The 2026 selling season is about to look very different. We’re at peak adoption of consumers using LLMs, like ChatGPT, Copilot, Gemini, and Perplexity, to shop. Because of this, the consumer journey and path to conversion are now more complex. The trust consumers place in LLM responses to their shopping prompts is surprisingly high, with most shoppers relying entirely on the AI to shape the decision all the way to purchase. 

95% of shoppers who get a product recommendation from an LLM won’t visit a brand or retailer website to verify it. Among 18–27 year olds, 64% buy on the recommendation alone.

Twelve months ago, most brands were still focused on ranking in traditional search results. Consumer shopping behavior has changed more in the last 12 months than in the previous five years combined. That shift is going to define how every peak event plays out in 2026. 

Amazon and Walmart are blocking LLMs from crawling their product data. So, keeping consumers within the Amazon ecosystem to maximize Prime Day returns and overall conversions is critical. 52% of all U.S. consumers still begin their online product searches directly on Amazon,^¹ which is reassuring for Prime Day activity. But when we factor in the LLM component, our strategy to maximize Prime returns gets more nuanced. 

Previously, we wouldn’t have as heavily considered our clients’ .com websites as part of the Prime Day journey, but if LLM recommendations are relying on Google and other general product data feeds, then ensuring product data hygiene from those outside sources is critical. 

How should brands reach shoppers on Amazon and LLMs at the same time?

• For consumers already starting their journey in Amazon search, keep them engaged, targeted with appropriate content and ads, and slam dunk the conversion. 
• For consumers starting their search in LLMs, make sure client .com product data feeds are accurate, updated, and organized so the right recommendations entice consumers to seek out the shopping moment outside the LLM. 

There’s more to this year’s approach. Prime Day lands June 23–26 this year, the earliest window since 2021 and the second year at four full days.¹

Back-to-school demand is already overlapping with it. BFCM is less than six months out. And the LLMs shoppers are using to find and compare products have improved significantly since last peak season.

LLMs are pulling richer product details, making more accurate comparisons, and starting to support transactions directly. The brands and retailers who will perform best during peak 2026 are the ones whose catalogs are accurate, structured, and available everywhere the AI is looking. 

At Rithum, we work across 900+ channels with brands and retailers preparing for these events right now. We’re deep in the planning alongside them, from media strategy to product data to channel prioritization, and everything below reflects what we’re seeing and recommending heading into peak selling in 2026. 

What are the major 2026 peak sales events, and when do they happen? 

• Amazon Prime Day: June 23–26¹ 
• Back-to-school: May through September (peaks during and after Prime Day) 
• Labor Day: September 1 
• Amazon Prime Big Deal Days: October (dates TBD; October 7–8 in 2025) 
• Singles’ Day: November 11 
• Black Friday: November 27 
• Cyber Monday: November 30 

Beyond the timeline, two things connect every event on this list: AI is reshaping how shoppers discover products, and the brands with accurate, structured data across channels will capture more demand than those without it. 

How should brands prepare for Amazon Prime Day in June 2026? 

Prime Day runs June 23–26 across 35 categories, with deals dropping three times daily and new inventory going live every five minutes during peak hours.¹ Amazon is putting particular emphasis on fresh groceries and household essentials, building on 4 billion grocery and essentials items delivered via same-day delivery over the past year.² 

Last year’s Prime Day (July 8–11) generated $24.1 billion in U.S. online spend, up 30.3% year over year.³ Four of the top five items sold were household or grocery products.⁴ School supplies rose 175% and dorm essentials 84%.³

Across our own client base, the four-day format shifted buying patterns. Shoppers browsed Days 1 and 2, then converted late, with Day 4 GMV jumping 38% year over year. 

One Rithum client pivoted mid-event to back-to-school bundles and optimized product titles on Day 2. That shift drove a 15% sales lift over the prior year. The teams that adjusted in real time outperformed the ones that set a plan and left it. And that was before AI was deeply embedded in the Prime Day experience. 

What role does Alexa AI play in Prime Day 2026? 

This year, Alexa for Shopping (formerly Rufus) will sit directly in the Amazon search bar, search results, and product detail pages for every U.S. customer.¹ Amazon has told sellers that Alexa pulls from listing content to answer shopper questions and make recommendations. If your product attributes are incomplete, Alexa will recommend someone else’s.

During Prime Day 2025, shoppers who engaged Rufus were 60% more likely to complete a purchase. Alexa now helps shoppers build personalized deal guides, set price alerts, trigger auto-buy at target prices, and review 365-day price history. Every one of those features pulls from your product data. If your titles, descriptions, and images aren’t accurate, you’re not showing up in those conversations. 

Rithum’s retail media advertising connects spend to product performance across Amazon, Walmart, and Target so you can shift budgets while the event is live. Marketplace listings keep data accurate across 900+ channels so you can coordinate pricing, inventory, and promotions across Amazon and every channel competing for the same shopper during Prime Day. And inventory management prevents oversells and stockouts during the exact hours demand spikes. 

How does AI shopping change peak season strategy in 2026? 

When AI gets product information wrong, 58% of shoppers lose trust in the brand itself, not the AI tool that served it. And shoppers aren’t loyal to a single AI platform either: ChatGPT’s daily active user share has fallen steadily since early 2025, Gemini has more than doubled its share in the same period, and 20% of AI users now regularly use two or more chatbot apps. Brands that only optimize for one platform are leaving reach on the table. 

The shopping capabilities on these platforms have also matured. Perplexity offers one-click checkout through Buy with Pro and PayPal integration across thousands of merchants. Gemini supports agentic checkout through Google Pay, including auto-buy when a price target is met.

