Second in a series on building stronger AI-driven commerce with Rithum

At a glance  

• AI shopping agents evaluate your product data before they evaluate your brand. Incomplete or poorly structured catalogs get excluded from recommendations before a shopper sees them.  
• Most brands still rely on AI platforms scraping their product pages. Direct, structured feeds give you control over how products are represented inside AI environments.  
• Reaching an AI response and earning the recommendation are different problems. Without visibility into AI platform performance, improving placement becomes guesswork.  
• Payment infrastructure inside AI shopping environments is being built now. Brands that address the earlier stages will be positioned to capture those transactions as they scale.  

AI agents are already shaping what consumers see and buy. 70% of consumers have used an LLM to shop in the last three months, and 19% are now purchasing from brands they’d never encountered before those recommendations. But when it comes to making sure your products appear in those results, you’re operating against a black box. Are consumers seeing your products? Are they clicking? How will they purchase in an AI-driven environment? 

Demystifying that black box comes down to four stages: AI-ready product data, LLM connection, monitoring and optimization for AI engines, and in-LLM payments. Each one feeds the next. A gap at any stage means a leaky bucket for everything that follows. 

With Rithum, you can address each stage of the agentic commerce funnel today and prepare for where the space is heading next. 

Bad catalog data keeps products out of AI recommendations

AI shopping agents match product attributes against a shopper’s query. Missing specifications, inconsistent formatting, or outdated inventory signals knock products out of results entirely. 

When an AI assistant returns incorrect product information, shoppers blame your brand. Catalog quality becomes a brand trust issue, not just an operational one. Find out exactly what it’s costing you. 

The work on catalog structure, attribute coverage, and category alignment usually happens earlier in this process. Tools like Catalog Assist and Magic Mapper focus on those areas, handling attribute gaps and cross-channel categorization so product data is usable across AI-driven environments. With your catalog complete, structured, and current, you can tackle the remaining stages of the funnel with reliable inputs. 

Scraped data adds risk you cannot control  

AI platforms still rely heavily on crawling websites, marketplace listings, and third-party sources to assemble product information. Inconsistencies follow. Pricing, availability, and product descriptions can all drift away from the current state of your catalog.  

When that happens, the AI response reflects whatever information it was able to gather, not the current reality of your inventory. Very few shoppers click through to verify those details elsewhere, which turns the AI output into the primary version of the product. 

Rithum replaces that with direct, structured feeds into AI platforms. Rithum’s ChatGPT and Perplexity Feeds get your product data live and accurate on LLMs in three steps: your data is compiled into a feed, that feed is optimized for LLMs, then delivered directly for ingestion. Your brand owns its presence on LLMs instead of leaving it to crawlers. 

Rithum’s Stripe partnership extends this further by allowing brands to connect once and distribute product data across multiple AI platforms as they come online. Instead of building new integrations for each new surface, you can test across an assortment of LLMs and understand the ROI, all while keeping your product data updated and aligned. 

Getting into the system is not the same as getting selected  

AI-generated responses return a limited set of recommendations. Products compete for inclusion in that shortlist, and small differences in product data, relevance, or confidence signals can determine which products appear. 

Your feed is not a set-it-and-forget-it deliverable. You need to understand how your products are ranking across AI platforms and how those rankings shift over time.  

Our upcoming GEO (generative engine optimization) capabilities provide a way to track how products appear, move, and compare within AI-driven results. 

But monitoring only addresses half of the equation. Once you understand how you’re ranking, you need a way to improve those rankings. 

Rithum’s upcoming Performance Lab translates those signals into specific optimizations to improve how products appear in LLMs. Between GEO and Performance Lab, brands can move from “live but invisible in recommendations” to earning placement where it actually drives revenue. 

Because Rithum connects monitoring and catalog management in one place, you can act on performance signals directly. No exporting data, no cross-referencing tools, no guessing what to fix. There are plenty of myths about how agentic AI actually works. One of the most costly is assuming that presence alone drives results. 

Agentic checkout infrastructure is taking shape  

Payment is the final stage of the funnel: a shopper completes a purchase inside the same environment where they found the product. 

The current state of in-LLM checkout is uneven. Some platforms are testing in-conversation transactions, others are still building toward it, and some LLMs, including ChatGPT, have moved away from native in-chat checkout toward third-party app integrations instead. 

Rithum’s Stripe partnership provides the infrastructure for this layer. Product data flows from Rithum into AI platforms. When a transaction occurs, Stripe processes the payment while Rithum handles the necessary inventory updates and order orchestration. The brand stays the merchant of record and retains control of the post-purchase experience. 

Google’s UCP, available through Rithum’s Google Shopping feeds, opens another route, allowing brands to opt products into agentic checkout through AI-enabled search and Google Shopping, with support for loyalty programs and order management.  

Checkout only functions when the upstream stages are already working. Catalog data, platform access, and product-level performance all shape whether a shopper reaches the point of transaction. Getting those right now is the most direct path to capturing agentic commerce revenue as it grows. 

Rithum connects the full agentic commerce funnel  

Agentic commerce does not reward partial readiness. Every stage you leave unaddressed leaks value from the funnel. The teams gaining ground are the ones connecting these stages rather than running them as separate workstreams. 

Rithum and its upcoming agentic commerce capabilities connect these stages inside a single platform. Catalog improvements flow into AI feeds. Feed performance is measured. Optimization updates feed back into the catalog. When transactions occur, performance signals inform the next set of improvements. 

That full loop runs on one of the largest commerce datasets available: $50B+ in annual GMV, billions of SKU updates, and 3 out of 4 AI-driven optimizations accepted by clients. The scale and breadth to cover the full funnel from product data to AI-driven sale, in one system. 

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At a glance: 

• Shoppers who get a clear explanation of why an AI recommended a product are nearly 2x more likely to buy without verifying anywhere else. 
• Only 32% of shoppers named accuracy as the top trust-builder in AI recommendations. 49% chose a clear explanation of why a product was selected. 
• 47% of 28-to-43-year-olds say AI makes them faster decision-makers. Shoppers “very familiar” with AI tools are 3x more likely to purchase without verification. 
• When product data is wrong or incomplete, 80% of shoppers stay in the AI channel and ask again. 

