At a glance: 

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

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

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

Why shoppers value AI explanation over accuracy in product recommendations 

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

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

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

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

AI-powered shoppers buy faster and verify less 

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

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

How to optimize product content for AI recommendations 

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

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

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

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

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

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

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

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

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At a glance: 

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

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

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

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

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

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

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

Brands have the answers, but shoppers are asking somewhere else 

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

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

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

A growing share of shoppers skip verification entirely 

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

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

The verification step itself is disappearing 

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

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

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

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

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

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

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

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

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

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

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

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

What this looks like across customer groups 

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

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

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

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

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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. 

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• Snapshot: eTail Insights surveyed marketplace and ecommerce leaders for Mastering the marketplace in retail and ecommerce to benchmark marketplace trends including what’s working, what’s breaking, and where budgets are going next. 
  Why it matters: These benchmarks show how marketplace teams describe their day-to-day reality right now. 

• Listing optimization no longer separates anyone. In the survey, every team rates its listing optimization capability as good or better, which tells you where the “easy wins” have already been harvested. 
  Why it matters: Listings may look “done,” but they stop being the lever that moves growth. 

• Retailer and brand teams lose time and margin on coordination. Inventory coordination across platforms ranks as the top challenge at 40%, followed by marketplace data integration at 33%. 
  Why it matters: When systems disagree, the fallout lands in fulfillment, customer service, and returns. 

• Leaders are budgeting for trust optimization above all else. Over the next 12 months, the top investment is customer service and review management systems at 51%, ahead of marketplace advertising and content expansion at 46%. This makes sense, as credibility optimization is key to meeting both customers’ and agentic shopping tools’ high standards. 
  Why it matters: Teams are funding the places where customers notice problems first and where reputations change fastest. 

• Profit pressure is shaping the stack. Competitive and dynamic pricing tools sit close behind at 41%, a signal that fees and price competition are forcing sharper math. 
  Why it matters: If teams can’t see the true cost per order, pricing tools can push them into underpricing. 

• Marketplaces are moving from “channel” to “center of gravity.” 81% expect marketplace sales to become more important to overall strategy, and 73% expect that shift within the next year. 
  Why it matters: Marketplace execution is becoming core operations, not an add-on project. 

A customer adds a refrigerator water filter to their cart. They’ve bought the brand before, and everything looks familiar. One click from paying, they pause. The model name is close, but not identical.  The customer taps the chat bubble in the corner of the screen to make sure they’re purchasing the right part.  

That bubble used to connect them to a live agent. Now it’s often an AI assistant, trained to answer product questions quickly, so the shopper doesn’t abandon checkout. 

“Is this the filter for model X?” 

The assistant replies instantly: “yes, it’s compatible.”  

But it’s not.  

The filter fits a similar model with a near-identical name. The product listing doesn’t clearly distinguish the two, so the AI fills in the gap with the most plausible-sounding answer. The customer buys anyway. The filter arrives, doesn’t fit, and gets returned. 

The return reason isn’t “wrong model.” It’s “defective.” The frustration isn’t just with the product. It’s with the guidance the shopper trusted. 

This is the kind of mistake customers try to avoid. When it happens anyway, they don’t blame the model, they blame the seller. Marketplaces penalize getting it “almost right” with returns, bad reviews, and support costs. The warning appears in performance long before you see it on a dashboard. 

The new marketplace problem: the storefront that moves faster than operations 

Most organizations aren’t dabbling in marketplaces anymore. Nearly everyone sells on Amazon. Most also sell on Walmart.com, according to eTail’s Mastering the marketplace in retail and ecommerce report. 

But while most respondents say their marketplaces strategy is at least somewhat effective for revenue, there is a deeper problem. Each marketplace comes with its own rules and requirements. Retailers and brands don’t describe their biggest marketplace challenges as “getting listed” (the industry has evolved past that point). They describe them as keeping platforms aligned. 

