tool allows real-time inventory updates, take advantage of it.

Custom labels in your feed are one of the most valuable seasonality tools. Try labeling your products by margin, best seller status, promotion type, limited stock, or seasonality. This allows you to structure campaigns around business priorities, not just categories or sub-types.

For example:

  • Increase bids on high-margin or high-conversion products
  • Lower bids or pause products with low inventory
  • Separate promotional items so they receive dedicated budgets and messaging

Performance Max and Shopping campaigns require even more attention. In my experience, it’s common to see PMax concentrate budget on a narrow slice of the catalog while other SKUs receive little to no impression share.

If that pattern doesn’t match your merchandising goals, segmenting high-priority product groups and tightening feed signals usually helps. If you don’t segment campaigns thoughtfully or monitor product-level performance, the algorithm may stall.

Consider using a mix of Standard Shopping and PMax when you need more control over key seasonal categories. Standard Shopping can provide the structure you need, while PMax can help with scaling.

Just make sure they serve different roles to avoid internal competition.

Campaign structure should work hand-in-hand with inventory strategy. The goal is to ensure your best products get visibility when demand spikes and that you don’t waste spend on items you can’t fulfill.

4. Work With Internal Teams During Peak Demand

In normal months, PPC managers can operate with relative independence.

During major retail seasons, that approach can create problems.

Demand fluctuation affects far more than media spend. It touches logistics, merchandising, pricing, site operations, and customer experience.

For example, if marketing pushes a product heavily but the warehouse can’t fulfill orders quickly enough, conversion rates could drop, and customer complaints can arise.

If a PPC offer launches a “50% off” ad before the site reflects the discount, you’ll likely pay for unqualified clicks or see conversions drop.

If inventory runs low but product promotions continue, you’ll burn budget on products that can’t convert.

During peak periods, cross-functional alignment is necessary for optimal performance. Be sure to establish regular communication with:

  • Inventory and fulfillment (stock levels, restock timelines, shipping delays).
  • Merchandising (featured products, bundles, hero SKUs).
  • Pricing and promotions (exact discount timing and margin impact).
  • Creative (messaging changes, urgency vs. value).
  • Site operations (traffic capacity, potential downtime, landing page readiness).
  • Customer service (policy changes, support volume expectations).

Even short daily syncs with these teams can prevent costly mistakes. Something as simple as a delayed shipment or pricing error can change campaign performance within hours.

When teams are aligned, marketing decisions become less reactive and more strategic.

Also, be prepared to change messaging quickly. If shipping times increase, adjust ad copy or landing page expectations. If a product is selling out fast, highlight “limited availability” or shift spend to similar in-stock alternatives.

5. Plan For Post-Peak Performance And Future Seasons

When the surge ends, the work isn’t over.

The post-peak period can feel unstable. After peak periods, I’ve experienced many advertisers observe a short re-balancing window: Conversion intent normalizes faster than bidding pressure does. This is where many marketers overreact and cut budgets too aggressively, causing campaigns to lose momentum.

Instead, treat the cooldown as a transition phase. Reset any seasonality bid adjustments. Reevaluate ROAS or CPA targets. Gradually adjust budgets to align with current demand, rather than slashing them immediately.

Shift campaign focus to retention and LTV where appropriate. Remarketing, post-purchase offers, loyalty initiatives, and subscription promotions can help turn seasonal traffic into long-term value. The conversion window doesn’t always end when the sale does.

This is also the most important time to analyze. Don’t wait weeks to reflect; be sure to capture key insights while the data is fresh.

When analyzing, ask questions like:

  • Which categories or SKUs exceeded (or missed) expectations?
  • Were budgets or bids too slow to adjust?
  • Did any campaigns cap too early in the day?
  • Were there inventory issues that hurt performance?
  • How did different bidding strategies respond under pressure?
  • What messaging/ad copy resonated best with users?
  • What would you start earlier or stop entirely next time?

