Prime Time for Merchant-Publisher Deals?

AI-generated search results are crushing some digital news publishers, and it’s oddly an opportunity for ecommerce brands.

The problem for digital publishers is simple enough. Searchers who query Google often see the answer on-site, eliminating the need to click to, say, CNN, HuffPost, or Business Insider. Moreover, the purpose of ChatGPT, Perplexity, Claude, and others is to provide answers, not links.

Zero Clicks

Google’s AI Overviews have reportedly cut CNN’s site traffic by about 30%, with HuffPost and Business Insider suffering closer to a 40% reduction, according to a September 2025 report from AI development firm Yazo.

Meanwhile, an August 2025 Digiday article suggests that, in general, digital publishers have lost about 25% of their search traffic due to Google’s AI Overviews.

These dramatic drops in search traffic have prompted some publishers to reassess their content and monetization strategies. Some are going directly to the culprits and securing licensing deals with Google, OpenAI, and others.

News Corp., for example, signed a multi-year deal with OpenAI in 2024, which could generate $250 million in revenue for permitting ChatGPT’s parent company to scrape its content. Hearst, Reuters, Condé Nast, The Financial Times, and many others have reportedly sought similar AI licensing arrangements.

Many digital publishers are experiencing traffic drops due to zero-click search results.

Alternative Revenue

At the Digiday Publishing Summit in Miami this month, attendees shared ideas about how digital publishers, small and large, can recover or even boost revenue in this era of AI-driven everything.

Frequent ideas, perhaps surprisingly, involved ecommerce and cost-per-action deals.

It is surprising because many of these same publishers already have product recommendation sites — think CNN’s Underscored — that struggle against AI shopping for affiliate revenue.

The shared ideas fell into three categories.

Direct advertising deals

Digital advertising is chock-full of intermediaries, brokers, and would-be agents, who take upwards of 70% of an ad’s price.

Most publishers have their own direct sales teams, but these teams have traditionally sold ad impressions, which are relatively more difficult to generate owing to the declining traffic.

Hence some conference attendees proposed direct relationships with branded ecommerce sites or marketplaces. One publisher promised a six-figure bounty for someone who facilitated a direct revenue-sharing deal with Amazon.

Others contemplated portals or Shopify apps that would permit even the smallest retailers to post products on a CPA basis.

Pay walls

A second ecommerce-related strategy was to share custom deals with paying subscribers.

In this scenario, publishers would augment their paid services with Groupon-style discounts. Participating merchants would offer some set level of discount — perhaps, 35% — and pay a CPA fee for each sale.

The publisher generates revenue from the purchases while enjoying a new way to sell its premium content. Membership in a publisher’s insider subscription delivers exclusive content unavailable to AI bots and the best deals on products.

Merchants benefit from exposing their goods on publisher websites and pay only when a sale occurs.

Micro marketplaces

The last and most ambitious idea was for publishers to establish their own ecommerce marketplaces and allow participating merchants to list and sell products directly.

Imagine a cooking magazine with a million print subscribers, 15 million monthly website impressions, and 2.5 million newsletter subscribers directly hawking cast-iron skillets and hand-sewn tablecloths for a share of the sale.

The implementation could utilize a marketplace platform such as Mirakl or include only the publisher’s white-label products.

In each case, the publisher generates income from facilitating ecommerce sales.

Opportunity

Ecommerce brands can encourage these sorts of deals. It’s a prime time for publishers and merchants to explore direct collaborations.

Retail Media’s Performance Dilemma

Retail media has become a significant revenue stream for enterprise-level merchants, but for some, selling ads to suppliers has altered the traditional relationship.

“Because of retail media, the power dynamics have shifted a little bit,” said Drew Cashmore, chief strategy officer at Vantage, a retail media management platform, during an interview.

Retail media has, in some cases, transformed suppliers into advertising clients, forcing retailers to compete for those marketing budgets.

Amazon launched “Product Ads” in 2008, pioneering the practice of online merchants selling advertisements to suppliers, now called “retail media.”

Co-op Advertising

Suppliers have long provided retailers with “cooperative advertising” rebates to promote the suppliers’ products.

Co-op ad budgets are typically 2-3% of a retailer’s total purchases. So a chain that bought $1 million worth of products from a given supplier might receive a rebate of $20,000 or $30,000 to spend on advertising.

