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. 

The post Which consumers will embrace agentic commerce, according to Gartner  appeared first on Rithum.

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.

TL;DR 

• Product journeys, from discovery to decision, are shifting to AI. Among AI-active shoppers, 90%+ use LLMs to research products and compare options, and 53% use them to decide where to buy. 
• AI is diminishing buyer loyalty. 19% say they now buy from brands or products they had not heard about before, and 13% say they are more likely to switch retailers or products after using an LLM. 
• Brand and retailer sites have less time to influence the decision. 32% of shoppers spend less time browsing other sites after using LLM tools, and only 5% verify AI shopping information on a retailer or brand site. 
• Product information now plays a bigger role in trust. 49% say a clear explanation would do the most to increase trust in an AI recommendation, 67% say price is the most important detail to get right, and 58% say trust in the brand drops when an LLM provides incorrect product information. 

Rithum’s new report, The new discovery engine: How consumers are using AI to find, trust, and choose brands, and what’s at risk for those they never see, has a clear message for retailers and brands: the shopping journey is no longer confined to shelves, search results, category pages, or product detail pages. 

Based on a survey of 1,046 online shoppers in the U.S. and U.K., the report shows how LLMs have become the entire shopping journey and a one-stop shop where products get researched, compared, narrowed down, and chosen. Among AI-active shoppers, more than 90% use LLMs to research product information and compare options, while 53% use them to decide where to buy. By the time a shopper lands on a product page, the filtering may already be over. 

That shift to AI is creating room for brands that were not previously in the mix. In the survey, 19% of shoppers say they now buy from brands or products they had not heard about before. Established brands are facing a tougher version of the same market. Recognition still helps, but it has less force when AI is doing more of the sorting, ranking, and explaining before the click. 

The shortlist is forming earlier  

AI shopping adoption is the starting point for 80% of shoppers ages 18-to-27 and 80% among shoppers ages 28-to-43. Among households earning $100,000 to $150,000, it reached 84% adoption. These are commercially important shoppers, and they are already weaving LLMs into their purchase journey. 

The filtering that used to happen across tabs, retailer sites, and review pages is now happening inside LLMs. A product that appears high in the response moves ahead. One that doesn’t can disappear before the shopper has seriously considered it. 

ChatGPT’s product comparison feature adds to that shift. Shoppers can compare products side by side inside the chat, with price, features, reviews, and other details presented in one place instead of scattered across multiple retailer tabs. 

More than half of shoppers already trust AI tools as much as brand websites, and among high-income households, confidence in AI accuracy climbs as high as 80%. That trust gives LLM recommendations real weight early in the decision process, according to the report. 

Retailers now have more riding on how products are represented on their page, even before a shopper lands on the site. Brands face the same pressure, but with fewer natural intervention points. A retailer may still appear as the place to buy, even if a brand is filtered out earlier. If a brand’s product information is incomplete, inconsistent, or hard for AI to explain, it can be dropped from consideration before its own product page or brand story has a chance to influence the decision. 

New brands are finding room where familiar brands once had an easier ride 

You can see the LLM effect far beyond just initial research, with ripples into what shoppers buy. Nineteen percent of shoppers say they are more likely to buy from brands or products they had not heard about before if an LLM suggests it. Another 13% say they are more likely to switch retailers or products after using an LLM. Together, those numbers create a shopping environment where familiar brands have less room to rely on recognition alone. 

That creates an opening for challenger brands. A newer brand does not need years of broad recognition to get in front of a shopper. It needs usable, consistentproduct information and enough context for AI to present it clearly and convincingly. 

Established brands have less room to lean on familiarity alone. Customer loyalty still helps, but it no longer ensures that they go to your site first. Nearly half of shoppers say a clear explanation of why a product or brand was chosen would do the most to increase trust in an LLM recommendation. What carries weight here in an LLM recommendation is not name recognition but whether the recommendation feels specific, informed, and ready to act on. 

