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.

There was a time when brands could simply list a product online and expect it to sell. Today, success requires a coordinated strategy across retail media, the digital shelf, pricing, and inventory. That strategy should be informed by data, but with thousands of SKUs across numerous marketplaces, brands not only have to interpret endless insights but also act on them. At scale, consistent execution becomes nearly impossible to manage manually.

In a recent survey, nearly half of business leaders said their data isn’t actionable, and more than 40% cited a lack of data accessibility or decision-making time as a core operational challenge. 

Teams don’t need more visibility — they need the ability to keep up with and act on the data they already have. It’s impossible for e-commerce teams to adjust every bid, fix every product description, and catch every stock issue themselves. To close that gap, brands need to rethink how the work gets done.

Why Today’s Execution Model Can’t Keep Up

Teams aren’t lacking data; they have too much of it. Dashboards have become more sophisticated, but seeing more doesn’t necessarily help teams do more.

Every optimization still requires someone to pull the report, identify the issue, decide what to do, and then execute. That delay between spotting a problem and fixing it is where performance starts to degrade. By the time a team corrects a content issue, the marketplace algorithms have already shifted.

What retailers are experiencing is a gap between insight and execution. The old approach was to do more: more reports, more meetings, more manual tweaks, more tools. But today, teams can spend most of their time looking for what’s broken and patching the same issues over and over.

The way the work used to get done won’t keep up with how fast modern e-commerce moves. With AI-powered discovery through Amazon’s Rufus and Walmart’s Sparky shaping how shoppers see and buy, the pace will only continue to accelerate.

Many brands have tried to address this by turning to agencies for support. In a recent survey, 67% of commerce teams said they rely on agencies, and about half said they spend 15% to 30% of their budget on agency fees alone. Yet 55% said those costs are too high for the results, and 40% said response times can’t keep up with the speed of algorithms.

While agencies can provide expertise, they don’t operate 24/7 — and the marketplaces retailers sell on never pause. Retailers can’t match that speed by adding more headcount or additional agency hours. 

Brands today need an algorithm to keep pace with the algorithm. They need a new execution model that can handle the volume and velocity of decisions in modern ecommerce.

What an Agentic Retail Strategy Looks Like

The shift happening now is toward agentic AI execution, where human teams are no longer tasked with doing the heavy lifting of weeding through data, reports, and manual workflows. Instead, they drive strategy and make final decisions while their agentic AI counterparts analyze large volumes of data and identify growth opportunities. 

This approach lets brands continuously extend execution across their entire catalog around the clock, rather than focusing only on top-performing SKUs. In practice, this looks like specialized agents that operate across the digital shelf:

• A content agent identifies and resolves product page issues across thousands of SKUs simultaneously, reducing the time required to update product detail pages from 35 minutes to 35 seconds.
• A media agent optimizes campaigns using dozens of signals at a scale no human team could match. 
• A sales agent monitors pricing and promo performance to flag gaps or opportunities.

To keep pace with the speed of modern ecommerce, retailers don’t have to add more tools on top of the same manual process; instead, they need to fundamentally change how the work is done. With agents handling the execution layer, the brand manager’s role changes. They spend less time pulling reports and making fixes and more time setting guardrails and deciding priorities.

The Real Shift Starts in Execution

Teams already know the old model isn’t working. The speed required to compete today has outgrown what humans can manage manually. There’s too much data to interpret and too many actions required to keep up with how quickly the digital shelf changes. 

Agent‑driven execution removes that bottleneck. According to a recent report, 82% of commerce leaders expect AI investment to increase in the next 12 to 18 months, and 71% are already familiar with or actively using AI agents. 

With the industry moving in this direction, retailers can’t afford to be left behind. The next era of e-commerce will belong to the companies that can execute at the same speed as the market.

The post Dashboards Created Visibility, but They Didn’t Solve Commerce Execution appeared first on MartechView.

I once spoke with a marketer at a B2C brand who told me that if they can make a consumer feel something, they’ve done their job.

As a B2B CMO, my job is done when I’ve hit my numbers. That difference isn’t about one side being right or wrong — it’s a difference in perspective. Tech and CPG marketers don’t think alike.

This year, that theory played out clearly on the Super Bowl LX stage.

At Zappi, we tested every ad that aired during the game — more than 65 spots in total. Tech brands made up just under a third of the lineup, spanning AI companies, devices, platforms, and apps. When we looked at overall performance using a sales-impact score that captures distinctiveness, brand recall, and purchase impact, tech ads underperformed non-tech ads by an average of 47 percent.

That’s a jarring gap. And it points to a simple conclusion: tech just doesn’t get the Super Bowl. Let’s look at the good, the bad, and the ugly of tech at Super Bowl LX.

Why Tech Ads Miss the Mark

Expectations matter

Super Bowl viewers show up primed for humor, spectacle, and the occasional emotional ad that pulls at the heartstrings. Categories like beer, snacks, and entertainment are “native” to the game because they naturally tap into these shared cultural feelings. Tech often leads with features instead of feeling, asking viewers to think at the exact moment they’re ready to feel.

Where technology can excel is in removing pain points for consumers. It’s less emotionally charged and more functional. Even within that constraint, Grubhub stood out by solving a familiar frustration: excessive fees. At a time when many consumers are feeling pinched, Grubhub used its Clue-styled spot featuring George Clooney to position the brand as the hero — the one willing to “eat the fees” on orders over $50. The message was clear, memorable, and accessible, and it showed up in performance: above-norm distinctiveness and more than double the benchmark for laughter.