Co-op rebates can be a point of negotiation. The buyer at a retailer could, in effect, obtain a discount on product purchases via a higher co-op amount.

Suppliers certainly have input into how retailers employ co-op dollars, including evidence that the merchant spent the funds on advertising. But retailers are typically in charge.

This model gives retailers leverage. Retailers control how funds are spent, and suppliers have limited insight into performance.

But the balance changes with retail media. Suppliers purchasing ads expect transparency, outcomes, and accountability, creating both a challenge and an opportunity for retailers to build stronger, performance-based partnerships.

Retail Media

Co-op ads have not created retail media. Amazon was the catalyst with its 2008 launch of “Product Ads,” followed by a few other ecommerce sites. However, thanks to co-op monies, many traditional retailers seized the opportunity to earn revenue beyond selling goods to consumers.

When it uses cooperative advertising funds to buy magazine ads, a retailer benefits from increased sales, presumably.

If, however, it published a magazine, the retailer could use the same co-op dollars to buy space there. Effectively, this is retail media.

A more formal definition is something like, “Retail media is advertising that retailers sell on their own websites, publications, and in their physical stores.”

Regardless, large merchants view retail media as a way to have cake and eat it, too. The advertising drives the companies’ product sales while generating non-merchandising revenue.

A side effect of moving co-op advertising dollars to retail media is performance.

This shift is significant because it elevates expectations. Suppliers increasingly view retail media as part of their broader digital advertising strategy. They compare it to search, social, and programmatic buys and expect the same level of reporting, targeting, and performance. For retailers, meeting these expectations is key to retaining and growing suppliers’ budgets.

The Dilemma

As Vantage’s Cashmore put it, “The monies that used to go into the retail space … are going to go away unless there’s mutually beneficial value.”

Retailers must prove that their media channels are as effective as competing options. Hence many retailers now act like advertising networks. They provide the tools — reporting, analytics, dashboards — that media buyers at consumer brands are accustomed to receiving, or risk losing the revenue.

Benefits of Winning

Conversely, merchants that excel in retail media can position themselves as critical growth partners to suppliers, strengthening loyalty and unlocking incremental spend that supports both merchandising and marketing goals.

Winning at retail media promises to have several benefits:

• Incremental revenue. Retail media generates high-margin income that complements product sales. Once platforms and processes are in place, the ads deliver recurring revenue with low incremental costs.

• Increased sales. Retail media can help sell more products.

• Strong supplier relationships. Winning at retail media positions the merchant as a key marketing partner, which, in turn, strengthens loyalty and encourages suppliers to allocate larger portions of their advertising budgets to that merchant.

• Competitive moat. A well-executed retail media program helps a seller defend its market position against both rival merchants and digital ad networks.

• Actionable insights. Retail media provides data on shopper behavior, campaign performance, and category trends. Retailers can utilize these insights to enhance their merchandising, pricing strategies, promotions, and supplier negotiations.

• Shopper experience. Thoughtfully targeted retail media offers timely and relevant ads and recommendations, helping to build long-term shopper loyalty.

Flash Sales without Brand Damage

The era of “40% off everything, today only” as a revenue driver is mostly gone. Nowadays, sophistication is key since repeat customers expect more than discounts.

For experienced marketers, the challenge isn’t just timing or promotion. It’s building urgency in a way that aligns with long-term goals, brand positioning, and channel constraints.

Urgency

Discounts alone no longer create urgency. Merchants need a mix of temporal, social, and product-based cues that build momentum throughout a campaign.

Consider:

• Inventory scarcity. Communicate low stock thresholds dynamically. Tools such as Fomo and Convert inject real-time signals like “12 left in stock” or “selling fast.”

• Tiered unlocks. Reward speed with value. For example: “First 100 customers get 30% off; next 200 get 20% off.”

• Personalization. Segment by purchase behavior, and run a flash sale for your most loyal customers with unique timers per buyer cohort. Klaviyo and Iterable dynamically adjust expiration based on email open or site activity.

These approaches build urgency without sacrificing profits across all customers.

Paire, a sustainable clothing brand, teases limited-quantity gift-with-purchase offers that unlock at specific spending thresholds.

