An e-commerce merchandiser’s day is a constant negotiation between forces that rarely agree: what customers are clicking and buying, what inventory exists and at what depth, what marketing is pushing this week, what the brand wants front and center, and what revenue and margin targets demand. Aesthetics. Creativity. Inspiration. Each pulls in a different direction. Merchandisers reconcile all of them — every day, across a vast catalog of products and collections.

Experienced merchandisers often have sharp instincts. But the forces shaping 24/7 ecommerce are dynamic, complex, and unrelenting. Under that pressure, there is rarely any confirmation that a merchandising decision outperformed what it replaced — or what else was possible. Teams won’t know for days or weeks, and by then, shoppers’ preferences, brand preferences, and market signals will have already shifted.

Every merchandising decision is a trade-off. Most are made without ever knowing what the alternative would have cost. That gap between decision and consequence is where revenue quietly slips away.

New Arrivals, Best Sellers, and Hidden Winners

Ask any senior merchandiser how they decide what goes where, and they’ll walk you through the logic: new arrivals get prominent placement to build velocity, best sellers stay visible because they convert, slow movers get deprioritized to protect real estate. It sounds rational. For many years, it worked well enough.

But in the fast-moving reality of modern e-commerce, “rational given my experience” and “optimized” are two very different things. Both matter. The gap between them, however, costs real revenue.

Consider new arrivals. Most teams give them a fixed window at the top — a week, two weeks, sometimes a month — regardless of how they are actually performing. The logic is understandable: new products need exposure to get a fair shot. But this approach treats every new arrival the same. A dud sits in a premium position just as long as a breakout hit. That is not a strategy. That is a schedule — and it burns valuable catalog real estate on products that have already signaled they won’t earn it.

Now consider best sellers. A product that ranks in your top ten overall is not necessarily a top performer in every collection. A bestseller in “Evening Wear” might underperform in “Casual,” where it occupies a prime position while stronger contextual fits remain buried. What works across a full catalog does not always work within a collection — and promoting it heavily in the wrong context wastes resources that could be driving real conversion elsewhere.

Then there are the hidden winners: products with strong conversion signals that never get the traffic to prove themselves because they’re sitting on page four, crowded out by slower-moving items consuming the placement and promotion they deserve. Without a system to surface them, they stay buried. Teams never know what they missed — or realize it only in hindsight, too late to act. And customers never find what they would have loved.

Short on Time, Not Signals

Most e-commerce teams measure what they did or conduct retrospectives on what happened days, weeks, or months ago — clicks, conversions, revenue per session, return rate. These are reasonable metrics. But very few teams systematically measure what they didn’t do: the product that could have been promoted but wasn’t, the placement that could have driven conversion but went to something weaker, the new arrival that earned its spot on day three but stayed there until day fourteen because the calendar said so.

Opportunity cost — the revenue left on the table by not surfacing the right product in the right place at the right time — is the most consequential metric almost no one is tracking. The reason is straightforward: you cannot measure what you cannot see. And most merchandising systems are not built to make the alternative visible.

This is not a data problem. Most brands have plenty of data, and AI is producing more of it every day. It is a decision intelligence problem. The data needed to make better calls already exists — it is simply not being assembled in a way that supports the actual decisions merchandisers need to make, in the moment they need to make them.

What AI Merchandising Support Needs to Do

As AI becomes embedded across e-commerce, the goal should not be piling on more tools and dashboards. It should be deploying a genuinely intelligent merchandising approach — not “here are your top ten products” reports, not weekly performance summaries, but real-time decision support that reconciles competing constraints, surfaces what even the most experienced merchandiser cannot possibly see, and quickly tells you whether the arrangement you just published is performing better or worse than the one it replaced.

In practice, this means an AI model that knows when a new arrival has accumulated enough signal to be called a winner or a loser — and alerts a merchandiser, or automatically adjusts placement, rather than waiting on a fixed schedule. It means understanding that a product’s rank in one collection says almost nothing about how it should be positioned in another. It means flagging overexposed products that crowd out items with stronger contextual fit and surfacing quiet performers before the window to act on them closes.

The goal is not to replace the merchandiser. It is to handle what the merchandiser cannot: processing more variables simultaneously than any human can track, across more collections than any team can actively monitor.

The teams that will win in the next few years are not the ones with the most data or the most sophisticated tech stack. They are the ones that close the loop between decision and outcome fastest. E-commerce waits for no one. The catalog changes. The customer moves on. The question is whether your merchandising intelligence moves with it — or whether you’re still finding out what happened last week.

The post Merchandisers Are Drowning in Data and Still Flying Blind appeared first on MartechView.

Before a customer logs in, loads a product page, or checks their order history, a cascade of invisible processes has already run. A database has retrieved its information. An API has connected the relevant systems. An automation has triggered the right response. A messaging system has delivered the update they needed. None of it is visible. All of it is felt.