Document everything. Don’t assume you’ll remember next year.

Seasonality repeats, but consumer behavior and the corresponding algorithm responses evolve every year. The teams that improve each cycle are the ones who treat post-peak as planning time, not recovery time.

Then, build your playbook for the next season. Define earlier ramp-up timing if needed. Establish bidding and budget frameworks. Create inventory and messaging coordination workflows.

When the next seasonality surge comes, you’ll be ready to scale strategically.

Sustain Stability Through Every Season

Managing demand fluctuation is more about staying in control when the market becomes unpredictable. That requires preparation, data awareness, cross-team coordination, flexible bidding and budgeting, and deliberate post-peak analysis.

Demand shifts will always happen. The difference between chaotic seasons and successful ones comes down to how well you anticipate, adapt, and learn from each cycle.

The marketers who treat seasonality as a workflow system (not an event) are the ones who can turn volatility into growth.

More Resources:

Featured Image: Roman Samborskyi/Shutterstock

Ecommerce MGMT 0 Comments
Aug 29 2025
Why WooCommerce Slows Down (& How to Fix It With the Right Server Stack)

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

Wondering why your rankings may be declining?

Just discovered your WooCommerce site has slow load times?

A slow WooCommerce site doesn’t just cost you conversions. It affects search visibility, backend performance, and customer trust.

Whether you’re a developer running your own stack or an agency managing dozens of client stores, understanding how WooCommerce performance scales under load is now considered table stakes.

Today, many WordPress sites are far more dynamic, meaning many things are happening at the same time:

  • Stores run real-time sales.
  • LMS platforms track user progress.
  • Membership sites deliver highly personalized content.

Every action a user takes, from logging in, updating a cart, or initiating checkout, relies on live data from the server. These requests cannot be cached.

Tools like Varnish or CDNs can help with public pages such as the homepage or product listings. But once someone logs in to their account or interacts with their session, caching no longer helps. Each request must be processed in real time.

This article breaks down why that happens and what kind of server setup is helping stores stay fast, stable, and ready to grow.

Why Do WooCommerce Stores Slow Down?

WooCommerce often performs well on the surface. But as traffic grows and users start interacting with the site, speed issues begin to show. These are the most common reasons why stores slow down under pressure:

1. PHP: It Struggles With High User Activity

WooCommerce depends on PHP to process dynamic actions such as cart updates, coupon logic, and checkout steps. Traditional stacks using Apache for PHP handling are slower and less efficient.

Modern environments use PHP-FPM, which improves execution speed and handles more users at once without delays.

2. A Full Database: It Becomes A Bottleneck

Order creation, cart activity, and user actions generate a high number of database writes. During busy times like flash sales, new merchandise arrivals, or course launches, the database struggles to keep up.

Platforms that support optimized query execution and better indexing handle these spikes more smoothly.

3. Caching Issues: Object Caching Is Missing Or Poorly Configured

Without proper object caching, WooCommerce queries the database repeatedly for the same information. That includes product data, imagery, cart contents, and user sessions.

Solutions that include built-in Redis support help move this data to memory, reducing server load and improving site speed.

4. Concurrency Limits Affect Performance During Spikes

Most hosting stacks today, including Apache-based ones, perform well for a wide range of WordPress and WooCommerce sites. They handle typical traffic reliably and have powered many successful stores.

As traffic increases and more users log in and interact with the site at the same time, the load on the server begins to grow. Architecture starts to play a bigger role at that point.

Stacks built on NGINX with event-driven processing can manage higher concurrency more efficiently, especially during unanticipated traffic spikes.

Rather than replacing what already works, this approach extends the performance ceiling for stores that are becoming more dynamic and need consistent responsiveness under heavier load.

5. Your WordPress Admin Slows Down During Sales Seasons

During busy periods like seasonal sales campaigns or new stock availability, stores can often slow down for the team managing the site, too. The WordPress dashboard takes longer to load, which means publishing products, managing orders, or editing pages also becomes slower.