By contrast, Genspark fell flat. On the surface, the ad checked familiar Super Bowl boxes — a celebrity cameo, a self-aware joke, a wink to the audience — but it stopped short of clearly stating what it is. In Genspark’s focus on being relatable, consumers left feeling confused about what it actually does.  On a night when brands are expected to show their best selves, Genspark used its airtime to gesture at scale and disruption without clarifying relevance. The result felt lightweight, especially compared with brands that translated innovation into tangible results.

Focus and cues misfire

Tech ads tend to veer in one of two directions: they either drift so far into abstraction that the product disappears, or they try so hard to be part of the spectacle that they miss the point entirely.

Squarespace leaned heavily into arthouse visuals. The spot was visually compelling, with real star power — but did it make anyone want to buy a domain during the game? Not really.

Salesforce went the opposite route, anchoring its ad in internet culture by centering it on MrBeast and gamifying the visuals with a million-dollar giveaway. MrBeast is instantly recognizable to my kids, but far less relevant to people who actually buy enterprise technology. Advertising to kids to influence purchase decisions is an age-old practice — just not in SaaS. The idea landed, just not with an audience that would ever convert.

Across the AI category, the issue wasn’t technical depth — it was vanity. Broad use cases layered with thin jokes, frenetic energy, and unsettling music dominated OpenAI’s ads. Others, like Anthropic, spent more time signaling scale and taking shots at competitors than actually appealing to potential users. Innovation became noise.

You can see how differently this plays out within tech itself. The Oakley and Meta collaboration landed because the feature at its core had clear, exciting implications. Viewers didn’t just see something futuristic — they understood that the product unlocked possibilities. That clarity made the brand easy to consider both now and later, placing the ad in the top tier of performance.

Emotional resonance beats prestige

Consumers remember stories, not specs. 

Too many tech brands chase prestige or vague signals of “innovation” without anchoring them in a relatable human truth. The result is often admiration without action — or indifference altogether.

One brand that stood out was Google, with its Gemini ad. Unlike other AI spots, Gemini avoided jokes and spectacle in favor of showing real applications — from dropping photos into a chatbot to visualizing life changes in concrete ways. While the ad still struggled with brand recall, it resonated emotionally: love scores exceeded benchmark levels, suggesting the message connected even if the branding lagged.

The takeaway isn’t that tech should avoid the Super Bowl. It’s that the Super Bowl demands a different kind of discipline. This is a moment built for people, not products. Even the most advanced technology needs an emotional hook to earn attention.

Tech can succeed on this stage — but only when ambition is balanced with accessibility, and amid the spectacle, you can provide clarity.

The post Tech Doesn’t “Get” the Super Bowl — and the Data Shows Why appeared first on MartechView.

For decades, the Super Bowl commercial was a singular cultural artifact—an expensive, high-wire act that lived for 30 seconds on a Sunday night and then disappeared into memory.

Those days are gone.

Today, the big game is less an advertising event and more the climax of a meticulously engineered, weeks-long media campaign. Brands no longer wait for kickoff to make an impression. They seed teasers, leak celebrity cameos, premiere extended cuts online, and court viral buzz long before the first whistle.

What used to be a moment has become a season.

“Companies don’t just want airtime anymore,” one creative executive recently told Sportico. “They want to own a multi-week cultural moment.”

And in 2026, that cultural moment has started earlier—and louder—than ever.

From Apple’s “1984” to the Era of Everything, Everywhere, All at Once

There was a time when mystery fueled Super Bowl advertising. Apple’s legendary “1984” spot aired once, stunned the world, and vanished into history.

Now, secrecy is considered a bad strategy.

Rocket, Ro, Salesforce, Pepsi, and dozens of others have already begun rolling out their Super Bowl narratives across LinkedIn, TikTok, Instagram, and YouTube. The commercial is no longer the product. The conversation is.

Take Pepsi’s latest campaign. President of PepsiCo Beverages U.S., Michael Del Pozzo, recently framed the company’s Super Bowl 2026 ad as “a new chapter” in the cola wars. Directed by Taika Waititi, the spot revives the Pepsi Challenge with a mischievous polar bear that seems suspiciously familiar to fans of a certain rival brand.

But, as Pepsi’s VP of Marketing Gustavo Reyna insists, “This is not about cola wars. This is about cola facts.”

That distinction—between rivalry and narrative, between advertising and entertainment—is the tightrope every major brand is trying to walk this year.

The Real Star of Super Bowl 2026: Artificial Intelligence

If there is one unavoidable theme looming over Super Bowl 2026, it is AI.

Greg Zakowicz, an e-commerce advisor to Omnisend, joked recently, this year’s unofficial motif may well be “AI slop.”

From AI-generated commercials to ads for AI companies, to brands cheekily poking fun at the technology itself, artificial intelligence is expected to dominate the advertising landscape.

And not everyone is thrilled.

“If the average person wasn’t tired of hearing about AI by now,” Zakowicz observed, “they will be after the Super Bowl.”

The irony, of course, is that the same technology being satirized on screen is increasingly being used behind the scenes—to target audiences, optimize ad buys, generate creative concepts, and even personalize campaigns in real time.