That invisibility is the measure of a successful backend. When these systems work, customers experience so little friction that they never think about what is happening beneath the surface. When they fail — when a page loads slowly, a transaction stalls, or a notification arrives too late — trust in the platform erodes immediately. Users rarely articulate why an app feels unreliable. They simply stop using it.

For technology leaders focused on customer experience, understanding how backend infrastructure shapes what users actually feel — not just what they see — is becoming a strategic priority.

Databases: Speed Is the Product

A database stores everything that keeps an application running: customer profiles, order histories, product catalogs, login credentials and activity records. Every time a user interacts with a platform, the system is querying that database in real time.

The margin for error is narrow. Research has shown that a one-second delay in page load time reduces conversions by seven percent. At scale, that figure compounds quickly. A database that responds in milliseconds produces a customer experience that feels instant. One that lags results in a customer leaving.

Integrations: One Experience Across Many Systems

Most businesses today operate across a dozen or more software tools simultaneously — customer relationship management platforms, payment processors, marketing automation systems, analytics dashboards. Without integrations, these systems operate in isolation. With them, they share data in real time and present a coherent experience to the user.

The practical effect is visible in small moments. When a customer updates their phone number and sees the change immediately reflected in their support correspondence and email communications, that seamlessness is the product of well-built API integrations working behind the scenes. The same infrastructure enables personalization: research suggests that around 72% of recipients engage only with messages tailored to them, making the ability to link customer data to behavioral signals a direct driver of revenue.

Automations: Removing Friction Before Users Notice It

Automation handles the repetitive processes that keep operations running without human intervention — confirmation emails sent after purchases, support tickets assigned to the right team, profiles updated, and marketing messages scheduled. For users, these automations are invisible. What they experience instead is speed, consistency and the feeling that the system anticipated what they needed.

More sophisticated applications are emerging. AI-powered behavioral analysis can reveal why users abandon items in their shopping carts, identify friction points in checkout flows and suggest layout changes that improve conversion — all derived from patterns in backend data that no human analyst would have the capacity to review at scale.

Messaging Systems: Timing Is Everything

Customer expectations around communication have sharpened considerably. Research indicates that around 40% of users expect a response within an hour, and 79% within 24 hours. When a response arrives while a concern is still relevant — whether from a human agent or a well-designed chatbot — it builds confidence in the platform. When it arrives late, the moment has passed and the damage is done.

Real-time messaging infrastructure makes transparency possible at scale. Delivery updates, appointment reminders and account alerts arrive exactly when they are needed, without manual intervention. For smaller businesses operating with limited customer service resources, this infrastructure allows them to meet expectations that were previously achievable only by larger organizations.

The Backend Is the Brand

A polished interface can attract a user. It is the backend that keeps them. The systems that store, connect, automate and communicate are not technical details — they are the foundation on which every customer relationship is built. Users may never see them. But they feel them with every interaction, and they remember how the experience made them feel long after they have forgotten what the interface looked like.

The post The Invisible Infrastructure Behind Every Great App appeared first on MartechView.

Retailers are tightening policies in 2026 as a response to snowballing returns that totaled nearly $850 billion last year, roughly 15.8% of annual sales. But while it’s true that brands have to do something to protect shrinking margins, the question is whether introducing shorter return windows, additional fees, and extra hoops for all customers is the right move.

Imagine a longtime shopper, one who’s spent thousands with your brand over the years, trying to return a pair of shoes that didn’t fit, only to be met with obstacle after obstacle. From their perspective, nothing changed — except that a brand they trusted suddenly stopped trusting them. 

Stricter returns can help deter abusers, but they can also drive loyal customers to spend with a competitor whose policies feel fairer. The retailers that see return policies as a CX advantage in 2026 won’t aim to have the most lenient or the strictest rules. Instead, they’ll strive for precision: cracking down on fraudsters while maintaining a frictionless return experience for customers who’ve demonstrated trust over time. ​

Blanket Policies Punish Your Best Shoppers

For years, retailers raced to make returns as quick and painless as possible. But as the gap between when a refund was issued and when a return was validated continued to grow, “no questions asked” began to turn into “no consequences.” 

The line between normal shopping behavior and policy gaming has blurred. Many shoppers believe practices like wardrobing (wearing and then returning clothing) and bracketing (ordering multiple sizes or colors to try on) are acceptable.

Now, with margins under pressure, retailers are pushing back. In 2024, returns and claims cost retailers about $103 billion. Those are real losses, and stricter return policies are a knee-jerk reaction many retailers are already putting into place.

But blanket policies that tighten everything at once and are easy to roll out tend to punish loyal, low-risk customers while repeat abusers just find new ways around the rules. Not only that, but refunds slow down, exceptions become inconsistent, and customers shift future spending to brands that still feel easy to deal with. 