This slowdown happens because both shoppers and staff are using the site’s resources at the same time, and the server has to handle all those requests at once.

Modern stacks reduce this friction by balancing frontend and backend resources more effectively.

How To Architect A Scalable WordPress Setup For Dynamic Workloads?

WooCommerce stores today are built for more than stable traffic. Customers are logging in, updating their carts, taking actions to manage their subscription profile, and as a result, are interacting with your backend in real time.

The traditional WordPress setup, which is primarily designed for static content, cannot handle that kind of demand.

Here’s how a typical setup compares to one built for performance and scale:

Component Basic Setup         Scalable Setup
Web Server Apache NGINX
PHP Handler mod_php or CGI PHP-FPM
Object Caching None or database transients Redis with Object Cache Pro
Scheduled Tasks WP-Cron System cron job
Caching CDN or full-page caching only Layered caching, including object cache
.htaccess Handling Built-in with Apache Manual rewrite rules in NGINX config
Concurrency Handling Limited Event-based, memory-efficient server

How To Manually Setup A Performance-Ready & Scalable WooCommerce Stack

Don’t have bandwidth? Try the easy way.

If you’re setting up your own server or tuning an existing one, are the most important components to get right:

1) Use NGINX For Static File Performance

NGINX is often used as a high-performance web server for handling static files and managing concurrent requests efficiently. It is well suited for stores expecting high traffic or looking to fine-tune their infrastructure for speed.

Unlike Apache, NGINX does not use .htaccess files. Rewrite rules, such as permalinks, redirects, and trailing slashes, need to be added manually to the server block. For WordPress, these rules are well-documented and only need to be set once during setup.

This approach gives more control at the server level and can be helpful for teams building out their own environment or optimizing for scale.

2) Enable PHP-FPM For Faster Request Handling

PHP-FPM separates PHP processing from the web server. It gives you more control over memory and CPU usage. Tune values like pm.max_children and pm.max_requests based on your server size to prevent overload during high activity.

3) Install Redis With Object Cache Pro

Redis allows WooCommerce to store frequently used data in memory. This includes cart contents, user sessions, and product metadata.

Pair this with Object Cache Pro to compress cache objects, reduce database load, and improve site responsiveness under load.

4) Replace WP-Cron With A System-Level Cron Job

By default, WordPress checks for scheduled tasks whenever someone visits your site. That includes sending emails, clearing inventory, and syncing data. If you have steady traffic, it works. If not, things get delayed.

You can avoid that by turning off WP-Cron. Just add define(‘DISABLE_WP_CRON’, true); to your wp-config.php file. Then, set up a real cron job at the server level to run wp-cron.php every minute. This keeps those tasks running on time without depending on visitors.

5) Add Rewrite Rules Manually For NGINX

NGINX doesn’t use .htaccess. That means you’ll need to define URL rules directly in the server block.

This includes things like permalinks, redirects, and static file handling. It’s a one-time setup, and most of the rules you need are already available from trusted WordPress documentation. Once you add them, everything works just like it would on Apache.

A Few Tradeoffs To Keep In Mind

This kind of setup brings a real speed boost. But there are some technical changes to keep in mind.

  • NGINX won’t read .htaccess. All rewrites and redirects need to be added manually.
  • WordPress Multisite may need extra tweaks, especially if you’re using subdirectory mode.
  • Security settings like IP bans or rate limits should be handled at the server level, not through plugins.

Most developers won’t find these issues difficult to work with. But if you’re using a modern platform, much of it is already taken care of.

You don’t need overly complex infrastructure to make WooCommerce fast; just a stack that aligns with how modern, dynamic stores operate today.

Next, we’ll look at how that kind of stack performs under traffic, with benchmarks that show what actually changes when the server is built for dynamic sites.

What Happens When You Switch To An Optimized Stack?