Inventory coordination across platforms ranks as the top challenge at 40%. Marketplace data integration follows at 33%. 

Those aren’t minor headaches. They sit underneath the moments customers remember: the delivery promise that never was, the unwelcomed out-of-stock surprise, the placement part that “should have worked,” or the one-star review that deters others from buying your product. 

Meanwhile, marketplace importance keeps rising. 81% say marketplaces will become more important to overall strategy, and 73% expect that shift to happen within twelve months. So, as opportunity grows, the friction grows along with it. 

Where marketplace execution starts to separate teams

Listing optimization is not the weak point: every respondent rates their listings as good or better. But when everyone feels strong in the same area, it is no longer a competitive advantage. Instead, the advantage shifts to what’s more difficult to keep consistent at scale, such as inventory accuracy, data consistency, review health, margin control, and service quality. 

It also moves to the places where organizations admit weakness. In this case, one-third rate “review and reputation management” as fair or poor. 

That gap matters because reviews aren’t just social proof. They shape ranking, conversion, and what shoppers learn before they buy. They also feed the summaries shoppers increasingly rely on, whether that’s a marketplace recap, a review highlight, or an AI-style shopping assistant answer. A shopper who doubts your listing doesn’t read your FAQ. They read your reviews. 

Marketplaces reward speed, but trust decides who keeps the sale 

Marketplaces raise expectations without lowering complexity. Shoppers expect quick answers, fast shipping, and painless resolution even when the catalog and fulfillment reality are messy.  

Trust decides whether a retailer or brand keeps the sale. On marketplaces, customers judge trust through what happens when something goes wrong and what other buyers say happened to them. That’s why the report’s investment priorities matter. Leaders are investing in customer service and review management systems at 51% over the next year, ahead of advertising and content expansion at 46%. 

Competitive and dynamic pricing tools follow close behind at 41%, underscoring how quickly the fees and shifting costs can erode margins. Teams don’t lead with service systems unless service continues to break. 

The catalog is the experience now 

The story of a mismatched water filter isn’t really about AI. It’s about what happens when product information fails under pressure and gets repeated across channels. 

Marketplaces have always punished unclear product information, but the old failure mode looked different. A customer read the listing, guessed, and learned the truth when the package arrived. 

Now the failure happens earlier. Customers ask questions mid-journey and even when the catalog is incomplete or inconsistent, the assistant still answers. When two SKUs look nearly identical, the assistant still picks one. When variation structure is messy, the assistant still tries to resolve it. If your product data leaves room for interpretation, the assistant will fill it, and you will own the consequences. 

What breaks first when teams scale marketplaces 

Most marketplace failures don’t start with a dramatic crash. Then one day, someone notices reviews shifting in tone. Support tickets spiking. Returns rising. A marketplace rep flags an issue. A finance team sees margin slip and can’t tie it to one decision. 

Coordination can be the biggest bottleneck. Inventory coordination and marketplace data integration top the list. If you can’t keep systems aligned, you can’t keep promises aligned. 

Three shifts that make marketplace growth easier to scale 

Most teams don’t need a radical reinvention. They need to tighten the parts that create expensive downstream effects. 

These three easy shifts  map directly to what leaders say is slowing them down. 

1) Treat inventory coordination like a revenue problem 

Inventory coordination across platforms ranks as the top marketplace challenge. This sits at the center of marketplace performance. 

When inventory is wrong, you don’t just lose a sale. You lose ranking and trust. You waste customer support time. You pay for returns. You create reviews that never should have existed. 

Start by making your inventory view boring and reliable. Establish one source of truth for what’s sellable. Push that truth to every platform on a cadence you can defend. 

That shift prevents the “invisible losses” teams learn to tolerate, including cancellations, refunds, angry reviews, and time spent explaining mistakes. 

2) Treat review management as a system, not a reaction 

One-third of survey respondents rate “review and reputation management” as fair or poor. That’s one-third of teams saying they still treat reviews reactively—and know that it’s hurting them. 