How Brands Can Protect CX Without Encouraging Abuse

The key to getting returns right in 2026 is using automated decisioning to maintain the same CX that loyal shoppers have come to expect while adapting quickly as abuse patterns evolve.

Risk-Based Routing

Instead of a blanket policy that treats repeat customers and repeat abusers the same way, risk-based routing segments shoppers by trust levels (trusted, standard, and high-risk) based on signals such as their return rate, claim history, support interactions, and ordering behavior. This moves loyal shoppers through the process quickly, while those exhibiting potential harm hit a speed bump.

Automated Approvals

With unified post-purchase visibility, patterns can be spotted in real-time. When a refund is initiated, the trusted customer is automatically approved, while the high-risk request triggers additional verification steps or manual review. The result is seamless CX for the customer you want to keep, without extending the same ease to likely abusers.

Personalization Through Automation

Not only could two shoppers be routed and approved differently, but they could also receive different offers. The loyal customer may receive a prepaid shipping label and an instant refund, while a repeat offender may be limited to store credit or required to cover their own return shipping. Therefore, leniency can no longer be assumed; it must be earned.

Putting Friction Where It Belongs

The customer journey doesn’t end when the package is marked as delivered. What happens next, whether your return policy feels effortless or frustrating, could be the moment a shopper decides if it’s worth coming back.

In 2026, protecting your margins won’t come from being the strictest, nor from staying the most lenient. It will come from using data to create trust-based experiences and being precise about where to place friction. Instead of building your customer experience around bad actors, provide loyal shoppers with the seamless CX they’re used to, and set guardrails against high-risk behaviors. This sends a clear message: trust is a two-way street, and we’ll still make returns easy for the customers who’ve earned ours.

The post Protecting Loyal Customers From Your Own Return Policies appeared first on MartechView.

When Target’s recommendation algorithm began identifying purchasing patterns consistent with pregnancy — prenatal vitamins, unscented lotion, cotton balls bought in bulk — the retailer did what any data-driven marketer would do. It acted on the insight, mailing a coupon book for cribs and baby clothes to the customer’s home address.

The problem was that the customer was 15 years old. Her father called the store to complain, accusing the chain of encouraging teenage pregnancy. He later called back to apologize. His daughter, it turned out, was pregnant — a fact he had not yet known. Target’s algorithm had figured it out before her family did.

That story, now a fixture in marketing case studies, captures the central tension of personalized marketing in a single episode: the same capability that makes recommendations feel helpful can, without warning, make them feel like a violation. The line between the two is not where most brands think it is.

The Infrastructure Behind the Insight

Modern recommendation systems can track consumer behavior down to mouse movements, dwell time and keystrokes. Search engines, email platforms and social media make it straightforward to monitor purchases, preferences and browsing habits in real time. What feels like casual scrolling — saving a destination photo, browsing furniture, pinning bathroom tile ideas on Pinterest — generates detailed behavioral profiles that brands and advertisers can access, often without the consumer’s awareness.

The infrastructure making this possible operates largely out of sight. Spy pixels, tracking cookies and browser fingerprinting have become standard tools in the personalization stack. Third-party data brokers collect, categorize and sell the behavioral data these technologies generate, frequently without consumers’ explicit knowledge or consent. The discomfort that results does not come from receiving a relevant advertisement. It comes from the realization of how comprehensively ordinary behavior was tracked, packaged and monetized.

The scale is significant. A Boston Consulting Group survey of 23,000 consumers found that while 75% are comfortable with personalized experiences, nearly 70% have had at least one experience they found invasive or inaccurate—and in many cases, they responded by unsubscribing or ending business with the company entirely. Separately, around 62% of consumers say they will remain loyal only to brands that personalize their experience, while almost 80% express concern about how companies collect their data. Both things are true simultaneously, and the gap between them is where trust is won or lost.

When Precision Becomes a Problem

The most common personalization failures fall into two categories: acting on misread data, and acting on data the consumer did not know you had.

The first is a technical problem. An algorithm that recommends a product to someone who just purchased it has simply made a mistake — it has failed the implicit promise that tracking behavior should, at minimum, benefit the person being tracked. The annoyance is mild but corrosive: it signals that the system is watching without understanding.

The second is more serious. A push notification that reads “We see you’re in the mall — stop in for 50% off” is not a helpful reminder. It is a demonstration of geolocation capability that many consumers did not realize they had consented to. The offer is irrelevant. What the message actually communicates is surveillance.

The same principle applies when brands venture into sensitive life stages — pregnancy, illness, divorce, bereavement, job loss — without being invited. Sending coupons for infant formula to someone experiencing infertility, or congratulating a couple on a pregnancy they have not announced, converts a data asset into a liability. The algorithm made a reasonable inference. The brand failed to ask whether it should act on it.