Super Bowl 2026 may well be remembered as the year advertising became self-aware.

The Second Screen Becomes the First Screen

Another defining shift this year is the formal acceptance of what viewers have already made clear: television is no longer the main event.

The real battleground is the phone.

QR codes, interactive polls, social media tie-ins, and instant shopping integrations are expected to feature heavily in this year’s commercials. The goal isn’t just to entertain viewers—it’s to activate them.

“TV won’t be the destination medium,” Zakowicz predicts. “It’ll be the conduit to the phone.”

Even the structure of ads is changing to accommodate this behavior. Short “floater ads” appearing during brief in-game breaks may become more valuable than the traditional 60-second blockbuster, precisely because they are designed for quick, multi-screen engagement.

The audience no longer watches ads. They participate in them.

When the Hype Outshines the Reveal

There is, however, a growing downside to the modern Super Bowl ad machine: anticipation fatigue.

With so many commercials released days—or even weeks—before kickoff, the actual broadcast risks becoming an anticlimax. The element of surprise, once the Super Bowl’s greatest advertising asset, is steadily disappearing.

To combat this, brands are leaning heavily into teaser strategies—dropping cryptic previews, partial reveals, and behind-the-scenes clips to keep audiences guessing until game day.

The commercial has become a movie trailer for itself.

The Game Behind the Game

What all of these points to is a fundamental truth about Super Bowl 2026: the commercials are no longer interruptions to the event. They are the event.

Brands are not simply buying 30 seconds of airtime; they are orchestrating complex, multi-platform narratives designed to live far beyond the final score.

In an era of fragmented attention and algorithmic feeds, the Super Bowl remains one of the last true mass media moments. But even that moment has been stretched, repackaged, and optimized into something much bigger.

Football still brings the audience together.

Advertising is what fights for their attention once they arrive.

Conclusion

Super Bowl 2026 will, as always, crown a champion on the field.

But off the field, a far more consequential competition is underway—one fought with polar bears, AI punchlines, QR codes, and celebrity cameos.

The question is no longer which brand will win the night.

It’s which brand that will win the month.

The post Super Bowl 2026: When the Commercials Became Bigger Than the Game appeared first on MartechView.

As the holiday shopping season approaches, marketers are finalizing their plans, but the largest gains this year will not stem solely from discounts. The promotional period has evolved into a protracted marathon of discovery, comparison, and intent stretching from late September into the new year.

This period remains the most significant in the U.S. retail calendar, marked by extraordinary traffic and spending surges. Data from the previous year underscores this intensity: Thanksgiving 2024 saw orders increase 56.8% above the October average, Black Friday orders surged 172%, and Cyber Monday’s growth exceeded 200%.

In financial terms, Americans spent more than $994 billion during the last season, with $241 billion from online sales. For 2025, despite heightened consumer uncertainty, the National Retail Federation projects total spending will surpass $1 trillion.

Success this year hinges not just on price, but on a brand’s ability to respond to a consumer base that is more deliberate, data-driven, and open to trying new options. Every day counts, and strategic presence now can dictate annual results.

From Bargain Hunting to Exploration

During the promotional season, shoppers are primarily in exploration mode, actively searching for new products, brands, and categories. Fashion advertisers, for example, typically see up to a 70% increase in unique visitors during this window.

These are not merely bargain hunters; they are high-intent audiences with curiosity. The key takeaway for brands is to build campaigns that fuel exploration rather than focusing solely on price drops. Data shows that 61% of products bought were not viewed beforehand, suggesting discovery is happening directly within the ad experience when creative is personalized and relevant.

The Race Starts Early

Visibility becomes expensive and harder to secure as inventory prices begin to rise weeks before Thanksgiving. Waiting until the week of Black Friday forces brands to pay more for less reach.

Winning brands adopt a “Grow Ahead of the Market” strategy, extending their promotional windows and building consistent awareness as early as September. This approach, which prioritizes early exposure over a last-minute blitz, often delivers up to twice the performance compared to scaling too late. Early campaign launches efficiently populate retargeting pools, which translates directly into stronger conversion efficiency when customer intent peaks.

Navigating the Complex Path to Purchase

The modern path to purchase is rarely linear. For nearly half of all shoppers, the journey involves multiple touchpoints—a user might see an ad, browse, add to cart, leave, and be retargeted five or six times before completing a purchase. This pattern is most pronounced in high-consideration categories like luxury fashion, electronics, and home goods.

Consistency is paramount: consistent presence, creative, and messaging across the full journey ensure the audience does not complete their path with a competitor. Marketers are increasingly leveraging Deep Learning-based advertising, which excels at servicing these longer paths by tailoring ads based on individual behavioral patterns rather than simple recency, maintaining brand awareness until the moment of conversion.

Activating New and Returning Shoppers

Intent surges across all audience segments, but activation requires segmentation. Returning customers are 17% more likely to purchase during this period, making it an opportune moment to reward loyalty with early access or personalized recommendations. Conversely, new visitors require assurance and discovery cues, needing to understand why a brand is worth their attention. Brands that balance both—running distinct campaigns for new customer acquisition and repeat conversion—achieve stronger overall lift.

Don’t Let Carts Go Cold

A significant conversion opportunity lies in cart re-engagement. Many shoppers begin building carts as early as mid-October but pause before finalizing the sale. In the fashion category, conversion rates can drop from 25% to 19% as the season progresses, indicating customers are waiting for the optimal offer.