ChatGPT launched native shopping features in late 2025. These are live commerce channels, and every one of them relies on structured product data to generate accurate recommendations. 

We built ChatGPT and Perplexity Feeds for this: product data compiled, optimized for LLMs, and delivered for ingestion so your brand controls how it appears in AI recommendations. Our upcoming GEO (generative engine optimization) pilot provides a way to track how products appear, move, and compare within AI-driven results. Rithum and Stripe are also building the commerce infrastructure for transactions that happen inside the AI conversation itself. 

The path from catalog accuracy to LLM delivery to in-conversation purchase is what determines whether your products get recommended during peak season. Every step depends on the one before it, and most brands are still missing at least one. 

How early does back-to-school shopping start in 2026, and what should sellers prioritize? 

Back-to-school retail is projected at $85.42 billion in 2026, up 3.3% year over year.⁵ 67% of families started buying by early July 2025, up from 55% the year before. With Prime Day now in June and school supplies already among its top-performing categories (up 175% during Prime Day 2025³), back-to-school effectively starts during Prime Day. 

84% of parents say prices are too high. 82% are prioritizing value over brand. But 80% still say buying the specific products their child wants is important.⁵ Parents are cutting back on clothing and switching to store brands in some categories while spending full price in others. Brands that understand which of their products fall into which bucket can plan promotions accordingly. 

We recommend spreading promotions across May through September rather than concentrating spend in a single burst. Rithum’s product catalog feeds push near-real-time updates to price, stock, and product data across every channel where shoppers and AI agents are looking. For brands selling across multiple regions, automatic localization for language, currency, and regional specs keeps listings accurate without rebuilding feeds from scratch. 

What should brands plan for across Q4 2026 peak events? 

September through November packs five major sales events into 90 days, and performance in each one can help shape the strategy for the next. 

Labor Day (September 1) — The last major sales moment before Q4 takes over.

Brands use it to move seasonal stock in a smart way. They set pricing rules based on demand signals, not flat markdowns. This helps them carry less dead stock into the holiday rush.

Rithum automates promotional updates across marketplaces. Order management routes to the best fulfillment location based on your rules. 

Prime Big Deal Days (October) — Amazon’s separate October event, distinct from summer Prime Day.

The 2025 event (October 7–8) outperformed the prior year globally.³ Shoppers complete holiday purchases here, and the performance data directly informs Q4 decisions. Brands that connected product feeds to LLM platforms earlier in the year will have compounding advantages by October.

Singles’ Day (November 11) — $202 billion across major Chinese platforms in 2024, making it the largest single shopping event globally.³

The event continues to expand into Southeast Asia, where Chinese ecommerce players now account for up to 50% of B2C GMV in key markets. U.S. and European retailers saw an 11% year-over-year lift in influenced revenue last November. Rithum supports 600+ global marketplaces with built-in localization and dedicated account strategists across EU, US, and APAC. 

How should brands prepare for Black Friday and Cyber Monday 2026? 

Black Friday (November 27) and Cyber Monday (November 30) — BFCM 2024 delivered $10.8 billion (Black Friday) and a record $14.3 billion (Cyber Monday 2025).³

Promotions started climbing November 16 last year, nearly two weeks early. Mobile drove 56% of online holiday spend last season.³ AI-assisted shopping volume, which surged during Prime Day 2025, will be even higher by November as more consumers adopt LLM-based product discovery. 

During BFCM 2025, we advised clients to spread budgets from October through December. Clients increased spending by only 3% on average and still hit efficiency goals with a 1.7 advertising cost of sales. Here’s how those Prime Day lessons apply to holiday planning. 

Across all of these events, the operational requirements are the same: accurate product data everywhere you sell, budgets that can move in real time, and listings that don’t break under pressure. Rithum’s product catalog feeds deliver data to every channel and AI platform in the right format. Paid search and shopping ads optimize across Google, Microsoft, and Meta with feed-level precision. And marketplace listings catches broken or non-compliant listings before they cost you revenue during the highest-traffic weeks of the year. 

How do you prepare for peak season across 900+ channels at once? 

Our 2026 Commerce Readiness Index found that 43% of retailers and 37% of brands are prioritizing AI enablement across operations. The best teams have one thing in common at their busiest times. Product data, ad spend, inventory, and fulfillment all run in a single connected system. When something changes mid-event, they can act right away. 

Rithum brings all of that into one platform across 900+ channels and AI platforms. If you’re planning for peak 2026 and want to talk through your approach, our team is here. 

Footnotes
¹ Amazon Press Release, “Mark Your Calendars: Amazon Announces Prime Day Event from June 23–26,” June 2026. press.aboutamazon.com
² Forbes, “Amazon Prime Day 2026 Moves to June—This Time With Alexa AI Powering the Cart,” June 2026. forbes.com
³ Adobe Analytics, various reports including “Prime Day Event Drove $24 Billion in Online Spend Across U.S. Retailers” (July 2025), Black Friday 2024, Cyber Monday 2025, holiday season 2025, and Singles’ Day data. business.adobe.com
⁴ Numerator, “Amazon Prime Day 2025 First Half Results,” July 2025
⁵ MRI-Simmons, “Back-to-School Shopping Trends of the 2026 Season,” May 2026. mrisimmons.com | Accio, “Back-to-School Trends 2026.” accio.com

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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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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.  

The post The hidden cost of ecommerce automation is verification work appeared first on Rithum.

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.

The post The Shopping Bracket: What the NCAA tournament tells us about today’s commerce appeared first on Rithum.