Paire unlocks limited-quantity gift-with-purchase offers at spending thresholds. Click image to enlarge.

Bombas, the sock and apparel company known for its one-for-one donations, deploys personalized flash sales for loyalty segments with precision. A bright yellow “Expires Tomorrow” banner creates urgency, while strategic messaging reinforces societal impact: “3 million pairs donated.”

Bombas combines a yellow “Expire tomorrow” banner with the social impact of “3 million pairs donated.” Click image to enlarge.

Both approaches — Paire and Bombas — demonstrate how discount messaging can reinforce brand purpose when properly segmented.

Channels

Flash sales can be effective beyond holidays or end-of-quarter pushes.

• Lagging products. Got seasonal overstock or SKUs with poor velocity? Run a micro-flash sale triggered by inventory data — target customers who viewed or added those products but didn’t convert.

• Lifecycle churns. If your average repurchase window is 45 days, schedule a flash sale on day 40 with a time-sensitive reorder incentive. Use your customer data platform or email service provider to identify likely churn cohorts.

• List fatigue. If open or click rates dip by 20% across core segments, test a one-day flash event as a reactivation lever, especially if your emails emphasize content or brand rather than promos.

Flash sales affect multiple channels: email, SMS, and paid. Alignment is essential to avoid:

• Inbox fatigue. Repeated discounts can lower click-to-open rates and prompt inbox filtering by internet service providers. Suppress habitual non-clickers (except for products with longer consideration cycles) or create a dedicated “sale-only” segment where users can opt in.

• Ad dilution. Frequent promos can tank click-throughs. Use exclusions (e.g., customers who purchased in the last 30 days) to protect your evergreen campaigns.

• List degradation. Flash-sale-only buyers are likely to churn. Consider delaying welcome offers after those subscribers convert organically.

Better Flash Sales

Here’s a three-phase approach integrating urgency and control.

Warm-up (1–2 days prior):

• Tease the sale via SMS or email to high-value segments. Use early access as a loyalty perk.
• Target visitors by deploying browse abandonment emails or early ad previews.

Launch (24–48 hours max):

• Use a single call-to-action across channels.
• Pull in zero-party data where possible (“You liked [product A]. It’s 20% off today only.”)
• Embed social proof or low-inventory signals in product detail pages and ads.

Cool-down:

• Use a final chance message for non-purchasers, then suppress or retarget based on funnel behavior.
• Analyze performance by segment (e.g., new vs. returning, email vs. SMS) to understand who buys from urgency and who waits for discounts.

Beyond Revenue

A strategically executed flash sale can double as an insights tool.

• Which channel drove the fastest conversions?
• Who purchases early vs. last-call messaging?
• Did discounts elevate average order values or only volume?

Tools such as Daasity, Triple Whale, and business intelligence dashboards can provide the post-sale data to answer those questions. Then refine not just your sales cadence but audience planning and creative strategy.

In short, flash sales should not compromise a brand. When planned with data, segmentation, and restraint, they can re-energize a list, clear inventory, and deliver real margin — much more than a short-term rush.

Another Privacy Pivot in Ad Targeting

Ad targeting attempts to improve the odds that the person seeing an offer or promotion will take action.

A few decades ago, almost all ad targeting was contextual. A golf shop bought ads in golf magazines or the golf section of the phone book.

Fast forward to 2025, and artificial intelligence tools capable of analyzing huge stores of consumer data have upended ad targeting. An entire advertising infrastructure can now target specific shoppers, not just related content, based on their browsing and purchasing habits.

Golf ads might appear on a news site or next to an unrelated social media post because targeting has shifted from context to people.

Such personalized ads typically produce better results and are preferred by consumers, who now see messages that interest them.

Personal Privacy

Google collects much individual data, as shown on its My Ad Center.

Targeting individuals, however, has a downside. Attempts to understand personal preferences, affinities, and behaviors lead to serious privacy concerns.

Take Google’s My Ad Center, for example. It shows the topics Google associates with a given person and lists several recent ads and brands.

Certainly Google complies with privacy regulations. Yet consumers are often surprised by how much of their online activities are public.

In a sense, the advertising industry overreached. Regulators responded with legislation such as the E.U.’s General Data Protection Regulation and the California Consumer Privacy Act (CCPA).