Persistent, well-timed retargeting remains critical for recovering these high-value conversions. It is often worthwhile to maintain re-engagement efforts with shoppable creative or countdowns well into late December, long after peak weeks have passed, as many undecided users are still considering purchase.

The mindset shift is clear: the holiday promotional season is equally a discovery event as it is a discount one. Success belongs to brands that understand this fundamental shift in consumer behavior and remain strategically relevant from the first impression to the final click.

The post Holiday Shoppers Want Discovery, Not Just Discounts appeared first on MartechView.

For brand marketers, retail promo days like Black Friday, Singles’ Day, and Prime Day can feel like unmissable opportunities. The industry narrative is always the same: spend big, stand out, and ride the sales spike.

But here’s the uncomfortable truth: just because sales rise doesn’t mean your ads caused it.

At INCRMNTAL, we’ve analyzed more than $2 billion in verified ad spend across retail, gaming, fintech, and DTC brands—much of it tied to seasonal campaigns. And time and time again, we find the same pattern: performance looks excellent on paper, but actual incremental lift is far harder to find.

Take Prime Day 2025. Across fintech, E-commerce, and app marketers, we saw dramatic swings in ROAS and CPA—sometimes day-to-day—even when spend held steady. Some of the highest returns came from advertisers spending under $5,000 per day, while those who ramped up aggressively saw plateauing installs and diminishing returns. Standard attribution painted a rosy picture, but incrementality revealed the truth: many campaigns were riding the wave, not driving it.

And that’s hardly unique to Prime Day. The bigger the shopping moment, the easier it is to confuse correlation with causation.

Promo Days Don’t Guarantee Performance

Advertisers often enter sales periods convinced that more spend will automatically translate into more results. It’s understandable: when your board expects a Q4 spike, it feels safer to follow the herd than test assumptions.

But what we consistently see is that seasonal demand does much of the heavy lifting without paid media. If consumers are already primed to buy, some of your best results may happen with or without your campaign.

And when everyone is bidding aggressively in crowded auctions—especially on platforms like Amazon—costs spike quickly. That’s a dangerous place to be without real-time visibility into what’s working and what’s waste.

Scale Isn’t Strategy

One of our biggest takeaways is this: precision beats scale.

Smaller, targeted campaigns often drive stronger incremental performance than large, broad bursts. Promo days attract intent-rich audiences—you don’t need to shout to be heard.

At the same time, spend is spreading across more channels than ever: CTV, influencers, DOOH, retail media, and more. While diversification brings opportunity, it also creates more surface area for misattribution. Without robust measurement, brands risk rewarding the loudest channels, not the most effective ones.

Attribution ≠ Impact

Promo days create the perfect storm for flawed attribution. Spikes in conversions often get credited to whichever ad was last seen, regardless of whether it actually moved the needle.

The result?

Platforms look like heroes.
Budgets shift based on incomplete data.
Inefficiencies snowball into the next quarter.

Incrementality measurement cuts through this. It reveals the real effect of your spend—not just what happened, but what wouldn’t have happened without your ads. That’s the metric marketers need when CPMs surge and budgets are on the line.

Three Principles for Promo Season 2025 and Beyond

With Black Friday, Cyber Monday, and January Sales ahead, marketers can approach seasonal campaigns more strategically:

• Measure continuously, not just after the campaign.
  Always-on incrementality lets you adjust spend mid-flight and avoid pouring money into saturated markets.

• Don’t mistake demand for campaign success.
  A conversion spike doesn’t mean your ads drove it. Focus on lift, not volume.

• Use seasonal insights to inform long-term planning.
  Promo season learnings should shape evergreen strategy. Don’t scale what looked good—scale what actually worked.

What’s Next for Seasonal Performance?

Seasonality isn’t going anywhere—it’s getting more complex. Micro-moments like flash sales, influencer drops, and even weather-driven shifts now shape real-time purchase behavior.

That means performance marketing needs a rethink. You can’t rely on last-click attribution or platform-reported ROAS to guide decisions. And you definitely can’t assume that promo day conversions equal success.

Marketers must move from chasing spikes to understanding their causes. The key is embracing privacy-safe, always-on measurement approaches that reveal true drivers of performance—especially during high-pressure shopping periods.

The best campaigns of 2025/26 won’t be the loudest. They’ll be the ones powered by insights that show what’s really driving results—not just what happens during the spike.

The post Big Spikes, Small Truths: The Myth of Promo-Day Performance appeared first on MartechView.

Last year, U.S. shoppers spent $10.8 billion online on Black Friday, a 7.5% increase from 2023. It was a record-setting total, with the action unfolding in real time: between 10 a.m. and 2 p.m., spending averaged $11.3 million per minute.

At that speed, even small delays compound. With thousands of campaign variables—bid costs, search rankings, inventory velocity—shifting faster than human teams can track, the Turkey 5, the five days that begin with Black Friday and end with Cyber Monday, favors brands built for instant decisions, not those waiting for insights to load.

That’s why more retailers are beginning to replace traditional analytics tools with AI assistants and agents that not only report on performance but act on it. Here’s how that shift is redefining what it means to keep up during the most volatile week of the retail calendar.