Not all performance challenges come from code or plugins. As stores grow and user interactions increase, the type of workload becomes more important, especially when handling live sessions from logged-in users.

To better understand how different environments respond to this kind of activity, Koddr.io ran an independent benchmark comparing two common production setups:

  • A hybrid stack using Apache and NGINX.
  • A stack built on NGINX with PHP-FPM, Redis, and object caching.

Both setups were fully optimized and included tuned components like PHP-FPM and Redis. The purpose of the benchmark was to observe how each performs under specific, real-world conditions.

The tests focused on uncached activity from WooCommerce and LearnDash, where logged-in users trigger dynamic server responses.

In these scenarios, the optimized stack showed higher throughput and consistency during peak loads. This highlights the value of having infrastructure tailored for dynamic, high-concurrency traffic, depending on the use case.

WooCommerce Runs Faster Under Load

One test simulated 80 users checking out at the same time. The difference was clear:

Scenario Hybrid Stack Optimized Stack Gain
WooCommerce Checkout 3,035 actions 4,809 actions +58%
Screenshot from Koddr.io, August 2025

LMS Platforms Benefit Even More

For LearnDash course browsing—a write-heavy and uncached task, the optimized stack completed 85% more requests:

Scenario Hybrid Stack Optimized Stack Gain
LearnDash Course List View 13,459 actions 25,031 actions +85%

This shows how optimized stacks handle personalized or dynamic content more efficiently. These types of requests can’t be cached, so the server’s raw efficiency becomes critical.

Screenshot from Koddr.io, August 2025

Backend Speed Improves, Too

The optimized stack wasn’t just faster for customers. It also made the WordPress admin area more responsive:

  • WordPress login times improved by up to 31%.
  • Publish actions ran 20% faster, even with high traffic.

This means your team can concurrently manage products, update pages, and respond to sales in real time, without delays or timeouts.

It Handles More Without Relying On Caching

When Koddr turned off Varnish, the hybrid stack experienced a 71% drop in performance. This shows how effectively it handles cached traffic. The optimized stack dropped just 7%, which highlights its ability to maintain speed even during uncached, logged-in sessions.

Both setups have their strengths, but for stores with real-time user activity, reducing reliance on caching can make a measurable difference.

Stack Type With Caching Without Caching Drop
Hybrid Stack 654,000 actions 184,000 actions -7%
Optimized Stack 619,000 actions 572,000 actions -7%
Screenshot from Koddr.io, August 2025

Why This Matters?

Static pages are easy to optimize. But WooCommerce stores deal with real-time traffic. Cart updates, login sessions, and checkouts all require live processing. Caching cannot help once a user has signed in.

The Koddr.io results show how an optimized server stack:

  • Reduces CPU spikes during traffic surges.
  • Keeps the backend responsive for your team.
  • Delivers more stable speed for logged-in users.
  • Helps scale without complex performance workarounds.

These are the kinds of changes that power newer stacks purpose-built for dynamic workloads like Cloudways Lightning, built for real WooCommerce workloads.

Core Web Vitals Aren’t Just About The Frontend

You can optimize every image. Minify every line of code. Switch to a faster theme. But your Core Web Vitals score will still suffer if the server can’t respond quickly.

That’s what happens when logged-in users interact with WooCommerce or LMS sites.

When a customer hits “Add to Cart,” caching is out of the picture. The server has to process the request live. That’s where TTFB (Time to First Byte) becomes a real problem.

Slow server response means Google waits longer to start rendering the page. And that delay directly affects your Largest Contentful Paint and Interaction to Next Paint metrics.

Frontend tuning gets you part of the way. But if the backend is slow, your scores won’t improve. Especially for logged-in experiences.

Real optimization starts at the server.

How Agencies Are Skipping The Manual Work

Every developer has a checklist for WooCommerce performance. Use NGINX. Set up Redis. Replace WP-Cron. Add a WAF. Test under load. Keep tuning.