Reviews deserve a better approach because they aren’t random. They’re patterns. 

A shopper complains that the filter “didn’t work.” Another says the product “felt defective.” A third says the listing “misled them.” The issue is expectation. 

Route review themes back to the source. If customers keep confusing models, fix the variation structure. If customers misunderstand compatibility, tighten attributes. If customers misread product claims, update bullet copy. 

When review management becomes a loop instead of a fire drill, you stop treating bad reviews as a cost of doing business. 

3) Align marketplace data before you scale ads 

Marketplace data integration ranks as the second-biggest challenge at 33%, and it’s easy to underestimate how costly those integration speed bumps become once you push harder on growth levers. 

Incomplete integration creates quiet inconsistencies: price mismatches, delayed updates, missing attributes, inventory errors that propagate across systems. 

Then advertising scales and performance doesn’t follow. You spend more to acquire customers you can’t keep, and you blame the campaign when the problem lived upstream. 

Tighten the data path before you scale spend. Your future self will thank you in fewer support tickets and cleaner margin. 

Where AI fits without hijacking your strategy 

AI can help marketplace teams move faster. It can spot inconsistencies, flag missing fields, draft content variants, and speed up service workflows. 

It cannot rescue messy foundations. Give it incomplete data and it produces confident answers built on gaps. That’s why product truth has become a performance lever: AI increases the speed of both accuracy and error. 

What mastering the marketplace looks like in 2026 

Most teams feel good about listings. The report shows the friction elsewhere: inventory coordination, marketplace data integration, and review management. That’s why next year’s spend tilts toward customer service and review systems ahead of content and advertising. 

Marketplace growth requires consistency. Keep one version of the product true across platforms: what it is, whether it’s available, what it costs after fees, and what the customer should expect. The report shows why this is hard at scale. Inventory coordination ranks as the top challenge at 40%, followed by marketplace data integration at 33%. When those basics slip, the cost surfaces as returns, heavier support volume, and reviews that live on the listing. 

Micah McGuire is Senior Product Marketer, Brands, at Rithum. 

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During Prime Days 2025, one client brand Rithum works with saw their conversion and average order value slide early. Instead of throwing more budget at weak campaigns, the team held spend, watched the data, and waited for cleaner signals. When performance improved later in the event, they pushed onward and finished strong. 

An apparel brand during the same period saw similar issues. But they decided not to wait. Reliable performance signals showed a shift toward back-to-school demand. So, the team pivoted mid-event and adjusted assortments to focus on back-to-school products, introduced bundle offers, and optimized product titles and keywords. That shift resulted in a 15% increase in sales year over year vs. the previous Prime Day. 

Both brands took different approaches, based on having the visibility and confidence to act on what the data was telling them. There wasn’t a one-size-fits-all approach, but the key was data and agility. 

But according to the 2026 commerce readiness index, a survey of 200 brand and retail leaders across the U.S. and U.K, many brands don’t have that level of confidence and visibility to make it work.

According to the survey data, economic instability and inflation are the top hurdles to market expansion for both retailers and brands. But close behind sit technology and data challenges, rising operational costs, shifting consumer behavior, and supply chain issues.  

Some of that drag is the market. But lot of it is self-inflicted. Using the survey responses from commerce leaders, these are the three internal drags you say you feel the most . . . and what you can about them. And one external headwind you can only prepare for. 

Hidden drag 1: Manual ecommerce operations that slow omnichannel growth 

This drag shows up in the everyday work that still runs on spreadsheets and email, even as your sales channels and partners multiply. 

According to the survey, leaders say they’re “stuck at spreadsheet speed.” Fully automated workflows are rare, with many retail and brand leaders saying that 26% to 50% of their workflows still rely on manual steps like spreadsheets and email. 