The Illusion of Visibility

Retailers have never had more data—or more ways to get lost in it. In one survey, 40% of marketers cited data timeliness as their top challenge when advertising on retail media networks, followed closely by fragmentation and inconsistent reporting standards. 

Teams now juggle a dozen different performance views, each with its own definitions for impressions, attributed sales, and incrementality. Share of search might update hourly in one system, ad efficiency daily in another, and content health weekly in a third. The fragmentation forces teams to spend as much time reconciling metrics as improving them.

During high-velocity events like Black Friday, this noise intensifies. Decision cycles stretch as teams verify conflicting data or chase down the root cause of a performance miss. Meanwhile, campaigns overspend on SKUs already ranking organically, keywords underperform because of out-of-stock PDPs, and creative mismatches drag down click-through rates.

It’s the illusion of visibility: teams have more access to data than ever, yet they are farther from action.

From Dashboards to AI Assistants to Agents

After years of relying on dashboards and reports that surface metrics for humans to interpret, retailers are beginning to adopt AI assistants and agents that understand intent, prioritize competing signals, and take action in real time.

An assistant functions as an always-on analyst, monitoring performance, detecting anomalies, and recommending actions aligned with goals such as share of search, return on ad spend, or margin. An agent goes further by executing those actions automatically.

Here’s where AI assistants and agents can make the greatest impact during the Turkey 5:

Redirect Spend Toward New Opportunities

When bids surge or inventory starts moving faster than expected, agentic AI can instantly shift spend toward high-potential campaigns or underexposed products. This preserves margin and incrementality while sustaining rank through the day’s price wars.

Protect Margins Before the Market Moves

Agents can combine live availability data with campaign performance, pausing ads before they waste impressions on out-of-stock SKUs. On a weekend when sell-through rates can spike by 300%, this eliminates the lag between an item selling out, dashboards catching up, and a human intervening—keeping every dollar focused on products that can still ship.

Have Campaigns Evolve With Intent

These systems monitor which combinations of copy, imagery, and targeting drive the strongest engagement and automatically scale the winners. Campaigns evolve with shopper intent without waiting for manual reviews.

Respond to Competitors in the Moment

When a rival SKU drops in ranking or changes price, an AI agent can adjust bids or promotions instantly. During the Turkey 5, when top positions can turn over dozens of times an hour, acting within seconds often determines who stays visible when shoppers are ready to buy.

Make Retail Performance Operate as One System

Instead of managing each retailer or media network in isolation, AI assistants can synthesize signals across Amazon, Walmart, and Target to maintain consistent pricing, content, and share of shelf. They surface near real-time comparisons across retailers, alerting teams when pricing deviations could confuse shoppers or erode performance.

How to Approach This Turkey 5

The Turkey 5 may be just weeks away, but it’s not too late to adopt AI assistants and agents. Even modest automation across bottlenecks can unlock real-time responsiveness when it matters most.

Choose the Right Tool

Start with an AI platform built for commerce—one that unifies trusted retail, media, and digital-shelf data and layers AI on top to automate decisions teams typically make manually.

Pick One Workflow to Automate

Identify a single repetitive process that consistently slows the team down, such as pausing out-of-stock SKUs, shifting spend away from cannibalized keywords, or reconciling performance reports. Automating even one of these tasks before peak season can reclaim hours of execution time.

Use Role-specific Insights

Agentic AI works best when trained on how commerce teams operate in the real world. Role-specific assistants can tailor actions for media, sales, or category teams—automatically flagging margin erosion for sales while optimizing bids for retail media.

The Turkey 5 lasts just one week, but it offers the clearest preview of how real-time retail will operate all year. By replacing dashboards with AI assistants and agents sooner rather than later, retailers can prepare for the constant volatility that now defines modern commerce.

The post Black Friday Speed Demands AI, Not Dashboards appeared first on MartechView.

Every year, Black Friday pushes Amazon’s systems—and its shoppers—to the edge. Tens of millions of products, millions of buyers, a blizzard of reviews, endless deals dropping by the minute. Chaos for most retailers. A controlled, data-driven machine for Amazon.

Behind that machine is AI.

Artificial intelligence now powers almost every moment of Amazon’s Black Friday experience—what customers see, what they buy, and how securely they check out. From hyper-personalized recommendations to fraud detection that operates at millisecond speed, Amazon’s AI ecosystem is the quiet force shaping the biggest shopping event of the year.

Here’s how it works.

AI-Powered Personalization: The Deal Discovery Engine

Black Friday is a choice overload problem. Amazon uses AI to fix that.

Machine learning models process billions of data points—search queries, browsing history, purchase patterns, abandoned carts, even time-of-day behavior—to build real-time recommendations.

For a shopper in Seattle who loves gadgets? Tech deals rise to the top.
A parent in Atlanta? Toys and home goods dominate the feed.
A beauty buyer in Miami? Limited-edition holiday drops are surfaced first.

These AI cues don’t just personalize the shopping trip—they shorten it. Amazon’s recommendation engine is responsible for 35%+ of total purchases on normal days. On Black Friday, that influence skyrockets as consumers lean on algorithmic shortcuts to navigate the noise.

Voice AI—through Alexa—adds another layer. Shoppers can ask:

• “Alexa, what are my Black Friday deals?”
• “Compare the top offers on noise-canceling headphones.”

The assistant responds instantly, ranking deals based on personal preferences and global trends.