A shopper asks ChatGPT for noise-canceling headphones for an open office, under $300. One result explains why it fits: isolates low-frequency hum, weighs less than comparable models, includes a transparency mode for conversations. Another lists a name, a rating, and a price. 

In a Rithum and Retail Dive survey of 1,046 U.S. and U.K. online shoppers, 49% named a clear explanation of why a product was chosen as the top trust-builder in an AI recommendation. Always-accurate information came in at 32%. And shoppers who get that explanation are nearly twice as likely to buy without checking anywhere else. 

Why shoppers value AI explanation over accuracy in product recommendations 

Shoppers expect AI to get the basics right. 67% named price as the top detail AI needs to be accurate on, followed by reviews and availability. But when asked what would most increase their trust, they reached past accuracy. 49% chose a clear explanation of why a product was selected. Always-accurate information came in at 32%. 

Any ecommerce team has seen this on a product detail page. Accurate price and clean specs keep a listing live. Rich attributes are what make it sell. The same applies to AI. An LLM builds its explanation from whatever product data it can find. If your listing includes driver size, noise cancellation type, and a note about comfort for all-day wear, the AI has something specific to say. If it doesn’t, the AI defaults to price. 

A jacket listed with fabric composition, weight, care instructions, and a note that it runs slim through the shoulders gives AI something to work with. A jacket listed as “men’s jacket, blue, available in S-XL” gives AI a price to compare. 

Newer brands with complete, attribute-rich product data already use this to their advantage, earning more persuasive recommendations than established names running on thin listings. When product data is wrong or incomplete, 80% of shoppers stay in the AI channel and ask again. The next answer is built on whatever data is available at that point. 

AI-powered shoppers buy faster and verify less 

47% of 28-to-43-year-olds say AI makes them faster decision-makers, compared to 21% of shoppers 60 and older. Shoppers who are “very familiar” with AI tools are 3x more likely to purchase without verification.   

For these shoppers, the explanation in the recommendation has to do the work that a product page, a review site, or a friend’s opinion used to handle. When the explanation falls short, the shopper moves to the next option in the response. There is no second visit, no follow-up search. The sale goes to whichever product explained itself best.   

How to optimize product content for AI recommendations 

Product content built for explanation earns stronger AI recommendations than content built only for visibility.   

• Enrich product attributes beyond the minimum required fields. Include use cases, compatibility notes, and sizing context. 

• Keep pricing and availability current across every channel where AI pulls data. 

• Monitor where shoppers verify AI recommendations and ensure your data holds up in those places too. Shoppers turn to search engines first at 28%, followed by online reviews at 19% and friends and family at 17%. 

• Test your own visibility: ask an LLM about your product category and evaluate whether your products appear with a clear, specific reason attached. 

• Prioritize data hygiene: validate and standardize titles, attributes, categories, and inventory/pricing sync so AI doesn’t amplify broken inputs across channels. 

Prioritize data hygiene: validate and standardize titles, attributes, categories, and inventory/pricing sync. AI can’t fix bad data. It can only move faster with whatever you give it, and when the inputs are off, that speed works against you. 

For a full breakdown of the data, download The New Discovery Engine report. 

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At a glance: 

• 53% of shoppers trust AI tools, including an AI shopping assistant, as much as brand websites, according to a Rithum and Retail Dive survey. This trust is reshaping AI shopping verification behavior and expectations for AI in retail. 
• When shoppers verify an AI recommendation, retailer and brand sites rank near the bottom at 5%, showing how AI shopping verification is happening away from owned channels. 
• 64% of shoppers ages 18 to 27 say they’re likely to purchase based on an AI recommendation without verifying it anywhere else. 
• AI-referred visitors now convert 42% higher than non-AI traffic.
• Retailers like Walmart, Target, and Instacart are enabling purchases directly inside AI conversations, accelerating a future where the entire shopping journey happens in one place. 

A year ago, shoppers arriving through AI tools browsed but left without buying. A year later, those same shoppers are 42% more likely to buy than shoppers arriving through traditional channels¹. In the same month, Walmart deployed its AI shopping agent inside ChatGPT², joining Target, Instacart, and DoorDash in letting shoppers browse, compare, and buy products directly inside the conversation. 

In Rithum and Retail Dive’s survey of 1,046 online shoppers across the U.S. and U.K., 53% already trust AI tools as much as brand websites. And when they want a second opinion on what AI told them, they’re going everywhere except the brand to get it. 

When shoppers double-check, they go everywhere but the brand site 

When shoppers verify an AI recommendation, they’re choosing channels outside the brand’s control. Search engines are the top destination at 28%. Online reviews come next at 19%, followed by friends and family at 17%. Retailer and brand sites rank near the bottom at 5%. 

A brand’s own product page, no matter how thorough, is still the brand talking about itself. Shoppers want independent voices, and they’re finding them everywhere else. 

That puts more pressure on the product data traveling through those channels. If the search engine is the second stop after AI, the data you’re pushing into Google, Bing, and other platforms needs to be accurate and complete. If a shopper pulls up a review site and finds specs that conflict with what the AI told them, the brand absorbs that cost. In the same survey, 58% of shoppers said trust in the brand decreases when an AI recommendation contains incorrect product information, and 16% abandon the purchase entirely. 

Brands have the answers, but shoppers are asking somewhere else 

A shopper asks an AI tool to recommend a running shoe for flat feet under $150. Three options come back. The shopper likes one but wants to confirm the arch support claim before buying. 

They type the product name into Google. They scan a couple of review sites. They text a friend who runs. The brand’s product page may have the most detailed answer to their question, but the shopper has already moved on to other sources. 

Product information accuracy across your entire distribution footprint now carries more weight than the quality of your own site experience. Feeds, marketplace listings, third-party retailer pages, and structured data that AI tools can parse all shape what the shopper encounters during verification. The brands investing in that full footprint are the ones staying in the consideration set. The ones focused primarily on their own site are building for a shopping journey that fewer customers follow. 