Brand-owned sites get fewer chances to influence the outcome 

The shopping journey used to leave more room for second thoughts. A shopper could open a few tabs, compare prices, read reviews, leave, come back, then change course. LLMs have shortened that process.  

In the survey, 32% of consumers say they spend less time browsing other sites when using LLM tools to shop. Another 36% say they make faster decisions, while 34% say they feel more confident about their purchases.  

These three stats don’t live in a vacuum. They indicate a continual trust-building experience for the shopper: they’re saving time, they’re finding what they need faster, and they feel better about their purchases. Why would they leave that experience to go back to a retailers website? 

The same pattern appears in how people verify what they see. Shoppers who double-check an LLM recommendation rarely begin with looking for confirmation on a brand or retailer site. Twenty-eight percent turn to search engines (which is likely also relying on AI tools), 19% specifically look for online reviews, 17% ask friends and family, and only 5% go to a retailer or brand website. A beautiful, brand-forward website won’t convince them to buy your product. They won’t even see it. But a PDP with in-depth specifications, GEO-optimized keywords, and highly relevant descriptions will impact consumers’ decisions, even if they don’t see the page. 

The recommendation is only as strong as the product story behind it 

Ask an LLM why it chooses one product over another, and it has to build that answer from the product facts it can find: materials, dimensions, compatibility, intended use, etc. The recommendation that an LLM givesis assembled from those pieces in real time. 

For brands, that raises the standard for product content. Copy, attributes, use cases, and supporting details are no longer sitting off to the side as content maintenance. They are becoming part of the recommendation itself. When the product story is thin, generic, or inconsistent, the answer reads that way too. 

Retailers feel the same pressure across the assortment. Pricing, inventory, attribute completeness, and feed quality all shape how products are represented before a shopper ever reaches the site. Anyone who has spent time inside a catalog has seen how quickly that can start to fray. A bad price, a missing dimension, or stale availability can make a solid product look less reliable than it is. 

The harder question is whether the product story still holds together everywhere that LLMs are pulling from. This includes product content, syndication, pricing, availability, and the systems that keep those details aligned. It also includes sources brands and retailers cannot fully control, such as reviews, forums, and social discussion. When those external signals surface alongside structured product data, inconsistencies become more visible. That makes it even more important for the information you do control to be accurate, complete, and easy for AI to explain. LLMs only give recommendations they can trust, based on the information that holds it together. 

Trust is moving closer to the data itself 

The survey leaves little ambiguity on price. In an AI shopping recommendation, 67% say it is the most important detail to get right. Reviews, availability, where to buy, and technical specifications all come after it in the list of prioritization 

That order will feel familiar to anyone who has watched shoppers abandon a cart over a mismatch or lose confidence over a number that does not look right. A wrong price or stale detail does not stay in the background. It becomes part of the recommendation, which means it becomes part of the shopper’s impression of your brand. 

The report puts numbers behind that. When an LLM provides incorrect product information, 58% say trust in the product or brand decreases, and 16% say they leave the purchase altogether. 

At that point, the issue is no longer confined to data quality. The recommendation may come from the model, but shoppers are not spending time sorting out where the error began. They decide whether the information feels reliable, and the brand lives with the result. 

The next phase is close enough to shape decisions now 

The report also looks ahead to a shopping flow where the model takes on more of the decision itself. More than 25% of AI-active shoppers say they are already very likely to hand purchasing decisions to AI, and another 39% say they are somewhat likely to consider doing this, if and when it’s available. Among the most AI-active shoppers, 65% say they are very or somewhat likely to use an AI agent that would buy for them. 

What AI sees already shapes what shoppers buy. Thin product content, stale pricing, patchy attributes, and inconsistent availability all weaken the recommendation before the shopper has done anything beyond type in a prompt. 

The priorities are clear. Keep your product story consistent. Keep pricing accurate. Keep availability current. Make products easier to compare, easier to explain, and less likely to be misread. New brands already have more room to enter the conversation. Established brands have less room for weak information, stale details, or missing context. 

