Commerce

Retail Media Trends and Outlook for 2026

By Jenn Wheatley Mar 05 2026

The Skinny: Retail media has evolved into a multi-billion dollar cornerstone of commerce, shifting from simple search tactics to a full-funnel ecosystem that integrates first-party data across CTV, social, and advanced in-store “store mode” app experiences. Driven by AI orchestration and shoppable media, the landscape is moving toward a “love growth, hate waste” philosophy that prioritizes incremental sales and closed-loop attribution.

Retail media has evolved from a niche tactic into a core pillar of commerce media. It is now the connective tissue among onsite search, display, CTV, social, and in-store experiences, all powered by retailer first-party data and closed-loop attribution. As third-party signals erode and competition for shoppers’ attention intensifies, retail media networks are uniquely positioned to connect media spending to real sales, not just clicks or views.

The past year reinforced how quickly that landscape is shifting. In-store is no longer just about endcaps and screens; “store mode” in retailer apps is turning phones into the most valuable inventory in the building, guiding shoppers with real-time offers, navigation, and scan-and-go checkout. At the same time, preparations for global tentpoles like the 2026 World Cup are accelerating upgrades in TV hardware and expanding shoppable CTV opportunities in the living room. Against that backdrop, brands are grappling with a new wave of AI-driven tools, emerging media networks in categories such as financial services and travel, and a dense web of partnerships among platforms that blur traditional channel lines.

The dollars tell the same story. US retail media ad spend is projected to surpass 60 billion dollars in 2025 and approach 70 billion in 2026, growing faster than the broader digital ad market. Tinuiti’s Q4 2025 Digital Ads Benchmark Report (DABR) shows advertisers ramping up investment in Amazon and Walmart, with Sponsored Products, DSP, streaming video, and offsite display all seeing strong momentum.

For marketers who love growth and hate waste, the challenge now is clear: move beyond surface-level ROAS, use advanced measurement to understand what is truly incremental, and treat retail media as a full-funnel, accountable system that can continuously redirect budget from noise to proven drivers of business outcomes.

Why Are Advertisers Interested in Retail Media?

Marketers are drawn to retail media because it solves problems that traditional digital channels struggle with: reaching high-intent shoppers, accessing durable first-party data, and measuring outcomes via closed-loop attribution. RMNs offer audience segmentation based on real purchase behavior, enabling brands to target by category, basket composition, loyalty, and more.

Investment momentum is clear in DABR data. Clicks on Amazon Sponsored Products rose 23% year over year in Q4 2025, while CPCs declined 1%, driving strong efficiency alongside growth. Advertisers active on Amazon DSP increased spend 31% year over year, as impressions climbed 32% and CPMs fell 1%, demonstrating the appeal of premium, data-rich display and video inventory on and off Amazon. On Walmart, Sponsored Products spend grew 14% year over year in Q4 2025—decelerating from 48% in Q3 but cycling against a massive 53% surge in Q4 2024, keeping two-year growth robust.

Retail media is also aligned with how people actually shop. Nearly every category now has a meaningful digital touchpoint, even when the final purchase happens in-store. During peak periods, Amazon Sponsored Products sales for the median retailer rose by more than 30% on key days like Cyber Monday, reinforcing RMNs’ ability to capture demand at the moment of decision. For brands, that means fewer wasted impressions and more opportunities to meet shoppers where purchase intent is highest.

Finally, marketers are seeking truth in measurement. Retail media’s closed-loop attribution lets them trace exposure through to online and in-store sales, and increasingly to incrementality.  That’s the essence of a love growth, hate waste mindset: invest where you can see real, incremental impact, and pull back where spend isn’t moving the business forward.

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8 Retail Media Trends to Watch in 2026

The next chapter of retail media is defined by AI, interoperability, and full-funnel activation. These eight trends will shape where growth concentrates and where waste gets exposed.

1. AI Orchestrated Advertising Experiences

AI is transforming retail media buying from manual tuning into orchestrated, self-optimizing systems. Self-service platforms are rolling out AI-powered tools that automate audience building, bidding, creative testing, and budget reallocation, helping marketers move faster without losing control. On Amazon alone, advertisers are increasingly embracing automated campaigns and DSP optimizations to find the best mix of placements, formats, and audiences.

At Tinuiti’s Amazon & Retail Media Summit, multiple sessions focused on how AI is reshaping Amazon DSP and search, including how Performance+ and Amazon Marketing Cloud (AMC) are evolving measurement and optimization.

The takeaway: AI isn’t just about efficiency; it’s about making smarter decisions with every impression. When brands connect AI-curated campaigns with independent measurement, they can quickly shift budget toward the combinations that produce incremental growth and away from tactics that only look good in siloed dashboards.

2. AI-Curated User Experiences Through Agentic Commerce

On the shopper side, AI is quietly rewriting discovery. In the first session, Elizabeth broke the AI commerce landscape into three buckets:

• Standardized, which is what’s already embedded in bidding, budgeting, and creative 
• Experimental, such as platform agents that help you ask “how’s my campaign doing?”
• Aspirational, including fully autonomous agentic shopping that handles discovery, purchase, and returns end-to-end

Agentic commerce sits firmly in that aspirational bucket today, but early tests in conversational search, store-mode experiences, and agent-assisted planning are already changing how shoppers move from research to purchase.

In that environment, merchandising and messaging must be designed for both humans and machines. Product content, reviews, and structured data all influence how AI systems evaluate and rank items. For marketers, this raises the bar: if your data is incomplete, your reviews are thin, or your creative doesn’t match AI-detected intent, you risk being filtered out before a shopper ever sees your brand. A Love Growth. Hate Waste. approach means investing in the content and data that help AI make better recommendations, rather than overspending on ads that point to weak product experiences.

Just because the AI functionality exists doesn’t mean people are going to suddenly change their habits. You have to start with convenience, build usage and trust, and be realistic about where we really are.

-Elizabeth Marsten, Vice President of Innovation & Growth, Commerce Media

3. Growth of Shoppable Connected TV Advertising

Shoppable connected TV (CTV) is becoming a cornerstone of full-funnel retail media. As Prime Video and other streamers scale ad-supported tiers, retailers and brands are weaving in commerce-enabled formats that let viewers scan, click, or save offers directly on-screen. 

Tinuiti’s DABR shows streaming video ad spend outside YouTube rising 13% year over year in Q4 2025, with impressions up 14%, driven in part by the rapid expansion of Prime Video ads.

Prime Video investment alone grew 31% from Q3 to Q4 2025, with year-over-year spending up 127%. Pair that with retailer sales data, and you get a powerful combination: upper-funnel storytelling with commerce-grade measurement. That’s growth you can quantify. And growth as an antidote to opaque TV buys that soak up budget without clear attribution.

4. Expansion of Programmatic Retail Media

Programmatic is increasingly how retail media scales. Brands are accessing retail audiences and inventory through demand side platforms (DSPs), treating retail media as part of their broader display and video strategy rather than a standalone silo. On Amazon, DSP now accounts for 40% of total Amazon ad budgets for advertisers active in both Ad Console and DSP, underscoring its role as a core channel, not an experimental add-on.

This shift offers new levers: frequency management across channels, consistent creative frameworks, and more granular control over where commerce signals are applied. But it also raises measurement expectations. Without advanced analytics, it’s easy to double-count conversions or over-value lower-funnel touches. A strategy that champions growth and reduces waste requires tying programmatic retail media back to incrementality by identifying where DSP is generating net new demand and where it may simply be intercepting buyers late in the journey. 

5. Improved Retail Media Interoperability

As more RMNs emerge, brands are demanding interoperability: common standards, cleaner data feeds, and easier integration with their existing analytics stacks. Retailers are responding by improving taxonomy consistency, enhancing reporting APIs, and partnering with measurement platforms to allow for cross-network comparisons. Interoperability is the bridge between “a bunch of separate dashboards” and a single, coherent view of how retail media drives business outcomes.

This becomes even more critical as new networks emerge beyond traditional retail, from quick-serve restaurants and health & wellness to travel, financial, and even in-car environments, each bringing its own data, inventory, and measurement quirks. Without interoperable standards, it’s nearly impossible to compare performance and avoid over-investing in the loudest new entrant instead of the most effective one.

Retail media is no longer just “media”; it is a core source of marketing intelligence. Marketers want to know how performance varies by retailer, format, and audience, and to feed those learnings back into planning across search, social, and CTV. When RMNs enable that kind of interoperability, brands can consolidate insights, reduce redundant tests, and reallocate spend more quickly from low-yield networks to those that demonstrably drive growth.

It’s going to get weird. Amazon and Microsoft, Walmart and Google—these partnerships are going to change how data flows and how media gets bought, and you have to be ready for that.

-Elizabeth Marsten, Vice President of Innovation & Growth, Commerce Media

6. More Investment in Offsite Retail Media

Offsite retail media, using retailer data to target audiences off the retailer’s owned properties, is gaining share as brands seek reach plus relevance. Tinuiti’s DABR shows that 60% of Walmart self-serve display spend in Q4 2025 went to offsite inventory, even though onsite placements commanded a higher CPM and a larger share of spend relative to impressions. Offsite impressions accounted for the majority of volume, giving brands more scale at efficient prices.

This offsite expansion supports a true full-funnel approach: reaching high-intent audiences as they browse the open web, watch streaming video, or scroll social, while still measuring outcomes through retailer sales. The opportunity and risk is in balance. Brands that understand the incremental contribution of off-site placements can lean into combinations that drive net-new sales, while dialing back tactics that merely re-target shoppers who were already likely to convert.

7. Increased Demand for Advanced Measurement Tools (and In-Store’s Data Upgrade)

As retail media matures, standard dashboards aren’t enough. Brands want advanced measurement: cross-channel attribution, incrementality testing, competitive benchmarking, and the ability to customize models around their specific growth objectives. They also want in-store activity to be measured with the same rigor as digital media, from digital shelf and endcap displays to audio and QR-enabled signage.

Tinuiti’s Bliss Point technology is built for this level of accountability. By ingesting impression-level data from RMNs, layering in retailer and first-party sales, and modeling cross-channel impact, Bliss Point helps brands identify which investments are truly driving incremental growth and which are over-credited. This is where love growth, hate waste moves from slogan to system: continuous budget reallocation based on what the data proves, not what individual platforms report. 

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8. In-Store Retail Media Advancements

In-store media is evolving from static signage into a measurable, data-driven extension of retail media networks. Retailers are rolling out digital screens, smart shelves, and audio inventory that can be targeted using the same first-party data that powers onsite and offsite campaigns. At the same time, “research online, purchase offline” behavior continues to grow, which means in-store media must be planned alongside digital touchpoints, not as a standalone shopper tactic.

In the session, Elizabeth emphasized that the most meaningful in-store innovation isn’t just more screens, it’s “store mode” inside retailer apps. Experiences from Sam’s Club, Target, and Walmart show how geo-fenced, in-store modes can surface aisle-level navigation, personalized offers, and even scan-and-go checkout in real time, effectively turning the phone into the most valuable retail media surface in the building. For marketers, that means coordinating in-store media plans with app experiences and ensuring product data and promotions are clean enough to power these journeys without frustrating shoppers.

For brands, the opportunity is to design coordinated journeys: use onsite and offsite media to drive store visits and consideration, then rely on in-store placements and store-mode experiences to reinforce messaging at the shelf and capture final-mile decisions. The key is measuring in-store exposure and app interactions against sales and connecting those outcomes to your broader retail media mix. When in-store is folded into the same closed-loop, incrementality-driven measurement framework, it stops being a black box and becomes another lever in a pro-growth system.

We talk a lot about digital endcaps and all the screens you see when you walk in, at checkout, even out in the parking lot. But I still believe the nearest best place to be is in ‘store mode’ inside the mobile app.

-Elizabeth Marsten, Vice President of Innovation & Growth, Commerce Media

How to Build an Effective Retail Media Strategy in 2026

In a landscape this dynamic, retail media strategy can’t be a checklist; it has to be a disciplined, measurement-driven system. The most successful brands are aligning goals, data, and activation under a single full-funnel commerce media vision.

Set Goals

Effective retail media programs start with sharp, SMART goals (specific, measurable, achievable, relevant, and time-bound). Instead of defaulting to generic ROAS targets, leading brands define success in terms of incremental revenue, share of search, category penetration, or new-to-brand acquisition.

Having clear objectives changes the mix of formats and partners you choose. For instance, growth-stage brands might lean more heavily into upper-funnel display and shoppable CTV to build awareness, while mature brands emphasize Sponsored Products and DSP to maximize profitability and defend share. In both cases, goal clarity makes it easier to identify what’s actually fueling growth versus what’s inflating vanity metrics.​

Understand Your Current Data Landscape

Before scaling retail media, brands need an honest assessment of their data reality. What first-party data do you have? How often is it updated? How far back does it go? Is it clean enough to power audience segmentation and advanced attribution?

Equally important is understanding the data you get back from each RMN and how it flows into your analytics stack. Brands that invest in data hygiene, identity resolution, and governance early are better positioned to unlock AI-driven optimization, agentic commerce opportunities, and cross-retailer measurement later. Without that groundwork, it’s easy to overspend in channels that “look good” on paper while missing deeper signals about where growth is really coming from.

Fine-Tune Your Marketing Analytics Capabilities

Retail media ROI depends on more than platform reports. Brands need analytics capabilities that can answer questions like: How does this RMN perform relative to others? How does retail media interact with search, social, and CTV? What’s the incremental lift versus baseline demand?

The DABR shows that performance can vary widely by platform—YouTube, Prime Video, and retail DSPs all play distinct roles across the funnel. To navigate that complexity, marketers are combining experimentation (geo-splits, audience holdouts), media mix modeling, and multi-touch attribution with independent platforms like Bliss Point. That combination enables them to make confident decisions about where to trim waste and where to double down.

Utilize Online and In-Store Experiences Across Retail Media Networks

Retail media is no longer limited to onsite search results and banner ads. RMNs can now activate a mix of touchpoints such as onsite display, in-store screens, direct mail, coupons, audio, and even geofenced messaging, under a unified strategy. The opportunity is to design journeys that reflect how people actually shop: researching online, buying in store, and then being retargeted with relevant offers.

For example, brands can use onsite Sponsored Products to capture active demand, Walmart display to extend reach offsite, and in-store retail media to influence last-mile decisions. When these elements are planned together and linked through closed-loop measurement, marketers gain a more complete view of campaign effectiveness and more levers to reduce waste, whether that means pruning underperforming formats or shifting budget into higher-impact combinations.

Work With a Seasoned Partner

Even sophisticated teams can struggle to keep pace with changing RMN offerings, privacy rules, and measurement methodologies. An independent partner provides the connective tissue: aligning strategy across networks, centralizing reporting, and pushing platforms to deliver the data needed for serious measurement.

Tinuiti’s combination of channel-specific expertise, DABR insights, and Bliss Point measurement is designed for brands that want to make retail media their growth engine. By applying a love growth, hate waste lens, Tinuiti helps brands continuously redirect spend from low-yield impressions into media that consistently drives incremental revenue, share, and profitability. 

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Frequently Asked Questions

How Is Retail Media Ad Spend Trending?