Demand Forecasting & Inventory AI: Predicting What America Will Buy

Black Friday’s biggest sin is simple: running out of stock.

Amazon uses AI forecasting models trained on years of holiday sales, regional trends, and real-time behavior spikes. This helps Amazon:

• Predict demand down to the ZIP-code level
• Pre-position inventory in nearby fulfillment centers
• Adjust pricing dynamically when demand surges
• Reduce overstock by anticipating when interest dies off

Chicago might over-index on winter apparel.
Phoenix might spike in home electronics.
New York might lean heavily on premium kitchen appliances.

AI sees these patterns before humans do, preventing the dreaded “Out of Stock” button that kills both revenue and shopper trust.

On the logistics side, route-optimization AI ensures Black Friday orders move through a system that’s stretched to its limits. In cities like L.A. and Philadelphia—where traffic can ruin delivery promises—AI helps Amazon keep its “arrive tomorrow” magic intact.

Marketing Intelligence: AI That Knows What Will Trend Before It Trends

During Black Friday, Amazon’s marketing engine operates like a newsroom on deadline.

AI analyzes:

• Trending searches
• Social sentiment
• Competitor pricing
• Deal performance in real time
• Engagement signals across cities and states

That intelligence fuels dynamic ad placements—across Amazon, Google, social platforms, and connected TV—that shift on the fly.

In San Francisco or Washington, D.C., where tech adoption is high, Amazon can push deal ads that adapt instantly based on a user’s behavior. If beauty gifts are trending in New York by mid-morning, Amazon’s AI can update creatives, placements, and relevance in minutes—not days.

Black Friday is no longer a campaign; it’s a self-optimizing system.

AI vs. Fraud: The Quiet Battle Behind the Buy Button

More shopping means more fraud.

Fake reviews, payment fraud, bot-driven scams, impersonation attacks—Black Friday is peak season for bad actors. Amazon uses AI to police them aggressively.

Fraud Detection Algorithms

AI models watch transaction patterns in real time:

• Sudden changes in purchase behavior
• Unusual device or location fingerprints
• Suspicious account activity
• High-velocity orders

If anything seems off, the AI system flags or freezes the transaction instantly.

Fighting Fake Reviews

Fake reviews are a billion-dollar problem—and AI has made them harder to spot.

Amazon now uses:

• Machine learning models that analyze grammar patterns
• Linguistic signatures of AI-generated text
• Temporal patterns across multiple reviewers
• Verification cross-checks for “Verified Purchase” claims
• Behavioral signals (e.g., review farms posting in clusters)

Amazon has shut down massive fake-review networks and escalated legal action, including efforts targeting platforms that scrape or impersonate its marketplace.

Guardrails for Business Buyers

For Amazon Business customers, AI tools like Spend Anomaly Monitoring detect unusual purchasing patterns and alert administrators before a costly mistake occurs.

AI isn’t just protecting Amazon—it’s protecting the millions of shoppers who trust the platform.

AI Chatbots & Service Automation: Handling the Holiday Stampede

Black Friday pushes customer support to the brink.

AI-powered assistants—via chat, Alexa, and Amazon’s help portals—resolve:

• Delivery updates
• Returns
• Refunds
• Order tracking
• Product comparisons
• Deal-specific questions

Amazon’s service AI reduces resolution times from minutes to seconds, cutting strain on human reps and keeping frustration low during peak chaos.

What’s Next: The Future of Amazon’s AI-Driven Black Friday

The next wave of AI innovation is already underway:

• AR try-ons for fashion, beauty, and home decor
• AI Deal Advisors that compare discounts across years
• Personalized Black Friday “storefronts” built entirely by AI
• AI-generated product content paired with AI quality-control agents
• Ultra-localized demand forecasting that predicts trends neighborhood by neighborhood

As Amazon continues to push deeper into generative AI (Bedrock, Titan, etc.), Black Friday will evolve from a shopping event into a personalized, predictive experience.

AI Isn’t Behind the Black Friday Experience — It Is the Experience

Amazon’s Black Friday success isn’t luck, scale, or brute force.

It’s AI.

AI chooses the products.
AI sorts the deals.
AI predicts the demand.
AI secures the checkout.
AI personalizes the journey.
AI polices the marketplace.
AI routes the delivery truck.

For shoppers across New York, Chicago, Miami, Seattle, and everywhere in between, Amazon’s AI makes Black Friday feel effortless—even when millions of interactions happen every second.

Black Friday may look like chaos from the outside.
Inside Amazon, it’s an algorithmic ballet.

The post How AI Became Amazon’s Secret Black Friday Engine appeared first on MartechView.

For the past decade, retail search has been a race for visibility. Brands learned how to climb SERPs, win bids in retail media, and fine-tune every product detail page for keywords and conversion — all to be found first when shoppers started Googling.

Now, retail search is quietly entering its next evolution, and it’s not happening on Google. Across major AI platforms like ChatGPT, Claude, and Amazon’s Rufus, consumers now generate more than 2 billion queries a day, and roughly four in 10 U.S. shoppers already use an AI assistant weekly for product discovery or advice. 

GenAI search and, subsequently, Generative Engine Optimization (GEO) are still in their early days, and no one yet knows exactly which signals these systems will reward. But the direction is unmistakable: discovery is becoming conversational, and visibility is narrowing. So, what can brands do now to ensure they’re part of the solution?