A growing share of shoppers skip verification entirely 

Among shoppers ages 18 to 27, 64% say they’re likely to purchase based on an AI recommendation without verifying it anywhere else. Higher-income shoppers are twice as likely to trust AI without visiting another site. And across all demographics, 32% say they spend less time browsing other sites after using an LLM. 

For these buyers, the AI recommendation from an AI shopping assistant is the decision. The brand site is largely absent from it. And shoppers who verify through search and reviews encounter a broader set of options than they would on a single brand’s site, giving unfamiliar brands a real opening to enter the consideration set with accurate, well-distributed product data. Shoppers who skip verification altogether are relying entirely on whatever the AI already knows about your product. 

The verification step itself is disappearing 

64% of shoppers already take AI at its word. The platforms coming next are built to make that feel even more natural.

AI agents could mediate $3 trillion to $5 trillion of global consumer commerce by 2030, according to McKinsey and Co. Two competing open protocols are already live and processing transactions end to end: OpenAI and Stripe’s Agentic Commerce Protocol and Google’s Universal Commerce Protocol. The AI agent handles product discovery, comparison, checkout in one place. 

Now picture that same running shoe shopper six months from now. They ask ChatGPT the same question. A product card appears with an image, price, and a “Buy” button. They tap it, confirm their saved payment method, and the order ships. The entire transaction happened inside a single conversation. 

The shoppers who still verify aren’t going back to the brand to do it. They’re checking reviews, search results, other people. And as AI agents take on more of that process, the brand’s window to influence the answer gets smaller. The data has to be right before the question is ever asked.”

Your product data is now your pitch to an AI buyer that will never visit your homepage 

Getting product data right across every channel is the minimum. It’s expected. The question is where that data lives: search engines, review platforms, marketplace listings, and the structured data feeds that AI agents read when they decide what to recommend. AI-readable product content needs to be complete, consistent, and built for machines to parse. For a growing number of shoppers, that content is the only version of your brand they’ll ever see. 

The distance between discovery and purchase is collapsing. Sometimes it’s a single conversation with an AI agent. The brands feeding that conversation with accurate, well-distributed product data are the ones the agent recommends. 

For a full breakdown of the data, download The New Discovery Engine report. 

Sources:
1: https://www.retailtouchpoints.com/features/the-agentic-commerce-paradox-its-already-here-and-its-also-still-evolving/618945/  
2: https://www.cbsnews.com/news/ai-agentic-artificial-inteligence-what-is-it/

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Before brands and retailers move further with agentic commerce, they need a clearer read on which shoppers want that kind of help and which do not.  

In Quick Answer: Which Consumers Will Embrace Agentic Commerce? Gartner writes that “U.S. consumers show some signs of openness to agentic commerce, with readiness varying significantly by generation, income level and community type. While acceptance among younger, more affluent, and urban consumers might be expected, successful digital commerce leaders will adjust their investments more precisely to align with their target customers.”  

For brands and retailers, the next step is to consider what that may look like for their own customer base. 

What this looks like across customer groups 

These findings will not apply the same way to every customer base. For example, “Specifically, 55% of respondents within the high-income and affluent demographics express willingness to adopt AI-assisted shopping.” 

A brand or retailer serving those shoppers may see more openness than one serving a different mix of customers. That should shape how teams think about where to test, how fast to move, and how much of the shopping journey shoppers are ready to hand off. 

For brands and retailers, the focus comes back to the customer. The better a company understands who it serves, the better it can judge where agentic commerce fits, where a lighter touch may work better, and where shoppers may want to stay in control. 

Want the full picture? Download the report for the complete findings. 

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At a glance:

• More than a third of surveyed commerce leaders say key tasks are partially automated but still require manual checks. Many workflows still depend on a human proof step before go-live, according to the 2026 commerce readiness index.  
• Another 29% said data is scattered and stockouts show up after orders, turning routine pricing and inventory updates into avoidable cleanup work.  
• 34% of commerce leaders said dashboards are available, but teams are still forced into manual exports to confirm what they’re seeing.  

Retailers and brands have spent years layering ecommerce automation into pricing, inventory, listings, and media. But many still stop for a manual proof step before go-live.  

The double-check has not gone away. It often comes just before a team is ready to act. A price looks right in one system but off in another. Inventory looks stable until the channel view suggests otherwise. The team stops to verify the basics before moving ahead. That pause turns automated work back into manual work. 

Ecommerce automation still needs a proof step  

In the 2026 commerce readiness index, 34% of commerce leaders said key tasks are partially automated but still require manual checks. Nearly half of executives said 26%-50% of workflows still depend on spreadsheets, and repetitive tasks like manual data entry, and approvals.  

Promotions punish messy handoffs  

Marketing campaign promotions don’t leave much room for hesitation. Price, inventory, product content, and media all move at once, yet teams are often working against slower decision cycles: more than half of retailers said they act on meaningful performance signals within 48 hours, while brands are more likely to take three to five business days.  

The readiness assessment helps explain why: 32% said signals are spread across tools and reports, while 29% said they have dashboards and alerts but unclear workflows. When product feeds, pricing, availability, and catalog updates refresh on different schedules, a promotion can look ready until something slips and the team has to stop to confirm what is actually true. Rithum’s retail media guide describes the same problem from the campaign side: by the time the dashboard reflects it, time and budget may already be gone. 

Scattered data turns ordinary work into manual work  

The trouble is often small at first. A price changes here but not there. Inventory moves, but not everywhere at once. Reporting can show that something changed without showing where it began. In the index, 29% of respondents said stockouts appear only after orders come in. Routine work turns into cleanup.  

Dashboards lose their authority when something shifts  

A dashboard can feel reliable until something starts moving faster than your reporting can explain. When a product moves faster than expected, a promotion performs differently across channels, or a price update appears in one system but not another, the dashboard can flag the issue without showing its cause. The team has to look elsewhere to confirm what changed.  

The readiness assessment points to the same problem: 34% said core dashboards are standardized, but edge cases and new channels still depend on manual exports they know are unreliable. Another 26% said the data they work from is incomplete, late, or manually tweaked, but they still use it because it is all they have. The gap is not only in dashboard coverage, but in confidence in the data underneath it. 