For more details on the survey and a full breakdown of the results, download the report here.  

Methodology 

Rithum’s 2026 report is based on a survey of 1,046 online shoppers in the U.S. and U.K. Some questions look at behavior in the last 3 months, some category questions use the last 6 months, and some trust and behavior questions are broader and are not tied to a single recall window. 

The post A brand your customer had never heard of just won the sale 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.

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. 

The post What ecommerce teams are focused on right now in 2026 appeared first on Rithum.

Shoppers rarely start their online search with a perfect query. It’s usually an idea of something, like “I need a couch good for a small apartment,” or “storage that can fit underneath a bed.” And Generative AI is making it easier for shoppers to get trustworthy recommendations, from those questions. 

In the Gartner report, Use GenAI to enhance commerce search and discovery experiences, they write: “Generative AI (GenAI) offers customers the ability to search by asking natural language questions or queries of what they intend to buy.” GenAI turns a shopper’s question into a useful set of products and context. Then there’s agentic AI, which goes one step further and takes actions, like narrowing choices and moving a shopper toward checkout. That flow still starts with discovery, and this report focuses on the GenAI patterns that support it. 

With GenAI, results are accompanied with context alongside products with recommendations connected to the shopper’s question. With guided selling, a shopper’s questions narrow down options, so it is important to have the most accurate and up-to-date product catalog information to ensure your products are included in those responses. 

When selling across multiple channels, consistency gets harder to maintain. If a shopper is directed to a webpage only to find that the product doesn’t match or is out of stock, they move on to another option and someone else gets the sale. GenAI can help shoppers decide faster. 

Download the Gartner report to see the full examples, diagrams, and recommendations.  

Gartner, Use GenAI to Enhance Digital Commerce Search and Discovery Experiences, By Aditya Vasudevan, Mike Lowndes, 27 November 2024 

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved. 

The post When shoppers phrase their search as sentences, your product catalog has to be the answer  appeared first on Rithum.

At Rithum LIVE—our flagship event that brought brands, retailers, and partners together in New York and London—the pattern was clear: shoppers are changing how they find products, clean data is more important than ever, AI is everywhere (though not being optimized), and retail media is being redefined. As CEO Lou Keyes put it, “The battleground is shifting from persuasion to precision . . . You can no longer convince consumers to buy with just more ads or louder ads.”  

Here are five of our favorite big moments from the sessions, focused on how leaders are fixing data quality, proving AI ROI, preparing for agentic shopping, adjusting retail media, and turning dashboards into decisions. If you’re curious about what’s coming for commerce in 2026, start here—then dive into the full talks on Rithum LIVE On-Demand.  

Data readiness: close the confidence–accuracy gap 

Suzin Wold Chief Marketing Officer at Rithum, ran a reality check in her Rithum LIVE keynote: “80% of you know the data you are using is bad, while 100% feel confident in your performance reports. You have really super high confidence, but you have really, really low accuracy,” Suzin said. Her rule is straightforward: “It is not about acting faster. It is about reacting smarter.” This takeaway, and the rest of the data analysis from her keynote, was built on The 2026 commerce readiness index, which reports on 200 retail and brand executives’ responses to industry-landscape questions. It points to heavy manual effort and inconsistent data as the root of slow, error-prone reactions. Bad data doesn’t just slow work; it produces the wrong results like mispriced items, wasted ad spend, and higher returns. 

Watch Suzin’s full session for more industry trends from the readiness index, which is built on a survey of brands and retailers.  

AI and ROI: clean inputs, measurable outcomes 

95% of AI projects fail to deliver ROI and only 5% move beyond pilots, Ali Irturk, Chief Technology Officer at Rithum, said. “If you have bad data you’re going to make bad decisions a lot faster.” 

Here’s what it looks like for retailers and brands when the inputs are right. “We found that 200 SKUs were driving 15.5% of the returns,” said Seb Spiegler, Head of AI at Rithum, in discussing one Rithum client use case. Updating titles, materials, and size charts fixed the problem. “Returns went down, margins went up,” Seb said. 