How the shopper journey is collapsing into one conversation

For years, digital commerce followed a predictable rhythm. Shoppers moved step by step, from recognizing a need to browsing a sea of results, comparing features, reading reviews, and finally clicking “add to cart.” Each step offered multiple touchpoints for brands to intercept or influence the journey.

Generative AI collapses that process into a single interaction. Instead of typing “best protein powder for runners” and scanning 30 listings, a shopper now asks an AI assistant, “What’s a good protein powder for recovery after long runs?” and gets one or two tailored answers, often complete with reasoning and direct links to buy. 

The result is a shorter path from intent to purchase, with far fewer opportunities for discovery along the way. Early data from AI-powered shopping environments indicate that customers arriving via generative queries are 10% more engaged and convert at a faster rate than those using traditional search methods. This fundamentally changes where and how influence happens.

The new rules of being found

For brands, the compression of the shopper journey is both a gift and a challenge. It makes the path to purchase frictionless, but also front-loaded. Once a model generates its answer, the window to appear effectively closes. Instead of optimizing for dozens of mid-funnel interactions, brands now have to win a single conversation.

While it’s too early to say exactly what these systems will prioritize, early patterns suggest four factors likely to shape which products surface first:

• Complete, structured data: Models rely on context, not just keywords. Products with detailed, well-tagged attributes (such as ingredients, use cases, benefits, and certifications) are easier for AI systems to understand and recommend.
• Credible sentiment: Generative AI places significant weight on trust signals. Fresh, consistent, and credible reviews are more likely to appear in the model’s responses, while low sentiment can quietly push a product out of view.
• Cross-platform consistency: Discrepancies between retailer PDPs, brand sites, and media copy can confuse AI crawlers and erode confidence in what’s authoritative.
• Conversational authority: AI models favor clear, natural language that answers a shopper’s question over keyword-stuffed or jargon-heavy product blurbs.

What brands can do now?

Generative search is still a small slice of traffic today, but the behaviors behind it (including faster decisions, fewer options, and higher trust thresholds) are already influencing how people shop. Here are three steps brands can take today to future-proof discoverability.

Be AI-visible

Though the algorithms behind GenAI search remain opaque, brands can still act on the fundamentals that have historically improved discoverability (and are likely to matter even more in AI-driven environments).

The first step is making product data intelligible to machines. That means ensuring every PDP includes attributes such as size, use case, benefits, and ingredients, with schema markup that allows models to understand the context, not just the text. 

Because LLMs draw from multiple sources, ranging from retailer listings to brand sites, consistency across these platforms determines whether a product is even included in the recommendation set. Some tools can automate that consistency at scale, auditing PDPs, validating schema markup, and ensuring every SKU remains discoverable across channels.

Write for questions, not keywords.

Traditional SEO playbooks wax and wane about the importance of matching phrases for search visibility. GEO, however, depends on understanding intent. Product copy should read like an answer to the questions shoppers actually ask: “What’s the best protein powder for recovery?” or “Which moisturizer works for acne-prone skin?” Writing for that kind of natural language teaches the model which use cases a product solves, something keywords alone can’t do.

Leverage social proof as training data

Reviews, Q&As, and expert recommendations have always been important for persuasion, but in a generative search environment, they also matter for visibility. LLMs increasingly pull context and authority cues directly from these sources, learning not only what people buy but also why they trust it.

When feedback is recent, specific, and detailed (“it absorbs quickly without residue” or “my dog’s coat looks shinier after two weeks”), models can infer product benefits, use cases, and sentiment patterns. Those insights feed back into the recommendation logic and determine which products the AI considers “safe” to surface.

That makes review cadence and credibility management a new form of discoverability maintenance. A product with thin, outdated, or polarized reviews risks being overlooked entirely, even if it performs well on the shelf. Brands that regularly refresh reviews, close the loop on customer feedback, and encourage detailed commentary are effectively training the next generation of search.

Unify paid and organic signals

If current retail media trends are any indication, paid and organic signals will eventually converge as GenAI platforms learn to blend them into a single ranked recommendation. That means brands must ensure that every piece of creative, messaging, and attributes across channels reinforces the same structured, trustworthy story.

Performing all this manually across hundreds or thousands of SKUs is unrealistic. This is where technology, such as CommerceIQ, comes into play by helping brands align their creative, attributes, and spend, so paid and organic signals reinforce each other in a single performance view. The result is a feedback loop that turns retail data into a discoverability advantage.

The post GenAI Search Is Rewriting the Shopper’s Playbook appeared first on MartechView.

The best Halloween campaigns of 2025 proved one thing: in the attention economy, October belongs to brands that give people something to do, not just something to see. From limited-edition packaging and monster-themed menus to AR try-ons and fandom tie-ins, this year’s standout campaigns transformed products into props, and weekends into full-blown experiences.

These are the Halloween campaigns that defined 2025 — collectible, interactive, and built to move effortlessly across stores, screens, and neighborhoods.

A Snapshot of the Season

• Global takeover: Fanta teamed up with Universal/Blumhouse to feature horror icons on collectible cans and cinema activations worldwide.
• QSR theatrics: Burger King launched its “Monster Menu,” remixing classics with ghoulish flair.
• Packaged-goods spectacle: Heinz Brazil released jet-black “Mayo Halloween,” embracing horror tropes with tongue-in-cheek style.
• Beverage buzz: Bacardí unveiled a limited Skeleton Bottle, pairing it with Halloween cocktail events and social content.
• Retail anchor: Spirit Halloween opened 1,500+ stores, cementing its role as the season’s costume trend barometer.