Rithum’s retail media guide points to what’s missing: product context alongside campaign performance. When teams can see which products absorbed the budget, what changed in price or availability, and which issues need attention first, reporting stays useful while teams are still deciding what to do.  

The window to act is getting tighter  

The gap becomes more expensive during peak shopping events. In the report, more than half of retailers said they act on meaningful performance signals within 48 hours, while brands are more likely to take three to five business days.  

Rithum’s Prime Days 2025 data shows how timing can be problematic. One brand held spend until conversion and AOV recovered, then pushed harder. Another brand pivoted mid-event toward back-to-school assortments, bundles, and sharper titles and keywords, finishing 15% above the prior year’s Prime Day. The advantage was a timely read on what had changed, while there was still room to respond.  

What teams should standardize now to achieve the benefits of ecommerce automation  

Instead of adding more ecommerce automation, retailers and brands should look at where to cut back on verification work. Start with the handoffs that break trust most often. Set clear source-of-truth rules for product data, pricing, and inventory so a routine channel change does not trigger a manual review. Exceptions should surface early, with enough context for teams to understand what changed without going back into spreadsheets.  

Where Rithum helps by providing automation tools for ecommerce  

Rithum helps cut down the verification work that piles up between systems by offering automation tools for ecommerce. Error tracking flags mismatches earlier. Automated tools and workflows reduce the same reconciliation loop playing out over and over. Connected commerce and media insights bring pricing, listings, inventory, fulfillment, and performance into a view teams can actually use.  

For retail media teams, product changes and campaign performance sit closer together, so it’s easier to spot what shifted, what needs attention, and where to act first. As a result there are fewer exports, less back-and-forth, and less time spent confirming what should already be clear.  

Download the full 2026 commerce readiness index to see where retailers and brands are still losing time, and what it takes to move faster without adding risk. Then take our readiness assessment to see where you stand in comparison.  

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Every commerce team has a peak season playbook covering Black Friday, Prime Days, back-to-school and other big tentpole events. But we’d bet our bracket that few commerce teams strategize for the day UConn beats Duke.

We analyzed six weeks of Rithum network data over the course of the 2026 NCAA Men’s Basketball Tournament, looking at order patterns across states, channels, and game days. What we found was something rarely see quantified this cleanly about how Americans shop, and what drives their purchases.

The quick takeaways:

• Thirty-one of 32 tournament states had shopping rates above their baseline on game days.
• On Sweet 16 Thursday, national ecommerce orders ran 40% above the pre-tournament average.
• The day UConn beat Duke, Connecticut’s shopping numbers did the wave right along with the fans.
• In all four Elite 8 matchups, the state that shopped more was the state whose team won, even adjusting for population.

This is a small data snapshot, but it tells a story of a much bigger moment. Even as AI optimizes your feeds, tariffs reshape your margins, and new channels multiply your reach, there are non-high-tech, non-global-impacting signals that still matter. Like when a state collectively loses its mind over a basketball win.

The brands that can read those moments—and move fast enough to meet them—have an edge that can’t be planned six months out.

Connecticut broke its own record the day UConn beat Duke

On the day UConn knocked off Duke in the Elite 8, ecommerce orders in Connecticut hit the state’s highest single shopping day across our analyzed six-week window. That Sunday came in 12% above the next-highest volume shopping day and 32% above a typical Sunday. In fact, 10 of the top 11 highest-shopping days of the last 6 weeks in Connecticut were during the tournament.

And just to be clear, this isn’t a basketball merchandise story. Connecticut didn’t suddenly buy 32% more jerseys. The entire state’s ecommerce activity surged—across categories and channels—because Connecticut was having a great week.

The commerce data predicted every Elite 8 winner

In all four Elite 8 matchups, the state that shopped more per capita in the 6 weeks analyzed was the state whose team won.

Connecticut out-shopped North Carolina. Michigan out-shopped Tennessee. Illinois out-shopped Iowa. Arizona out-shopped Indiana.

Now, we’re not suggesting you build your bracket prediction model on this data. But we are saying that this basketball story is disguising a commerce story about what always-on retail has made possible. A fan in New Haven checking the injury report at halftime is two taps from buying something. Maybe they didn’t plan to shop, but hey, their phone is already in their hand, and if the right ad has been built for the moment, or the right TikTok influencer pops up . . .

Commerce used to require intent. Now, especially with social shopping, it has essentially become ambient. We can’t say the tournament created demand, but it made opportunities for shopping. And understanding this is key to knowing where to show up next to meet those cultural moments.

California spiked 38%. And their team lost

When Saint Mary’s tipped off in the Round of 64 on March 19, California’s ecommerce orders surged to a 38% spike over its average Thursday order rate and had the biggest same-day jump of any state with 10M+ residents. St. Mary’s competitor’s home state of Texas was the only other large state that came close, with a 34% spike.

Saint Mary’s, a 7-seed, lost that same day.

California didn’t shop because its team won. California shopped because its team was playing on Californians’ screens.

Anticipation and access drives commerce. Participation and brand presence drives commerce. Being emotionally invested—regardless of outcome—drives commerce. The brands positioned to capture these moments aren’t the ones running the best sale. They’re the ones who showed up in the right place, with the right product presence, consistently, when the emotion and moment was already there.

What this means beyond March Madness

One caveat: orders were trending upward across the whole 6-week period for every state, tournament or not, so some of this reflects seasonal momentum. But the day-specific spikes—40% above normal on Sweet 16 day, for example—suggest something else happening on top of the trend. The correlation between game days and order volume is too consistent, across too many states.

The NCAA Tournament is a six-week, state-by-state experiment in what happens when consumer attention concentrates around a shared cultural moment. And the answer, across 31 of 32 states, is the same: commerce goes up.*

So, what do you do with this information, other than have a cool factoid for your next cocktail party?

The FIFA World Cup kicks off June 11, spanning 16 cities across the US, Canada, and Mexico for 39 days. Six billion people are expected to watch. America’s 250th anniversary arrives July 4th with a year of cultural programming around it. The Tour de France runs through July, and in recent years has been drawing its largest American audience yet.