What Responsible Personalization Looks Like

The distinction between personalization and surveillance is not technical. It is one of consent and expectation. Consumers are comfortable with brands using data they have knowingly provided, in ways they can reasonably anticipate, to deliver experiences that serve them rather than expose them.

Macy’s offers a workable model. The retailer aggregates first-party behavioral data with real-time insights to personalize communications across its Star Rewards loyalty program, where members have actively opted in and understand the exchange. Fifty percent of messages to program members are now personalized, with more than 500 million custom offers sent since launch — a scale achieved without the invasive inference that has damaged other brands.

The principle scales down as well as up. A florist sending a birthday coupon featuring the recipient’s birth month flower is using a small, delightful piece of data to create a moment of genuine connection. The customer feels seen, not watched. That distinction — between being known and being monitored — is the one that personalization, at its best, is supposed to resolve.

First-party data, compiled from purchase history and direct customer engagement, is almost always preferable to third-party profiles purchased from brokers. It is more accurate, more current and carries none of the ethical ambiguity of data the consumer never knowingly generated. Before acting on any data point that touches sensitive territory — marital status, health, financial circumstances, family composition — brands should ask not only whether they have the information, but also whether the customer knows they have it, and whether acting on it will feel like service or exposure.

The Real Cost of Getting It Wrong

The Target story endures not because it is exceptional but because it is legible. Most personalization failures are quieter — a recommendation that misses, a notification that unsettles, a message that arrives at the wrong moment with the wrong assumption — but they accumulate in the same direction. Each one runs a small deficit against the trust that personalization is supposed to build.

The goal of personalization is to make customers feel understood. When it works, the transaction is invisible — the right offer at the right moment, and the customer reaches for it without thinking twice. When it fails, the mechanism becomes visible, and what the customer sees is an unhelpful brand. It is a system that has been watching them.

The capability to know more about customers than they know about themselves is not, by itself, a marketing strategy. Judgment about when to use it, and when to hold back, is what separates the brands that earn loyalty from the ones that learn, too late, what they should not have said.

The post Automated Recommendations Feel Like Surveillance appeared first on MartechView.

The tab-switching is the problem Yahoo is trying to solve. Stock price in one window, sports scores in another, inbox buried somewhere underneath. Most people start their day toggling between the same five destinations. Yahoo’s answer is to collapse all of them into one.

On Tuesday, Yahoo introduced MyScout, a fully customizable homepage inside Yahoo Scout, the company’s AI answer engine currently in beta. It is, Yahoo says, the first AI answer engine to offer this kind of dynamic, user-driven personalization — and it pulls from across the Yahoo ecosystem to do it.

What It Actually Is

MyScout is less a search feature than a configurable daily briefing. Logged-in users can build a homepage that surfaces whatever matters most to them: emails from their Yahoo Mail inbox, stocks from a Yahoo Finance watchlist, live scores and schedules for favorite teams, trending news, local weather, shopping comparisons, and games including Trivia IQ. Users control both what appears and how it’s arranged, with tiles that can be added, reordered, or built around nearly any query.

Some tiles update in real time — stock prices, for instance. Others refresh throughout the day, including weather, sports scores, and breaking news. The result is a single view of the day’s most relevant information, assembled around the individual rather than a generic news feed.

“Staying on top of your world shouldn’t require a constant cycle of toggling between tabs and apps — yet that’s exactly what most people do,” said Eric Feng, Senior Vice President and General Manager of Yahoo Research Group. “We’ve introduced MyScout to bring all the information you need together in one place.”

The Data Advantage Yahoo Is Leaning On

The pitch behind MyScout is inseparable from Yahoo’s scale. The company reaches roughly 90 percent of US internet users each month, drawing on 500 million user profiles, a knowledge graph spanning more than one billion entities, and 18 trillion consumer events annually. That accumulated signal is what Yahoo is positioning as its structural advantage over newer AI search entrants — three decades of understanding how people actually search, decide, and act online, rather than months.

Whether that heritage translates into meaningful personalization or simply more targeted noise is the question MyScout will need to answer in practice.

Also Read: Your Rebrand Is Failing. Have You Tried Listening?

A Simultaneous Bet on Publishers

Alongside MyScout, Yahoo News is introducing publisher brand pages and a follow feature — giving publishers a centralized hub on Yahoo that brings together their articles, video, and social feeds, and enabling users to receive curated newsletters of followed content directly in their inbox.

The move responds to a genuine tension in AI search. A recent Morning Consult survey found that more than 75 percent of Americans think it is important for AI search tools to show original sources — and that a tool making it easier to click through to those sources would make them more likely to use it. Yahoo is deliberately arguing that AI answers and publisher health are not in conflict.

“Publishers are fundamental to the open web, and its future depends on a healthy, thriving ecosystem,” said Kat Downs Mulder, General Manager of Yahoo News. “We’re focused on making Yahoo a place where great content is discovered and valued.”