Rithum’s Magic Mapper, powered by RithumIQ, cut categorization and attribute mapping from days to minutes in 100+ channels and in more than 30 languages, Seb said. Because publishing, inventory, and advertising run on the same source of truth in Rithum, those changes travel quickly to the places that matter. “Models matter, but outcomes matter even more,” Seb said.

Watch Ali and Seb’s session where they show how they spotted the 200 SKUs, which edits they prioritized first, and where the gains surfaced (returns, margin, rank).  

Agentic shopping: make claims machine-readable 

Discovery is changing as consumer behavior changes. “In the past, [discovery] was user initiated. Now it’s going to be AI agent initiated,” Arun Kumar Global Head of AI at Accenture Song, said. Agentic AI doesn’t react to slogans or ad spend. “Your brand actually is the moat and agents see it as data and rules.” They verify off the website, too: “If you are best in something, I want proof that you are best in something. I’m going to go to Reddit. I’m going to read your reviews.” 

Arun suggests that the biggest thing to do now is to encode the facts—materials, price, availability by location, shipping cutoffs, and returns policy—in your website so assistants can confirm them. For the rest of his best practices and examples on getting ready for agentic shopping, watch his session here.  

Retail media: let spend listen to inventory and returns 

Media works harder when it runs on commerce truth. “We decided to move back and double down on our own platform,” Louis Camassa, Director of Product for the Brands Platform at Rithum, said. “We’re using that data to show where clients could spend and get the best ROI.” 

Shelf control completes the loop. “See where you rank, your brand’s share of shelf or share of voice, then make strategic decisions from an advertising and organic perspective,” Meghan Barden, Director of Global Retail Media at Rithum, said. When retail media and commerce data live in one platform, bids and budgets can adjust to stock, margin, and delivery promise in real time, steering spend toward products that can ship and convert. Keeping media and commerce signals together inside Rithum helps avoid wasting spend on items that are out of stock or likely to bounce back.

Watch Louis and Meghan’s session for examples of routing budgets to in-stock, high-margin SKUs. 

Decision intelligence: define the choice, then act 

“Companies don’t have insight problems. They have decision problems,” Daniel Hulme, Chief AI Officer at WPP, said. Pick the wrong formulation and the option set stretches “longer than the age of the universe.” Pick well and a machine solves it in milliseconds. 

The talk reframed AI, saying most companies don’t suffer from a lack of insight—they struggle to turn insight into consistent, high-quality decisions. According to David, our “fast brain” loves intuition. But the real world runs on hard trade-offs where the wrong algorithm can turn a millisecond task into an “age-of-the-universe” problem. He drew a sharp line between automation and AI, where automation just repeats yesterday’s choice. AI, properly defined, is goal-directed and adaptive—it makes a call, learns from the outcome, and updates the next call. 

“Large language models are really good at knowing things about the world,” says David. “They are not good at making predictions. They’re definitely not good at making complex predictions.” The business gains show up when you pair them with explainable machine learning and optimization for things like allocations, pricing, routing, and channel mix.  

The takeaway was refreshingly human: start by naming the decision you need to get right, be explicit about the objective and constraints, and then choose the method. If AI learns over time and can explain why it works, you’re building intelligence—not just another dashboard. For the full talk, watch Daniel Hulme’s keynote here. 

Watch these sessions and more on Rithum LIVE On-Demand here. 

Quotes have been lightly edited for clarity. 

The post 5 big moments from Rithum LIVE: What retailers and brands are doing differently in 2026  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.

The post Your customer journey is leaking revenue. Here’s how to fix it.  appeared first on Rithum.