In practice, this might look like retail vendor analysts pulling late-order reports into Excel, pivoting them, emailing suppliers one by one, and manually editing product descriptions before listings go live. On the brand side, manual work often involves pulling performance signals from multiple platforms, reconciling conflicting reports in spreadsheets, and chasing analysts for validation before anyone can act. 

You can see the impact of that manual overload in the data. Nearly three quarters of leaders say they at least sometimes make decisions based on inaccurate or inconsistent data, and more than a third say it happens often or all the time. 

53% of retailers say they act on important performance signals within 48 hours; brands are more likely to need three to five business days. Even the fastest groups say they are still acting on incomplete or inconsistent data and largely manual processes. 

If you’re selling through marketplaces, dropship programs, retailer.com sites, and your own direct-to-consumer (D2C) website, this is more than an internal annoyance. Manual listing updates, inventory syncs, and routing decisions become the places where channels drift and small errors balloon across your entire network. 

To turn this drag into an advantage, teams are: 

• Automating onboarding of assortments, content updates, inventory synchronization, and order routing where possible. 

• Consolidating product, inventory, and order data so a change to a SKU is reflected wherever you sell it. 

• Replacing “hero” spreadsheets with shared rules and playbooks that run on current, accurate data. 

Hidden drag 2: AI running on messy product and inventory data 

This drag appears when AI is built on data that is incomplete, inconsistent, or scattered across systems. 

AI is already live for many of the retailers and brands surveyed. 41% of retailers and 29% of brands use AI-based automation across functions like pricing, inventory, and marketing, and another 57% of brands and 41% of retailers say they are getting ready to implement it. 

At the same time, nearly three in four leaders say AI is advancing faster than their organizations can apply it effectively. 

The gap is visible: 

• 49% of retailers and 62% of brands say they still struggle with too many manual processes. 

• 91% of retailers and 78% of brands say poor data quality is a challenge. 

The same leaders rolling out AI across pricing, inventory, and marketing are also telling us they don’t fully trust the data underneath it. When catalog attributes are inconsistent, stock numbers are unreliable, or order and return data live in different silos, AI trained on that information doesn’t fix the issues, it magnifies them. 

This can show up as: 

• Retail media campaigns bidding on SKUs that are already out of stock on key partners. 

• Pricing models making decisions based on incomplete fees or cost data in certain channels. 

• AI “optimizing” assortments based on stale sell-through and margin data. 

How to make AI actually useful 

Start by fixing the inputs. Clean up product data, improve inventory accuracy, and connect orders and returns back to their source channels so you know what really happened and where. 

Then shorten the path from insight to action. If every AI-driven price or bid suggestion still has to be pasted into a spreadsheet and debated in a meeting, you will never see the benefit.  

Apply AI where it matters most: margin pressure from fulfillment and logistics, inventory stock-outs, and wasted media on low-quality traffic. Those are natural places to focus AI, once the data is ready. 

Hidden drag 3: Margin erosion across marketplaces and retail media 

This drag shows up in the small gaps where money and customers slip away across channels. 

Brands say the biggest hits in the past year came from fulfilment, logistics, and product costs. Retailers point first to tariffs and trade disruptions. Both groups also call out discounts, paid media inefficiency, and listing errors or inaccurate product data as other margin drains. 

Retailers most often lose shoppers before checkout, especially when ads do not match the product experience or when payment fails. Brands are more likely to have problems after the sale, in customer care and returns or refunds. 

At the same time, 91% of retailers and 84% of brands say they have changed their marketing channel mix in the last year, often in response to shifting consumer behavior and strategy.  

Some examples where customers and profit are lost in operations: 

• Broken links or mismatched product pages that cause pre-checkout drop-off. 

• Ads driving to SKUs that are out of stock or unprofitable to ship once fees and costs are counted. 

• Returns and service policies that vary by channel, leaving some experiences noticeably worse. 