Every one of these is a moment where consumer attention spikes. And as the NCAA data shows, when attention spikes, so do orders.

The brands that follow a retail calendar alone—through peak season, off-peak season, promote, pause—will miss the revenue that cultural moments create. The brands with the channel infrastructure, inventory visibility, and pricing flexibility to move when consumers are paying attention are the ones who capture it.

Three things worth doing before the next moment arrives:

1. Map your commerce calendar to cultural moments, not just retail tentpoles. The World Cup, America 250, Tour de France, and Fashion Week are all coming in the next six months. Each one is a potential Connecticut-level spike.
2. Make sure your channel presence is ready. Game-day-style lifts happen fast. Brands that are already visible on the right marketplaces with accurate inventory and optimized listings are there in the right moment.
3. Make sure you can see, and follow, the data in the moment. Rithum’s network data showed the tournament signal clearly because we were looking at order patterns in-depth. Your own commerce data will show you which moments move your buyers only if you’re set up to see it. The data is always there. The question is whether you’re able to read it accurately, with the right partner.

The retail calendar tells you when to run a promotion. The cultural calendar tells you when your buyers are paying attention. Rithum can help you connect all of these calendars and moments to be ready when and where the opportunity lives.

*Maryland, home of UMBC, was the exception. UMBC bowed out in the First Four; their orders dipped a fraction of a percent.

Methodology note: Data sourced from the Rithum commerce platform across 8.2M unique products and 20K+ suppliers. Order rates are normalized to per 100,000 residents to enable state-by-state comparison. Some client data excluded to preserve anonymization. Results reflect the composition of brands and retailers active on the platform during the period analyzed.

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The Strait of Hormuz—a 21-mile-wide channel between the Persian Gulf and global markets—has been effectively closed to commercial shipping since late February 2026. Over 150 vessels are anchored outside the strait,¹ and for the first time in modern history both Middle East major maritime corridors are blocked at once.² 

Negotiations are ongoing and partial passage is being allowed for some vessels. But the disruption has already persisted long enough to ripple through multiple layers of the supply chain and land on the desks of commerce teams managing margins, inventory, and marketplaces. 

It’s a stressful time. But the commerce world has been here before. Here’s what past disruptions tell us, what’s happening now, and what you can do about it. 

Lessons from past chokepoint crises 

When the Ever Given ran aground in March 2021, it delayed 432 vessels carrying an estimated $9.6 billion in cargo per day. The impact on commerce logistics was almost immediately felt: retail late shipments spiked 11% in the week of the blockage, and Nike, Gap, and Steve Madden all cited the incident as a direct driver of inventory shortfalls that quarter.³ 

Brands with inventory already in domestic warehouses were largely protected. Brands relying on in-transit or just-in-time replenishment were not, and the global supply chain took more than two months to absorb what was only a six-day delay. 

The pandemic showed us what happens when a disruption compounds across quarters rather than resolving in days. Stockouts triggered over-ordering, which created margin erosion, which led to reactive discounting that took quarters to recover from. It was a cycle, and the brands caught in it were the ones still treating each disruption as a one-off logistics problem rather than a structural exposure.

The brands that came out structurally stronger weren’t the ones waiting for ports to reopen. They used the disruption as a forcing function to build lasting infrastructure: inventory visibility across channels, pricing systems that could respond to cost changes at scale, and channel flexibility that didn’t depend on any single supplier or route. The real question today is whether that muscle held through years of relative calm. For some brands, it has. For others, this moment is exposing the same structural gaps all over again.

The repeating lesson: waiting for resolution to plan prolongs the pain. 

Landed costs are moving  

According to Rithum’s 2026 Commerce Readiness Index, 91% of retailers and 87% of brands say pricing power is shaped more by external conditions than by their own strategies. The Strait of Hormuz is the latest confirmation. 

Hapag-Lloyd, a leading global container shipping company, is absorbing $40+ million in additional costs per week due to surging bunker fuel, war risk insurance, and emergency surcharges that now run $1,500 per container for Gulf-bound cargo.^4,5 Base freight rates from Shanghai to Jebel Ali more than doubled in the first two weeks of March alone.^6,7 

For brands, those costs move into pricing decisions, promotional planning, and SKU prioritization. And for brands with lean inventory strategies, this impact will likely ripple through into Q2 and Q3, even into back-to-school and early holiday planning.  

Gartner research puts numbers on what’s at stake: during a major supply chain disruption, nearly two-thirds of companies expect to lose revenue and supply chains experience an average 40% surge in cost-to-serve post-disruption.⁸ That figure holds whether you sell through two channels or twenty. But it also holds that brands with real-time visibility into pricing and inventory are in much better shape to navigate through disruption cycles.  

This can be overwhelming, as it lands out of your control. But what you can control is acting quickly on what your data is telling you. 

What you can do right now 

Audit your inventory position across channels. Know what’s in your warehouses, what’s in transit, and what hasn’t shipped yet. For brands selling across multiple marketplaces, that picture is often fragmented across systems. Consolidate it now so you can make decisions from data, not estimates.  

Revisit your pricing architecture. If landed costs are moving, your margin profile is moving. Identify which SKUs are most exposed and whether your current pricing across marketplaces reflects the new cost reality. Brands with centralized pricing management can make those adjustments at scale. 

Prioritize your assortment. Not all SKUs are equal under margin pressure. Identify which products have the most runway at current landed costs and consider whether promotional strategy needs to shift toward higher-margin items while the disruption persists. This is also a moment to identify SKUs with the highest exposure to affected supply chains—electronics, petrochemical-adjacent goods, and anything sourced through Gulf or Southeast Asian routes facing extended transit windows. 

Communicate proactively with retail partners. If you’re a brand selling through retail dropship programs, your retail partners are managing the same pressure. Getting ahead of availability conversations—rather than responding to stockout flags—protects the relationship and the shelf. Retailers are already managing their own inventory and margin exposure; being a predictable, communicative supplier is a competitive advantage right now. 