Summary: Holiday 2025 ecommerce strategy

• Data readiness is uneven. Nearly 3 in 4 retailers and brands say they’re making decisions on incomplete or inaccurate data. Retailers more often act within 48 hours when signals spike, while more brands take 3 to 5 days. 
• Shopper timing has shifted. As covered in our recent webinar, during summer sales season carts were built early and many purchases landed on day 2 to day 4. 
• Where conversion happens is evolving. Social commerce is the top current conversion driver, with site experience close behind. Think live shopping, influencer unboxings, and social-first storytelling that leads directly to purchase. 
• Returns impact profit. 60% of shoppers made at least one return last year. Average processing cost is about $30 per returns. U.S. online returns are projected above $363B; globally, returns account for roughly 17% of sales value. 
• Policy influences purchase. 41% say return policies affect the decision, 88% prioritize free returns, and 47% have stopped buying from a retailer or brand due to policy. 
• Two case notes. Monitoring hourly and leaning back in late improved AOV and conversion by the end of a four-day event. An apparel brand’s mid-event pivot to seasonal assortments, bundles, and cleaner titles delivered a 15% lift over the prior year. 

Shoppers plan early and purchase later

The competing big sales events of July, September, and October have given us an outline of the shape of things to come for holiday season just around the corner. The big themes? Shoppers are planning early and often building carts early then buying late. In our recent webinar, Countdown to holiday 2025: Strategies for growth and agility, experts Kellie Martin, Nistaar Chandhok, and Storm Morgan summed up a new pattern of shopper behavior: “Shoppers were browsing less and buying more deliberately. Cart building happened early, but conversion often didn’t occur until day two or four.” 

The earlier cart building points to the competition of today’s sales season, and the ease of comparison shopping across big sales (especially when shoppers are aided by AI). While early cart building is slowing down the path to purchase, it also gives you longer windows to act on your live sales data—the key is getting the right information quickly, understanding the patterns, and knowing when to change course during a sale (and when to stay steady).  

The 2026 commerce readiness index shows that commerce leaders report uneven data quality and response speed, which matters when promotions land and search behavior shifts.  

Here are some scenarios to help you plan around that pause and move based on what you see as part of your holiday 2025 ecommerce strategy.

Path A: Improve visibility 7 to 10 days before the peak window for your holiday 2025 ecommerce strategy

If data shows you that your discoverability is weak, begin here. The webinar tied early launches to stronger CTR and lower CPC. “You can’t win if you show up late,” Storm said. 

• First, match real searches. Tune product titles and keywords to seasonal queries across the marketplaces and channels you sell through, including your site and social shops. 
• Next, strengthen the product page. Use enhanced product pages with clear bullets and consistent value messaging so comparison shoppers can decide quickly. 
• Then, add early creator content. The webinar highlighted authentic influencer campaigns that show the product in use as a lift when paired with strong product pages. 

Watch CTR, CPC, PDP views, and add to cart. If carts grow but orders land later, go to Path B. If traffic remains thin, stay on Path A and keep improving titles, images, and keywords. 

Path B: Convert day-2 to day-4 buyers without deep discounts 

If carts are building while buyers compare, lean into the timing covered in the webinar. More than a third of shoppers price-check across sites before they commit. 

• First, show value rather than cutting deeper. Bundled offers and loyalty programs lifted average order value in the examples that Storm discussed in the webinar, without the cost of blanket discounts. 
• Next, keep it current. Update titles, keywords, and product page copy to match trending queries and seasonal context during the event. 
• Then, find ready-to-buy-shoppers. AI-driven formats like Google Demand Gen and Performance Max helped when the message was specific to winning new customers. 

Track day-2 and day-4 conversion, AOV, and cart resume rate. If the margin tightens, go to Path C. If traffic softens, return to Path A. 

Path C: Protect margin with value and channel fit 

If revenue grows while profit slips, use the levers shown in the readiness index and webinar. 

• First, tap into pricing agility. Dynamic pricing helped retailers and brands respond to competitors’ moves in real time without broad cuts. 
• Next, set the products and price for each channel. Multichannel shopping is normal. Set assortment and pricing strategy to each channel’s strengths. In the holiday readiness webinar, Storm noted that buyers will pay full price when value is clear, especially for scarce items or well-timed bundles. 
• Then, watch results hour by hour. Rithum saw clients who watched SKUs and profit hour by hour and adjusted as needed. 