The fix is to connect performance metrics with operational and margin data to see where the problems really come from. Are you losing money because of traffic quality, or because of content, availability, or fulfillment issues that could be fixed centrally and rolled out across channels? 

The drag you can’t fix: External volatility and ecommerce expansion 

This force comes from outside your walls, but it still shapes how fast you can grow and where. 

When retail and brand leaders rank their top hurdles to expanding into new markets, economic instability and inflation come first for both. Close behind are technology and data challenges, rising operational costs, shifting consumer behavior, supply chain issues, regulatory complexity, and tariff or trade uncertainty. 

Tariffs in particular stand out. 46% of retailers and 60% of brands say they are at least somewhat concerned that tariff and trade shifts will disrupt their sourcing strategies. More than 60% of both groups say they are re-evaluating sourcing relationships to prepare, while many are also cutting business costs and investing in supply chain resilience. 

You cannot control that volatility. However, you can decide how much internal drag you stack on top of it. Focus on what you can change by building stronger operations, reducing technology barriers, and creating more flexible, integrated customer experiences so you can pivot channels, partners, and assortments when conditions change. 

For a deeper look at the data behind these drags and more benchmarks you can use in your own planning, download the full 2026 commerce readiness index report. 

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Returns are no longer a background cost or occasional loss. They are influencing how consumers shop, what they expect from retailers and brands, and whether they come back. Our newly published 2025 Global Returns & Profit Impact Report offers a detailed look at where returns are happening, why they happen, and what retailers and brands can do to reduce the impact.  

Based on responses from more than 6,000 consumers worldwide, our report highlights the behaviors and expectations that are shaping the post-purchase experience—and what separates companies that manage returns well proactively compared with those still taking a reactive approach. 

Shoppers plan to return before they even check out 

The data from that survey suggests that returns are not just a post-purchase inconvenience—they are a core part of online shopping today. According to the consumer survey, 36% of shoppers admit to overbuying with the intention of returning part of the order. This behavior, also known as “bracketing,” is especially prevalent in apparel categories, such as shoes and clothes. It’s also an even stronger trend among shoppers under the age of 35, where 50% say they commonly buy more than they need (for example, multiple sizes and colors), knowing they will return part of the order. 

Bracketing is likely a strong factor behind why apparel and footwear dominate return volumes, with 68% of consumers saying they returned clothing or shoes in the past 12 months. Electronics also rank high among returned items, particularly in Europe, where more than half of German and UK consumers returned an electronic product last year.  

Poor product content is still driving returns 

One of the top reasons cited for returns is poor fit, mentioned by 61% of consumers surveyed. But returns are not only about sizing. A third of respondents said they returned products because the item didn’t match its online description or images. This mismatch highlights the need for retailers to go beyond generic content and ensure that each listing reflects the reality of the product. While consumers many not always say it directly, this lack of confidence often leads to bracketing. When shoppers aren’t sure how any item will look, feel or fit as expected, they buy extras to cover their bases. 

Customer reviews are also playing a larger role in the purchase decision, especially in apparel. Half of consumers say they rely heavily on reviews when buying clothing or shoes. This makes transparency not just a bonus, but a competitive requirement. Inaccurate listings, missing details, or outdated visuals aren’t just conversion risks, they lead directly to a cycle of poor reviews and costly returns. 

Return policies are becoming make-or-break 

Return policies are shaping not just immediate buyer behavior, but long-term loyalty. According to the survey, 88% of consumers now expect free returns to be a standard feature, and 41% say they consider a retailer’s return policy before making a purchase. Nearly half (47%) said they’ve stopped shopping with a retailer because the return policy didn’t meet their expectations. 

This shift makes return policies a delicate balance. Offering free returns may cut into margins in the short term, while unclear or overly restrictive policies can damage trust and reduce repeat business. Retailers and brands are finding success by combining accessibility with just enough friction. One effective lever is time: 51% of global consumers consider a return window of 14 days or less to be reasonable. When positioned well, these types of limits can feel fair—especially when paired with fast, convenient return options and clear communication.