TL;DR

• “Returnuary” is a myth: January is the second-lowest return month of the year, with a 7.5% average return rate across 8.2M products and 20,000+ suppliers on the Rithum platform
• The real returns crisis is summer: June peaks at 9.35%, July at 8.78%
• TikTok Shop’s U.S. return rate is 0.42%, compared to an 8.25% ecommerce average, meaning traditional channel shoppers are 18x more likely to return a product
• Even in fashion, women’s jeans are 2.4x less likely to be returned via TikTok Shop than through non-social channels.Zalando reduced size-related returns by 10% after implementing more precise sizing tools

Every January, retail teams brace for the post-holiday return wave. It’s become almost a mythical event, with its own nickname: “Returnuary.”

But does it actually exist?

We analyzed return behavior across our more than 8.2 million unique products and 20,000+ suppliers generating billions in global sales. And across three years of data, January consistently ranks as the second-lowest return month of the year, with an average return rate of just 7.5%.

In today’s ecommerce driven gift season, it seems it’s more likely for items to be regifted or recycled to thrift stores than returned.

The actual return crisis is waiting in the sunny summer shadows: June has the year’s highest return rates, with peaks at 9.35%. July follows at 8.78%.

These are driven almost entirely by seasonal apparel. Shoppers buy swimsuits, dresses, and summer pieces sight-unseen, often in multiples, often with the full intention of returning most of them. Two-piece swimwear alone sees return rates as high as 64%.

This pattern has held consistently from 2022 through 2025. While there are nuances to it—retailers who don’t sell clothes likely aren’t hit as hard, for example—the big takeaway is that if your returns strategy is built around January, you’re focusing on the wrong month. The Returnuary monster is a tall tale, distracting from the spikey June lurking around the corner.

Social commerce is cracking returns wide open

In recent returns analysis, we also looked closely at how social commerce is changing the returns landscape.

TikTok Shop’s U.S. return rate is 0.42%.The overall ecommerce average sits at 8.25%. That means shoppers on traditional channels are 18x more likely to return a product than TikTok Shop buyers. The UK TikTok Shop return rate is 0.75%—still a fraction of conventional channel performance.

The instinct is to chalk this up to product type, with the assumption being that TikTok sells cheap, low-consideration items that aren’t worth the hassle of returning.

That’s the easy answer. But we don’t see that holding true. Even in fashion—the highest-return category in all of ecommerce, where 68% of consumers have made a return and traditional return rates run 50-70%—TikTok Shop buyers behave fundamentally differently. Women’s jeans, one of the most-returned items in fashion, are 2.4x less likely to be returned through TikTok Shop than through non-social commerce channels.

The mechanism is straightforward: TikTok gives consumers more confidence before checkout. In traditional ecommerce, a consumer is gambling on product photos and written descriptions. On TikTok Shop, they can see exactly how a product fits on a real body: the stretch, the sizing, the way it looks in motion. There are no surprises at unboxing. Which likely leads to far less bracketing, far fewer returns.

This has meaningful implications beyond TikTok specifically. It’s a green flag about what actually reduces returns: better pre-purchase information. Our last year’s consumer survey of 6,000+ global shoppers backs this up: 39% said better size and fit recommendations would significantly reduce their returns, and 31% say they’d be less likely to return a product if it included real-life customer photos.

Bracketing is a young person’s game. And it’s here to stay.

The summer apparel spike is largely a bracketing problem, and bracketing is a generational one. More than 50% of shoppers under 35 admit to regularly buying more items than they intend to keep, specifically planning to return the rest. Globally, 36% of all consumers do it, with Germany at 43% and the U.S. at 39% leading among surveyed markets.

The behavior is especially entrenched in European markets, where platform refund rates in countries like Austria and Poland can exceed 60% on fashion items. And it accelerates when conditions are favorable: free shipping, generous return windows, and frictionless return processes all make bracketing easier.

The irony is that the policies designed to drive conversion are often the ones amplifying the return problem. One European retail brand implemented a 100-day return window with free logistics. Fashion refund rates soared above 60%. The policy had turned the brand into a fitting room at scale, with all the handling, restocking, and margin erosion that entails.

Friction as a feature

Return friction has traditionally been treated as something to minimize at all costs. The new thinking—and the data—suggests a more nuanced approach.

1 in 5 shoppers says they’ve wanted to return something but didn’t, citing hassle, shipping costs, or distance to drop-off. This intentional friction, placed thoughtfully, can reduce casual returns without damaging the experience for buyers who genuinely need to return.

Return windows are similarly flexible. 51% of global consumers consider a 14-day or shorter window reasonable—and acceptance is even higher in heavy-return markets like Germany (57%) and France (64%). The assumption that consumers expect 30-day windows as a baseline isn’t supported by what consumers actually say they would accept.

Sustainability framing is emerging as another lever, particularly for younger segments. 60% of shoppers say they’d be open to consolidating return shipments to reduce environmental impact. More than half say they’d accept return limits or small fees if positioned as planet-positive. Among Gen Z, 85% say their return behavior is influenced by environmental concerns—the highest of any age group, and a clear signal about where consumer expectations are heading.

Your return policy is a fitting room

The thread running through all of this—the social commerce returns, the summer spike, the bracketing behavior—is that most returns are a pre-purchase problem masquerading as a post-purchase one. And that returns are more complex than they used to be.

But overall, shoppers who know exactly what they’re buying return far less.

The operational and policy fixes are just symptoms. The information gap is the disease.

Close the information gap first. High-quality images, real customer photos, detailed sizing guides, and fit tools are among the highest-ROI investments a retailer can make in return reduction. Zalando reduced size-related returns by 10% after implementing more precise sizing tools. The upfront investment pays for itself quickly.

Build better return policies. Tie free returns to loyalty tiers or order thresholds. Use SKU-level profitability data to determine where you can absorb free return costs and where you need to draw a line. Test variable fees on low-margin products or with repeat returners.