That positioning is pointed. As AI-generated answers increasingly displace the click-throughs that sustain publisher revenue, Yahoo is staking out a different relationship — one where the AI surface and the open web reinforce each other rather than compete.

MyScout is available now in beta for US users at Scout.com and in the Yahoo Search app on iOS and Android.

The post Yahoo Scout’s MyScout Wants to Be Your Daily Dashboard appeared first on MartechView.

Raj De Datta has spent over a decade building Bloomreach into one of the most quietly formidable companies in commerce technology. With $260 million in ARR and customers like American Eagle and SPANX, the platform’s AI engine — Loomi — isn’t a bolt-on feature. It’s the whole architecture. But as generative AI rewrites the rules of how people shop, discover products, and interact with brands, De Datta is less interested in celebrating how far things have come and more focused on what comes next. 

In this conversation, he makes a case for why the traditional e-commerce site is becoming infrastructure rather than interface, why personalization and automation are converging faster than brands are ready for, and why the winners of the next era of commerce won’t be those who automate the most — but those who automate responsibly.

Excerpts from the interview; 

Bloomreach just crossed $260 million in ARR, powered by Loomi AI and brands like American Eagle and SPANX. What did you get right in scaling an AI-native platform — and what nearly broke along the way?

We got a few core decisions right early. First, we built Loomi AI as the foundation, not as a feature. It is the intelligence layer that powers our applications and agents across email, search, personalization, and conversational shopping. That architecture meant that as AI advanced rapidly over the past few years, we remained ahead of the curve.

Second, we focused on real-time data and first-party context. Loomi AI combines customer and product data with real-time infrastructure, AI decisioning, and orchestration across channels. That allowed us to deliver personalization that compounds — every interaction feeds the system and improves the next one.

Third, we invested early in agentic capabilities. Nearly half of our customers now use at least one next-generation AI tool, and that adoption has more than doubled in the past year. We also saw strong engagement with our conversational shopping agent, including a significant uptick during the recent holiday season. That momentum reflects a clear product direction: move from static tools to systems that can take action.

The one ongoing challenge — though really more of an opportunity — was the pace of change in AI. Every day unlocked a new possibility. We constantly had to examine what we were building to ensure it was maximizing AI’s potential. That meant a lot of rapid iteration and required us to be willing to take products we thought were great and challenge ourselves to make them even better.

The past two years have rewritten the rules of AI almost quarterly. How do you build a durable product strategy when the underlying technology shifts this fast?

The technology will keep changing, and our strategy is to stay one layer above it. We built Loomi AI as the intelligence layer beneath all of our applications and agents. Because that foundation is consistent, we can adopt new AI capabilities as they emerge without rebuilding the core each time the model landscape shifts.

Durability comes from that architecture — every interaction feeds back into the system, so the platform continuously improves. The goal isn’t to predict the next breakthrough. It’s to build a system that seamlessly incorporates it. That’s what allows us to move quickly and keep innovating while maintaining stability for enterprise customers.

You’ve spoken about agentic commerce. What changes when AI stops assisting shoppers and starts transacting on their behalf?

When AI moves from assisting shoppers to acting more autonomously, commerce shifts from reactive experiences to outcome-driven ones. Instead of simply answering questions or suggesting products, AI can understand context, predict intent, and guide the entire journey in real time — from discovery to decision to purchase.

But this changes a great deal for brands. It means they risk losing control over customer journeys and relationships. That’s why this is such an inflection point for businesses right now. The brands actively investing in agentic commerce — preparing catalogs for agentic discoverability, building their own apps — are the ones that will be prepared for this new era of commerce.

If AI becomes the primary interface for product discovery, what happens to the traditional e-commerce site? Is the homepage becoming obsolete?

It’s more a case of “the website is dead… long live the website.” In reality, the website becomes infrastructure rather than interface, as we know it today. All of the data it houses — about your products, about your customers — remains as relevant as ever. But that data and infrastructure will transcend the website, too. It’ll extend into conversational experiences in AI apps, and into channels like email and mobile, as it already does today. The homepage isn’t obsolete. It just isn’t the front door anymore.

Every AI transformation has its blind spots. What assumptions about AI in commerce turned out to be wrong?

There were assumptions about speed and scale that were somehow both too big and not big enough. At the onset of generative AI and large language models, there was real fear that e-commerce would become irrelevant almost overnight. Obviously, that hasn’t been the case.

But at the same time — it will, and already is, wholly transforming what commerce looks like. The way people discover products, compare brands, build wardrobes — all of that is now done with AI in ways we couldn’t have imagined a few years ago. And I think the scale of that transformation hasn’t even peaked yet.

At what point does personalization cross into automation? And how do brands ensure agency remains with the consumer?