Retail media remains one of the fastest-growing segments in digital advertising. US retail media spend is expected to exceed $60B in 2025 and approach $70B in 2026, outpacing overall digital ad growth as more brands treat RMNs as essential performance channels. Within that, Tinuiti’s DABR shows strong momentum for Amazon Sponsored Products (23% click growth and 22% sales growth in Q4 2025) and Walmart Sponsored Products (14% spend growth despite tough comps), as well as rapid expansion in Amazon DSP and offsite display. Growth is particularly strong in commerce-linked formats such as search, DSP, and CTV, where first-party data and closed-loop measurement give marketers clearer proof of impact.

How Do Marketers Use Retail Media in Their Strategy?

Marketers use retail media across the full funnel: upper-funnel display and CTV to generate demand, mid-funnel offsite and audience campaigns to nurture interest, and lower-funnel sponsored search and product ads to capture ready-to-buy shoppers. For many brands, RMNs sit at the intersection of media and shopper marketing, combining shopper insights, trade investment, and performance budgets under a single commerce media strategy. 

Tinuiti’s clients increasingly treat Amazon and Walmart as test-and-learn laboratories where they experiment with creative, pricing, and assortment, then scale what works across channels. The most advanced brands integrate retail media with search, social, and streaming video, using unified measurement to optimize for overall business outcomes rather than channel-by-channel ROAS.

What Are the Most Popular Retail Media Ad Formats?

Sponsored product and search ads remain the workhorses of retail media. On Amazon and Walmart, Sponsored Products account for the vast majority of search ad spend, reflecting their role in capturing high-intent shoppers on product detail and category pages. Sponsored Brands, video units, and onsite display complement these placements by building brand equity and driving exploration. Offsite display and audience campaigns are growing quickly as brands tap retailer data to reach shoppers across the open web, social platforms, and streaming video. Shoppable CTV, in-store digital screens, and audio are newer but rapidly scaling formats that connect storytelling to measurable sales, particularly when activated through DSPs and measured via retail sales data.

How Do Marketers Measure Retail Media Strategies?

Most marketers start with platform analytics such as impressions, clicks, sales, and return on ad spend, but quickly layer on more advanced methods to get a truthful picture. Incrementality tests (such as geo-splits and audience holdouts) help distinguish net new sales from those that would have happened anyway, while media mix models and multi-touch attribution reveal how retail media interacts with search, social, and CTV.  

Tinuiti’s Bliss Point technology unifies these approaches by connecting impression-level exposure with sales across channels to estimate incremental lift by network, format, and audience. That level of measurement enables brands to live the love growth, hate waste philosophy, systematically shifting budget toward tactics that contribute measurable incremental growth and away from those that simply absorb spend. 

Privacy

What's Media Mix Modeling? Examples & More

By Jenn Wheatley Mar 02 2026

The Skinny: Media Mix Modeling (MMM) is a privacy-resilient, top-down statistical technique that uses historical aggregate data and regression analysis to measure how various marketing channels and external factors (like seasonality and economic shifts) contribute to sales. By calculating the incremental impact and diminishing returns of different tactics, MMM enables marketers to optimize budget allocation, forecast future revenue, and justify investments across both online and offline channels.

Have you ever felt in the dark when it comes to understanding the real impact of your marketing dollars? 

Determining where and how conversions are occurring is crucial in optimizing your budget to drive the most impact with your marketing budget. But in a fragmented media environment, optimizing your marketing performance is challenging. How can we measure how our paid TikTok ads impact instore sales? We know our target audience watches a certain television channel, but during which time slots? Are those TV ads even providing a positive return on ad spend?

Media mix modeling (MMM) holds the answers to all of these questions and more. It’s a powerful tool for marketers seeking to optimize their marketing investments. By providing a holistic view of how various factors contribute to sales and conversions, MMM enables data-driven decisions that enhance marketing efficiency and business growth.

In this article, we explore how media mix modeling works, and will provide examples of how MMM drives smarter ad spend decisions.

What Is Media Mix Modeling?

Media mix modeling (MMM) is a statistical analysis technique used to measure the impact of marketing activities across multiple channels. It accomplishes this by ingesting historical data, then running a regression analysis to understand the relationship that independent variables (like channel marketing spend) have on dependent variables (such as net new sales).The process isn’t one-and-done either – the model should continuously optimize itself based on actual consumer responses and business outcomes. With each iteration, marketers are able to gain an even higher degree of clarity and certainty in their everyday decision making. 

The method is similar to marketing mix modeling, although media mix modeling focuses on optimizing the performance of promotion channels, rather than other aspects of the marketing mix (like personnel, place of sale, and pricing).

To put it simply, MMM uses the predictive and causal capabilities of regression analyses to accurately explain how a combination of disparate marketing activities lead to a single desired effect. For most marketers, this desired effect is improved ROI – but MMM can also be used for more complex purposes, like measuring brand equity or predicting stockouts.

What Can Media Mix Modeling Do for Marketers?

MMM gives marketers a top-down, privacy-resilient way to understand how media and non-media factors work together to influence key outcomes. It moves beyond channel-by-channel performance reports and helps answer the bigger questions: where to invest, when to invest, and what tradeoffs you’re making across the portfolio.

Below are some of the most common ways MMM is used in practice, along with how the model actually solves each.

Measuring omnichannel marketing effectiveness

MMM combines performance data from all your channels and your business outcomes to estimate each channel’s contribution to KPIs, showing how every touchpoint drives results, even when conversions happen offline.

Optimizing budget allocation

MMM shows how shifting budget between channels is likely to impact results by surfacing marginal returns at different spend levels, so you can move dollars toward the optimal mix and away from saturated or underperforming areas.

Improving ROI forecasting

By estimating how changes in spend and external factors affect outcomes, MMM lets you run “what if” scenarios before you invest, giving finance and marketing a data-backed way to pressure test plans.

Promoting integrated marketing strategies

MMM quantifies how upper- and lower-funnel tactics support each other, such as brand campaigns boosting search performance, helping you design integrated strategies instead of siloed channel plans.

Justifying marketing investments

MMM translates media activity into incremental revenue, retention, and new customer growth, giving you the evidence to defend budgets and argue for more investment where it truly drives impact.

Adapting to market disruptions

By accounting for shifts in consumer behavior, competition, and macro conditions, MMM helps you see how disruptions affect performance and make smarter pivots rather than reacting on guesswork.

Accounting for environmental factors

MMM explicitly accounts for seasonality, holidays, promotions, and weather, so you don’t misattribute performance changes to media when they’re actually driven by timing or external context.

Scenario planning

MMM supports multi-channel scenario planning (comparing maintain, cut, or accelerate plans) and reveals the trade-offs in revenue, profit, and other KPIs, so leaders can align on the best path.

Personalizing marketing strategies (at the portfolio level)

MMM can be built by region, product line, or segment, enabling more tailored strategies such as shifting spend by market or category based on how different groups respond to your media.

Benchmarking performance

With regular refreshes, media mix modeling shows how channel and tactic effectiveness changes over time, providing an objective benchmark beyond platform reports to see whether media is working harder or softer than before.

Guiding long-term strategy

Paired with experiments and channel-level testing, media mix modeling becomes a strategic compass for multi-year planning, helping you decide where to invest in new channels, products, and growth bets.

Marketers can answer these questions and determine the true influence of media buys across diverse platforms by using a simple byproduct of their marketing campaigns: Aggregate data. Brands can set up data feeds for online and offline advertising channels like social media, print advertising and streaming TV advertising. Then, the data is layered with other performance-related factors, seasonality and economic conditions. From there, marketers can use that data to understand how each touchpoint and externality impacts outcomes like sales revenue, brand health, new customers, and conversions.

Media mix modeling is a top-down privacy resilient approach to attribution in the sense that it applies statistical methods to analyze and interpret marketing data, providing a systematic understanding of how different marketing channels contribute to overall business goals in the broader context of the market. The quality of insights derived from MMM heavily depends on the quality and granularity of the data used.

— Annica Nesty, Group Director of Marketing Science at Tinuiti

How Media Mix Modeling Works

At its core, an MMM is built to optimize a KPI (or set of KPIs) that are influenced by multiple inputs. Things like media spend by channel, pricing changes, seasonality, promotions, and macroeconomic trends. The model uses regression analysis to estimate how each of these inputs affects your outcome of interest, and how those effects change as you increase or decrease spend.

Three core modeling concepts are especially important: reach (sometimes called saturation), adstock (sometimes called time decay), and additional external factors.

Adstock & Time Decay

Media rarely drives impact only on the day it’s delivered. A strong TV spot, for example, may influence behavior for days or weeks after it airs. MMM handles this with “adstock” or time decay functions that spread the effect of a media input across multiple time periods. The model estimates how quickly each channel’s impact decays, whether it’s short-lived (like a flash sale) or longer-term (like a brand campaign that builds awareness over time). This is crucial for understanding how campaigns contribute to results beyond the immediate reporting window.

Reach & Saturation

Every channel has a point at which adding more spend produces diminishing returns. MMM captures this by modeling non-linear relationships between media spend and performance. At lower spend levels, incremental dollars may drive significant gains; as you raise spend, the model often shows the curve flattening, signaling that the audience is saturated or that additional impressions are less impactful. These saturation curves are a key input to budget optimization, helping you avoid overspending on channels that have already hit their sweet spot.

External factors

Finally, MMM incorporates non-media factors that can significantly influence performance, such as:

• Economic conditions (inflation, unemployment, consumer confidence)
• Competitor activity (major launches, heavy promotions)
• Product reviews and ratings
• Weather or regional events
• Internal factors like price changes, new store openings, or site outages

By including these variables, the model can separate what’s truly driven by media from what’s driven by the broader environment, giving marketers a clearer read on the incremental effect of their campaigns.

Constraints and limitations

Even a strong MMM has constraints. It needs sufficient historical data to produce stable estimates, often for at least a year. It works on aggregate data, so it doesn’t offer user-level insights or granular path analysis. And depending on how it’s implemented, it may be slower to update than platform dashboards. The key is to recognize these limits and pair MMM with complementary measurement tools, such as experiments and user-level attribution, where appropriate.

Data Required to Set Up a Media Mix Model

Successful MMM starts with the right data foundation. The more complete, clean, and consistent your data is, the more accurate and actionable your model will be.

Below are the core categories of data you’ll typically need, along with guidance on structure and history.

Sales or KPI data

You’ll need a clear, consistent time series of the primary outcome(s) you want to model. That might be:

• Revenue
• Transactions or orders
• New customer acquisitions
• Leads or qualified opportunities
• Brand metrics (awareness, consideration, etc.)

Ideally, this data is aggregated at a regular cadence (daily or weekly) and covers at least 18–24 months. Longer histories enable the model to capture multiple seasonal cycles and major media or business shifts.

First-party data sources

First-party data is critical, especially in a privacy-first world. Examples include:

• Site analytics (sessions, conversions, engagement)
• CRM and loyalty data
• Email and SMS activity
• Owned social performance
• Press placements and PR activity

These signals often act as both inputs and intermediate indicators, helping the model understand how media influences engagement that eventually leads to conversions.

Marketing spend and media data

For each channel and key tactic, you’ll typically provide:

• Spend (by day/week, channel, tactic, or campaign group)
• Impressions, GRPs/TRPs, or other volume metrics
• Clicks or views (for digital channels)
• Flighting details (when campaigns were on/off or pulsed)

Consistency is more important than granularity. It’s better to have a clean, consistent view at a slightly higher level than messy, incomplete data at a very deep level.

Control variables

Control variables help the model avoid attributing performance changes to media when something else is at play. Common examples:

• Price changes or promotions
• Product assortment changes
• New store openings or closures
• Site or app changes that affect conversion rate

These variables provide context and reduce the risk of spurious relationships.

External factors

External variables provide important context for demand and behavior, such as:

• Macroeconomic indicators (consumer confidence, unemployment)
• Category demand indices or search volume trends
• Weather (especially for categories sensitive to seasonality or climate)
• Major cultural or industry events

Including these signals helps the model distinguish between media effects and wider environmental shifts.

Internal variables and events

Internal variables capture changes within your control that aren’t pure media, like:

• Brand campaigns that span multiple channels
• Product launches and rebrands
• Loyalty programs
• Operational disruptions (inventory shortages, shipping constraints)

These are often modeled as “event” variables that spike or change at specific times.

Holidays and seasonality

Holidays and seasonal periods (Black Friday, Cyber Monday, back-to-school, major sporting events, etc.) should be explicitly flagged in the data. Even if your KPIs clearly spike during these periods, the model still needs structured indicators to understand and quantify those patterns.

Geographic data and experiments

If you have sufficient data, MMM can be built at the geographic level (e.g., by DMA, region, or country). This supports more precise insights and enables:

• Geo-based lift tests (holdout vs. exposed regions)
• Regional optimization strategies
• Understanding how different markets respond to the same media mix

Where possible, you’ll want to incorporate results from lift tests and other experiments as part of your MMM setup to ground the model’s assumptions in real-world behavior.

Media Mix Modeling Examples & Case Studies

With the right media mix model, a business can measure their past marketing performance to improve future ROI by optimizing the allocation of the media budget by channel and/or tactic, including: traditional and digital media channels, promotions, pricing, competitor spend, economic conditions, weather, and more.

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1. Linking digital media to in-store sales

Poppi, a top-selling prebiotic soda brand, wanted to understand how their digital campaigns influenced in-store purchases. However, the problem was that they utilized a lot of promotion channels, and sold their drinks across thousands of retail locations. While they had clear results for digital sales from platforms like Amazon Online Video, Instacart, and TikTok, measuring the impact on in-store purchases was a challenge.

To address this, our team worked with Poppi to leverage Crisp, a platform that connects real-time sales data from retailers down to individual SKUs and ZIP codes. Then, our software experts developed a custom model in Bliss Point that analyzed the relationship between Poppi’s digital campaigns and in-store sales across different geographic regions.

The model gave Poppi a detailed breakdown of how each campaign influenced in-store purchases, allowing them to optimize their media mix for maximum ROI. One key insight was that TikTok ads made their target customer 80% more likely to purchase Poppi instore, leading them to allocate more of their budget to this platform.

2. Optimizing a full-funnel media mix

Leafguard, a home services brand, needed to understand how its full-funnel media mix was driving leads and revenue across markets. With investments spanning upper-funnel channels like TV and streaming, as well as lower-funnel channels like search and social, the team needed a clearer view of which combinations worked best—and where to scale back.

Tinuiti implemented a custom MMM that ingested Leafguard’s historical media spend, performance data, and key demand drivers across regions. The model quantified the incremental contribution of each channel and tactic to core KPIs, and revealed how brand-building channels were feeding retargeting and search demand downstream. Using these insights, Leafguard was able to reallocate budget, increase investment in high-ROAS combinations, and better balance short-term lead generation with long-term brand growth.

3. Forecasting revenue and maximizing ROI

An international ecommerce brand wanted to forecast their second-half of the year and create an optimal media mix to make their marketing dollars work smarter. A combination of the client’s data, marketing data, and machine learning were required to create a powerful, custom media mix model. 

To build the model, the business used over 2 years of digital marketing and revenue data, analyzing it by market, tactic, and day. The data was then used to create model to assess future spend showing how changes in investment across channels could impact revenue and sales.

The full digital media mix model gave the ecommerce brand a detailed analysis of where to optimize their spend across all digital marketing channels. 

One recommendation was to shift dollars away from social—which historically had been at or near 30%—to paid search. This recommendation came with another layer of insight: The brand realized they were overinvesting in awareness campaigns, and needed to invest more heavily in capturing current demand during the second half of the year.

Working with a robust media mix model, the brand was able to break down how much media spend was needed by each channel in order to achieve the 30% YoY revenue goal they targeted. 