Don’t wait for resolution to plan. The Suez blockage lasted six days and took two months to clear from supply chains. The Red Sea crisis stretched well over a year. The planning decisions made now around inventory, pricing, assortment, and channel mix will determine your margin position heading into H2, regardless of when the strait reopens. 

Resilience is the strategy 

The brands and retailers best positioned to navigate this are the ones who built operational flexibility into their commerce infrastructure before the disruption hit, ensuring inventory visibility, centralized pricing, channel diversification, and the ability to make fast decisions from clean data. The data story about your products, the accuracy of your pricing, the visibility into your inventory—those are things you can control right now. Rithum was built for moments like these. If you’d like to talk through what this means for your business, please reach out. Our team is ready to help you navigate it.  

Sources 

1.  Carra Globe, Strait of Hormuz Closure 2026: What It Means for Your Supply Chain, March 2026. 
2.  CNBC, The Strait of Hormuz crisis explained: What it means for global shipping, March 2026. 
3. Wikipedia, 2026 Strait of Hormuz crisis. 
4. Supply chain impact figures from post-Ever Given analyses; retail late shipment data widely reported. See also Easyship, Strait of Hormuz Shipping Disruption (2026). 
5. UNCTAD, Strait of Hormuz Disruptions: Implications for Global Trade and Development, 2026. 
6. Wikipedia, 2026 Strait of Hormuz crisis. Brent crude peaked above $126/barrel, March 2026. 
7. Sourcing Journal / Reuters, Hapag-Lloyd Faces $40–$50 Million Weekly Costs Due to Middle East Conflict, March 2026. 
8. Container News, Hapag-Lloyd introduces war risk surcharge for Gulf cargo, March 2026. $1,500/TEU standard; $3,500/TEU reefer. 
9. Couriers & Freight, Middle East Conflict: Major Carriers Add Shipping Surcharges, March 2026. 
10. TTL Co., War Risk Surcharge on Gulf Shipping — Verified Carrier Rates (March 2026). Freightos Terminal data. 
11. Easyship, Strait of Hormuz Shipping Disruption (2026): Impact on SMBs. Cape of Good Hope rerouting adds 10–14 days per shipment. 
12. CNBC, How Strait of Hormuz closure can become tipping point for global economy, March 2026; citing Andrei Quinn-Barabanov, Moody’s. 
13. ISM / Gartner, The Impacts of the Iran Attack on Supply Chains and Global Business, March 2026. 

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Ulta Beauty is now live on TikTok Shop, bringing one of retail’s most recognized beauty retailers into a channel where discovery and purchase increasingly happen side by side. 

For a retailer built on experience, assortment, and community, the move feels like a natural next step. TikTok Shop gives Ulta Beauty a new way to reach shoppers in the moment, when they are discovering a product for the first time, following a trend in real time, or ready to buy without leaving the platform. 

A new channel for beauty discovery, shopping, and growth 

The launch reflects a broader shift in how people shop. More consumers are finding products through content and expecting a faster path from inspiration to purchase. For Ulta Beauty, TikTok Shop offers a more direct path from discovery to checkout in a channel that is already shaping how beauty shoppers browse and buy. 

Rithum served as Ulta Beauty’s integration partner for the launch and ongoing management, supporting the work behind the scenes to bring the new channel to market. The launch builds on the long-standing relationship between the two companies. 

Ulta Beauty’s launch on TikTok Shop reflects how retailers are bringing discovery and purchase closer together. We’re proud to support Ulta Beauty as they expand into this channel and to build on the strong partnership we’ve established over time.

– Blaine Nielsen, President, Retailers at Rithum

The move gives Ulta Beauty a new way to connect with shoppers as they discover and buy on TikTok Shop. It also gives the retailer another way to reach new shoppers while deepening engagement with existing ones. 

A curated experience built for TikTok Shop 

Ulta Beauty’s TikTok Shop launch reflects that strategy through a curated experience designed for the platform, including exclusive bundles, early-access moments, and creator-led discovery. The result is a shop experience that feels native to TikTok while staying grounded in what customers already expect from Ulta Beauty. 

TikTok Shop is an exciting extension of Ulta Beauty’s discovery-led ecosystem, giving us another way to meet guests where the beauty conversation is happening and bring our trusted curation to life in a TikTok-native format. We see this as a complementary, incremental channel, and we appreciate Rithum’s partnership in bringing it to market.

– Josh Friedman, SVP, Digital and Ecommerce, Ulta Beauty

As social commerce continues to grow, TikTok Shop is becoming an increasingly important channel for retailers and brands that want to connect discovery and purchase more closely. Ulta Beauty’s launch extends its discovery-led approach into a new environment. 

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TL;DR 

• ROAS stays useful when you read it inside one retailer and within specific placements. Once you carry it across retailers, the comparison gets noisy unless you bring context with it. 

• Every retail media network behaves like its own environment. People shop differently, products appear differently, and performance attribution varies greatly. 

• Strong retail media strategy starts retailer by retailer, using insights on a retailer-by-retailer basis to meet the customer where they are in their shopping journey. 

• Price, availability, and assortment shape shopping outcomes in ways ROAS does not show. 

• Reporting is most valuable when it connects ad spend to product-level outcomes and uses the language that aligns with the business 

On the weekly call with the retailer’s ecommerce team, the deck opens with ROAS (return on ad spend) and a tidy spend line. Everything looks good, until someone asks about which products are they trying to move the next week. Answering that question requires looking beyond the fast sellers to address the products that haven’t been as successful to find out how to change that. 

It’s easy to keep spending on the products that already sell efficiently. Brands and retailers also have other priorities, like giving a new launch enough visibility to earn repeat purchases, supporting a focus line ahead of a seasonal moment, or putting weight behind an item that reliably introduces shoppers to the rest of the brand. That’s why ROAS belongs in the overall update, but it won’t tell you whether the budget supported the week’s priorities. 

Picture a snack brand whose variety pack always delivers strong ROAS. Left alone, the plan keeps feeding that winner. Then the retailer flags next week’s focus: a new flavor launch tied to a seasonal feature. The brand shifts some spend to the new item and a few related products shoppers often buy together. The report may still credit the variety pack, but the plan does the job the retailer asked for, and the brand can watch whether more of the lineup starts moving. 