Personalization becomes automation the moment systems start acting without asking. That’s the real inflection point. For years, personalization meant better recommendations or more relevant messaging. Now, with agentic AI, systems can execute decisions across channels. The question isn’t whether that’s possible — it’s how it should be governed.

Brands need to be clear about intent. Automation should reflect what a customer has already signaled, not replace it. When AI is grounded in real customer context and real-time signals, it can reduce friction and help people complete their journey. When it drifts from that, it stops being helpful and starts becoming opaque.

Agency remains with the consumer when technology is designed to respond to their intent, stay transparent in its decisioning, and keep humans in control of outcomes. The more autonomous systems become, the more important those guardrails are.

In this next phase of AI, the differentiator won’t be who automates the most. It will be who automates responsibly — and keeps the customer at the center of that automation.

In 2030, what will feel outdated about how we shop today — and what will Bloomreach have built to stay ahead of that shift?

Today, much of e-commerce still centers on browsing: navigating pages, filtering options, and manually managing campaigns across channels. But the shift we’re already seeing is toward agentic experiences — systems that understand intent and take action in real time. As AI moves from insight to execution, customers will expect more conversational, personalized, and responsive interactions, not just better search results or product recommendations.

What will matter most is whether brands can operate across touchpoints as one connected system. Experiences won’t be confined to a single website or channel. They’ll need to work across email, messaging, mobile, search, and beyond — with intelligence that adapts instantly to customer context.

The post Your Homepage Isn’t the Front Door Anymore appeared first on MartechView.

Xometry, an AI-driven marketplace connecting buyers and suppliers in custom manufacturing, has introduced new predictive lead-time and dynamic pricing models designed to improve sourcing accuracy and speed across its platform.

The company said the new Enterprise Machining Lead Time Prediction Model and enhanced pricing intelligence expand the capabilities of its Instant Quoting Engine, the core system that connects engineers and procurement teams with a global network of manufacturing partners.

Together, the upgrades aim to deliver more accurate production timelines, faster delivery options and improved operational efficiency for enterprise buyers.

Also Read: The End of the Predictable B2B Buyer Journey

Predicting Manufacturing Lead Times With AI

Xometry’s Instant Quoting Engine uses machine learning models trained on historical production and delivery data generated across its global supplier network. The newly released lead-time model significantly expands the scope of that predictive intelligence.

According to the company, the system now analyzes a training dataset four times larger than previous versions, incorporating new variables such as specialized certifications, additional materials and advanced finishing requirements.

Key improvements include:

• Higher prediction accuracy, with measurable improvements in RMSLE performance compared with earlier models
• Expanded rapid-delivery options, including optimized one-day lead times for a broader set of materials and part geometries
• Improved operational throughput, enabling shorter standard lead-time estimates for customers

The model continuously learns from supplier performance data, allowing the platform to refine delivery forecasts and improve execution reliability over time.

Personalized Pricing for Manufacturing Orders

Alongside lead-time improvements, Xometry has also upgraded the dynamic pricing logic within its quoting system.

Rather than relying on static price tables commonly used in supply chain software, the company’s model analyzes part geometry, quote configurations and a customer’s historical purchasing data to generate a price-response function for each individual quote.

Following testing in late 2025, the personalized pricing models are being rolled out more broadly to U.S. customers during the first quarter of 2026.

The company says the changes are designed to improve both the buyer experience and revenue efficiency across the marketplace.

Also Read: From AI Vision to Retail Personalization at Scale

A Closed-Loop Manufacturing Intelligence System

“These updates represent more than incremental improvements,” said Vaidy Raghavan, Xometry’s chief product and technology officer. “By closing the data loop with our partner network and accelerating model training cycles, we are reducing the time from insight to real-world production impact.”

Xometry’s marketplace integrates quoting, supplier selection, production performance and delivery outcomes into a continuous learning system. Each completed order feeds additional data back into the platform’s AI models, improving future predictions for pricing, delivery and supplier matching.

The company says the approach is designed to address long-standing inefficiencies in manufacturing procurement, where sourcing custom parts has traditionally required manual coordination across fragmented supplier networks.

By embedding predictive intelligence directly into engineers’ workflows, Xometry aims to transform the process into a faster, data-driven system that links digital design with real-world production.

The post Xometry Expands AI Models for Faster Manufacturing appeared first on MartechView.

Tim Hortons on Monday relaunched its annual Roll Up To Win promotion in the United States, offering customers a chance to win prizes ranging from free coffee and doughnuts to travel packages and consumer electronics.

The promotion runs through March 22 and includes more than one million food and beverage prizes, along with gift cards and Tim Rewards points. Larger prizes this year include a Cirque du Soleil Las Vegas VIP package, Nintendo Switch 2 gaming consoles, Oura Ring 4 wellness trackers and gift cards from brands such as Uber Eats and Shell. No purchase is necessary to enter, according to the official rules.