But not every team has the bandwidth to maintain all of it.

That’s why more agencies are using pre-optimized stacks that include these upgrades by default. Cloudways Lightning, a managed stack based on NGINX + PHP-FPM, designed for dynamic workloads is a good example of that.

It’s not just about speed. It’s also about backend stability during high traffic. Admin logins stay fast. Product updates don’t hang. Orders keep flowing.

Joe Lackner, founder of Celsius LLC, shared what changed for them:

“Moving our WordPress workloads to the new Cloudways stack has been a game-changer. The console admin experience is snappier, page load times have improved by +20%, and once again Cloudways has proven to be way ahead of the game in terms of reliability and cost-to-performance value at this price point.”

This is what agencies are looking for. A way to scale without getting dragged into infrastructure management every time traffic picks up.

Final Takeaway

WooCommerce performance is no longer just about homepage load speed.

Your site handles real-time activity from both customers and your team. Once a user logs in or reaches checkout, caching no longer applies. Each action hits the server directly.

If the infrastructure isn’t optimized, site speed drops, sales suffer, and backend work slows down.

The foundations matter. A stack that’s built for high concurrency and uncached traffic keeps things fast across the board. That includes cart updates, admin changes, and product publishing.

For teams who don’t want to manage server tuning manually, options like Cloudways Lightning deliver a faster, simpler path to performance at scale.

Use promo code “SUMMER305” and get 30% off for 5 months + 15 free migrations. Signup Now!

⚡ Power Up My WooCommerce Store

Image Credits

Featured Image: Image by Cloudways. Used with permission.

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

Ecommerce MGMT 0 Comments
Jul 10 2025
OpenAI Quietly Adds Shopify As A Shopping Search Partner via @sejournal, @martinibuster

OpenAI has quietly added Shopify as a third-party search partner to help power their shopping search, which shows shopping-rich results. The addition of Shopify was not formally announced, but quietly tucked into OpenAI ChatGPT search documentation.

Shopify Is An OpenAI Search Partner

Aleyda Solís (LinkedIn profile) recently noticed that OpenAI had updated their Search documentation to add Shopify to the list of third party search providers.

She posted:

“Ecommerce sites: I’ve found that Shopify is listed along with Bing as a ChatGPT third-party search provider! OpenAI added Shopify along with Bing as a third-party search provider in their ChatGPT Search documentation on May 15, 2025; a couple of weeks after their enhanced shopping experience was announced on April 28.”

OpenAI Is Showing Merchants From Multiple Platforms

OpenAI shopping search is returning results from a variety of platforms. For example, a search for hunting dog supplies returns sites hosted on Shopify but also Turbify (formerly Yahoo Stores)

Screenshot Showing Origin Of OpenAI Shopping Rich Results

The rich results with images were sourced from Shopify and Amazon merchants for this specific query.

At least one of the shopping results listed in the Recommended Sellers is a merchant hosted on the Turbify ecommerce platform:

Screenshot Of OpenAI Recommended Retailers With Gun Dog Supply, Hosted On Turbify Platform

OpenAI Shopping Features

OpenAI recently rolled out shopping features for ChatGPT Search. Products are listed like search results and sometimes as rich results with images and other shopping related information like review stars.

ChatGPT Search uses images and structured metadata related to prices and product description, presumably Schema structured data although it’s not explicitly stated. ChatGPT may generate product titles, descriptions, and reviews based on the data received from third-party websites and sometimes may generate summarized reviews.

Merchants are ranked according to how the merchant data is received from third-party data providers, which at this point includes Bing and Shopify.

Ecommerce stores that aren’t on Shopify can apply to have their products included in OpenAI’s shopping results. Stores that want to opt in must not be opted out of OpenAI’s web crawler, OAI-SearchBot .

Featured Image by Shutterstock/kung_tom

Ecommerce MGMT 0 Comments