Geo-based incrementality testing

A premium luggage brand wanted to test the impact of YouTube ads on driving incremental site visits and purchases during the holiday season. Specifically, the brand wanted to measure the effectiveness of its always-on spend and audience-targeting efforts on YouTube.

To execute this test, we worked with the brand to leverage Google’s external geo-based incrementality testing feature, integrated into their Bliss Point platform. This feature allowed our teams to measure not just site visits and purchases, but also the incremental impact of these conversions. Using the brand’s own historical data to target the right audience with tailored ads, they ran the test during the 6-week holiday season and reported back with actionable insights on how YouTube ads drove incremental results.

This test was highly successful. It drove a 7% incremental lift in site visits, representing 202,000 additional site visits. This turned into a 6% incremental lift in purchases, clearly demonstrating the value of external geo-based incrementality testing on YouTube for driving retail performance.

You need incremental measurement in 2026.

Incrementality can help marketers identify sources of ad waste and understand which touchpoints promote growth. Our playbook explains it all.

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The Benefits & Challenges of Media Mix Modeling

MMM helps you accurately connect all the dots, leveraging (ideally) a wealth of provided data, to understand how disparate aspects of marketing campaigns work together in helping you reach your business goals. The nature of these benefits is multifaceted, offering marketers a strategic edge in navigating the intricacies of their advertising efforts. Let’s dive into each benefit in detail:

• Omnichannel Campaigns: MMM excels in providing insights for omnichannel campaigns, allowing marketers to understand and optimize the impact of their initiatives across various channels. This capability is crucial in today’s interconnected digital landscape, where consumers engage with brands through diverse platforms.
• Improved Oversight Over Media Spend Impact: MMM provides a comprehensive view of the impact of media spend, enabling marketers to assess the effectiveness of their investments. This improved oversight ensures a clearer understanding of how each component of the media mix contributes to overall campaign success.
• Media Spend Optimization: With MMM, marketers can optimize their media spend by identifying the most impactful channels and touchpoints. This data-driven approach allows for strategic adjustments in budget allocation, ensuring that resources are directed towards the avenues that yield the highest return on ad spend.
• Effective Targeting of Audiences: MMM’s analysis helps in refining audience targeting strategies. By understanding which elements of the marketing mix resonate most with specific demographics, marketers can tailor their campaigns to effectively reach and engage their target audience segments.
• Forecasting with Certainty: One of MMM’s strengths lies in its ability to forecast results with a high degree of certainty. This forecasting capability empowers marketers to make informed decisions based on predictive analytics, aiding in long-term planning and goal setting.
• Reduced Reliance on Personally Identifiable Information (PII): MMM minimizes the reliance on personally identifiable information for analysis. This is especially crucial in an era where privacy concerns are more important than ever. 

In the post-cookie and post-IDFA landscape, where privacy concerns and regulatory changes limit access to individual user-level data, media mix modeling has become a pivotal analytical tool. MMM’s emphasis on overall marketing spend allocation and its proficiency in establishing cause-and-effect models, address the challenges posed by the diminishing availability of explicit conversion information, providing marketers with a privacy-respecting and insightful approach to navigate the evolving digital advertising ecosystem.

Overcoming the Challenges of Media Mix Modeling

Overcoming the challenges of media mix modeling (MMM) involves addressing key factors like model fit, data quality, and the frequency of insights. Let’s take a closer look.

1. Ensuring the model is a good fit

A strong media mix model should be accurate, adaptable, and grounded in reality. This can be a challenge, because MMM makes its predictions based on historical data – meaning that if a model isn’t built with care, it will just display results that are too dependent on your previous data.

For example, brands looking into MMM should ensure the model has measures to prevent overfitting. Overfitting happens when a model clings too tightly to historical data, basically showing what’s already happened in a similar situation, rather than making new insights based on real market trends. It might seem highly accurate in theory, but will struggle to predict future results. Great MMM models should use cross-validation and base their predictions on a wide variety of internal and external factors.

The importance of accounting for external influences can’t be overstated. For example, imagine that you’re running advertisements on the Boston subway. You’ve been consistently building brand value in the metro area, until ad performance suddenly drops in November. A basic MMM wouldn’t explain much – just that your subway ads aren’t performing well in this market.

This very small lack of clarity can lead to massive mistakes in decisionmaking. Intuition might tell us this is caused by seasonal ridership declines, leading you to reallocate spend to billboards on the highway. But in reality, the true problem could be advertising fatigue caused by oversaturating the market. In this case, your simple assumption could lead to wasted spend and damaged brand equity.

2. Data volume & data quality

Working with high-quality data is important in any measurement initiative, but for MMM to work effectively, it also needs a lot of data to build a reliable model. Remember, the model is analyzing complicated relationships between multiple channels, touchpoints, externalities, and more, to determine a business outcome with a high degree of accuracy. The high volume of data helps it detect patterns, while the quality of that data ensures those patterns aren’t spurious correlations.

For example, if you want your model to analyze the impact of seasonality on a planned media buy, it ideally needs at least three full seasons (or three years) of data to recognize patterns and produce accurate insights. However, this doesn’t mean you need to wait three years after implementing an MMM solution to start seeing seasonality-driven insights. Instead, brands can connect an MMM platform to historical data sources from digital media platforms they have already been using for years, allowing the model to leverage existing information from day one.

One way to meet this demand is through automated, programmatic data connections, such as integrating data from Facebook’s API or streaming TV providers like Paramount. These connections feed the model with actual campaign data from all corners of your marketing strategy inputs, providing a rich and trusted source of data.

3. Frequency of readouts

Generally speaking, MMM is a ‘long game’ initiative with infrequent reporting by its nature. Brands and advertisers who are more accustomed to daily or weekly updates may struggle with ‘waiting out’ the analysis. This is because MMM uses aggregate data – not user-level data – to produce its performance insights. For that reason, most of the basic models on the market offer limited insights on brand impact, personalized targeting, and customer experience.

While most of the standard models on the marketplace can only offer monthly readouts and require months of training to return reliable information, this challenge is becoming less pronounced due to recent advancements in MMM. Models like Bliss Point by Tinuiti use “Rapid Media Mix Modeling” (rMMM) to provide highly granular insights with speed, precision, and transparency. While these models are few and far between, it is possible.

So, make sure your chosen MMM vendor is able to provide a high quality of information as frequently as you need it before you sign any contracts.

Common Misconceptions About Media Mix Modeling

Media mix modeling, like many other analytics solutions, has also become a marketing buzzword that has generated its fair share of misconceptions.

Here are a few of the most common misconceptions around media mix modeling.

Media Mix Models Are Not Transparent

With large datasets and statistical analysis involved in media mix modeling, the methods behind the technique have been critiqued for their obscurity. If there is no perceived transparency in the process, how does a brand know if its media mix model is really accurate?

Any organization specializing in media mix modeling should provide a transparent approach, with deliverables such as outlines, milestones, and performance reports. Additionally, you may want to consider partnering with an agency that truly understands how media mix modeling aligns with your needs and expectations. Every business is unique and each media mix model is based on multiple factors.

Media Mix Models Do Not Provide Real-time Data

Today, results are often measured by the timeliness of their delivery, with the current digital marketplace allowing for almost instantaneous real-time data. Media mix models do actually provide compelling real-time marketing insights, perfect for evaluating new campaigns, new competitors, and assessing pricing actions or changes in promotional strategies. 

A powerful partner in media mix modeling will provide sophisticated tools and real-time approaches to satisfy your business performance assessments. Your partner should also be able to provide forecasting, simulation, or AI- and machine-learning-integrated models to suggest future movements. 

Media Mix Modeling is Biased to Offline Channels

Though media mix strategies do integrate and consider offline channels in their approaches, media mix modeling also considers all digital channels — including display, email, paid search, social, and more. Remember—it’s considering your media mix. If that includes ten different channels and you provide enough high-quality data for each, they will all be considered in your marketing mix analysis. 

In fact, as customers have become more intertwined with digital channels, media marketing models have adapted to go even deeper into the analyses provided by those channels’ respective insights to support better budgeting choices and customer segmentation reports. 

Behind the Model: The History of Media Mix Modeling

In our modern world, we’re surrounded by all types of algorithms and statistical models. While we use these processes almost every day, it’s not always apparent how they work. However, with adoption on the rise, marketers should have a basic understanding of how media mix models work behind the scenes.

The Media Mix

First, the concept of the “media mix” emerged in the 1950s from Neil Borden, a Harvard professor of advertising. He thought of marketers like chefs in a kitchen, trying to find the perfect mix of ingredients to induce a consumer’s response. As the decades went on, researchers tried to define exactly what that perfect mix of ingredients were.

Since each business, product, and consumer is unique, 20th century marketers probably felt they were searching for the holy grail. “The perfect mix” was, inherently, a moving target. 

Innovations in Statistics

Speaking of moving targets, in the early 1700s someone named Isaac Newton was trying to predict how the movement of the Earth impacts celestial alignments – specifically, the equinox. Since the earth is tilted, the equator doesn’t easily align with its orbit of the sun. There were independent variables (such as the earth’s axial tilt, gravitational influences, and the time of year) that produced a dependent variable: The exact time of the equinox. While Newton didn’t quite crack the code, he came close to developing the first statistical regression model. It would take 200 more years for mathematicians to develop a reliable model for predicting the earth’s position in the cosmos.

Unfortunately, running regression analyses was extremely laborious in the early 1900s. Accurately calculating regressions could take a day or longer of punching numbers into simple digital calculators – and with computers being few-and-far-between until the 1980s, it wasn’t feasible or obvious to use regression to find the perfect marketing mix.

Synthesis

While large companies like Coca-Cola were using basic regression-based MMM as early as the 1970s, it was exceedingly uncommon to see marketers leverage media mix models until the 1990s. This is due to two key advancements: First, computers weren’t just becoming more powerful – they were becoming more affordable and ubiquitous as well. Second, statisticians were using computers to research new regression models like Bayesian learning and time-series. This allowed models to place less importance on historical data and outputs, while prioritizing more timely and relevant information. Now, MMMs wouldn’t be burdened by historical data – rather, it could be used to massively improve accuracy over time.

However, it wasn’t until the 2000s that the third key advancement brought a spark to the powder keg: The rise of digital advertising. Compared to traditional advertising, internet advertisements allowed brands to access granular and timely ad performance data. This strengthened the reliability of MMM models. On the flip side, the internet also brought a lot of uncertainty to the world of marketing attribution. This enhanced the demand for MMM, which promised clarity.

More than twenty years later, MMM is still iterating and improving, especially with the rise of artificial intelligence and deep learning. At this point, media mix modeling is an indispensable tool for measuring the impact of media spend and predicting the future outcome of marketing investments.

Media Mix Modeling vs. Data-Driven Attribution Modeling

Like media mix modeling, attribution modeling also studies the efficiency of marketing strategies — but there are important differences.

Attribution modeling is a general term that refers to tracking engagement to better understand how specific tactics drive action at the user level. This modeling works well for analyzing specific customer touchpoints, focusing on elements like how a consumer converted, which creative on which channel led to that conversion, and what the expected ROI could be if more ad budget were shifted to that channel. 

Media mix modeling takes a higher-level, more comprehensive picture. This modeling isn’t designed to measure user-level engagement like impressions and clicks, rather its primary function is measuring the impact of an entire touchpoint on specific marketing objectives. 

Data-driven attribution modeling and MMM each have their own set of strengths. It’s not a matter of one being better than the other, rather one being better-suited to different types of marketing analysis. 

For example:

• The precision of the data-driven attribution: Let’s assume you want to invest more spend in a social ad campaign during the holiday season. While MMM is an option for determining where to allocate those dollars, data-driven attribution excels in dissecting the intricate customer journey, offering a microscopic view of user interactions. For instance, if you’re keen on understanding the exact value of a single click from your social media campaign, data-driven attribution can illuminate the path. 

• The holistic perspective of the media mix modeling:  Media mix modeling, can consider the impact of offline actions and initiatives. Unlike the more narrowly focused attribution models, which might overemphasize the first or last touchpoint, MMM assesses the collective impact of all channels over time. This makes it an indispensable tool for strategic planning and long-term investment decisions in your marketing portfolio.

“Attribution modeling is based on a bottom-up approach while media mix modeling takes a top-down approach. Media mix modeling provides a long-term view of the marketing ROI of media activity, while attribution modeling evaluates individual-level activity to provide a short term view of marketing ROI.” 

— Annica Nesty, Group Director of Marketing Science at Tinuiti

What to Look for in Media Mix Modeling Tools

Selecting an MMM partner or tool isn’t just about the model; it’s about how well it integrates with your business and measurement ecosystem. Key considerations include:

• Data integration: The ability to ingest your media, business, and external data sources reliably.
• Speed and flexibility: How quickly the model can be updated as new data and questions emerge.
• Transparency: Clear documentation, interpretable outputs, and the ability to interrogate assumptions.
• Scenario planning: Built-in tools for forecasting and scenario analysis to inform planning cycles.
• Support and expertise: Access to marketing science experts who understand both the math and the realities of media buying.

How Tinuiti’s MMM Approach Works

Tinuiti’s approach, powered by Bliss Point by Tinuiti, focuses on rapid, privacy-safe MMM that connects media and measurement under one roof. The goal is to eliminate waste and reveal the true drivers of growth across channels, tactics, and markets.

Our teams work with clients to:

• Define the right KPIs and scope for the model.
• Build robust data pipelines that bring together first-party, media, and contextual data.
• Develop custom models that reflect each brand’s unique business dynamics.
• Translate model outputs into practical recommendations for planning, in-flight optimization, and long-term strategy.
• Continuously refine the model with new data, experiments, and learnings.

How Our Model Is Unique

While many MMM solutions provide point-in-time readouts, Tinuiti’s approach emphasizes:

• Speed: Rapid MMM (rMMM) that can be updated more frequently as new data comes in.
• Granularity: Models tailored to the level at which decisions are made (channel, tactic, region, product line).
• Integration: Tight alignment between MMM outputs and media activation across performance, brand, and commerce.
• Actionability: Clear recommendations, scenario planning, and cross-functional alignment so insights don’t just live in a slide deck.

The result is a measurement engine that closes the loop between media, measurement, and growth.

Conclusion: MMM Closes the Loop on Marketing Performance

In an ever-evolving digital landscape, MMM’s adaptability to the post-cookie/post-IDFA world positions it as an essential tool for marketers. As businesses seek to connect the dots, leverage data, and make strategic decisions, MMM is a crucial ally in the dynamic realm of mixed media advertising.

At Tinuiti, we know, embrace, and utilize MMM. Our Rapid Media Mix Modeling sets a new standard in the market with its exceptional speed, precision, and transparency. 

Our proprietary measurement technology, Bliss Point by Tinuiti, allows us to measure what marketers have previously struggled to measure – the optimal level of investment to maximize impact and efficiency.  But this measurement is not just to go back and validate that we’ve done the right things. This measurement is real-time informing what needs to happen next.

Curious about how we can tailor strategies to hit your unique marketing bliss point, including Rapid Media Mix Modeling? We’re eager to chat. Contact us today for details.

Media Mix Modeling: Frequently Asked Questions

What’s the difference between MMM and multi-touch marketing attribution?

MMM uses aggregated data to understand how total media and external factors drive business outcomes over time, across both online and offline channels. Multi-touch attribution (MTA) assigns credit at the user or event level across specific touchpoints in a digital journey. MMM is better suited for cross-channel budget decisions and long-term strategy; MTA is more focused on tactical optimization within digital ecosystems.