If returns start to rise or policy questions distract buyers, move to Path D. 

Path D: Reduce the cost of returns while keeping trust 

Returns are the swing variable in Q4. The 2025 Global Returns & Profit Impact Report quantifies what drives them and what stops them. 

• First, fix issues before purchase. 60% of consumers made at least one return last year. Poor fit is the top driver in apparel at 61%, and about a third of shoppers returned because items did not match descriptions or photos. Clear sizing, accurate copy, and strong images reduce avoidable returns. 
• Next, set clear policies that compete. 41% of consumers say return policies influence purchase, 88% prioritize free returns, and 47% have stopped shopping with a retailer or brand due to policy. If you change terms, make them easy to understand and consistent. 
• Then, plan for common behaviors. Bracketing is a common habit by 36% of global shoppers and over half among shoppers under 35. Two-thirds used third-party drop-off to return at least once in the last 12 months. 51% consider 14 days or less a reasonable window, with higher acceptance of shorter returns windows in some European markets. Understand some of the key levers to lower returns, and lean in.  

Keep an eye on total cost data. The average processing cost is about $30 per return. U.S. online returns are projected above $363B in 2025. 

If operations are steady and you want more demand, go to Path E. 

Path E: Adjust live and capture after-event demand 

If mid-event performance wobbles or you want to extend gains, follow the measured moves shared by Rithum’s experts during the holiday readiness webinar. 

• First, decide with hourly signals. One retailer started slow, held budget, watched hourly, and leaned back in; by the end, AOV and conversion improved, said Storm. 
• Next, pivot toward what is trending. An apparel brand shifted to back-to-school assortments, introduced bundles, and tightened titles and keywords, finishing 15% above the prior year’s Prime event. Per Nistaar: “They were pretty happy with that.” 
• Then, keep campaigns live after the rush. After-event demand is real; in October, we even saw some spending surge higher the day after an event ended than during the first day of the event. Test urgency messaging and retarget saved carts with offers that match the week’s story. 

If visibility still lags, return to Path A. If carts build again and buyers wait until later in the window, go back to Path B. 

Regional notes to localize any path 

If you sell in multiple regions, tune the plan to what shoppers actually do there. In Europe, shoppers compare across Amazon and regional marketplaces like Otto or Allegro, which means titles, keywords, and offers should reference local norms and events. In APAC, Singles Day and other game-like promotions drive activity, so timing and creative should match those mechanics and calendars. In North America, shoppers react strongly to fast delivery promises and a smooth checkout, so make shipping dates and returns information easy to see on the product page. Use these differences to pick which path to start with and which levers to pull first. 

Quick map for retailers and brands 

• If you are not being seen, use Path A. 
• If carts are full and buyers wait, use Path B. 
• If margin is slipping, use Path C. 
• If returns threaten profit, use Path D. 
• If performance is uneven mid-event or you want the tail-end lift, use Path E. 

Start where you are, switch when signals change, and keep each move tied to your data. 

If you’re ready to turn these paths into results, learn how Rithum can help.

Storm Morgan is a Senior Technical Account Manager at Rithum. 

Sources: The 2026 commerce readiness index, Global returns & profit impact report, Countdown to holiday 2025: strategies for growth and agility webinar 

The post What full carts and slower checkouts mean for your holiday 2025 ecommerce strategy appeared first on Rithum.

October’s Amazon Big Deal Days, flanked by rival events at Target and Walmart, confirms that for today’s shoppers, extreme sales operate as planning windows more than purchase windows. Billions still move through carts, but before they click purchase, consumers are comparing prices, checking delivery windows, and weighing return policies—and with the help of agentic AI, they can do it all without even visiting the retailer’s website. 