Localization matters as one size does not fit all 

Return behavior varies widely by region. In Europe, shorter return windows are more accepted: 57% of German consumers and 64% of French consumers believe 14 days or less is reasonable. In contrast, North American shoppers tend to expect longer windows and often treat the return period as an extension of the shopping process. 

Product category also influences return behavior across regions. In parts of Europe, it’s common for over 60% of apparel purchases to be refunded. Meanwhile, beauty and personal care products are more likely to be returned in North America, reflecting regional norms around trial and satisfaction guarantees. 

These patterns make clear that brands and retailers need region-specific policies and messaging. A policy that feels fair in one market may feel restrictive in another, and a uniform global return process risks alienating loyal shoppers. 

Turn returns into an advantage 

While return rates rise, margins are under pressure. Free shipping, free returns, restocking, and reverse logistics all eat into profit. But many of these costs are avoidable with the right tools and strategy. Our new report identifies key ways that returns are often a costly, but avoidable, downstream effect of poor upstream processes. With the right data and a proactive approach, retailers and brands can reduce return volume, recapture margin, and retain more loyal customers. 

The 2025 Global Returns & Profit Impact Report is a guide for doing exactly that. Download the full report to explore category-specific trends, country-level return insights, and steps you can take today to reduce costs and protect profit. 

Read the full report here. 

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Three key benefits for brands selling on marketplaces

Guest speaker Emily Pfeiffer, principal analyst at Forrester, noted the importance of data integration, and transparency and compliance, for sellers when selling through marketplaces. Pfeiffer also highlighted three key benefits for brands.

1. Expanded sales channels: Selling through marketplaces provides sellers with increased brand exposure and more sales channels to reach new customers.
2. Inventory liquidation: Marketplaces create opportunities for brands to liquidate excess or slow-moving inventory in a more profitable way compared to bulk liquidation.
3. Assortment testing: The endless digital shelf space of marketplaces allows brands to more easily test different product assortments and combinations to see what resonates with customers.

During her presentation, Pfeiffer also emphasized the importance of data-driven decision-making. This is achieved by using data from marketplaces to make informed decisions about inventory and customer preferences.

Rithum Managed Marketplaces helps ensure sellers comply with legal requirements for marketplace transactions. Rithum’s platform also provides robust data analytics tools, allowing brands to make data-driven decisions and optimize their product offerings.

The webinar also went in-depth about Rithum’s commissioned “The Total Economic Impact™ of Rithum Managed Marketplaces” study by Forrester Consulting, published in February 2024. According to guest speaker Veronica Iles, senior consultant at Forrester, the study found that by using Rithum Managed Marketplaces, four brands were able to expand to new marketplaces faster. Brands were able to reduce the time and effort required to onboard each new marketplace.

Sellers also increased GMV in a profitable way. Rithum helped sellers be where their customers are, optimized their listings and pricing, leveraged data insights, and controlled their margins. The study also found that sellers using Rithum Managed Marketplaces improved operational efficiency. By automating manual tasks such as data integrations and resolving marketplace issues, sellers were able to free up employees to focus on more strategic initiatives.

How to be successful on Walmart Marketplace?

Walmart Marketplace global e-commerce sales reached more than $100 billion in 2024. It continues to experience significant growth, with a 45% jump in Walmart U.S. Marketplace revenue in 2024, and a 17% increase in Q4 U.S. e-commerce sales. Walmart Marketplace offers end-to-end solutions and deep retail expertise to help sellers succeed on their marketplace, said Drew Lichvar, manager – channel partnerships, at Walmart Marketplace. Sellers selling through Walmart Marketplace can use Walmart’s fulfillment logistics network and easy in-store returns. The mass merchant has more than 4,600 U.S. stores combined with digital channels.