How is media mix modeling related to incrementality in marketing?

Incrementality is about understanding what results would have happened without a given marketing effort. MMM estimates incremental contribution by comparing modeled outcomes with and without specific media inputs, controlling for other factors. When combined with experiments like geo lift tests, MMM can be calibrated and validated against observed incremental lift, giving marketers a robust view of how much value media truly adds.

What’s the difference between media mix modeling and marketing mix modeling?

The terms are often used interchangeably, but there is a subtle distinction. Marketing mix modeling traditionally covers the broader 4Ps (product, price, place, promotion), incorporating variables like distribution, pricing, and product changes. Media mix modeling focuses more narrowly on the “promotion” component—media channels and tactics—while still accounting for key non-media factors, such as pricing and distribution, as controls.

What’s an example of a media mix?

A media mix is the combination of channels and tactics a brand uses to reach consumers. For example, a retail brand’s media mix might include streaming TV, linear TV, paid search, paid social, display, online video, retail media, and direct mail. MMM helps determine what proportion of the budget to allocate to each, and how to adjust that mix over time to maximize growth.

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Email & SMS

ESP Migration: A Guide to Switching Email Platforms

By Amy Brighter-Sax Feb 10 2026

The Skinny: Migrating to a new Email Service Provider (ESP) is a complex process that, if managed correctly, offers a strategic opportunity to eliminate technical debt and improve marketing efficiency. Successful transitions require a five-phase approach: rigorous pre-migration audits, data architecture planning, tech stack integration, and a careful IP warming strategy to protect deliverability.

Migrating technologies is never an easy transition, and an ESP (Email Service Provider) migration brings its own unique set of challenges. It can take longer than expected, cost more than expected, and cause more organizational headaches than expected.

However, if you fully prepare for an ESP migration and take special account of your marketing stack and related workflows, you can avoid many of the issues that seem endemic to the process. Not only that, your migration will present a strategic opportunity to find hidden efficiencies. 

Want to ensure a successful, stress-free transition that maximizes the ROI of your new technology? Keep reading.

Why Migrate Your ESP? Knowing When It’s Time to Switch

Knowing when it’s time to move on from your current system is key to understanding if an ESP migration will be worth the effort and investment. Most migrations are kicked off due to one of the following reasons:

• The current email service provider is expensive and you’d like to shop around to find similar functionality at a lower cost
• There is dissatisfaction with the current email service provider
• The current technology is functioning, but your growing program needs new features, tools, integrations, vendor support or other capabilities not offered by your current ESP. This is the most common reason to move. 

Understanding the why behind your migration will help you evaluate your current needs and help determine your desired future state in a new technology partner. 

At Tinuiti, we are constantly evaluating and assessing different ESP partners, and can assist in helping you find an email service provider that works best with your needs. 

Key Steps to a Successful ESP Migration

There are 5 main phases to an ESP Migration: 

1. Pre-Migration Preparation and Planning
2. Data and Architecture Planning
3. Tech Stack Integrations
4. Deliverability and IP Warming Strategy
5. Launch and Optimization

Let’s cover each step in more detail.

Phase 1: Preparation & Success Planning (Pre-Migration)

Before making the move, it is critical to perform a comprehensive documentation of your current ESP program. This allows you to identify and deprecate outdated workflows, stagnant audiences, or legacy triggers that are no longer driving results. Moving “messy” data or underperforming automations only carries old problems into a new environment.

Automations Audit

Take a look at what is currently running in your system. Determine what items are needed and should be moving, as well as how they are triggered and any key data that powers your messaging. If any A/B tests are running, determine winners so that a clean transition can occur. 

Based on migration timing, it’s also best to set a cutoff date for any changes in your legacy system to ensure parity when migrating. This is also a good opportunity to set priority order for determining key pieces of your marketing strategy (such as welcome series or abandon messaging) that are key revenue drivers and should be migrated first.

Segmentation & Data Audit

Review your standard segmentation for sending and identify any filters needed for automations. This is also a good time to identify if any data is no longer needed or duplicative.

Also review any event and profile data that is being piped into your system and determine what pieces are needed to power automations or other information within your system. Knowing how that data is being sourced and how it is currently being piped into your ESP will help you determine what is the source of truth and what integrations need to be leveraged for your new ESP.

Standardizing Templates and Assets

Finally, assess your creative needs for your program. Identify message templates and any building blocks such as headers and footers that are consistently used across assets. Audit your currently live messaging for out-of-date branding, as moving systems is an excellent time to unify branding to ensure consistency in your messaging.

Additionally, if you are currently using your legacy ESP for image hosting, those images will need to be migrated to your own asset management system or will need to be rehosted in your new ESP. Any content being moved will need to be audited for those links to ensure items are rehosted. 

Phase 2: Data Planning & Architecture

Now that key messaging has been identified, it’s time to analyze your data architecture. An ESP migration is a pivotal time to deeply reassess your data structure, ensuring consistency across systems and keeping data in sync throughout the transition.

Identifying Data in Use

Most ESPs divide data into two categories, real-time data (such as purchase events or onsite activity) and profile data (demographic data of a single contact). Create a list of necessary data, where it is sourced from, and document examples of the data’s format. 

Every ESP has its own preferences for data formatting and ingestion, so determining any data transformation that needs to occur can help you ensure that data is able to be utilized in similar ways across systems. 

Data Cleaning and Validation

Ensuring data integrity and cleanliness is another key part of ensuring a successful migration. The most crucial phase of a migration is the warming process that will establish the reputation of your new sending system. By cleaning our bad contacts before they ever make it into your new ESP, you can help get ahead of any deliverability issues by reducing the risk that these bad contacts impact your warming, which can cause issues and extend the migration timeline if not properly mitigated. 

Validating your data can also help you determine the accuracy and consistency of any data fields. Are there duplicative fields that can be removed or combined in the new system? Is demographic data such as country being brought in a consistent format? If it is possible to standardize this information, it can help simplify your data schema and improve the accuracy of your segmentation, reducing the risk that data is missed. 

Phase 3: The Tech Stack & Integrations

ESPs rarely exist in isolation anymore, so understanding the broader marketing ecosystem around your system can help you plan what integrations need to be in place to fully power your messaging. 

Data In

Identify what data needs to be brought into the system to power your messaging and where it comes from. Do these sources have out-of-the-box integrations with your new ESP, or will additional engineering assistance be needed? 

Potential data sources: 

• Signup sources
• Data enrichment such as loyalty and review platforms
• Purchase data
• Site events
• Preference centers
• Customer Data Platform (CDP) data
• Transactional events (if being run through the same system)

Data Out

Likewise, ESPs can be a crucial part of feeding your broader marketing ecosystem with engagement and first party data. 

These destinations can include:

• Reporting tools
• CDP data
• Marketing tools such as social and digital ad platforms. 

See the Latest Data

Download our Digital Ads Benchmark Report for a deep-dive on data across Google, Meta, Amazon, and more.

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Phase 4: Deliverability & IP Warming Strategy

Beyond the cleanup, migrating requires reestablishing your sending reputation—a key factor in keeping your messaging in the inbox and out of the spam folder. 

Moving ESP platforms also means that you are migrating you sending IP and infrastructure. This is a crucial part in ensuring that your program is able to transition to your new ESP smoothly. A negative reputation can risk your emails landing in the spam folder, or never even making it to users at all. 

With this in mind, having a solid warming plan is key to getting your reputation in a strong position from the first send.

Preparing for Warming

The warming process starts with identifying your target warmed audience. This should be your average send size that is sent to on a consistent basis. If you are a daily sender, this may be your average daily send volume. If your cadence varies more widely, your largest send size over a week or month may be a better target. 

Next, determine your engaged audience sizes. Because IP reputation is built on consumer engagement with your messaging, you want to be targeting your most engaged audiences first, and expand your list from there until you have reached your target audience size. 

It is also recommended to set up tools like Google Postmaster and Microsoft SNDS in order to monitor your reputation, particularly for these key inbox services. 

Finally, you will want to select high-engagement content to make up your warming messaging. Select evergreen content that does not need to be sent at a specific time, as you may need to make pivots to your warming schedule based on how your reputation is developing. Bumps in the road during warming is incredibly common, so having contingency plans for backup content is always helpful. 

Phase 5: Launch & Optimization

Once your IP is warmed, it is best practice to send out of your new system. Throughout the warming process,  you should also be launching automations, which can add additional volume during the later phases of warming, and sunsetting messaging from your legacy system. 

As you reach parity in your new system, you can start taking advantage of new tools and features in your new system to begin to optimize your program and create an even more personalized experience for your customer. 

The Ultimate ESP Migration Checklist

Why Work with an ESP Migration Agency?

An ESP Migration is a large-scale change and requires a lot of knowledge and planning to accomplish. Working with an agency that specializes in ESP migration services lets you take advantage of a team that  regularly assists in migrations. This grants you unique access to expert advice to ensure your project goes smoothly. Depending on your team’s needs you can get ESP migration consulting for everything from helping audit your systems, determining your data schema for your next system, planning warming, and assistance in moving content, data and logic from your current system to your next. 

Tinuiti’s migration services have worked across a variety of ESPs, giving us the unique position of being able to help your team get up to speed and comfortable with your new system, as well as being able to help your team assess what system might best fit your unique needs.

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Frequently Asked Questions About ESP Migration

How do I migrate email flows to a new ESP? 

Each ESP has its own mechanisms and tools for automations and logic. During your audit of automations, it’s best to document the overall logic of your automations, so you can best translate the functionality of an automation from one system to another. Taking note of any data points needed and how they are used can also help you identify how data needs to be implemented in the new system to maintain functionality. 

How does migrating from one ESP to another affect deliverability? 

Moving ESP systems requires you to set up a new subdomain and new IP addresses as your sending identity. This resets your sending reputation, so you need to re-establish your reputation with ISPs. This process of warming is a gradual increase in sending to prove to ISPs that you are a valid sender and are sending mail wanted by your recipients. 

Is tracking lost when migrating ESPs?

Historical click and open data usually stays in the old system, so it’s best to do final exports of any legacy data before system end of life. Any tracking of new sends must be set up and validated in the new system to ensure parity and consistency of conversion data. 

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Search

Using Prompt Methodology for AI Visibility Optimization

By Jen Cornwell Feb 04 2026

The Skinny: Traditional keyword research is evolving into a Prompt Methodology that targets full-sentence, natural-language queries used in AI search. Success depends on optimizing for user personas and “intent families” rather than specific platforms, ensuring your brand is cited as a trusted source. By hosting authoritative, first-party content on your own domain, you move from merely tracking visibility to actively influencing AI-generated answers.

SEO teams have always lived and breathed their keyword research.

We built methodologies for choosing them: user research, intent mapping, volume thresholds, difficulty scores, SERP features, business value. Then we pushed those keywords into rank trackers and dashboards and content strategies.

AI search works the same way at a higher resolution:

Prompts are the new measurement unit and they need a methodology just as rigorous as keyword research.

At the same time, measurement alone isn’t enough. Just as keyword research only became valuable once teams could act on it through content, AI search insights need an execution layer.

This is where many brands are starting to evolve their approach. Tinuiti partners with FERMÀT’s AI Search Commerce Engine to help teams move beyond tracking prompt visibility and toward operationalizing it by using first-party content to influence how and where brands appear in AI-generated answers.

The goal isn’t just to observe AI search behavior, but to turn prompt insights into something teams can actually use.

A key operational consideration in AI search is what happens after content is created — specifically how that content is deployed, hosted, and surfaced as first-party signals to AI models.

“With FERMÀT, the content we generate is first-party by default and deployed directly on the brand’s main domain. There’s no additional hosting, no separate CMS, and no handoff to engineering just to get content live. That matters because AI models place more trust in content that’s clearly authoritative, first-party, and connected to real product data. The trust is much harder to establish with agents and consumers when content lives off-domain or behind extra deployment layers.” – Josh Feiber, Engineering Lead, FERMÀT AI Search 

Lots of tools will let you track how often your brand appears and is cited for specific prompts in ChatGPT, Google AI Mode, Perplexity, and more. 

But none of that matters if your prompt list is a random selection or a quick ChatGPT prompt (no matter how many times you ask, it won’t know what people are searching for).

This article walks through a practical, repeatable prompt selection methodology you can plug into your existing SEO workflows and adapt to your business to make sure that no matter how you’re tracking, you can expand and extend your strategies. 

Regardless of how you feel about AI visibility tracking or whether you view AEO/GEO/AI SEO as just an extension of SEO, I believe that this exercise forces you to think bigger about how users actually search now. 

The Most Effective Strategy for Improving AI Visibility 

Stepping back from traditional keyword volumes and mapping full conversational journeys opens up richer opportunities for both on-site content and off-site influence. It sharpens your understanding of user intent in a world where questions are phrased naturally, decisions happen faster, and the “search results page” is no longer a page at all.

1. Start where keyword research starts: with outcomes

In classic SEO, you don’t start with the keywords you hope matter, you start with the business outcomes you need to support. That usually means asking:

• What products or services drive revenue?
• Where do we lose buyers in the journey?
• Which topics actually influence decisions?
• What problems are we solving as a brand?
• Who is our target audience and how do they evaluate options?

Prompt selection follows the same logic. Before you generate a single prompt, define:

• Your business goals (growth areas, priority categories, competitive battles)
• Your audience personas (jobs-to-be-done, motivations, objections, language patterns)

Having these clearly mapped gives you a filter for every prompt you consider. It lets you ask, “Will tracking this prompt help us understand the journeys and decisions that actually matter for the business?”

When prompt selection is grounded in outcomes, it also becomes easier to connect insight to execution. High-value prompts naturally map to priority categories, competitive gaps, and moments where content can influence decisions.

In practice, this approach allows teams to treat prompt research less like a static analysis and more like an ongoing workflow. This way prompts inform what content gets created, refreshed, or expanded, and performance is evaluated based on whether that content is actually referenced in AI responses.

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2. Optimize for personas over platforms

One of the biggest mistakes teams make is trying to build an AI search strategy around specific platforms. With ChatGPT, Google AI, Perplexity, Gemini, and a dozen others evolving weekly, chasing platforms becomes endless and ineffective.

A better approach, and the one that actually scales while benefiting your traditional SEO strategies, is to optimize for personas.

Your users will adopt different AI assistants for different reasons: subscription access, built-in AI features, device preferences, UX comfort, or simple habit. Some will stick with Google. Some will ask everything in ChatGPT. Others will interact with AI only through social platforms or their operating system.

When you build your prompt strategy around personas:

• You identify the questions they ask, no matter the platform
• You map their decision journey from early research to final choice
• You uncover the topics that influence them most
• You stay aligned with real behavior, even as tools change

This mindset is the foundation of a strong prompt methodology: The person matters more than the platform.

Use persona-driven research to pinpoint high-priority topics and build a prompt set strategy that better aligns with real user behavior

This type of mapping highlights patterns across personas — and those shared needs become your highest-value prompts. It also reveals persona-specific questions that help you build deeper topical authority.

3. Build your AI prompting strategy from topics, not keywords

Once you’ve mapped your personas and the themes they’re likely to ask about, the next step is turning those journeys into a structured set of topics. This is where your keyword research becomes useful not as the starting point, but as a way to confirm the demand behind the topics you’ve already identified.