First introduced in 1986 as Roll Up The Rim To Win®, the campaign has become one of the company’s signature marketing events in both the United States and Canada, distributing millions of prizes annually.

“Launching Roll Up To Win® every year continues to be a way for us to thank our loyal guests,” said Maria Posada, vice president of marketing. She added that even smaller rewards — such as a free coffee or doughnut — are designed to “celebrate everyday wins.”

Customers can participate digitally through the Tim Hortons app by purchasing eligible items, including hot or cold beverages, breakfast sandwiches, wraps, avocado toast or Omelette Bites. Delivery and catering orders placed through the app also qualify. Users must have a free Tims Rewards account to receive and reveal digital rolls.

In-store customers can also participate by rolling up the rim of specially marked medium, large or extra-large hot beverage cups for a chance to win instant food and beverage prizes, while supplies last. Guests who scan for rewards in the app may receive both a physical and a digital roll for eligible purchases.

Also Read: From AI Vision to Retail Personalization at Scale

According to the company, odds of winning a cup-based prize are approximately 1 in 8 for medium cups and 1 in 6 for large and extra-large cups. Digital rolls must be revealed by early April, and prize claims are subject to verification.

The campaign underscores the chain’s continued investment in blending in-store traditions with app-based engagement as quick-service restaurants compete for customer loyalty in a crowded market.

The post Tim Hortons Revives Roll Up To Win in U.S. appeared first on MartechView.

Financial pressure is reshaping not only what Americans buy, but how they feel about spending.

A new survey from Omnisend, based on responses from 1,072 Americans, found that 66 percent have switched to cheaper products in the past year, 60 percent abandon online shopping carts in the hope of getting a discount, and 44 percent admit to hiding an online purchase from someone in their lives.

Taken together, the findings suggest that tighter household budgets are driving both behavioral and emotional shifts. As more consumers trade down to less expensive brands and delay purchases in search of deals, spending decisions appear to carry greater psychological weight.

Also Read: From AI Vision to Retail Personalization at Scale

Secrecy and Justification

Among those who said they had hidden an online purchase, 21 percent cited a spouse or partner as the person they concealed it from. Fourteen percent said they hid purchases from children in the household, while 12 percent pointed to parents or friends.

When asked why, respondents offered a mix of financial and emotional reasons: 17 percent said the item was expensive, 15 percent described it as unnecessary or impulsive, and another 15 percent said it felt personal or embarrassing.

Deal-driven shopping may be intensifying that tension. Fifty-eight percent acknowledged buying something primarily because it seemed like a good deal, even if it was not needed.

“People are feeling more accountable for every dollar, especially at home,” said Marty Bauer, an e-commerce expert at Omnisend. “When money is tighter, purchases carry more weight.”

Trading Down

Two-thirds of respondents said they had switched to cheaper alternatives either often or occasionally over the past year. Among them:

• 57 percent chose lower-priced brands
• 46 percent opted for private-label or store brands
• 29 percent selected simpler products with fewer features
• 26 percent purchased second-hand or refurbished items

Only 7.5 percent said they responded to higher prices by simply buying less rather than substituting.

For many shoppers, discovering that a lower-cost product performs adequately can permanently alter brand loyalty, Bauer said, as consumers recalibrate their expectations.

Also Read: When New Year Travel Strains Corporate VPNs and SASE

Waiting for the Real Price

Price sensitivity is also reshaping how Americans navigate online checkouts. Half of the respondents said they wait for sales or promotions before buying. Forty-three percent compare prices across multiple websites, and 40 percent search for discount codes before completing a purchase.

Sixty percent reported abandoning carts at least occasionally in anticipation of a follow-up discount or reminder email. Nearly 19 percent delay purchases even when they intend to buy.

Years of aggressive online promotions have conditioned shoppers to assume that the first price is rarely the final one, Bauer said. Waiting has become embedded in the purchase journey — and paying full price can feel like a misstep.

In an environment of economic uncertainty, the psychology of shopping appears to be evolving alongside household budgets, with thrift increasingly shaping not just transactions but the emotions surrounding them.

The post Financial Stress Is Reshaping U.S. Shopping Habits appeared first on MartechView.

Most retailers have a good idea of what personalization ought to look like. They recognize that customers expect individualized experiences that reflect their preferences, context, and timing, and they understand what delivering on those expectations would mean for their bottom line. But too often, retailers encounter operational barriers that prevent them from achieving personalization at scale. And those barriers typically stem from a breakdown between strategy formulation and practical implementation, or from the clash between organizational complexity and technological reality. 

While investments in AI and customer data platforms continue to grow rapidly, disconnected teams, slow activation cycles, and limited real-time decisioning often mean those strategies don’t translate into consistent customer experiences. Customer data teams develop sophisticated segmentation models, while loyalty teams operate their own programs with distinct customer tiers. Technology groups build impressive data platforms that merchandising teams rarely access effectively. When every retail department is pursuing personalization within its own domain, fragmentation becomes inevitable. 