How Rithum partners with Walmart Marketplace

Rithum helps sellers navigate Walmart Marketplace’s unique requirements and provides them with the necessary tools and resources to optimize their performance and profitability, Lichvar said. Sellers can accelerate their expansion on the Walmart Marketplace and better manage their operations.

“I’m really excited because Rithum just launched a full suite of new tools such as assortment growth, repricer – all while staying profitable,” Lichvar said. “Rithum has those capabilities with us.”

Lichvar added that Walmart Marketplace is also launching new technology as an omnichannel retailer. This includes virtual try-on tools so customers can try before they buy and view items in their home virtually.

Rithum’s global commerce network includes more than 40,000 brands, suppliers and retailers representing $50 billion in annual sales. Through a single e-commerce platform, Rithum connects hundreds of channels and more than 400 marketplaces and retail sites.

Walmart Marketplace and Rithum meet weekly to understand sellers’ needs, Lichvar said. And while technology can make selling on Walmart Marketplace more efficient, customers can still reach a person when they need to, Lichvar added. “We are a tech company that is people-led. We’re using technology to take us to the next level as a marketplace,” he said.

Learn how Rithum can help here.

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“Gartner Hype Cycles provide a graphic representation of the maturity and adoption of technologies and applications, and how they are potentially relevant to solving real business problems and exploiting new opportunities.”*

What is multichannel selling in ecommerce?

Multichannel selling is when sellers sell products on more than one sales channel. Gartner defines, “enterprise marketplaces as digital platforms that allow multiple sellers to sell directly to end customers.”

According to Gartner, “Enterprise Marketplaces (MOAs) enables marketplace operators to manage seller onboarding, drop-shipping programs, product catalogs, order routing and management, and seller compliance with marketplace policies.”

Gartner stated that this is important because “enterprise marketplaces can provide consumers with more options to choose from, leading to an improved customer experience (CX), streamlined procurement processes, and a resilient supply chain.” Rithum was previously recognized in the 2024 Gartner market Guide for Marketplace Operation Applications report.

In the Gartner Hype Cycle for Digital Commerce, 2024 report, Gartner states that one obstacle to sellers is that “creating an enterprise marketplace is a fundamental business model change that requires support from the highest levels of the organization. Marketplace operators will need to serve end customers as well as third-party sellers.”

What is 3P commerce?

Third-party (3P) commerce is when the inventory owner and the channel owner are different. A 3P commerce model allows brands to sell directly to consumers through a retailer storefront via a dropship or marketplace model. This allows sellers immense scalability, offering retailers reduced inventory risk and more choices for customers. Sellers increase market reach and connection with customers.

Developing a strategy around marketplaces requires a comprehensive approach. As stated by Gartner’s User Recommendations, “leverage vendors’ seller networks that can help with marketplace scaling efforts,”

Sellers can leverage Rithum’s network of over 40,000 brands, suppliers, and retailers that represent $50 billion in annual GMV.

Learn more about how Rithum can help you launch your dropship, marketplace or hybrid solution here.

Gartner, Hype Cycle for Digital Commerce, 2024, Sandy Shen, 8 July 2024 *Gartner Methodologies, Gartner Hype Cycle GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, and HYPE CYCLE is a registered trademark of Gartner, Inc. and/or its affiliates and are used herein with permission. All rights reserved. Gartner does not endorse any vendor, product or service depicted in its research publications. And does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research. This includes any warranties of merchantability or fitness for a particular purpose.

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“In 2024, we’ll see significant technological advancements disrupting the retail and e-commerce sectors. From AI powering more personalization to leveraging AR and VR to improve shopping experiences—it’s an exciting time for brands and retailers to adopt these new opportunities and seek transformation to remain competitive,” said Deborah Weinswig, CEO and founder of Coresight Research. 

“Building on the advent of technologies like generative AI and the explosion of retail media that we saw in 2023, we see a wealth of opportunities for retailers and brands alike to tap into this year. We’re excited about the opportunity to help companies take advantage of these new technologies to drive positive business outcomes, including benefits like reduced operating costs, scaled personalization, and increased speed to site for products,” said Pete Elmgren, Chief Revenue Officer of Rithum. 