Your existing keyword data helps you understand:

• Which topics have consistent search interest
• Where you already have topical authority
• Where competitors are earning attention
• Which themes appear across multiple personas

Instead of rewriting keywords one-by-one into prompts, focus on topic clusters that consistently show up in both your persona mapping and your keyword insights. Those clusters become the foundation of your prompt universe.

At this stage, your job is simply to validate: Do these topics align with real user behavior and real demand?

There are research tools that can help uncover additional prompt angles and intent variations based on how LLMs contextualize your category but the core inputs should always trace back to your personas.

This approach keeps your prompt universe anchored to meaningful topics, grounded in validated demand, and directly aligned with the questions your audience actually explores in conversational search.

Use prompt volumes to estimate visibility

One of the biggest challenges in AI search today is that there is no true equivalent to keyword volume like we have in traditional SEO. There’s no public AI search volume, no query logs, and no unified view of how many times a prompt has been asked across platforms.

Most tools offering prompt volume metrics are doing the best they can with what’s available: they use large clickstream datasets to model conversational demand and project trends. That data can be directionally useful but it’s important to understand its limitations.

Modeled prompt volumes often don’t account for:

• Uneven adoption rates across assistants
• Regional or demographic differences in platform usage
• Bots, scraper tools, and automated agents inflating prompt activity
• Noise from model-testing or API load, not real user questions

Because of this, I think of prompt volume tools as helpful signals, not absolute truth.

They’re best used to:

• Validate whether a topic seems active in conversational search
• Spot relative demand shifts, not exact numbers
• Explore how users might phrase certain questions
• Identify clusters of related prompts and intent types

Used thoughtfully, these tools help you shape a more complete view of conversational demand just without the illusion of precision we’re used to in keyword research.

The goal is to lean in and understand how users talk and what kinds of prompts your personas are likely to ask as AI assistants become a default part of LLM search behavior.

5. Group prompts into intent-based “families”

Once you’ve validated your topics and understood the conversational demand behind them, the next step is organizing your prompts by intent. Just like keywords, prompts fall into recognizable behavioral patterns but AI assistants introduce entirely new intent types that don’t exist in traditional search.

Your prompt methodology needs to account for all of them.

Traditional search is dominated by informational and navigational queries. ChatGPT, on the other hand, introduces two major new intent types:

• Generative intent: users asking for creation, synthesis, planning, drafting, and ideation
• No-intent / open-ended prompts: broad or exploratory questions that don’t map to classic SEO intent at all

These intent types dramatically change how users seek information and make decisions. To build a comprehensive prompt universe, you need a taxonomy that reflects both existing search intent and new AI-native intent.

AI Search Intent Families

What makes these intent families especially important is that many of them don’t map cleanly to traditional SEO measurement. Generative, exploratory, and planning prompts often influence decisions without producing a click, a SERP, or a clear conversion signal.

That’s why more teams are starting to evaluate AI search performance through citation and reference behavior across models.

This lens makes intent-based prompt grouping more than a taxonomy exercise. It becomes a way to understand where a brand is shaping outcomes inside AI-driven decision journeys, even when those journeys never touch a traditional search result.

This classification helps you:

• Capture the full range of user behavior inside AI assistants
• Ensure your prompts reflect real decision-making journeys, not just old SEO patterns
• Cover both direct questions and generative tasks where brand equity matters
• Build a prompt library that’s easy to tag, sort, audit, and scale

6. Generate prompt variations at scale

Once you know your categories, have validated them for demand, and identified the intent types you need to represent, it’s time to generate prompts.

This part should feel familiar as it mirrors the expansion stage of keyword research, but you’re working with natural language instead of queries.

Use multiple inputs so your prompt list reflects real user phrasing:

1. Topic → Prompt expansion: Take each topic and draft 5–10 variations that align to different intents, personas, and levels of sophistication.
2. Use LLMs to scale ideation: This is the one moment ChatGPT is useful: generating variations and surfacing alternative ways a user might phrase the same question. Just remember: LLMs help with phrasing patterns, not user demand.
3. Pull from internal knowledge sources: Your support team, sales calls, internal FAQs, and site search logs are often the richest source of real user language.
4. Traditional keyword insights: Keyword modifiers like “best,” “for women,” “near me,” “safe,” “vs,” “alternative,” or “cheap” all translate into natural-speech prompts.

7. Define core prompts vs. cyclical prompts

Not every prompt in your universe plays the same role year round. Some anchor your long-term strategy; others help you stay relevant as user behavior shifts.

Core Prompts: Your Always-On Set

These are the stable, evergreen questions tied directly to your products, category, and primary personas. They stay relevant year-round and define the foundational search journeys you always want represented.

Cyclical Prompts: Seasonal or Trend-Driven

These surge during specific retail moments, cultural cycles, or seasonal needs and should rotate in and out of your universe as behavior shifts.

Why both matter

Core prompts give you stability and long-term insight; cyclical prompts help you adapt to shifting interest and real-time behavior. A healthy prompt strategy blends both — steady coverage of what always matters, plus flexible coverage of what matters right now. Here’s how to maintain them:

• Keep core prompts consistent for benchmarking
• Add or retire cyclical prompts each quarter
• Monitor FAQs, trends, and category conversations to catch new ones early

This balance keeps your prompt universe strategic, relevant, and easy to manage.

8. How many prompts should you track?

There’s no magic number, but this is for coverage, not volume. The win comes from representing how users actually search across your categories, not from building an endless list of variations. You will exhaust your research (and it gets very expensive.)

Over time, you can:

• Add prompts for emerging themes
• Remove prompts that become irrelevant
• Expand categories as the brand grows
• Add persona-specific sets for new ICPs

The key is: start structured, scale intentionally.

9. Use research loops to refine your prompt set over time

Just like keyword research, a good prompt research strategy isn’t static.

Conversational habits evolve, new assistants rise, personas shift, and categories expand. Your prompt set should evolve with them.

Ongoing Prompt Refinement Tasks

• Revalidate categories and identify emerging trends
• Review persona gaps and overlaps
• Add new generative or no-intent prompt types
• Remove outdated or low-value prompts
• Monitor FAQs, support, and sales insights for new questions
• Track launches, seasonality, and topical shifts
• Refresh prompts in fast-moving categories
• Expand geographies or languages when needed

A lightweight refresh cadence keeps your prompt universe aligned to real user behavior without reinventing the wheel every month.

Ready to Improve Your Visibility in LLM Models?

As AI assistants reshape how people search, compare, and make decisions, the brands that win will be the ones who understand their audience and their brand deeply enough to show up in the questions, tasks, and conversations that actually drive intent.

A structured prompt methodology gives you that foundation. It turns “AI search” from a buzzword into a measurable, strategic part of your marketing engine—one that flexes across channels, personas, and platforms without losing its center of gravity.

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Search

The Future of SEO: 7 Predictions for Marketers in 2026

By Jen Cornwell Jan 22 2026

The Skinny: SEO has shifted from driving website traffic to managing “synthetic share of voice” within AI-generated summaries and answer engines. Success now requires brands to prioritize “agentic discovery” by using structured data and decision-grade content that allows AI agents to research and recommend their products autonomously. As zero-click searches become the default, the new SEO scoreboard focuses on AI visibility, citation share, and a consistent brand footprint across the entire digital ecosystem.

Strategic SEO in 2026 isn’t about competing with AI. It’s about accepting that AI now underpins SEO and deciding whether you want your brand to be in the answer box or outside the conversation.

People are searching more, using AI more, and expecting more thoughtful, contextual responses from both. The question is no longer “where do we rank?” It’s “are we the brand AI is comfortable recommending when it really counts?”

Key takeaways:

• Zero-click is the new baseline. With over 60% of queries now ending without a click, SEO is shifting from driving website traffic to managing brand presence within AI-generated summaries.
• Optimization is moving toward “Agentic Discovery.” Success in 2026 requires optimizing for AI agents—autonomous bots that research and recommend products based on technical schema and cross-platform authority.
• AI visibility and citation share are the new KPIs. Traditional rankings are being replaced by “synthetic share of voice,” measuring how often and how prominently your brand is cited in AI responses (GEO/AEO).
• Decision-grade content is on the rise. To win in an AI-first world, content must move beyond basic “what is” explainers and toward high-intent, data-backed insights that help AI (and humans) make a final selection.
• SEO is now an omnichannel discipline. Search visibility is no longer siloed to your website; it relies on a consistent “digital footprint” across community forums, reviews, and social signals that AI models use for training.

The data underpinning our SEO predictions

Our recent 2026 AI Trends Study found that 34% of U.S. adults use AI platforms daily, with another 21% using AI weekly. This means more than half of the U.S. population is in regular conversation with chatbots.

At the same time, more than one in three respondents say they’re using search engines more often since AI tools became widely available, compared to just 14% who say they use search less.

In other words: AI didn’t steal attention from search; it raised the bar on what search has to deliver.

For this reason, brands should approach SEO in 2026 by asking these questions:

• Are we part of the answer when AI responds?
• Do we help people decide, not just learn?
• Can we prove we’re shaping those moments—even when clicks disappear?

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How this data reshapes what “SEO” even means

Put those numbers together, and you get a very different brief for SEO in 2026. AI isn’t a side channel sitting next to search anymore; it’s the layer that interprets, compresses, and often delivers the search experience. People are still asking questions, still comparing options, still choosing brands; they’re just doing more of that work inside AI-generated answers instead of across ten blue links.

This new era of digital engagement is based on the seamless delivery of information from brand to consumer via agentic proxy, meaning users get a complete, ready-to-use answer immediately, without having to browse different websites. When agents can assemble and package everything a buyer needs in one response, from options and trade-offs to reviews and next steps, the only way to stay in the conversation is to make sure those agents have compelling, structured, and trustworthy information to work with.

Success in this environment largely depends on a comprehensive AI SEO strategy. Whether you’re calling it AEO (Answer Engine Optimization) or GEO (Generative Engine Optimization), the goal remains the same: earning visibility within AI-synthesized responses by optimizing the entities and structured data that these models trust. It is no longer about just being found; it is about being synthesized.

That’s why the rest of this piece focuses on forecasts, not a recap. Given what we’re seeing in the data, here’s where we believe SEO is headed and what you can do about it before these shifts show up in next year’s dashboards.

1. Zero-click becomes the default, not the exception

We expect zero-click behavior to become the baseline mode of search in 2026. People will still search, but more of those journeys will start and end inside the SERP or AI interface.

“Zero-click searches are becoming the norm, with over 60% of queries resulting in no clicks. That’s not a dead end — it’s the new front page of the internet. Brands that master AI SEO will own the ‘top answer’ space inside AI Overviews and answer engines, where consumer trust is being built.”

– Simon Poulton EVP of Innovation & Growth at Tinuiti

In our AI Trends Study, more than one in three respondents say they’re using search engines more since AI went mainstream, while only 14% say they use search less. Recent coverage in AdExchanger highlights major platforms testing AI-native ad formats within generative results, which only accelerates this shift.

Yet when AI-generated answers are present, respondents are slightly more likely to “just read what’s on the search results page” than to click through for more. At the same time, our data shows that over 60% of queries now result in no click, once you factor in modern SERP features and AI Overviews.

Among those who notice AI summaries, a clear majority say they improve Google results rather than worsen them, suggesting AI answers are likely to become the default interface.

Why it matters

If we keep treating “no click” as failure, we’ll undervalue content and channels that are actually influencing decisions. A buyer who reads a summary that cites us, then comes back later as “direct,” is still on a path we helped shape. We just won’t see it if our only SEO success metric is sessions.

What to do now

• Treat AI Overviews and AI Mode as inventory. For your highest-value queries, document whether an AI answer appears, which sources it cites, and whether your brand is visible.
• Adjust your strategy accordingly. Shift the goal for those queries from “grow traffic” to “earn inclusion and positive framing inside the answer,” and plan content and technical work against that.

2: The future SEO scoreboard shifts from volume to influence

We don’t expect traffic and CTR to disappear in 2026, but we do expect them to lose their place as the headline story. The most effective programs will judge SEO on its ability to shape AI-mediated decisions, not just raw volume.

Traditional rank-and-click metrics were built for a world where users saw a static SERP, clicked to a site, and moved through a linear funnel. In AI search, answers are composed probabilistically, personalized, and often served in a closed-loop. A “position” no longer maps cleanly to exposure, much less to revenue.

“”Intent matching, trust building, and ease of accessibility for AI agents are all necessary strategies to focus on as brands begin to drive AI search-specific KPI improvement.”

– Benjamin Grosse Head of Partnerships and Growth, Profound

We see six questions that matter more than rankings in 2026:

1. How often do we actually show up? AI visibility shows how often and prominently we appear in AI-driven answers to the prompts that matter.
2. Does AI quote us, or just mention us? Citation shares track how often our owned content is referenced directly as a source.
3. Who else is talking about us, and where? Third-party mentions reveal whether PR, reviews, and communities are supporting or undermining our positioning.
4. What tone does the internet use when it talks about us? Sentiment analysis across sources helps us understand whether AI is likely to frame us favorably.
5. Are AI crawlers actually using our content? Bot visits and crawl activity show which URLs are being used for indexing and training.
6. What happens when AI does send traffic? AI referrals, tracked separately from organic, enable us to compare performance with traditional search visits.

Another nuance we’re watching closely is the growing digital divide between free and paid AI tiers. Premium models are likely to have fresher data, richer reasoning, and more sophisticated guardrails, while free versions may answer with narrower, older, or more generic information. That means the “answer” you see when you test a prompt may not match what every customer sees—and measurement, research, and QA all need to account for that variance.

What this looks like in practice

In our recent engagement with Rough Country, we found that the automotive accessory brand had strong traditional rankings but zero visibility in AI Overviews or LLM answers.

After we implemented LLMs.txt, enriched product pages with AI-legible detail, and added structured data plus natural-language FAQs, AI visibility climbed to 22% share of voice across 180 non-branded, mid-funnel prompts, driving over 14,000 sessions from ChatGPT in 90 days and $23K in attributed revenue.

Results

Why it matters

If brands don’t add AI-era KPIs to our dashboards, they’ll struggle to justify investments that clearly move AI visibility and revenue, but not sessions. Meanwhile, teams that can show “we increased our share of voice inside AI answers, and AI referrals converted X times better than typical organic” will win budget and support.

What to do now

• Rethink your KPIs. Add AI visibility for a set of core prompts, citation share of your domains, and AI referrals as distinct line items on your 2026 scorecard.
• Test, document, and iterate. Run a single pilot where success is explicitly defined as “improve AI presence and sentiment for this cluster,” then document downstream brand search and conversion.

3. SEO and PPC converge inside AI answers

In an AI-first search world, organic and paid don’t just sit next to each other; they blend into one continuous answer. AI Overviews and AI Mode summaries already combine citations, snippets, and ads in a single, scrollable experience.

Google has signaled that ads in AI Mode are contextually aligned to both the user’s query and the AI-generated response, and while we drafting this piece, OpenAI announced their plans to start testing ads in the ChatGPT free and Go tiers of the platform. For both platforms, this means SEO and PPC will be influencing the same moment, whether your teams are coordinated or not.

We expect 2026 to be the year SEO and PPC convergence stops being a talking point and becomes an operational reality. As I’ve previously pointed out in Search Engine Land, the AI era makes it harder than ever to justify separate SEO and PPC silos. Smart brands will design unified search strategies in which paid and organic work together to own as much real estate as possible within AI answers—not just above or below them.