Two recent lawsuits against The Trade Desk, a “demand side” platform for advertisers, claim the company’s universal identification technology (UID2) is an invasion of personal privacy and violates the CCPA. Ironically, replacing third-party tracking cookies was a benefit of UID2.

Better Context

Perhaps recognizing the privacy risks of targeting individuals, several ad tech companies have revisited content.

A golf shop once advertised putters in golf magazines, targeting golfers generally.

Now, an ad platform might scan a specific article on a golf site about, say, 10 ways to improve putting. To some degree, the platform understands the context and displays an ad for a new putter. The targeting is content-level — putter ads on putter articles.

Google does this with at least two of its ad types: Contextual targeting for its display ad network and AdSense’s related search for content that analyzes a page and generates links to Google Search for related queries. Both technologies match words.

But the concept could go further. A new startup, AdZen, does more than word matching. It understands the intent of an article and inserts in-line native ad links.

An article about how professional golfers play the most difficult greens at the Augusta National Golf Club represents a different level of buying intent and interest than an article on improving putting. Both mention the word putter and putting, but the latter article gets a putter ad, while the former receives an offer for a premium subscription to the Golf Channel.

This is intent-level contextual targeting. It is more specific than putter ads in putter articles. For advertisers, it may also be as effective as targeting individuals.

Easier Ads

Google and AdZen are not the only examples. Dozens of companies are trying to use AI to improve contextual targeting.

The result could be contextual ads that are easier for advertisers.

For individually targeted ads, marketers collect data about shoppers, parsing demographics, psychographics, and behavioral data, all to build audiences. Some audiences are for retargeting. Some are for suppression. And some are lookalike audiences to attract new customers.

In theory, AI-driven contextual targeting does not need any of this data. It shows an ad based on what engages a prospect at that very moment.

Instead of building audiences, marketers would build detailed descriptions of their products and how to use them. The more the AI knows about the item’s features and benefits, the better it will target.

Perhaps best of all, it needs no personally identifiable information.

PII Again

But “needing” and “using” are different. As privacy rules and regulations evolve, ad tech companies could blend AI-driven contextual targeting with personalization.

The combination could place the best message in front of a high-intent shopper in a more privacy-safe way.

AI Will Help Advertisers and Shoppers

In what seems like no time at all, artificial intelligence has become a mainstream business tool. One of its most promising uses is in advertising.

AI will likely improve advertising efficiency, boosting key metrics such as return on advertising spend and conversions while engaging shoppers better.

Here’s how.

Personalization

In a recent Harvard Business Review article, academics from Babson College, the University of New Hampshire, and the University of South Carolina proposed a framework for how businesses should employ two forms of artificial intelligence for marketing.

The article distinguishes generative AI (ChatGPT, others) from analytical (data analysis), arguing that the latter helps predict shopper behavior and outcomes, while genAI is creative.

This tandem of AI tools will facilitate personalized ad messaging to individual shoppers or micro-segments at scale. It is the idea of precise ad targeting combined with personalization to drive conversions.

Romain Lerallut

“It’s no longer just about reaching a large audience; it’s about reaching the right audience with tailored, relevant messaging that delivers better outcomes,” wrote Romain Lerallut, head of Criteo AI Lab, in an email conversation with Practical Ecommerce.

Analytical AI will create target audiences and segments, while generative AI will deliver the just-right message, ultimately improving ad performance.

Targeted personalization is not easy, even for data scientists, but most advertisers will not have to manage it directly. Rather, advertising technology companies — think ad exchanges and ad services — will likely offer personalization as a service.

Budget Allocation

Allocating advertising budgets is instinct-driven and necessarily relies on human experience and data silos.

Most marketers can interpret relevant data and make correct budget decisions. Yet automated budget allocation in a fast-moving digital ad marketplace would be an improvement.

AI will improve budget management, which, in a sense, builds on the targeted personalization described above.

AI models will analyze performance, customer behavior, and real-time market signals to optimize budgets across channels and campaigns.

“Brands can target consumers more precisely and make every advertising dollar count,” wrote Criteo’s Lerallut.

Most advertising platforms will likely include predictive budget allocation tools that automatically shift ad spending toward the top-performing audience segments or channels. Advertisers will benefit from improved performance.