Moving Beyond Static Segmentation Models 

Let’s start with one of the most obvious barriers to true one-to-one personalization: customer segmentation. Traditional engagement approaches group customers into demographic segments or purchase-based categories, creating broad buckets that miss individual behaviors and context-dependent needs. A customer casually browsing winter coats during a weekday evening has entirely different requirements than someone urgently checking the same products during an unexpected winter storm. Age, income, and historical purchases provide no insight into these contextual differences. 

There is an antidote for this broad-brush problem: predictive, behavior-driven engagement that responds to individual actions, context, and timing rather than fixed customer groups. This approach tracks subtle behavioral signals, such as the pause before abandoning a cart, product-comparison patterns, and timing between browsing sessions, which reveal intent more accurately than a demographic profile. 

Leading pet retailers, like Petco, demonstrate this by tracking pet life-stage transitions rather than segmenting customers by pet type or spending levels. A customer gradually shifting from puppy to adult dog products triggers different marketing or recommendation sequences than someone researching products for multiple pet types simultaneously.  

This kind of predictive engagement reacts to in-the-moment customer behavior rather than assumed characteristics, but it’s only possible with access to rich customer data and AI-powered models to make sense of it. 

Breaking Down Data Silos for Connected Execution 

This is why connected data is so important. When customer, loyalty, promotions, and retail media data remain siloed, execution breaks down. Connecting these systems enables faster decisions, more relevant interactions, and better control over promotional spend. Traditionally, most retailers maintain separate platforms for loyalty programs, marketing campaigns, promotional offers, and commerce transactions. These disconnected systems prevent a comprehensive understanding of customers and limit personalization capabilities to narrow functional areas. 

Unified data architecture connects every customer touchpoint into coherent insights into individual customers. Transaction histories merge with browsing behaviors. Loyalty interactions integrate with promotional sensitivity. Marketing responsiveness correlates with customer service data. This holistic view enables real personalization that reflects the complete customer relationship. 

The impacts of connected data go well beyond customer experience improvements. Connected systems enable retailers to optimize promotional dollars by understanding which offers drive incremental spending rather than cannibalize existing purchases. Loyalty program investments can be redirected toward tactics and incentives that genuinely influence buying patterns. Retail media campaigns can target audiences based on demonstrated (and attributable) purchase behaviors. 

Retailers can also link customer data platforms to operational systems, enabling promotional campaigns that automatically trigger inventory adjustments or staffing recommendations in response to predicted demand spikes. Connected data enables a retail environment where every customer-facing action coordinates with backend operations to ensure consistent experiences. 

Execution Hinges on Organizational Alignment 

Consistency and coordination are also required to achieve advanced personalization. Success depends on aligning marketing, loyalty, data, and technology teams around shared goals and execution models. The most sophisticated algorithms and platforms fail without organizational structures supporting integrated execution across functional boundaries. 

Instead of separate departments managing acquisition, conversion, and retention through isolated systems, successful personalization comes from cross-functional teams responsible for the whole customer relationship. These teams share data, insights, accountability, and, most importantly, execution responsibility for the entire customer lifecycle. When teams align around customer value creation rather than metrics that govern their individual function areas, the retail brand can get the most out of its technology investments and personalization strategies. 

Leading retailers also establish clear decision-making protocols for real-time personalization. When AI systems identify optimal promotional timing for individual customers, operational teams and systems must be ready to respond. Inventory management has to make micro-adjustments based on demand predictions, just as HR and customer service must staff up based on traffic forecasts. These coordinated responses require organizational commitment beyond technological capability. 

Measuring Integrated Impact 

Traditional metrics evaluate personalization initiatives in isolation, missing cumulative impact across connected customer experiences. Comprehensive measurement frameworks track changes in customer lifetime value, cross-category expansion, improvements in engagement depth, and the strengthening of long-term loyalty. These metrics reveal whether personalization creates genuine value or simply redistributes existing spending patterns. 

Unified systems enable measurement that spans entire customer relationships rather than individual promotional responses. Machine learning algorithms continuously improve based on actual customer behavior, feeding insights back into operational systems for ongoing optimization. The entire ecosystem becomes more intelligent through connected measurement and response cycles. 

Realizing the Personalization Vision 

Retailers finally have access to the predictive AI tools and advanced technology platforms to achieve true one-to-one personalization. But bridging the personalization gap demands more: a commitment to execution, organizational alignment around customer value creation, connected data systems, and operational processes that respond to insights in real time.  

Retailers that find a balance between strategic vision and operational execution create competitive advantages that technology investments alone cannot provide. Only then will their personalization strategies come to fruition, and only then will they be able to implement them consistently at scale.

The post From AI Vision to Retail Personalization at Scale appeared first on MartechView.