Why it matters

• If organic and paid search still run on separate roadmaps and KPIs, you’ll miss the compounding effect of appearing both as a cited source and as a sponsored option in the same AI answer.
• Budget decisions that ignore AI-native formats will underfund queries in which a single AI response now carries more influence than a full SERP used to.

What to do now

• Build a unified search brief per category. For your priority themes, create one shared doc that covers the prompts and keywords that matter, the AI Overviews that appear, and where you want to show up organically and paid.
• Align reporting across SEO and PPC. Move toward dashboards that provide combined visibility across organic citations, AI visibility, paid placements, and AI referrals for the same set of prompts and queries.
• Coordinate creative. Use paid search and Performance Max learnings to inform which topics and angles your GEO and AEO content should emphasize, so your brand story is consistent whether AI pulls in an ad, a blog, or a product page.

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4. SEO goes omnichannel because AI already has

By the end of 2026, focusing SEO around what happens on your website will be outdated. AI systems already synthesize information from PR hits, creator content, forums, and reviews alongside our .com, and we don’t see that trend reversing.

Our AI Trends Study shows more than one in three respondents are using search engines more since AI’s rise, 29% are using social media more, and 32% are using Wikipedia less. People aren’t moving from search to a single new destination; they’re comfortable with AI pulling from many sources at once.

At the same time, when we audit answers in AI Overviews, AI Mode, and LLM conversations, we consistently see citations from:

• Trade publications and news outlets in the category.
• Reddit and other community forums.
• YouTube reviews and explainers.
• Retailer review modules and Q&A.

We expect social and user-generated content to play an even larger role in that mix over the next year. As models improve at parsing multimedia, citations from TikTok, YouTube, Instagram, public Facebook groups, and community threads are appearing more frequently in AI-generated responses as proof points and lived experiences.

That means the creator reviews, how-to videos, and community posts we’ve traditionally treated as “influence” now function as SEO assets, too, because AI treats them as first-class signals when deciding which brands to trust.

As such, SEO must be approached as an omnichannel discipline in 2026.

Why it matters

If PR, social, CX, and web aren’t aligned, AI may be amplifying narratives we never meant to lead with. A handful of out-of-date reviews, an old positioning statement on a directory profile, or a stray quote in a forum can show up in the most important answer our buyer sees.

What to do now

• Map your brand’s ecosystem. Build a simple “AI footprint” report: for your brand and top category queries, list which domains and content types AI currently cites.
• Collaborate across departments. Establish a quarterly working session where SEO, PR, social, and CX agree on the 3–5 narratives we want AI to repeat and align pitches, content, and fixes accordingly.

5. Decision-grade content becomes the new top of funnel

We expect more SEO-driven growth in 2026 to come from decision-grade content than from classic “what is X?” articles. AI is increasingly handling the explainer work; we need to own the “what should I do?” moment.

Data from Semrush shows that 88% of informational queries now trigger AI Overviews that answer questions directly. At the same time, our AI Trends Study finds that 48% of respondents would trust an AI assistant or chatbot to recommend products, and many have already used AI recommendations across multiple categories—from electronics and apparel to beauty and home and garden.

Data from our partners at Profound shows that about one in three AI conversations contain unprompted product recommendations, and over 41% mention specific brands without being asked. AI is already introducing brands on its own; the question is whether our content gives it a good reason to introduce us.

We also expect product discovery to become more proactive and personal by design. AI agents are starting to integrate signals from calendars, past purchases, and on-device behavior to anticipate needs, such as suggesting packing lists before a trip, recommending gear ahead of a season, or flagging replenishment opportunities before we run out.

That shift from “I search when I’m ready” to “my agent surfaces options when it thinks I’m ready” will reward brands whose content clearly ties products to real-world contexts, routines, and constraints.

Why it matters

Thin educational content is unlikely to be the piece AI leans on when it needs to point a user toward a choice; the sources that win that slot are the ones that bring real trade-offs, context, and proof. If we stay stuck in top-of-funnel explainer mode, we risk being the paragraph that gets summarized away, not the recommendation that gets repeated.

What to do now

• Establish a baseline. Audit your current SEO winners and ask: How many genuinely help someone choose, compare, or implement—versus define?
• Rebalance your 2026 roadmap. We’d recommend focusing on:
  • Honest comparison pages (including where a competitor might be a better fit).
  • Use-case stories that reflect how customers actually talk about their situations.​
  • Q&A content that mirrors real language and can be lifted cleanly into AI answers.
  • Buying guides that lay out clear, opinionated criteria.

6. Agents become a real audience, even if they don’t feel like it yet

We don’t expect 2026 to be the year everyone lets AI transact on their behalf, but we do expect more journeys to be mediated by AI agents who research, shortlist, and explain.

“We are moving into a world where every brand on the planet has a new customer, the AI agent. Ensuring that product listings can appear in agentic commerce channels is the first step, but optimizing for category-leading visibility and sentiment will be critical in driving agentic conversions.”

Benjamin Grosse Head of Partnerships and Growth, Profound

In the AI Trends Study, 48% of respondents say they would trust an AI assistant or chatbot to recommend products, but only 20% would trust AI to actually purchase products on their behalf. Younger consumers and daily AI users are more comfortable with both, and they’re also significantly more likely to opt into Google AI Mode.

This can be seen as the beginning of the agentic era, where AI agents browse, compare, and, increasingly, transact on a user’s behalf.

Why it matters

If an agent is doing the early research, it’s effectively pre-curating the shelf you appear on—or don’t. Product content that hides important details behind vague benefit copy makes it harder for agents to match you to the right user; structured, transparent detail makes it easier.

What to do now

• Think like a bot. Choose one high-value category and ask: “If an AI agent had to argue for or against recommending us, what facts would it have?” Then fill the gaps on your site and key third-party surfaces.
• Make structured data a priority. Product schema, rich attributes, semantic HTML, and an LLMs.txt strategy can help clarify what should be crawled and how for these agents acting on the consumer’s behalf.

7. SEO foundations and E-E-A-T become non-negotiable

Finally, we don’t see AI changing the fundamentals as much as it raises the stakes on them. Technical SEO and E-E-A-T (Experience, Expertise, Authoritativeness, and Trustworthiness) will be the quiet differentiators between brands that keep getting cited and brands that slowly vanish from the answer layer.

“Rank-and-click metrics were built for a world where search engines introduced users to a relatively static SERP, which then led to a brand’s site, where users were converted through an owned funnel.

In AI search, answers are assembled by probabilistic LLMs, so a single position or CTR no longer maps to exposure, much less conversion. The modern scoreboard centers on presence and proof inside answers.”

Benjamin Grosse Head of Partnerships and Growth, Profound

LLMs can only use what they can parse. Content locked in images, unstructured HTML, or unrendered JavaScript is effectively invisible, no matter how good it is. Conversely, content that’s clearly structured with schema, semantic headings, and clean internal linking is easier for models to segment, understand, and reuse.

We also expect search rules to get smarter and more explicit. Emerging standards like LLMs.txt give us a way to tell models what they can crawl, how they should treat different parts of our site, and where our most authoritative content lives. As those standards mature, GEO and AEO will move from experimental acronyms to core craft: structuring our sites and signals not just for search engines, but for answer engines and agents that sit on top of them.

E-E-A-T remains a key lens for how both search engines and AI systems evaluate content. First-hand experience, credible authorship, and consistent facts across the web make it easier for models to treat our claims as reliable enough to repeat.

Why it matters

If critical information is hard for machines to read or inconsistent across our ecosystem, AI is more likely to skip us, misrepresent us, or lean on a competitor whose signals are clearer.

What to do now

• Run a “can AI actually read this?” pass on your priority pages. Check crawlability, HTML structure, schema coverage, and FAQ formats.
• Elevate E-E-A-T from a checkbox to an editorial standard. Real experts on sensitive topics, updated content cadence, and aligned brand facts across your site, profiles, and key directories and review sites.

Where this leaves SEO leaders in 2026

Taken together, these forecasts point to a simple, uncomfortable truth: SEO success can’t be measured by rankings and traffic anymore. In 2026, the real work is about shaping how AI explains your category, which brands it recommends when it needs to make a recommendation, and whether it feels confident putting your name in front of your next customer.

The upside is that we’re not guessing in the dark. Our 2026 AI Trends Study shows how real people are using AI and search today—how often, on which platforms, and how much they trust AI to guide purchases. The AI in Search framework translates those behaviors into an SEO playbook built for AI-native search: new KPIs, omnichannel tactics, and agent-ready technical foundations.

If there’s one mindset shift to carry into 2026, it’s this: stop optimizing only for the click you can measure and start optimizing for the answer you need to shape. The brands that focus on this early by reframing their scoreboards, remapping their content, and re-architecting their presence for AI will be the ones AI cites, recommends, and remembers when it matters most.

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TV, Audio & Display

Building a Super Bowl Marketing Strategy That’s a Runaway Win

By Jenn Wheatley Jan 08 2026

The Super Bowl isn’t just the priciest media buy on the calendar; it’s one of the clearest tests of whether your marketing can turn culture into real, compounding growth. For C-suite leaders, the question isn’t simply “can we afford it?” It’s “can we turn this into a repeatable asset instead of a one-night gamble?”

This guide is built for marketers asking exactly that. It breaks down how to approach Super Bowl marketing from deciding if you’re ready, to choosing between national and more accessible plays, to building the creative, cross-channel, and measurement systems that ensure the investment pays off. Along the way, you’ll see how brands like e.l.f., eos, Instacart, Poppi, and Ramp have applied these principles in different ways.

“The greatest risk for brands is not considering where the Super Bowl fits into their larger brand journey. Marketers must pivot from asking if they can afford a spot to whether they should.

A winning strategy replaces the gamble of a single ad with a measurable, 360-degree approach that capitalizes on the full window of audience engagement.”

– Rachel Costanzo Senior Director of Media Investment at Tinuiti

Key takeaways:

• The Super Bowl is a full-funnel, multi-week marketing ecosystem—not a single TV ad. Winning strategies activate before, during, and after the game across channels.
• A national Super Bowl spot is optional. Brands can drive impact through CTV, streaming, local TV, social, creators, search, retail media, and CRM at lower risk and cost.
• “Surround the moment” outperforms one-and-done buys. The strongest Super Bowl marketing strategies orchestrate TV, digital, and performance media together.
• Not every brand is Super Bowl-ready—and that’s strategic. Readiness depends on audience clarity, brand distinctiveness, and proven performance foundations.
• Creative must be platform-native and rooted in brand truth. Ads that align with channel norms and clear product insights earn attention; trend-chasing erodes it.
• Real success is measured beyond game day. Effective Super Bowl campaigns deliver sustained lifts in awareness, consideration, and revenue using MMM, brand lift, and ROAS—not just buzz.

The old Super Bowl playbook is broken

Most classic Super Bowl playbooks were built for a different era: a single, linear feed, a short list of big sponsors, and a single “Monday morning watercooler” conversation. In that world, one great 30-second spot could reasonably carry the whole investment.

That world is gone. Today, the game spans streaming apps, alternative broadcasts, social feeds, gaming, and sports betting experiences. Viewers are just as locked into their phones as they are to the big screen, checking bets, scrolling reactions, and replaying ads on demand. 

What’s more, the window for capturing attention no longer starts and stops with the whistle. Social and search activity spikes before and after the game, as brands release teasers early and highlight clips circulate for days.

In that context, the riskiest move for a leadership team is clinging to a narrow definition of being in the Super Bowl: a single national TV spot plus a token social post. There are now multiple ways to show up, each with its own risk–return profile:

• A national in-game spot.
• Heavy alignment with pre- and post-game coverage and playoff ramps.
• Local buys in priority DMAs instead of the national feed.
• Streaming and CTV packages in and around the game.
• Second-screen and social programs that assume your customer is just as likely to see the work on TikTok as on CBS or Fox.

“The Super Bowl is no longer an exclusive club reserved for the Fortune 100. Today, brands of all sizes and budgets should consider how to lean into the cultural momentum of the game.

The fragmented media landscape has a wealth of opportunities for performance-driven brands to build a curated approach that aligns with their specific KPIs.”

– Rachel Costanzo Senior Director of Media Investment at Tinuiti

Common Super Bowl marketing strategies for prime time ad buys

Once you recognize that the Super Bowl is an ecosystem, not just a three-hour broadcast, the national TV spot becomes one option in a much bigger toolkit. It’s still the most visible play, and it’s also where the stakes are highest. Which is why it deserves its own strategy rather than defaulting to “we should be there because everyone else is.”

Timing and inventory basics

National Super Bowl spots are limited and usually sold well in advance, often during the spring upfronts. The cost of a national 30-second Super Bowl LIX spot went as high as $8+ million, excluding production, digital, and cross-channel investments required to support it. For many brands, the Super Bowl is the single largest line item in their annual media plan.

That’s why brands like Poppi started planning their Super Bowl presence in May prior, building a runway that included NFL playoff TV, streaming, and retail media to ensure the spot sat atop a broader system rather than standing alone.

“If you want to buy a national Super Bowl unit, you need to be participating in the upfronts. Planning the Super Bowl out further in advance and centering other efforts around the spot is significantly less stressful, more measurable, and leads to a noticeably stronger performance than deciding to do a spot at the last minute.”

– Kenny Bianchi Director, Client Partner, Tinuiti

Who you reach during the game

The Super Bowl remains one of the rare moments when nearly a third of the population is watching at once, across linear, streaming, and second screens. The 2025 showdown between the Eagles and Chiefs reached 126 million viewers across FOX, Tubi, FOX Deportes, Telemundo, and NFL properties, with Kendrick Lamar’s halftime performance drawing an estimated 131.2 million viewers.

Audience nuance still matters:

• First-half placements, especially near halftime, tend to deliver broader and more female-skewed viewership.
• Halftime talent and player narratives (for example, a major artist or a high-profile player relationship) can shift who tunes in and how engaged they are.
• Streaming simulcasts and alternative feeds (such as Tubi) can skew younger, more digitally savvy, and more likely to multitask on other devices.

Ad creative: Earning your place in the moment

On a stage where every brand brings its best, a Super Bowl spot has to do more than land a joke. It needs a clear story, emotional weight, and a strong connection back to the product or brand truth.

Winning Super Bowl creative usually:

• Has a clear role in the funnel. The Super Bowl spot should introduce or reinforce the brand story, not try to do everything at once.
• Invests in production where it matters. High-quality craft signals seriousness and helps stand out amid noise. Lo-fi can work, but only when it’s intentional and authentic to your brand.
• Leans into emotion. Humor, nostalgia, “happy-sad,” or a powerful human insight is often what makes a spot worth talking about after the game.
• Anchors in a real product or brand truth. e.l.f.’s first Super Bowl spot centered on Power Grip Primer’s “sticky” benefit. It tied it to Jennifer Coolidge’s cultural moment and her wish to “play a dolphin,” creating a memorable #dolphinskin platform.

“To truly earn your place in the Super Bowl moment, creative must be more than just a ‘fun ad’. It must be a synergistic blend of high-impact brand storytelling and platform authenticity. Success comes from developing a ‘true brand creative’ that is playful, emotive, and anchored in your product truth, while resisting the urge to simply ‘copy-paste’ that spot across the ecosystem.

The most effective campaigns recognize that while high-craft production signals seriousness, the real magic happens when you marry community, culture, and creativity to ensure your message feels organic and endemic to wherever your audience discovers it.”