Shopper Journey

AI can group disjointed data sources to better understand how shoppers become customers.

In practice, a predictive AI budget allocation tool will not rely on last-click attribution. Instead, analytical AI and machine learning tools will power marketing mix models and multi-touch attribution models.

Per Lerallut, “As consumers engage with brands across more touchpoints than ever — online, in-store, on social media, through apps, and more — AI can help unify and interpret these interactions to create a cohesive view of the customer.”

Advertisers will likely require marketing mix models and multi-touch attribution tools. Google and Meta currently offer free solutions.

Frequency Optimization

With a clear map of the shopper’s journey, AI can improve how and where ads appear, optimizing placement, frequency, and timing.

This could also mean optimization beyond a single platform. AI could suggest frequency caps on Meta, Google, and other platforms to avoid reader fatigue.

Perception

Combined, AI’s capabilities will result in a better shopper experience. Personalization, budget allocation, customer journey, and ad timing will lead to a deeper understanding and trust with the advertiser’s business.

Ultimately, AI will help both advertisers and shoppers.

The Rebirth of ‘Marketing Mix Modeling’

Lingering privacy challenges and ever-improving cloud and artificial intelligence technology are driving a marketing model renaissance.

Marketing mix modeling (MMM), launched in 1949, fell out of favor in the early 2000s when digital advertising took off. The data-driven technique had long helped marketers understand how variables such as advertising, promotion, and prices impact revenue.

Yet compared to tracking cookies and last-touch attribution models, MMM seemed complex and expensive.

Renaissance

In 2025, however, MMM has enjoyed renewed attention.

Meta and Google have released free, open-source MMM tools in the past couple of years — Google’s Meridian on January 29, 2025, and Meta’s Robyn in 2023.

Why does MMM interest two of the largest digital advertising platforms? I see three probable factors: tracking cookies, AI, and cloud computing.

Google launched Meridian, an open-source marketing-mix model, last month.

Cookie-less Advertising

Controversies surrounding tracking cookies are the first driver. Cookies are a foundational and useful technology. A first-party cookie on a browser keeps users logged into a website and retains their preferences.

However, third-party tracking cookies that catalog an individual’s behavior across web properties are a privacy pariah. Laws such as Europe’s General Data Protection Regulation and the California Consumer Privacy Act limit such cookies, and many browser companies have stopped supporting them entirely.

The potential of cookie-less ad targeting makes MMM attractive to large-scale advertisers and platforms.

Advertising performance. Third-party cookies, despite privacy concerns, drive ad targeting and thus performance. MMM should help advertisers identify which marketing channels and creatives produce the best returns. Coupled with new ad targeting techniques, MMM will almost certainly improve performance.

Meta’s Robyn, for example, helps advertisers analyze the performance of campaigns across Facebook, Instagram, and other channels. It gauges channel effectiveness and optimizes ad spend based on results.

The era of cookie-less targeting encourages the use of MMM. Some of the most forward-looking, high-budget advertisers are considering alternative targeting methods and new promotional channels. Monitoring those experiments requires complicated multi-touch attribution or MMM.

For example, Google’s Meridian MMM moved beyond standard regression models to a theory called “Bayesian causal inference,” which captures the impact of imprecise marketing actions, such as a social media post.

Personal privacy is yet another reason why MMM appeals to Google, Meta, and many advertisers. The model aggregates data and generally avoids personally identifiable information.

Artificial Intelligence

AI makes MMM relatively faster, more adaptive, and easier to scale.

Improved speeds come first from training the model quickly. The foundational, now-available models are a massive headstart compared to starting from scratch.

Second, AI helps process and clean large, complex datasets from multiple sources such as digital ads, TV, print, and online and in-store sales. The associated algorithms detect seasonality, outliers, and data anomalies, reducing manual work but still requiring data scientists to fine-tune the models.

Regardless, the AI behind Meta’s Robyn dynamically adjusts model variables, improving accuracy automatically. It is thus more adaptable and scalable.

Cloud Computing

Twenty years ago, the rise of web-based marketing produced massive datasets and hefty processing loads. Analyzing that info typically required custom-built infrastructure and expensive data warehouses

These limitations no longer exist thanks to cloud computing advances and affordability. Instead of spending $500,000 or more on MMM software and servers, a company can run Google Meridian in the cloud for a fraction of the amount, perhaps as little as $10,000 a year.