Here are the top 10 trends and predictions in e-commerce for 2024 from the report: 

Retail Companies to Increasingly use Customer Data Platforms to Gain Shopper Visibility

Retailers will increasingly adopt customer data platforms (CDPs) to gain a comprehensive understanding of both known and unknown shoppers. In the evolving data landscape, brands and retailers should integrate CDPs into their data management strategies to access customer data and construct comprehensive customer profiles to deepen personalization capabilities. 

Immersive Tech (AR/VR) to Improve the Shopping Experience

Augmented reality (AR) and virtual reality (VR) technologies are set to further elevate the shopping experience, ushering in a new era of consumer engagement. Outside of implementing current use cases, we advise companies to stay abreast of these technologies’ latest developments and explore new solutions and collaborations that enhance their conventional applications. 

GenAI-Powered Personalization to Revolutionize Customer Experiences and Drive Sales Growth

Generative artificial intelligence (GenAI) will revolutionize customer experiences by enhancing personalization and driving sales growth. Embracing GenAI requires a proactive approach. Brands and retailers can foster deeper customer connections by providing engaging, customized experiences that deliver high consumer satisfaction and build loyalty. 

GenAI-Created Product Descriptions and Imagery to Enhance Customer Engagement

GenAI will be adopted more widely to create product descriptions and imagery, streamlining content creation and enhancing the efficiency of marketing strategies. Retailers and marketers should seize the opportunities presented by GenAI to quickly create content that aligns with a brand’s voice and identity, while also maximizing shopper engagement. 

Retail Media Expansion to Provide Additional Revenue Opportunity

The retail media market is poised for substantial growth and transformation as more advertisers recognize the benefits of this channel and more retailers enter the market. To capitalize on the continued growth of retail media, retailers need to effectively differentiate their offerings through unique features and data insights. 

AI-Powered Automation to Drive Retail Pricing and Promotions

We expect brands and retailers to use AI to increase efficiency and adaptability, allowing them to align their pricing strategies more closely with dynamic market conditions and consumer preferences. Retailers can adopt AI to optimize their pricing strategies, fostering brand loyalty and driving personalization in promotions and sales. 

Evolving Checkout and Payment Systems to Reduce Friction Across the Shopping Journey

The checkout and payment landscape will likely continue to evolve due to the increased adoption of technologies such as computer vision and RFID (radio-frequency identification), which will address historical frictions and meet customer expectations for convenience, speed and security. Retailers should strategically assess the adoption and readiness of alternative payment/checkout technologies. Afterward, they should continue to focus on refining and optimizing existing implementations. 

Refined Last-Mile Delivery to Drive Retail Excellence

New advances in last-mile delivery are expected to play a pivotal role in superior customer service and propel business growth for those that effectively meet the demand for fast, convenient delivery. Retailers can leverage innovative technologies—such as route-optimization algorithms, automated delivery scheduling software and smart logistics platforms—to improve their last-mile and demand forecasting capabilities. 

Online Marketplaces to Continue Evolving

The rise of online marketplaces and dropshipping will reshape retail operations, enabling retailers to expand their reach, reduce costs and enhance the customer experience. Retailers should invest in unified solutions that encompass both marketplace integration and dropshipping process, enhancing operational efficiency, increasing flexibility and enabling them to respond swiftly to market shifts and consumer demand. 

Sustainable Returns and Reverse Logistics Platforms to Gain Traction

The integration of technology that supports sustainable returns and reverse logistics is set to witness a substantial upsurge as emphasis on environmental sustainability and efficient supply chain management grows. Retailers can adopt these platforms to efficiently manage the returns process, keep a strong emphasis on sustainability, reduce costs and improve customer satisfaction. 

Download the full report here.  

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