– Laura Ross Director, Client Partner, Tinuiti

Super Bowl marketing strategy matrix

Once you view the Super Bowl as an ecosystem rather than a single ad, the question becomes how to participate—not whether to show up at all. This matrix outlines the most common Super Bowl marketing strategies, mapped by budget, objectives, and tactics.

Before, during, and after: Orchestrating the moment

“The most impactful campaigns are a result of ‘surround sound.’ You want everything to work in concert, like an orchestra. The Super Bowl is the drums—it’s loud and amazing—but how much better do those drums sound when there is also a guitar, a piano, and strings? You need that harmony and synergy across the entire ecosystem to make the most noise and cut through in the biggest way.”

–Meg Crowley Group Director, Client Partner, Tinuiti

The brands that succeed in Super Bowl marketing treat the spot as part of a full-funnel marketing plan, not a one-off. Here’s a simple way to structure that system.

1. Tease (weeks before kickoff)

• Release teasers and cutdowns on TikTok, YouTube, and Meta.
• Tap talent and creators to build anticipation and start the conversation.
• Make sure search and shoppable experiences are ready so “Brand + Super Bowl ad” queries land on your properties first.

2. Game day

• Place the spot strategically (often in the first half, near halftime for brands with broad or female-skewing audiences).
• Launch the remaining media immediately after the spot airs: YouTube uploads, paid social, and high-impact digital placements.
• Monitor real-time performance and conversation for any needed pivots.

3. Post-game burst

• Max reach: Use high-impact units (TikTok TopView, homepage takeovers) to deliver tens of millions of impressions quickly.
• Contextual alignment: Show up where people are talking about the game—X, Reddit, TikTok content tied to Super Bowl coverage.
• Audience layering: Recenter your cor-category audience by layering segments (e.g., beauty + sports) to create deeper creative cuts.

4. Sustain (weeks after)

• Continue running evolved versions of the creative in relevant tentpoles (award shows, March Madness, major premieres) and always-on channels.
• Use exposure data to build retargeting audiences and continue engaging with the audience who saw the spot.

If you want a real example of how Super Bowl ad campaigns are built, our resident Streaming expert, Harry Browne, breaks it down for your below:

Super Bowl campaign strategies any marketer can afford

A national in-game spot is only one way to participate in Super Bowl marketing, and for many brands, it’s not the right strategy. What matters is finding the level of involvement that aligns with your budget, your stage of growth, and how clearly you can tie the spend to business outcomes.

That’s where “surround the moment” strategies come in. Instead of anchoring everything on a single 30-second spot, you can use a mix of local linear, streaming, social, creator, email, and retail media plays to tap into the Super Bowl moment at a fraction of the cost. All while still building a clear path from awareness to conversion.

Bob Avelino explains more:

Alternative broadcast placements

Beyond the main national broadcast:

• Pre- and post-game coverage. Spots in the hours around the game can be significantly more affordable while still benefiting from heightened attention.
• Family-friendly simulcasts. Channels like Nickelodeon’s kid-focused coverage open inventory for categories that don’t fit the main broadcast’s ad rules.
• Counter-programming (like Puppy Bowl). These events attract viewers who actively avoid the game but still participate in the overall cultural moment.
• Streaming-only presence. Broadcasters like CBS and NBC offer streaming-only in-game packages across their OTT platforms, allowing brands to create a marquee moment at a fraction of the cost of a full national unit, and to a younger, affluent audience.

Local and regional spots

Buying local linear TV in key DMAs instead of a national spot can significantly reduce costs while focusing on the markets that matter most to your business. For eos, for example, a local linear buy with pre-game and in-game units, supported by streaming, YouTube, and paid social, allowed the brand to spotlight its new body wash line without paying national-spot prices.

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Social media campaigns and the second screen

Most viewers will check their phones during the game for scores, bets, texts, and social feeds. That creates an opening for:

• Real-time social campaigns timed to ad breaks and halftime.
• Short-form video built around reactions, behind-the-scenes content, or challenges tied to the game.
• Paid social that reinforces your message while people are already in “scroll and share” mode.

Influencers, creators, and affiliates

Creators and affiliates can extend your reach into specific communities:

• Co-created content that drops before and after the game.
• Watch-party integrations where creators incorporate your product into their Super Bowl plans.
• Discount codes and trackable links to tie activity back to performance.

Riding the media buzz

Super Bowl generates days of commentary beyond the ads themselves. Innovative brands look for:

• Opportunities to provide expert commentary (e.g., on marketing trends, category topics).
• Timely press releases or thought leadership around the themes your brand can credibly speak to.
• Participation in post-game rankings and recaps that extend the life of your creative.

Email and CRM

Not every touchpoint has to be flashy:

• “Stock up for the game” emails with product bundles and recipes.
• Reminders about delivery cutoffs or limited-time offers ahead of kickoff.
• Post-game follow-ups thanking customers and encouraging repeat purchase.

For a brand like Poppi, sending email reminders to customers to “order sodas for the game” ahead of time can be a simple yet effective way to tie tentpole awareness to directly measurable sales.

Streaming and YouTube placements

Not everyone watches on linear. Many fans rely on:

• CTV streams of the game via apps like Tubi.
• YouTube highlight reels, recap shows, and creator breakdowns.

Advertising against this content—before, during, and after the game—can be an efficient way to tap into Super Bowl attention, especially for digitally native or younger audiences.

Retargeting: Turning awareness into action

Whatever mix of tactics you choose, retargeting is how you turn initial Super Bowl marketing impressions into revenue:

• Build audiences from exposed users on CTV, social, and YouTube.
• Use those segments in the weeks after the game across paid social, search, and retail media.
• Adapt creative to where people are in the funnel: more product and offer-driven messages as you move closer to conversion.

“Success in the Super Bowl ecosystem is no longer defined by a 30-second national spot; it’s about the strategic intersection of content and timing. Whether through digital takeovers, local broadcasts, or streaming-only packages, brands can activate across the entire event timeline. These levers allow brands to capture the cultural spotlight through a tailored fit without the national broadcast price tag.“

–Rachel Costanzo Senior Director, Media Strategy & Operations, Tinuiti

These aren’t backup options; they’re smart, right-sized Super Bowl marketing strategies that match where your brand is and how much risk you’re willing to take on.

Are you actually ready for the Super Bowl?

Not every brand should be investing in Super Bowl marketing this year. Being honest about that is one of the most important C-suite calls you can make.

The following readiness framework outlines how different brand situations should approach the Super Bowl and the non-negotiable foundations required before moving forward.

Two hard truths sit under this:

• If you don’t know exactly who your customer is and why they buy, you’re likely years away from the Super Bowl being your highest-ROI move.
• e.l.f.’s breakout wasn’t just about a clever spot; it rested on years of reading community comments, saturating Gen Z, and sharpening its brand voice before ever investing in the Super Bowl.

“The Super Bowl is not something I would ever recommend for a brand-new company just starting out. It is an incredible way to take a brand to the next level, but only once that brand has already done a good job building success with its core audience.

You have to ‘lay the logs’ and saturate your niche first so that when you finally light the match at the Super Bowl, the fire actually takes off. If a brand doesn’t even know who their audience is yet, they have years to go before this is the right move.“

–Meg Crowley Group Director, Client Partner, Tinuiti

Poppi followed a similarly disciplined arc. The brand didn’t wake up and decide to buy a Super Bowl spot; planning began during the prior May upfronts, with a TV ramp-up through the NFL playoffs and a clear view of how the game-day moment would sit atop retail and streaming integrations. If your team can’t sketch that kind of runway, your best move may be to build the foundation now and treat the Super Bowl as a future accelerator rather than a near-term fix.

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Creative that actually works on this stage

Super Bowl viewers reward sharp, on-brand creative, while exposing anything that isn’t. Two principles matter most: platform fit and funnel match.

Platform endemicity

Your creative has to feel native to where it runs:

• On TikTok and short-form social, people expect fast hooks, trend awareness, and content that feels like it belongs in their feed.
• On TV, CTV, and long-form YouTube, viewers tolerate (and expect) more produced storytelling and clearer branding.
• In retail media and commerce environments, shoppers are in a buying mindset; creatives should be product- and payoff-driven, with a clear path to purchase.

For leadership, this comes down to a budgeting decision. If the idea doesn’t fit the channel, you end up paying for impressions that are far less likely to drive meaningful brand impact.

“We encourage brands to throw out the ‘all or nothing’ playbook of the past.’ Today, the Super Bowl is approachable for all brands because the content waterfalls into every channel. It’s less about being  in the game itself and more about knowing your audience and finding the right pathway to be interwoven with the cultural moment.”

–Rachel Costanzo Senior Director, Media Strategy & Operations, Tinuiti

Funnel alignment

Match your story to where the customer is in the journey:

• Upper-funnel: Super Bowl spots and broad CTV should focus on brand storytelling and distinctiveness—what you stand for and why you belong in the conversation.
• Mid-funnel: Supporting video, display, and social can lean harder into product demonstrations, reasons to believe, and proof points.
• Conversion-ready: Retail media, search, and lower-funnel social need urgency and clarity. The job here is to remove friction, not add another twist to the story.

Two common failure modes are worth flagging:

• Sea of sameness. In categories like beauty, brands sometimes lean so hard into trends that their ads become interchangeable; if viewers can’t tell whether they saw you or a rival, you’ve effectively funded category awareness, not your own brand.
• Brand dissonance. When a brand chases a trend that doesn’t fit its identity, for example, a heritage luxury brand suddenly mimicking loud meme culture, it may grab attention. Still, it undercuts the story it has spent years building.

“The biggest misses happen when brands deviate too far from the ethos of who they are just to jump onto a trend that isn’t authentic to them. In a ‘sea of sameness’ where so many ads look alike, you lose the ability to stand out if your creative doesn’t tie back to your brand truth. You have to know who you are and why you’re unique well in advance of the ad buy, or you risk eroding your ability to actually connect with the audience.”

–Meg Crowley Group Director, Client Partner, Tinuiti

The Super Bowl magnifies both what’s working and what isn’t. It will reward a brand that knows exactly what it stands for and can translate that into a clear, memorable story, and it will just as quickly expose any confusion or drift in the narrative.

It’s not the moment to reinvent your brand identity or chase a trend that doesn’t fit. It’s the moment to show up as the sharpest version of your brand, grounded in the brand’s identity, aligned with your audience, and crystal clear about why your brand deserves a place in the conversation.

How to measure the success of a Super Bowl advertising strategy

For Super Bowl marketing to be investable, you need more than buzz; you need a clear framework for how success will be defined, tracked, and reported back to the business. That starts with setting expectations at the leadership level, so everyone is aligned on what good looks like before the spot ever airs.

1. Set expectations with leadership

Stakeholders often imagine instant website crashes or overnight sell-outs as the sign that a Super Bowl campaign worked. Those spikes still happen, but they’re no longer the norm, and they’re not the best way to judge success.

In practice, the healthiest outcomes look more like a structural step up: brand and business metrics settle at a higher floor after the halo fades.

“Any media spend can be wasted if you aren’t measuring it correctly. The biggest risk is committing to a high-impact moment without a strong measurement solution in place to track the actual impact of that spend. Without that framework, you’ll never know your actual return, and you’re essentially just throwing money into the void.”

– Laura Ross Director, Client Partner, Tinuiti

2. Choose the right KPIs

A practical Super Bowl scorecard includes:

Brand awareness and consideration

Track shifts in aided and unaided awareness, familiarity, and purchase consideration. Poppi, for example, saw almost a 13-point lift in brand awareness over two months after its Super Bowl spot, which is exceptional for an established brand.

Revenue and ROAS

Many Super Bowl programs are primarily brand plays, but Poppi’s near-1x ROAS on its 60-second spot shows that, with the right cross-channel architecture, even an eight-figure line item can defend itself on short-term revenue.

Earned Media Value (EMV)

For brands like e.l.f., EMV is often where the real upside sits—PR coverage, influencer content, and organic social can add up to value that exceeds paid media when community, talent, and culture all align.

Baseline shift

The most crucial question: what does your new normal look like? After e.l.f.’s first Super Bowl activation, the brand saw 57 billion global impressions, #1 brand sentiment among 2023 Big Game spots, and a 64% week-over-week lift in purchase consideration. Power Grip Primer sales jump from one sold every 8 seconds to one every 3.5 seconds, with a halo effect across the business.

3. Connect Super Bowl to your measurement stack

To make Super Bowl marketing accountable, you need to plan how it will be read before you spend:

• Use media mixed modeling (MMM) and BlissPoint-style models to estimate and then validate impact:
  • Rapid MMM for online sales.
  • Geo MMM for in-store.
  • Brand Equity MMM for awareness.
• Ensure your tagging and segmentation can distinguish Super Bowl-related exposure across channels so future models can capture the signal.

“We use Tinuiti’s MMM tools—specifically Rapid MMM for online sales, Geo MMM for in-store tracking, and Equity MMM for brand recognition—to see exactly which channels are driving the highest impact. 

Planning the Super Bowl further in advance and centering other efforts around the spot using this data makes the investment significantly less stressful and far more trackable. It allows us to create a feedback loop where we can confidently prove that a major bet, like an $11 million spot, actually delivered a noticeably stronger performance, achieving a 1x ROAS alongside a massive 13-point jump in brand awareness.”

– Kenny Bianchi Director, Client Partner, Tinuiti

Poppi’s 2025 campaign is a good example of this discipline: the team used MMM to calibrate channel investments, and the data clearly showed both short-term ROAS and long-term awareness lift.

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The C-suite checklist for a Super Bowl runaway win

When Tinuiti works with executive teams on Super Bowl strategy, the conversation usually centers on a few core questions.

Strategic fit

• Does this Super Bowl marketing plan clearly support our three- to five-year growth strategy, not just fill a spot on this year’s calendar?
• Are we confident we actually need an in-game spot—or would a surround-sound moment approach be a more innovative use of budget for where the brand is today?

Audience and brand clarity

• Can we say, in one sentence, who this campaign is for and what truth about our brand we’re putting on the biggest stage?
• Have we done the work to make sure we’re not just another face in a “sea of sameness” in our category?

Orchestration

• Do we have a clear cross-channel plan—tease, game day, post-game, and sustain—with defined roles for TV, CTV, social, creators, search, and retail media?
• If we do have a Super Bowl spot, is it acting as the crown jewel of that system rather than the whole show?

Measurement

• Are we set up to present this moment in MMM, brand-lift, and EMV reporting in a way that will withstand scrutiny from finance and the board?
• Do we have agreed-upon success ranges and a plan for how we’ll reinvest if we outperform expectations?

Risk and resilience

• Have we stress-tested the creative for potential misalignment with our brand or with cultural flashpoints that could distract from the story we want to tell?
• Are we clear on what we will not do, even if a last-minute trend tempts us to chase short-term attention at the expense of long-term equity?

Turning the Super Bowl from a bet to a growth lever

The Super Bowl remains one of the last true monocultural moments. It is a chance to put your brand in front of an audience you can’t replicate anywhere else.

But the real advantage doesn’t come from buying 30 second spots; it comes from treating the game as the center of a strategy that’s grounded in the basics you’ve just worked through: knowing whether you’re ready, choosing the right level of participation, building surround-sound media, getting the creative and timing right, and wiring the whole thing into your measurement stack.

When those pieces are in place, the Super Bowl stops being a high-stakes one-off and starts functioning like any other smart growth investment: it has a clear role in your multi-year plan, defined success ranges, and a path from awareness to revenue that your finance team can see and your board can understand.