Accurate modeling, however, requires some scale — businesses investing at least $500,000 per year in advertising likely benefit the most. But that could change if MMM becomes available as a service.

The New Era of Cookie-less Ad Targeting

Advertising technology has entered a new phase that pairs personal privacy with targeting.

For years, the ad industry has depended on third-party tracking cookies — tiny snippets of code downloaded to a user’s browser — to identify prospects, gather data about them, and employ that data for targeting. Consumers saw relevant ads, and advertisers enjoyed good, if not superb, returns.

Adobe’s new real-time customer data platform is one of many innovations in cookie-less ad targeting.

Cookies

Unfortunately, cookie-based tracking has privacy problems. In contrast to first-party cookies, which store preferences, third-party tracking cookies aggregate, share, and thereby expose lots of private information.

Just this month, Wired magazine reported, “Google’s advertising ecosystem reveals that a wealth of sensitive information is being openly served up to some of the world’s largest brands…Experts say that when combined with other data, this information could be used to identify and target specific individuals.”

Privacy advocates recognized this and began railing against sharing personal and sensitive information across advertising networks. Technology companies responded. Mozilla’s Firefox browser, for example, stopped allowing tracking cookies back in 2019.

Google, which operates one of the largest ad networks in the world and has the most popular web browser in Chrome, nearly killed third-party cookies in 2024, as it planned to block them in Chrome and move all advertisers to alternative targeting methods — but it didn’t happen.

“Google heard the alarm bells from the industry that nearly 20 years of ad tech infrastructure cannot be recreated in six months,” said David Stein, then the CEO of the data firm Audigent, in an April 2024 email to Practical Ecommerce.

Those alarm bells could have been Google’s ad exchange customers, its ad tech partners, and the anti-trust regulators concerned that ending cookie tracking would give Google a performance advertising monopoly.

Hence tracking cookies remained in Chrome, but ad tech providers did not stop innovating.

Innovation

The same day that Wired published its report, Adobe announced the general availability of its real-time customer data platform (CDP).

“Brands have long relied on third-party audience signals to power tailored digital ads. As consumers play a more active role in customizing their privacy preferences, a move away from third-party data means new tools are required for brands to identify relevant audiences and deliver personalized ad experiences,” read the Adobe press release.

Adobe’s CDP allows advertisers and publishers to securely collaborate on first-party data to enhance ad targeting while respecting user privacy. This relationship is often called second-party data sharing, and resembles Shopify’s Audiences and similar products.

Methods

Second-party data sharing is one of six primary methods ad tech companies are exploring as they try to maintain or improve ad targeting efficacy without raising privacy concerns.

• Second-party data sharing allows advertisers and publishers to collaborate, often sharing aggregate data.

• First-party data is when a business uses its own data to target or retarget individuals or cohorts. Many companies are buying publications to advertise to known customers and prospects.

• Unified ID solutions produce encrypted identifiers to share among systems. The Trade Desk’s Unified Solution is an example. Often, these IDs stem from an email address.

• Data clean rooms make it possible for analysts to use data without privacy concerns.

• Cohort-based advertising targets groups of shoppers with similar profiles instead of individuals.

• Contextual targeting analyzes the context — web page, app, video, email — to deliver relevant ads to consumers without relying on personal data. Artificial intelligence makes this more successful.

Used individually or in combination, these targeting techniques have significant promise. Most reports suggest that results vary among advertisers, yet the techniques will likely work better than third-party cookies.

Ad Experiments

Listing the ad tech companies developing cookie-less targeting methods would fill pages. Thus advertisers seeking to acquire customers might find it wise to experiment.

For example, marketing platform Zeta Global recently purchased the email advertising company LiveIntent. Some in the industry believe Zeta Global valued LiveIntent’s ability to identify website visitors based on email interactions.

Could Zeta Global’s demand-side platform be worth a look?

Similarly, Paved, another email ad platform, expanded its programmatic network, permitting advertisers to target and retarget shoppers safely and privately.

In short, ad targeting is changing, and the best options may not be familiar platforms.