Fanta x Universal/Blumhouse: Horror Icons Go Global

Fanta’s collaboration with Universal and Blumhouse brought classic horror characters to collectible cans, uniting film and beverage fandom in a single gesture. The campaign spanned retail, cinema, and social media — an ambitious, cross-market rollout powered by influencer-driven storytelling.

With global consistency and cinematic energy, Fanta pulled off what few mass-market beverages can: a Halloween campaign that transcended packaging and became an experience.

Editorial takeaway: When packaging becomes the medium, storytelling travels farther — from the shelf to the feed.

Burger King: “Monster Menu”

Burger King’s “Monster Menu” transformed its standard fare into limited-edition creatures of the night. Each item — reimagined with eerie naming and bold visuals — invited participation, not just purchase.

Across digital menus, OOH, and app-exclusive offers, the campaign fed into a steady stream of user-generated content. By making its menu the IP, Burger King ensured the campaign stayed alive — one post, one bite, one costume at a time.

Key move: A limited-time menu is a social calendar in disguise.

Heinz Brazil: “Mayo Halloween”

Heinz flipped its own brand mythology with a perfectly gothic twist — launching a jet-black mayonnaise infused with black garlic.

Shot in the aesthetic of vintage horror cinema, “Mayo Halloween” turned condiment aisles into cultural conversation. The product itself — strange, beautiful, and instantly Instagrammable — became its own form of earned media.

Why it worked: The product was the campaign.

Bacardí: Skeleton Bottle & Spooky Serves

Bacardí’s limited-edition Skeleton Bottle made the liquor shelf feel alive again. Paired with themed cocktail recipes, in-person activations, and influencer-ready photo ops, the campaign leaned into lifestyle rather than costume.

By bridging home décor, mixology, and nightlife, Bacardí proved that seasonal branding doesn’t have to feel seasonal — it can feel collectible.

Editorial note: When packaging meets ritual, brands earn permanence.

Dunkin: Spider Donuts, Buckets & Onesies

Dunkin’s Halloween rollout — complete with Candy Bar Lattes, Spider Donuts, and a new Munchkins Bucket — blurred the line between menu drop and merch drop.

A spider-themed onesie and limited-edition plush turned fans into walking (and sipping) brand ambassadors. Smart timing — two weeks before Halloween — kept Dunkin in feeds and newsrooms alike.

Smart play: Merch makes the menu media-worthy.

Haribo: The “Sourest City in America”

Haribo’s Sour Sodas pop-up in Houston — dubbed “The Sourest City in America” — gave the brand local PR gold. A limited early-access release created scarcity and hype, while TikTok “first taste” videos did the rest.

It was a case study in how geo-specific activations can create national buzz through local emotion.

HI-CHEW: The House of Mystery

HI-CHEW’s “House of Mystery” pop-up blurred flavor with folklore. Fans were challenged to guess a secret flavor before Halloween, turning product sampling into a social game.

Anchored by a Halloween Day reveal, the campaign kept engagement high and user content steady — proof that suspense is still a marketer’s best friend.

Spirit Halloween: Scale as Strategy

Spirit Halloween doesn’t just participate in the season — it defines it. With over 1,500 pop-ups, a 50,000-person hiring drive, and its annual trend guide, the retailer sets the national tone for what Halloween looks like.

Every store opening is a local headline. Every costume list is an earned media asset.

Insight: Scale, when well-branded, is storytelling.

LEGO: Family Fun Meets Fandom

LEGO’s Halloween sets — from the Jack-O’-Lantern Pickup Truck to the Altar of the Dead — dropped as early as July, planting seeds for autumn wish lists. By October, its “Brick-or-Treat Monster Party” at LEGOLAND tied the narrative together in physical form.

The campaign demonstrated how to merge retail rhythm with experiential play — one of 2025’s strongest examples of brand world-building.

IKEA: KUSTFYR Returns

IKEA’s KUSTFYR collection continued its reign as the chic, minimalist side of Halloween. With ghost throws, lanterns, and string lights under $25, it made seasonal décor accessible — and social media–ready.

The collection’s understated aesthetic extended its life beyond Halloween, proving that not every holiday launch needs to shout to be heard.

Maybelline: “Try On If You Dare”

Maybelline’s virtual Halloween hub invited fans to experiment with bold looks through AR try-on technology. With tutorials and shoppable links, the brand turned costume makeup into a digital-first beauty experience.

It’s a strong example of how beauty brands can use interactivity to reduce friction — and boost conversions — during the busiest social month of the year.

Why These Campaigns Worked

The best Halloween campaigns of 2025 shared three key traits:

1. Tangibility. Limited-edition products, merch, and pop-ups gave people something real to collect or visit.
2. Interactivity. Gamified elements — from guessing flavors to AR try-ons — created reasons to return.
3. Cross-channel design. Brands connected retail with digital, ensuring every SKU could live on a screen and in a cart.

From Fanta’s global horror icons to Dunkin’s spider bucket, these campaigns captured the season’s cultural pulse — turning October into not just a holiday, but a playground for creativity, commerce, and community.

The post How Brands Turned Halloween Into a Global Spectacle appeared first on MartechView.