Watch your calendar, not just your categories. If apparel is in your assortment, your peak return exposure is June and July, not January. Other categories might offer similar clues. There is no more singular commerce calendar. Plan inventory, staffing, and return logistics accordingly.

Take social commerce seriously as a returns strategy. Video-driven, creator-mediated product discovery fundamentally changes buyer confidence. That principle is portable across marketplaces: video content, real customer demonstrations, and fit-focused creative reduce returns across channels.

Want to learn more about the nuances of returns and how to prepare? Talk to us. We can help give you specific guidance and guidelines for making returns a profit lever, instead of a loss.

Data caveat: Rithum powers sales on social commerce across fashion, beauty, electronics, and other categories. Data sourced from the Rithum commerce platform, representing 8.2M unique products and 20K+ suppliers generating billions in global sales across ecommerce channels. Results reflect the composition of brands and retailers using the platform.

Gregor Kiddie is a manager, engineering, at Rithum.

The post How to prepare for the return crisis you’re not watching for appeared first on Rithum.

This time of year, marketplace leaders are all asking the same question: how do we scale? The answers usually revolve around supply, selection, and seller acquisition. Neel Grover, CEO of ShopSimon, asks something else. He starts with deciding what must stay protected as the marketplace grows, then builds backward.  “This is my fifth marketplace that I’ve done over the last two decades,” Neel said at the Rithum LIVE event in New York. “I’ve been in the marketplace business literally for 20 years now and wanted to do some things differently.” 

ShopSimon reflects those choices. ShopSimon is Simon Property Group’s digital marketplace for premium and luxury sale-priced products, with “guaranteed authenticity” and savings of up to 85% across fashion, handbags, shoes, beauty, and home. It lists more than 1 million products across thousands of brands, including Nike, adidas, Hugo Boss, and Puma. The business stays curated by design. It avoids buy box mechanics that pull marketplaces toward commoditization. It shares customer data with brands under specific conditions. And it treats Simon’s physical footprint as a fulfillment and discovery network with real operational demands, not a backdrop for brand storytelling. 

“We don’t really drive to a buy box,” Neel said. “We’re much more of a brand marketplace, and so we want brands to control the experience.” 

Neel has run marketplaces with thousands of sellers. “Whereas when I ran Buy and Rakuten, we had over 7,000 brands on our marketplace. Here we have about 450 today,” Neel said. That smaller footprint is intentional. ShopSimon stays selective by design, prioritizing a brand-controlled experience rather than buy box dynamics. 

Seller count looks like strategy until it changes behavior 

Seller count shows up in nearly every marketplace board deck because it’s easy to chart and hard to argue with. It changes how sellers compete and how the marketplace enforces standards. Price competition intensifies. Merchandising gets noisy. The customer experience begins to reflect the marketplace’s internal incentives rather than the brand’s intent. 

Neel has seen what happens when selection grows without guardrails, so he intentionally keeps ShopSimon selection tight. “We’re very curated,” Neel said. “We’re very selective on who can come on.” 

Curation protects the marketplace from the dynamics that turn premium brands into interchangeable inventory. It tells brands the marketplace won’t treat them like interchangeable listings, even as it grows.  

That tradeoff surfaced repeatedly during our conversation at Rithum LIVE. Scale is easy to measure, but brand trust depends on choices that limit it. 

For retailers building a marketplace, Neel’s approach forces an early decision. A marketplace can maximize selection, or it can protect an environment that brands trust enough to invest in. But it rarely does both well at the same time. 

That decision becomes real when retailers decide what the marketplace will enforce to protect brands, including: 

• Protect pricing behavior and merchandising consistency, even when sellers push for exceptions. 
• Give brands clear ownership of their experience on the site. 
• Make explicit what the marketplace will prioritize, especially as it grows. 

Neel acknowledged the tension that still exists in a curated model. Multi-brand sellers and distributors can surface lower prices and create brand friction. ShopSimon responds with posture and enforcement, not performative rhetoric. “We really want to be as true as we can to the brands,” Neel said. 

Brand trust starts with policy, not positioning 

Brands have heard every partnership pitch. They pay attention when a marketplace describes a rule set, especially around customer data. 

Neel framed ShopSimon’s approach as conditional collaboration, not open-ended generosity. “If a brand is exclusive with us in certain areas, we’ll actually share our customer data,” Neel said. “So, you can remarket back that customer back to your own website. To us that is true collaboration.” 

What stood out to me was how explicit the exchange is. Data sharing isn’t implied. It’s tied directly to exclusivity and collaboration, with a clear, practical benefit for brands. 

When brands evaluate marketplaces, Neel recommends a filter that starts with adjacency, not distribution volume. “Is this marketplace I’m looking at going to put my brand in a good light? Is it adjacent to how I want to be?” 

For brands, the best decision criteria rarely fit into a single dashboard metric. They come down to fit, context, and customer overlap. 

According to Neel, brands can best evaluate marketplaces by asking: 

• Does this environment strengthen how we want customers to see us? 
• Do we respect the brands we’ll sit next to? 
• Does the customer base align with where we want to grow? 
• Does the marketplace offer a real differentiator beyond another listing surface? 

BOPIS exposes whether a marketplace can operate in the real world 

Buy online, pick up in store (BOPIS) is where marketplace strategy collides with store reality. Neel built ShopSimon with a clear intent to connect online demand to Simon’s physical footprint. “We all agreed we could do things very differently,” Neel said, pointing to the ability to “tie into the 2 billion in store visits that happen in the malls and in the stores.” 