What ROAS tells you, and what it can’t 

ROAS is a strong efficiency signal for retail media strategy. Within one retailer, with consistent placements and stable measurement, it gives teams something practical to steer by when they manage bids and pacing. 

But overall brand profitability is a separate question. There are many dependencies for that level of measurement, including products sold, margin on the items that absorbed spend, and whether the outcome matched what the business needed that month. Those details often live outside the ad platform view, even on teams with solid measurement habits. 

A high ROAS can also happen when spend simply follows the easiest conversions. Budget concentrates on the fastest conversions, product conditions change midstream, and the report still looks strong. Nothing is “wrong” with the metric. It just doesn’t explain whether the spend supported the products the business needed to move. 

Retail media strategy is unique to each retailer environment

Retail media networks don’t follow one set of rules. A single approach template can feel efficient, but it smooths over the very differences that drive performance. What tactics may work in Amazon may not resonate the same in a Walmart or Target Roundel.  

Rithum points out just how scattered retail media still is: more than 220 networks, and each retailer reports results in its own format. 

Assortment varies by retailer, and a brand’s winners in one store may not even be listed in another. Shoppers also bring different expectations depending on where they shop, which changes how they respond to ads and merchandising. Retailer programs add another layer, from exclusives to promotion structures that alter which products deserve investment. Reporting varies as well, sometimes in small ways and sometimes in ways that change what a familiar metric appears to mean. 

Start by assuming the retailers won’t behave the same way. Then you can plan around the few things that change every time. 

What changes from retailer to retailer: 

• Assortment 

• How shoppers behave 

• How results get measured and reported 

Even one of these differences can force you to change plans. When combined, a one-size-fits-all or copy-and-paste strategy is risky. 

A retailer-by-retailer retail media strategy that holds up 

1. Decide what this retailer needs to do for you 

Start with intent before you touch the media plan. You need to ask what is this retailer supposed to deliver? 

Some retailer environments are volume drivers, while others are where shoppers discover and compare brands in a category, and still others matter because the relationship shapes promotions and visibility beyond media. Those differences should guide which placements you prioritize, which products get budget, and what you consider a good week. 

If you skip this step, the conversation turns into a debate about numbers that weren’t built to match. Rithum helps clients to look beyond the biggest networks, where a brand can often reach new shoppers and earn more visibility for the same effort. 

2. Choose the products that deserve the budget 

The question worth answering every week is what products is the retailer trying to sell more of next week? 

For instance, imagine a brand that sells both pantry staples and premium seasonal items. Last week’s ROAS winner might be the staple that sells year-round with a predictable conversion rate. But this week, the smarter list could look different. The retailer has a seasonal event running, the premium item is in stock and priced competitively, and the brand needs to build visibility for it before the moment passes. Meanwhile, a different product might be selling fine without paid support, or it might be tight on inventory, which makes it a poor candidate for extra spend. 

The right product list won’t be the same everywhere. Each retailer has a different assortment, different shoppers, and different moments week to week. 

Advertise based on where your target consumers are in their shopping journey. People come to each retailer with a purpose. Some visits are quick purchases; others are browsing and comparison. Creative works better when it matches that mindset instead of forcing one generic message everywhere. 

Retailer programs and exclusives matter here too. A promotion, a bundle, or an exclusive item can change which products make sense to push that week. 

3. Keep ads synced with what shoppers can buy 

Prices can change overnight. Inventory can tighten without warning. Monday’s campaign leans on the hero product; by midweek, shoppers can’t buy it, and the ads keep sending traffic anyway. 

Rithum’s Product Feeds materials describe low-latency syncing for inventory and pricing, with near-real-time changes to price, stock, and new items, with the stated goal of reducing wasted ad spend and preventing out-of-stock recommendations. 

4. Don’t let fast sellers swallow the budget 

Retail media spend tends to gravitate toward a handful of products that already convert. The signal is clean, and dashboards keep reinforcing the same winners. 

That pattern can crowd out the products you’re trying to grow. A best seller keeps getting budget because it makes ROAS look great, while a new line never gets enough exposure to prove itself. 

Rithum’s retail media advertising materials describe product-aware optimization that leverages inventory, pricing, and margin data insights powered by RithumIQ. 

5. Translate unique retailer metrics into business terms 

Retailers deliver performance data in their own formats, and standard metrics aren’t consistent across networks. Comparisons can still be useful, but they require clear definitions before anyone draws conclusions. 

Rithum’s retail media advertising materials also describe closed-loop reporting that ties spend to sales at the ASIN level for profitability measurement. 

The scenario where ROAS is enough 

ROAS can carry more weight when a program stays inside one retailer, within a stable set of placements, and the assortment doesn’t change much week to week. In that setup, the comparison is cleaner. 

As soon as a program spreads across retailers, the differences return. Assortment, shopping behavior, merchandising, and reporting depth still vary enough to change what “good” looks like. 

Where tools help, and where they don’t 

Most teams already know retailers work differently. The hard part is execution: keeping product reality, campaign decisions, and reporting connected without rebuilding the workflow every week. 

Rithum’s public materials describe product-aware optimization that leverages inventory, pricing, and margin data insights powered by RithumIQ, along with closed-loop reporting that ties spend to sales at the ASIN level for profitability measurement. 

Tools don’t replace judgment. They can make a retailer-by-retailer approach easier to run consistently. 

ROAS belongs in the update, but profit terms live at the product level. Look at the items that received budget in each retailer, the margin behind those sales, and the total margin dollars the week produced. Keep price and in-stock status in view at the same time. That view makes it easier to judge whether the budget supported the week’s priorities, not just the items that earned the cleanest attribution. Learn more about retail media strategy and how Rithum can help. 

Meghan Barden is Director of Global Retail Media at Rithum.

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To no one’s surprise, AI talk was everywhere at eTail West 2026. But the sessions that stuck with me focused more on returning to basics: Product information, checkout, measurement, customer trust, internal readiness and meeting the customer where they are in their shopping journey. Even as shoppers start their journeys using the advanced technology of AI, those basics carry more weight than ever. Here’s what I’m still thinking about after soaking up all eTail West had to offer.  