Most of all, they seem to be waiting. Many of the highest GMV days took place after the sales window. What does that mean for Black Friday, and the rest of the holiday season?

What we learned from October 2025 

Walmart opened its October event to everyone (with a head start for Walmart+), and Target deepened member-led offers. This broad access framed the holiday season’s opening moves.  Amazon buyers focused on everyday essentials with 58% saying they were satisfied with the deals, 23% saying they already purchased gifts, and 84% plan to buy holiday items on Amazon in the next three months. October sales, in other words, were the first round of the peak holiday shopping season, with shoppers starting early, but planning on more shopping to do. This is another sign that shoppers aren’t responding to urgency-driven hype the way they used to. They’re shopping on their own terms, watching closely, waiting longer, and buying based on intent, not impulse. 

Shoppers plan, retailers widen the window, and mobile and agentic AI set the terms. Here are some of the biggest patterns we saw in October’s sales push:. 

• Demand is real, but disciplined. Forecasts call for a sizable holiday spend, driven by mobile and a fast increase in AI-assisted shopping traffic, up 520% year over year. Shoppers are doing more research in less time. 

• Access beats exclusivity. Walmart’s “no membership required” positioning and Target’s member perks ran in parallel with Amazon’s event, giving shoppers many options  rather than anchoring them to one singular price drop  

• October is a staging ground. Reuters and Adobe flag October’s Amazon event as a multi-billion-dollar catalyst that pulls forward  holiday spend without exhausting demands. The shopping that started with October sales is expected to stretch longer, not be done earlier. 

• Category mix matters. Numerator’s early read shows baskets heavy on household essentials alongside selective gifting. Consumers are being mindful about what they spend. 

Shoppers are comparing across channels, pacing their purchases, and prioritizing convenience and trust. They check prices, delivery windows, and return policies before they buy. With Rithum, that behavior works in your favor: pricing, promises, and product details stay consistent across every touchpoint. Your returns policy doesn’t change from one channel to the next. And when the market shifts, you shift with it—because Rithum connects marketplace data, retail media, and fulfillment in real time. One update moves across platforms, so you can reallocate spend or inventory seamlessly.

Holiday 2025 retail strategy: next steps

Adobe expects U.S. online holiday spend to reach $253.4B, with mobile driving a majority share (56.1%) and buy now, pay later (BNPL) adding another $20.2B—evidence that convenience and flexibility, not just markdowns, will shape conversion. 

October sales set the tone for the season. Now is the time to stay consistent and make sure you’re keeping the same price, promise, and product facts everywhere. Push what is already selling and remove the friction that shoppers flagged. Start here: 

• Plan promotions around what shoppers actually want at different times, instead of relying on the same event-driven discounts.  

• Adjust product mixes and creative in real time to highlight what’s trending or to support categories that need a lift.  

• Connect messaging and offers across marketplaces, retail media, and owned channels so shoppers get a consistent experience wherever they buy. (Most teams are already shifting where they show up: 91% of retail leaders and 84% of brands changed their marketing channel mix in the last year.) 

• Use first-party data to personalize offers and reduce dependence on paid ads.  

• Measure success by long-term value don’t focus on short-term sales spikes. Repeat customers, lifetime spend, and loyalty are the your better long-term value drivers..  

• Partner with marketplaces and vendors to build limited-time experiences that create excitement and reach new audiences.  

• Re-engage peak-season buyers after the event with thoughtful follow-ups that turn one-time shoppers into loyal customers.  

• Put delivery dates and a clean returns promise on the page. Return policies are a decision filter at scale. 41% say the returns policy influences where they buy — so make the promise visible and easy to use. 

October shows that the 2025 shopper is steady, selective, and ready to buy when the facts are clear. Keep the same price, promise, and product story everywhere, make the mobile experience flawless, and turn returns into trust. To see how Rithum connects your marketplaces, media, and fulfillment so you can act on this now, contact Rithum to learn more. 

The post How October’s big sales set the stage for holiday 2025  appeared first on Rithum.