That ambition turned into a concrete initiative: buy online, pick up in store at a brand’s own location, launched by a marketplace. “We’re the first marketplace to launch buy online, pick up in store at your brand store,” Neel said. 

He also described why the hardest part doesn’t live in the integration layer. The hardest part lives in the store. “Operationally there are challenges,” Neel said. “The brand now has to train their employees to be able to not only handle their own BOPIS, but BOPIS through orders that come through us, which is not inconsequential.” 

ShopSimon’s framing of stores cuts through the usual omnichannel abstraction. “To me, those are little micro hubs and micro fulfillment centers,” Neel said. 

This store integration functions as a competitive moat. Software-only marketplaces can onboard sellers quickly and expand selection without taking on operational complexity. A marketplace that wants to route demand into stores must earn that capability through store workflows, training, inventory visibility, and service expectations. When the system works, it unlocks speed, proximity, and convenience that customers value. When it fails, it fails in public, at the counter, in front of customers. 

What stands out in Neel’s approach is that discipline often creates complexity rather than removing it. Curation requires enforcement. Data sharing requires clear rules. Store-based fulfillment requires systems that can reconcile marketplace orders with store operations in real time. These aren’t conceptual challenges. They’re execution problems, and they’re where marketplace strategies often stall. 

I’ve seen the same thing with retailers. The strategy isn’t the hard part. The hard part is building the operating model that can hold up once orders, inventory, and store teams are all in the mix. 

The ShopSimon team is working on lighter-touch capabilities that reduce friction for brands and store teams while expanding what customers can do with store inventory. “We are now looking at adding capabilities that will be even lighter touch on a brand side,” Neel said. 

Discovery works best when brands treat distribution as portfolio management 

Commerce leaders talk about discovery as a macro shift. Brands experience it as pressure to distribute widely. 

Neel takes a measured view. He believes brands benefit from broader product presence as discovery behavior evolves, but he also believes brands should resist the temptation to over-distribute and dilute control. “Getting a subset of your catalog out there so it can be discovered on other sites,” Neel said. “I’m not saying you have to have your whole catalog out there.” 

This distribution strategy treats marketplaces as differentiated surfaces with distinct audiences. Brands can choose whether to list the same subset everywhere or tailor assortment based on the customer base of each marketplace. “It may be really good for a subset of your products,” Neel said. 

He also pointed to why differentiation matters. “We’re deeply integrated into Simon,” he said, describing a “very large loyalty program” launching together. 

Marketplaces increasingly resemble each other on the surface. Brands will gravitate toward the ones that offer distinctive mechanics, clearer governance, and meaningful customer access. 

Cannibalization usually starts as a leadership decision 

Marketplace conversations often drift into cannibalization, as if the marketplace itself is the risk. Neel’s experience suggests a different diagnosis: organizations create cannibalization when they structure teams to compete. 

He has moved businesses from owned inventory to hybrid models and marketplace-first models. “We had to move from what I’d call an owned model to a marketplace model or a hybrid model,” he said. 

He described how internal competition showed up early. “There wasn’t a wholesale or owned inventory and a marketplace team, and it was competitive,” he said. 

He solved it by changing incentives. “I just ended up incentivizing the team on the overall sales of those products,” he said. 

That move aligns the organization around customer experience instead of channel politics. “At the end of the day you want to drive what’s going to be the best customer experience,” Neel said. 

Then he offered the line that should reset internal debates. “The customer doesn’t know if it’s wholesale or dropship or marketplace,” he said. “They don’t really care. They just want the product.” 

What serious marketplaces will prioritize next 

Neel didn’t frame ShopSimon as a marketplace that wins through volume. He framed it as a marketplace that wins through decisions other platforms avoid. It curates seller participation even when scale would be easier. It refuses to optimize around buy box mechanics even when price competition would drive short-term conversion. It treats customer data as a governed asset and offers brands a path to access it through clear collaboration. It invests in store-based fulfillment even though stores introduce training, labor, and process complexity. 

He also made clear why he took the role after spending two decades building marketplaces elsewhere. “I wasn’t looking to do another marketplace,” he said.  “We all agreed we could do things very differently.” For leaders who already understand marketplace mechanics, the takeaway is practical. Strategy begins with what you most need to protect, not what you can add. Smart constraints can help keep a marketplace consistent as it grows.  

Learn how Rithum supports marketplace operations teams.

Blaine Nielsen serves as President, Retail at Rithum. 

The post Building a new kind of marketplace: Lessons from ShopSimon CEO Neel Grover appeared first on Rithum.

During peak season, more than $1.5B in Cyber 5 sales moved through Rithum across channels, regions, and categories. That’s the first wave. The second arrives after the shipping notifications stop: returns season. Calendars reset, staffing normalizes, and inventory shifts back toward warehouses. For the shopper, the decision often happened earlier—at checkout, when they relied on whatever the product page made clear and whatever it didn’t. 

And as shopping shifts from product pages to AI agents that summarize and recommend, returns get harder to manage. When an agent gives the highlights, shoppers may never scan the full product detail page (PDP), compare specs side by side, or catch details that prevent a mismatch. If the summary is wrong, the purchase can still go through, and the retailer still absorbs the return. The harder part is accountability and diagnosis. If shoppers never see the original product page, you lose the trail of what they were told and you can’t easily trace a return back to a missing detail, a bad summary, or a mismatched listing. 

How can you prepare for returns season? Use January to audit the promises you made during peak: which products were misunderstood, which channels dropped key details, and which fixes would have prevented repeat returns. 

What returns show 

In the U.S., online returns will top $363B, driven by a 24.5% return rate, according to Rithum’s 2025 global returns & profit impact report.  