AI answers are a storefront now  

As the referral to external websites from LLMs decreases rapidly, retailers and brands need their products to be the answer when shoppers use AI, not a click along the way. Jessyca Frederick, Director of Digital Product at Wine Enthusiast, said: “LLM priority #1: answer your questions completely . . . prevent linking out.”  

Jessyca talked about how these systems gather information: “RAG agents pose fan-out queries to search engines.” RAG stands for retrieval-augmented generation. It means the system searches for sources, pulls what it needs, then writes using what it found. A fan-out query is one question that triggers several searches. Think of one shopper question, many search runs.  

“The more fan outs you appear in, the more likely you’re to be cited,” Jessyca said. 

AI answers reuse what they can read and trust. Product catalog management accuracy with an emphasis on data hygiene is key. The fit, price, availability, policies must be accurate. If your details are incomplete, inconsistent, or stale, the answer engine moves on.  

Jessyca suggested putting a window for time for “freshness” of that information of 3 months. She emphasized that teams need a routine for keeping key pages current across the places customers browse. The biggest thing to remember is that AI bots simply skip information they can’t access. Jessyca advised to put critical product details in formats they can read, keep pages fast, and clean up broken paths. 

Shoppers move. Your product story has to beat them there. 

The “everywhere customer” panel put a name to what many teams see in their analytics and support queues. With Jodi Williams from Ulta Beauty, Lauren Price from COS, Feliz Papich from Crocs, Jason O’Toole from Gildan, and Jacob Ross from PebblePost, this panel all agreed that brands need to stop thinking they can tell consumers where to shop. Discovery happens in one place, validation in another, purchase somewhere else. A TikTok Shop presence can end up influencing an Amazon transaction.  

Instead of focusing on where the conversion came from, the panel advised asking what shaped it. Measurement is important, but post-click attribution gives the final click most of the credit, even when the decision was made three touch-points earlier.  

“Post click conversions are heavily flawed.” according to the panel. Instead, the panel talked about the importance of incrementality. Measure lift against a baseline. Set up a holdout, compare outcomes, and see what changed. Try to reduce guesswork. 

Checkout decides revenue  

Once discovery is multi-surface and measurement is catching up, it’s tempting to treat checkout as an afterthought. The checkout panel pushed back on that.  

Pat Suh from Affirm (joined by Jack Phung from Newegg and Henry Spear from JD Sports) said, “Ideally the AI already knows the options, but we aren’t there yet.”  

While agentic commerce grows in popularity to discover products, consumers still abandon carts when checkout creates friction or surprise. Brands and retailers often treat checkout as a problem to solve later, then watch conversion drop after they’ve invested in everything upstream. It’s an expensive way to learn the lesson.  

Instead, panel members recommended that to reduce surprises, brands should put key payment details where customers look for them, make choices clear, and keep terms readable.  

Personalization needs intent, not a dossier  

Anna Downs, Digital Personalization Manager at The North Face, noted that consumers want to shop without feeling monitored. and Michiel Dorjee from Optimizely said, “A lot of people don’t like the creep factor of knowing too much about my persona.” The better target, they argued, is intent. “Think about it more of their intent and serve that to them faster, then their experience improves.”  

Intent is what the customer signals in the moment: what they search, what they click, what they compare, what they’re trying to solve. That’s more useful than demographic profiles and less likely to make someone feel surveilled.  

Automation raises output, then it raises the stakes  

The AI panel with Keri McGhee from Attentive, George Davis from Cozy Earth, and Tommy Kowalski from HeyDude covered what teams are truly using AI tools for: scaling creative volume, speeding up lifecycle messaging, and turning one idea into hundreds of variations. The panel cited AI-driven lifecycle journeys producing 200-300% revenue lifts in some implementations.  

Despite that growth, the panel noted there are still retention risks, because consumers will leave the brand if they have a bad experience with AI. Brands and retailers should understand what automation can do and where a human needs to be involved. Going back to the basics: Customers have always remembered when a problem is handled poorly. AI is no exception.

Trust keeps working after the purchase 

There is no replacement for consistency, according to the authenticity and transparency in retail panel. Angela Clark from Patagonia, Catherine Hayden from Kate Farms, Elton Graham from Sur La Table, and Sara Jensen from Hugh & Grace talked about the importance of brand stories that customers can recognize and believe.  

The big takeaways:

• Details like packaging inserts and QR codes drove high engagement to further brand loyalty.
• Customers want context after purchase. They want to understand what they bought and why it exists. It’s another way to retain customers.
• Clear context reduces returns, reduces support load, and strengthens repeat purchase behavior.  

Internal readiness decides who moves fast  

With all the technology changes happening so fast, it’s easy to fall into a reactive stance. A panel with Jennifer Conrad from Inc., Steve Schwartz from Art of Tea, Jonathan Weiss from Raw Sugar Living, Bridgit Lombard from Francesca’s, and Ron Tarter from MNEE Pay spoke about resilience as brand culture. 

“Give yourself permission to pause between stimulus and action.” They called that habit equanimity—the steadiness of mind to absorb new information before reacting. Brands and retailers need to move thoughtful, not make decisions in a half-panic. Be thoughtful and move faster when you need to, but don’t default to a reactive stance. 

The CMO panel echoed the same idea from the growth side. Kate Huyett from Bombas, Aaron Magness from Full Glass Wine Co., Ed See from Zeta Global, Richard Jones from Wunderkind, and Taryn Rayment from J.McLaughlin talked about what it takes to create profitable customers. The work crosses functions: inventory planning, finance, sourcing, marketing. When those conversations stay siloed, growth slows. The marketers gaining ground are the ones willing to get into other teams’ processes and build from there.  

In 2026, brand and retailer teams are fixing the basics. That way, any automation they use is working with accurate data. Looking for continued thought leadership? See how we’re partnering with Amazon MCF on stage at Shoptalk Spring 2026. 

If you’ll be at upcoming industry events, we’d like to compare notes. Meet with Rithum at Shoptalk Spring, booth #1775, March 24-26 in Las Vegas. 

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