Rithum’s consumer research shows the same drivers repeating: 61% cite poor fit and over a third say the item didn’t match the description or photos. 

Fit plays out differently by category. The “didn’t match” driver applies across categories. 

Electronics returns can trace back to compatibility or what was included. Home returns follow scale and finish. Parts and industrial returns surface around fitment, specifications, or installation assumptions. Across categories, the same thing happens: unclear listings leave customers to guess. 

In January, returns cluster. The same products come back through the same channels for the same missing details. In a Rithum returns fireside chat featuring Fabian Ortmann, Head of Returns, ZEOS; Kevin Brown, Director, Sales and Strategic Partnerships, Essendant Fulfillment Services; and Louis Camassa, Director of Product, Rithum, Ortmann argued that returns should be treated as a data source, not just a cost center, one of the clearest ways to pinpoint where expectations broke by product, channel, and market. 

That framing changes how brands and retailers should use January. For returns season, the work should shift from cleanup to finding out what went wrong. 

Where channel listings diverge 

Channel differences start costing money in January. During peak, products are listed, reformatted, and rewritten to fit each marketplace’s format. That’s expected. The risk is what gets lost. 

One channel may carry a compatibility field. Another may not. One listing spells out what’s included, while another assumes it’s obvious. One channel enforces short titles that drop (important) qualifiers where another keeps them in tact.  

Louis described this in the fireside chat: when listings leave room for interpretation, customers answer the question themselves. Sometimes they guess right. Sometimes they don’t and that can lead to a higher rate of returns. 

Why timing changes the math 

Returns also don’t happen on your schedule. In the same conversation, Essendant’s Kevin Brown described the gap between decision and action. Customers often decide quickly whether they’re keeping something. The return itself may take weeks to arrive. 

That delay narrows options. Inventory comes back later. Resale windows shrink. Seasonal goods lose flexibility. The cost isn’t only the refund. It’s what the business can no longer do with the product. 

January is also when slow fixes compound. If product information is inconsistent across channels and updates move slowly, the same mismatch keeps shipping while teams debate which version of the truth is “correct.” 

Rithum’s 2026 commerce readiness index points to why this is common. 91% of retailers and 78% of brands report poor data quality. Nearly 75% say inaccurate data leads to bad decisions. 

When product records aren’t consistent or trustworthy, January becomes more triage than correction. 

What policy can and can’t do 

Return policy shapes demand, but it doesn’t explain repeat returns. Policy still matters because shoppers read it before they buy, especially when they’re unsure. 

Rithum’s research shows return policies influence 41% of purchase decisions, 88% expect free returns, and 47% won’t click “buy” without them. 

Most preventable returns aren’t caused by policy. They’re caused by uncertainty that existed before policy mattered. 

What to check first 

Start with the returns that teach you something. Look at SKUs that return quickly after delivery. Fast returns tend to signal expectation breaks, not end-of-season cleanouts. 

Compare the same SKU across your top channels. Don’t assume it matches your internal record. Check titles, key attributes, images, what’s-included language, and variants. 

Track “didn’t match description/photos” returns by channel. Rithum found that 33% of shoppers cite mismatch with the description or photos as a reason for returns. 

Then move from insight to change quickly. If updates take weeks to reach every listing, the same mismatch keeps shipping. 

Where AI fits 

AI won’t fix messy product data. The eTail report’s preparedness finding reinforces the same issue returns already expose: only 2% of organizations describe themselves as fully prepared with structured product feeds and governance. 

Rithum’s 2026 commerce readiness index shows how widespread the gap still is: 91% of retailers and 78% of brands report poor data quality, and nearly 75% say inaccurate data leads to bad decisions.  

Whether the buyer is human, a marketplace algorithm, or an emerging shopping interface, the requirement doesn’t change. Product truth has to be consistent, category-appropriate, and fast to update. AI will move decisions faster, but it will also scale whatever your data gets wrong. 

What to do next 

The goal is to reduce the avoidable ones and recover value faster on the rest. 

The brands and retailers who do this well don’t wait for the next peak. They use January to tighten product truth, align it across channels, and push corrections while the signal is still fresh. Learn how Rithum can help.

Jordan Christensen is Director, Client Experience, Retailers at Rithum. 

The post Returns season is here. And AI raises the stakes  appeared first on Rithum.

special guest post by Caitlyn Ford and Brittny Cantor, AI Commerce Innovation Leads at Accenture.

For years, your product detail page (PDP) was the critical bridge to turning browsers into buyers by giving them specs, visuals, and the confidence to click “add to cart.”  

Generative AI has changed the path to that click. Shoppers aren’t reading your PDP first; agentic AI is. These agents scan your descriptions to decide whether a product is worth a shopper’s time—and sometimes even handle the recommendation, summary, and transaction themselves. Agentic AI assistants, such as ChatGPT, Gemini, and Perplexity, are now common starting points for product research, and Google’s AI summaries increasingly sit above the traditional blue links, often answering the query without a click. Analyses show that when an AI overview appears, the #1 organic result can lose roughly a third of its usual clicks, and more than half of Google searches already end without any website visit at all. 

That means your PDP now speaks to three audiences at once: the shopper, search algorithms, and the AI agents deciding what gets recommended. You’ve built for shoppers and search, but when agents answer a query, it gets more complicated. They synthesize data across multiple PDPs, reviews, listings, and brand content into one recommendation. To even be brought into their considering, your PDP must be accurate, consistent, structured, and easy for AI to parse. Because most AI summaries trigger on informational, top-of-funnel queries, discovery and education are increasingly happening inside AI layers, while the remaining clicks are skewing toward bottom-funnel, brand- and product-specific searches. That makes the quality of your PDPs even more important: when a shopper finally does click through, they’re usually closer to a decision than they used to be. 

This is Generative Engine Optimization (GEO) for your product page: optimizing the content so AI agents see your product as consistent, credible, relevant, and complete. In GEO terms, you’re optimizing not just for rankings, but for how (and whether) AI systems mention and cite your products inside their answers—whether that’s a link in a Google AI Overview or a product shout-out in a ChatGPT or Perplexity response.

GEO doesn’t replace SEO—it sits beside it. The fundamentals still matter, and you can start with a few practical moves to make your PDPs GEO-ready without losing ground with direct shoppers or SEO. Traffic from LLMs convert 9x higher than traditional search.  

1. Build for questions, not keywords 

Traditional SEO was about ranking for keywords. GEO is about being the best answer. AI agents scan content to understand intent. This includes who the product is for, what problem it solves, and why it’s trustworthy. Under the hood, modern search engines break complex prompts into many related fan-out queries (such as budget, location, use case, constraints) and then recombine the best matches into one synthesized answer. If your PDP doesn’t clearly map to those real-world questions, it’s less likely to be pulled into that synthesis. 

Start by rewriting your PDPs to sound more conversational. Frame details around real-world questions: “Is it waterproof?” “How long does it last?” “Is it safe for sensitive skin?” Product FAQs and comparison callouts matter more than ever because they train AI models on context, which is the very thing that makes a product relevant in generative results. 

Pro tip: The more structured and direct your answers, the more easily AI can cite and summarize them. Think in use cases: 

• Instead of: “Premium leather tote with zip closure and inner pocket.” 

• Try: “Looking for a work tote that fits a laptop and zips closed for travel? This one holds up to 15 inches and has a reinforced base for durability.” 

The goal is to pre-answer what a shopper might ask, because AI is looking for the best source to do just that. When generative systems are doing the research legwork on behalf of the user, you want them to find crisp, unambiguous language they can safely reuse or summarize on your behalf. 

2. Standardize data across every channel 

Whether you sell through Amazon, Walmart, Target, Instacart, or your own DTC site, the same PDP likely shows up in multiple places. For AI, inconsistency kills trust, as agents compare listings across channels. If your dimensions differ on HomeDepot.com and your own brand site, the agent is less likely to choose either. 

This isn’t just a data hygiene issue; it directly affects how AI systems model your products. Large models build their understanding of brands from many sources—retail PDPs, comparison sites, reviews, blogs—so if product specs conflict across those surfaces, the safest move for the AI is to ignore you or recommend a competitor with cleaner signals.  

Pro tip: Use shared taxonomies, normalize specs, and keep your product attributes up-to-date everywhere. AI agents can’t make sense of your product if your systems don’t agree on what it is. 

• For brands: Build and maintain a “single source of truth” for product data—ideally housed in your PIM and pushed everywhere via API or feed. 

• For retailers: Set clear PDP templates and data ingestion rules for third-party sellers to reduce conflict and duplication. 

Think beyond your own site, too. If comparison or review sites are important for your category, make sure your specs, naming conventions, and positioning match what appears there. AI answers often lean heavily on third-party sources—reviews, forums, round-ups—rather than a single brand’s website. 

3. Focus on proof, not promotion 

Agentic AI is trained to be skeptical. They value facts, third-party validation, and consumer signals. That means your PDPs need more than adjectives—they need evidence. It’s up to you to provide it.  

For example: 

• Instead of “super comfortable,” include “Rated 4.7/5 by 2,100+ customers for comfort.” 

• If you claim sustainability, link to third-party certifications like USDA Organic or OEKO-TEX®. 

• If your laptop is rugged, cite test conditions: “Drop-tested from 1.2 meters to meet MIL-STD-810G standards.” 

AI is scanning your product page for trust markers. Verified reviews, return policies, sourcing info, and warranty terms matter. This is valuable for customers as a rule of thumb, but is even more important now for the bots deciding what gets recommended. In AI search, these proof points do double duty: they help the model decide whether to feature your product at all and, if it does, they provide quotable snippets (ratings, certifications, test results) that can be pulled directly into summaries or comparison tables.  

Remember that a lot of this “proof” now lives off-site as well. If your products are reviewed on blogs, YouTube, Reddit, or niche forums, those conversations shape how AI systems describe you. Investing in credible third-party coverage and cultivating high-quality reviews does more than build social proof. It feeds the training data that powers AI recommendations in the first place. 

4. Automate freshness to avoid invisibility 

PDPs that are out of date—on price, inventory, delivery estimates, or product specs—will get skipped. AI tools now favor real-time accuracy. So if your shampoo shows as “in stock” in one feed and “out of stock” on your brand site, that confusion may cost you visibility altogether. 

• For brands: Connect your PDP data to real-time inventory systems and marketplace feeds. Set up automated triggers to refresh PDP content when price or availability changes. 

• For retailers: Sync PDP fields with seller dashboards and flag stale listings via scoring models or AI-based health checks. 

Timeliness isn’t a nice-to-have. It’s how you stay visible. An outdated PDP isn’t just underperforming. It’s invisible to AI. And as AI layers absorb more top- and mid-funnel traffic, the remaining clicks from search are increasingly tied to bottom-funnel, transactional experiences. That makes it even riskier to let inventory, pricing, or availability drift out of sync across feeds, because those are exactly the signals AI systems rely on when deciding what to surface for high-intent shoppers. 

5. Optimize for a multi-agent future 

AI agents are already embedded across search engines, voice assistants, mobile devices, and retail platforms. And they’re only getting more proactive. Soon, they won’t just answer your questions—they’ll anticipate and fulfill them. 

Your PDPs need to be ready for that shift now. Start by: 

• Structuring your content with pre-defined templates.

• Getting insights into current benchmarks and unlock opportunitites/risk with visibility analysis

• Evaluating prompt coverage. Which products are showing up for common category queries? Which competitors are dominating agent responses? 

GEO is the framework that ties this together: it extends classic SEO into a world where AI assistants summarize, compare, and recommend before a shopper ever sees a SERP. In practice, that means designing your PDPs so they’re easy for AI to understand and safe to quote, then measuring success not just in rankings and sessions, but in how often you’re mentioned or cited inside AI answers.  

Early data suggests that even if total traffic drops, the visits you do get from AI-mediated journeys can convert disproportionately well. In one analysis, LLM-originated visits accounted for a tiny share of total clicks but a materially higher share of sign-ups—because those users arrived later in the decision process. That’s exactly the kind of high-intent traffic your GEO-ready PDPs should be engineered to convert. 

Whether you’re managing thousands of SKUs across marketplaces or a curated set of products on your DTC site, your PDPs now serve a dual audience: the buyer and the buyer’s agent. 

Search behavior is changing. Make sure your PDPs are ready. 

AI agents are now the first (and sometimes only) step in a shopping journey. If your PDPs aren’t ready, you won’t make the cart. 

The good news is that GEO isn’t a separate track. It builds on what good PDPs already do: clear writing, accurate data, consistent structure, and real value to the shopper. You’ve got this (and Rithum can help). 

Caitlyn Ford and Brittny Cantor are AI Commerce Innovation Leads at Accenture.

The post 5 Ways to Make Your PDPs GEO-Ready  appeared first on Rithum.

Shoppers now ask AI assistants what they want to buy and get a short, curated list of recommendations with reasons, not a list of links to look up. Amazon’s Help Me Decide feature picks one product and explains why. Pinterest introduced a multimodal AI shopping assistant that turns voice, text, and image prompts into shoppable recommendations. Snap is building conversational search into Snapchat through a $400M partnership with Perplexity, bringing cited, in-chat answers to nearly a billion users starting in 2026. 

Assistant-led shopping is gaining ground, but The 2026 commerce readiness index, based on surveys of 200 global brand and retail executives, found that most retailers and brands are not ready. “Customer journeys are bleeding revenue. The cracks are everywhere: prospective shoppers bounce at broken links, irrelevant ads, and empty shelves, while current customers churn after bad service, costly returns, and radio silence from brands. Each leak bleeds profit.”  

Pouring more spend into acquisition won’t fix a leaky funnel. Instead, you must patch your highest-loss points first, then scale your spend to drive growth. 

Find the leaks first 

Before fixing anything, identify where journeys fail. According to the survey, retailers lose prospects before checkout. 26% point to gaps between ads and products, and 19% to payment processing issues. 

But brands lose customers after the sale. 22% cite customer care problems and 17% point to returns and refunds. 

These two realities suggest a practical order of work: stabilize pre-checkout for retailers and post-purchase for brands, supported by clean data and responsive operations. 

Why leaks persist (and why “move fast” isn’t enough) 

The index shows that while retailers and brands say they want to move quickly, their inputs and workflows slow them down. “Fully automated workflows are almost nonexistent,” according to the survey respondents. 

While 99% of brands and 100% of retailers say they feel confident measuring performance, high confidence is not the same as accuracy. Nearly 75% of brands and retailers admit they sometimes make decisions based on inaccurate data. And more than one-third say it happens “often” or “all the time.” 

These leaks are not random. They come from manual processes and imperfect data. Fix the inputs to steady the buyer journey. 

Start with these four leaks 

Start with the biggest-loss points. Each fix will reinforce the next. 

1. Ad → product page 
   26% of retailers said ad-to-product gaps proved most problematic. To fix it, make ads match reality. Tie placements to real-time catalog data: titles, images, price, and availability. Replace static landing pages with feed-driven product pages. Run daily link and variant checks. The result is fewer wasted clicks and a clearer path to cart. 
2. Payment processing  
   19% said payment processing is the second-largest failure point for retailers. Treat checkout errors as system signals. Track decline codes and 3D secure (3DS) loops. Tune fraud rules to reduce false positives. Align tax and shipping logic with inventory holds so payment retries go through. When payments clear reliably, your media and product page improvements (PDP) have a bigger impact. 
3. Customer care  
   For brands, customer service is the largest post-purchase leak with 22% citing it as their top breakdown. Give service agents one unified view of orders, inventory, and fulfillment. Publish status updates across channels so customers don’t need to ask. Faster resolutions protect margin and earn the next purchase. 
4. Returns and refunds 
   Treat returns as structured feedback. Feed return reason codes back into product copy, images, and targeting to fix fit, spec, and compatibility issues earlier in the funnel. Keep the returns process easy enough to preserve loyalty, but tighten controls where needed.  

Make speed and accuracy inseparable 

According to the index, “Data accuracy and speed can’t be trade-offs. They have to be solved together.” That starts with reducing manual work, checking data as it comes in, keeping prices, inventory, and delivery promises up to date, and stopping bad data before it goes live. 

When performance signals do light up, move quickly: over half of retailers say they respond within 48 hours, while brands are more likely to take three to five business days. Shorten the loop so fixes land while demand is still happening. 

What to scale once the leaks are sealed 

Social commerce leads on conversion, and site experience is close behind. Reach doesn’t matter if the landing page is slow or checkout trips people up. Fix the path first, then spend. 

Customers are shortening the distance from discovery to purchase. The Index’s guidance is simple: fix the biggest leaks first, then scale your spend.  

Download the full report here. To turn the index into an action plan for your team, talk to Rithum today.

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