That’s the core shift this playbook is designed to make: The shift away from “Do we dare?” to “How do we use this moment, at the right scale, to drive durable results for the brand?”

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Measurement

How Do Ghost Ads Measure Ad Performance?

By Liv Smith Dec 15 2025

The Skinny: Ghost Ads are a cost-effective method for incrementality testing that allows advertisers to measure an ad’s true impact without actually paying for control group impressions. By logging when an ad would have been served in an auction but withholding it, the platform creates a clean comparison between those exposed to the ad and a perfectly matched control group that saw organic content instead. This approach avoids the wasted spend of traditional Public Service Announcement (PSA) tests while providing more precise data than basic “intent-to-treat” models.

Imagine trying to determine which dog in a litter is the smartest.

• Dog A learned how to sit in two days
• Dog B learned in a week
• Dog C learned in two weeks.

Seems simple at first, but now what if Dog A is polite with older dogs and Dog C is the only one that learned how to fetch? Things get a little more complicated.

Marketing faces the same challenge. When we’re trying to determine if an ad campaign was effective, we need to keep in mind all of our other campaigns running in the background. If you didn’t buy that ad, would your target customer have bought your product anyway?

That is the power of incrementality testing. It identifies what advertising truly delivers beyond what would have happened naturally. But there are quite a few ways to measure incrementality, and Ghost Ads are one of the most effective ways of putting this concept into action.

What Are Ghost Ads?

Ghost Ads, also known as Ghost Bidding, is a method for incrementality testing that enables advertisers to identify the audience in the control group without serving them an ad. In environments where available, the platform logs instances when a consumer would have seen an ad but didn’t receive it. This creates a control group that can be analyzed directly against a test group that received the ad. 

How Ghost Ads Work

Historically, the most common incrementality experiments have been Public Service Announcements (PSA) holdout tests and “intent-to-treat” approaches. Both are described in the academic paper Ghost Ads: Improving the Economics of Measuring Ad Effectiveness by Johnson, Lewis, and Nubbemeyer.

For PSA Tests, imagine giving one friend group tickets to see a Boston Celtics basketball game and another friend group tickets to a workplace safety seminar. It’s nothing personal, you just selected these people at random because they’re technically part of your “audience.”

While both groups watched something, only the group that got to see the basketball game had the opportunity to interact with and experience the Celtics brand. Later in the season, you could quiz your friends on their Celtics knowledge or see if they attended more games afterward. By comparing each group’s recall and subsequent spend, we can isolate the impact attending the game had. However, you still had to spend money to send your friends to the workplace safety seminar, so there’s some opportunity cost and actual cost associated with this method.

For intent-to-treat, imagine sending five puppies to obedience school and leaving the other five with their owners at home with no professional training. Then, we compare their temperament to a control group of random dogs we found in the neighborhood to see if obedience class improves their overall behavior. The problem here is that some of the dogs left at home might have a chance to pick up skills from other dogs or were trained by owners. This makes it harder to measure the value of obedience school.  

Ghost Ads solve for these problems by recording when an ad would have been served in an auction but was withheld. This way, it’s like a PSA test, except we never had to pay to send out an advertisement. And it’s a bit like intent-to-treat, except we’re able to compare dogs that went to obedience school with dogs that almost went, but the school turned them away at the door for absolutely no reason. Because the groups are identical except for that last-second random chance, we can get a much more accurate view of user behavior while minimizing wasted spend.

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What Are the Benefits of Ghost Ads?

Ghost Ads provide a balance of efficiency and precision. There is no need to waste the budget on control impressions, and the lift can be measured against real market conditions since the control group mirrors the test group. Ultimately, this enables advertisers to understand what matters most clearly and concisely. Think of it like watching the Celtics in the playoffs rather than preseason scrimmages, or testing which puppy learns the sit command fastest by comparing them in the same household.

What Are Common Challenges When Deploying Ghost Ads?

Unfortunately, Ghost Ads are still not a perfect solution. Digital auctions are complex and constantly evolving, requiring periodic review and updates. In addition, some opportunity cost exists because part of the audience is held in the control. The group that never gets the tickets to see the basketball game misses out on the opportunity to interact with the Celtics. Jonson et al. highlight an additional challenge within their paper regarding ad formats. In scenarios where multiple ad formats compete in the same auction, the added complexity can make it difficult to isolate the actual impact. 

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Getting Started with Ghost Ads

With all the nuances of incrementality testing, it helps to have a structured game plan. Tinuiti lays out a five-step approach in the Incrementality Playbook that applies directly to Ghost Ads: 

1. Map your blind spots: Look across campaigns and ask, “Where are we making decisions without clear evidence or where platform data doesn’t tell the full story?” Without testing, you cannot know if a puppy got to the bowl first because it was the hungriest or simply the quickest. 
2. Identify testable opportunities: Ghost Ads are not available on every channel. Start with platforms that support user-level holdouts. 
3. Choose a design and commit to rigor: Set expectations early. Agreeing up front avoids the equivalent of a family debate over who is the cat’s favorite based on a head scratch here or a leg rub there, where every moment tells a different story. 
4. Build testing into the rhythm: Treat Ghost Ads as a routine part of planning, not a one-off project. The real value comes when tests accumulate over time, creating a baseline. Similar to the way the Celtics team runs plays, consistency turns individual moments into a winning performance. 
5. Don’t go it alone: Setting up correctly can be highly technical. Working with experts who have run these tests before ensures that the priority remains on insights rather than troubleshooting. 

Think of Ghost Ads as a feedback loop that sharpens with every cycle, helping you cut through noise and double down on what truly drives growth. 

Conclusion

Ghost Ads give advertisers a clearer and more cost-effective way to measure incrementality. By eliminating wasted impressions, creating realistic control groups, and cutting through noise, they reveal what advertising is actually driving outcomes. 

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BFCM Marketing Statistics: Ad Spend Trends for Black Friday & Cyber Monday

Andy Taylor VP, Research

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Ecommerce

BFCM Marketing Statistics: Ad Spend Trends for Black Friday & Cyber Monday

By Andy Taylor Dec 05 2025

This post was coauthored by Mark Ballard and Andy Taylor.

The Skinny: This post analyzes the 2025 Black Friday and Cyber Monday landscape, highlighting a shift toward a full-funnel, multi-channel strategy where the holiday shopping window now begins well before Thanksgiving. Key data from Tinuiti reveals record-breaking growth in Amazon DSP (up 34%) and Prime Video ads (up 650%), alongside a surge in YouTube engagement on TV screens and the dominance of AI-powered tools like Meta’s Advantage+ and TikTok’s Smart+ campaigns.

As marketers know all too well, Black Friday and Cyber Monday are now full-funnel, multi-channel, multi-screen events that require a comprehensive strategy to maximize effectively. Not only that, but the period that brands need to consider as part of this important stretch now starts well before the turkey gets carved on Thanksgiving Day.

Looking at same-store samples from the more than $4 billion in annual ad spend under management at Tinuiti, we’ve uncovered high-level takeaways from across the digital landscape to help inform the rest of your Q4. Here you’ll find data-backed insights across search, social, display, streaming, and commerce media.

Overview: The Top Black Friday & Cyber Monday (BCFM) Trends of 2025

We dove deep into the ad spend trends that defined one of the biggest retail events of the year. During our research, we found a few standout trends regarding where advertisers invested their ad dollars this year:

• Brands continue to see strong growth in sales attributed to Amazon ads, and are leaning into the DSP to help further reach shoppers during key holiday shopping days
• Walmart advertising provides brands with a massive opportunity to influence not only digital but also in-store purchases
• While AI platforms might be growing in popularity, there’s still massive opportunity that’s only growing on traditional search engines like Google to reach holiday shoppers
• YouTube is a golden opportunity to reach shoppers, not only on laptops and phones but also on TV screens
• Meta advertisers continue to benefit from newer placements and tools like Reels and Advantage+
• TikTok spend is rising as advertisers are more confident in TikTok’s staying power in the US, particularly in the rush to reach holiday shoppers
• Brands are leaning into Reddit for its unique audience and pipeline to AI citations

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How Did the Holiday Weekend Impact Amazon Sponsored Products Sales Growth?

This year, we saw Amazon Sponsored Products sales surge between Thanksgiving and Cyber Monday. In 2024, Amazon pulled its Black Friday deal messaging out a full week before Thanksgiving, resulting in advertisers seeing the strongest growth in sales attributed to ads in the week before Thanksgiving and slower growth over the weekend and into Cyber Monday. That trend was reversed in 2025, with sales attributed to Sponsored Products growing more than 30% from Saturday through Monday. Sales growth from the week before Thanksgiving through Black Friday was still strong, but slower than what was observed from Saturday onward.

This reversal can be attributed at least partially to the strength of year-ago comparisons, with days that saw stronger growth last year slowing in 2025 and vice versa. The two-year average growth of every period from the week before Thanksgiving through Cyber Monday was between 22% and 28%, as shoppers have ramped up ad-attributed buying on Amazon throughout the nearly two-week period.

Beyond the Ad Console, advertisers also leaned into the Amazon DSP to reach shoppers during this important stretch of holiday shopping.

How Much Did Amazon DSP Investment Grow Year Over Year?

Brands advertising through the Amazon demand-side platform ramped up spending 34% year over year, with a 40% increase in impressions and 4% decline in CPM. Advertisers use the Amazon DSP to secure placements both on Amazon’s app/website as well as across the web.

Amazon DSP spend growth has outpaced that of Ad Console campaign types for the last several years, and brands currently active on the DSP and Ad Console spent 31% of all Amazon investment on the DSP during the Cyber Five period.

Helping to drive up DSP spend has been the popularity of Prime Video ads, which have been adopted by both endemic and non-endemic advertisers alike. Across all advertisers, including those that didn’t advertise on Prime Video last year, investment in Prime Video ads jumped an astounding 650% year over year for the period between Thanksgiving and Cyber Monday.

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How Did Clicks and CPC Trend for Walmart Advertisers Ahead of Black Friday?

Clicks on Walmart Sponsored Products ads started to really pick up in the middle of November, and over the week before Thanksgiving averaged 63% higher than November 1. Volume dipped temporarily on Thanksgiving before surging into the weekend, with Sunday and Monday seeing more than 175% more clicks than November 1.

The cost of ad clicks rose during the crucial period between Thanksgiving and Cyber Monday as competition in ad auctions heated up, with daily CPC averaging 12% higher over that five day stretch than the beginning of November.

How Did Average Order Value Trend for Google Advertisers on Black Friday and Cyber Monday?

Retailer sales generated by Google search ads, including Performance Max, standard Shopping, and text ad campaigns, were up 8% year over year to start the fourth quarter, but growth accelerated into November and the run up to the Cyber Five period.

Retail Google search ads sales were up 15% year over year in early November and 13% during the week before Thanksgiving. That rate jumped to 17% on Thanksgiving itself before sliding to 12% on Black Friday. While Black Friday and Cyber Monday remain the biggest sales days of the period, customers have come to expect holiday deals to last throughout Cyber Week, if not beyond, and have spread out their purchases as a result.

Cyber Saturday through Monday saw particularly strong order growth for Google search ads with retailer orders up 17% on Saturday, 18% on Sunday, and 15% on Monday. All told, retail sales from Google search ads were up 20% on Saturday, 17% on Sunday, and 21% on Monday.

In general, the average order value for Google search ads has also run higher year over year since early November, with Cyber Sunday as the lone exception during the Cyber Five.

The average order value from Google search ads was up by an average of 4% year over year for retailers from Thanksgiving through Cyber Monday in 2025. In 2024, AOV fell for most of the Cyber Five, with an average decline of 1% year over year.

How Did Key Players like Amazon, Walmart, Temu, and Shein Approach Google Shopping Auctions?

Amazon remained notably absent from Google shopping ad listings during the Cyber Five, having withdrawn from Google shopping auctions in late July. Amazon had paused its Google shopping ads a few times in previous years, including during the early days of the pandemic, but this current break is now Amazon’s longest.

Temu and Shein also paused their Google shopping ads earlier in 2025, as tariff rates on Chinese goods skyrocketed and the de minimis exception on those goods was set to expire, but both have been active throughout Q4.

Zooming in on just the past month of results, it doesn’t appear that Temu, Shein, or Walmart or Target, for that matter, made a particularly big push to fill the gap Amazon left in Google’s shopping listings during the Cyber Five though. All saw their share of Google shopping impressions running at least slightly lower from Thanksgiving through Cyber Monday than it did over the first week of November.

Which Device Types Were the Most Important for YouTube Advertisers?

Retailer spending on YouTube ads was up 27% year over year during the Cyber Five period from Thanksgiving through Cyber Monday, with impressions up 35% and average CPM falling 6%.

Particularly for retailers, Google Demand Gen campaigns have become a major vehicle for purchasing YouTube inventory as Google began transitioning Video Action Campaigns to Demand Gen campaigns automatically in July 2025.

The difference in use between Demand Gen and traditional video campaigns for YouTube inventory (more action vs. more awareness) is reflected in the devices generating spending for each:

During the Cyber Five, TV screens accounted for 61% of retailer YouTube spending through video campaigns, but just 8% of YouTube spending through Demand Gen campaigns. On the flip side, phones accounted for 80% of Demand Gen spending on YouTube inventory, but just 21% of video campaign spending.

Did Facebook and Instagram CPM Growth Firm Up in the Weekend After Thanksgiving?

In the seven days leading up to Thanksgiving, CPM on Meta was down 13% year over year, with double-digit declines on both Facebook and Instagram. Pricing started to heat up on Thanksgiving, and from Saturday through Monday average daily CPM growth averaged just a 1% decline.

Naturally, not all advertisers saw CPM decline, and CPM rose for 37% of the Meta advertisers studied. With Instagram CPM growth far outpacing that of Facebook, part of what separates those brands that see positive pricing growth from those that see negative is the share of spend attributed to each platform.

CPM is also hugely impacted by the share of impressions coming from different placements, which has changed quite a bit since 2024.

How Much Did Reels Video Ads Impression Share Go Up Compared to Last Year?

Reels ads continue to become a more important part of how Meta advertisers reach Facebook and Instagram users during key holiday shopping days over the years. For the period from Thanksgiving through Cyber Monday, Reels video ads accounted for 28% of all Instagram ad impressions, up from 20% last year and equal to the share of impressions attributed to Feed ads.

Reels video placements also accounted for 15% of Facebook ad impressions, up from 12% last year. This doesn’t include Ads on Facebook Reels, which are banner ads shown on Reels and accounted for 13% of Facebook ad impressions, or in-stream ads for Reels, which accounted for 4% impression share. Neither of these placements are available on Instagram, highlighting even more runway for Reels-related inventory to grow from here.

With Facebook and Instagram Reels CPM lagging that of more mature placements like Feed and Stories, the growth of these ads continues to put downward pressure on pricing.

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Were Advantage+ Shopping Campaigns More or Less Important for Meta Advertisers This Year?

In 2024, Advantage+ shopping campaigns accounted for 26% of all Meta retail and ecommerce spend between Thanksgiving and Cyber Monday. That share rose to 33% in 2025, as advertisers continue to lean into the AI-powered campaign type that streamlines some aspects of campaign optimization.

The 33% share of retail spend attributed to ASC is also an uptick from the 27% share observed in Q3 2025. AI-powered campaign types are also becoming a bigger part of how advertisers reach shoppers on other social platforms like TikTok.

Have Advertisers Finally Shaken Off Concerns Over TikTok’s Future in the US?

The median advertiser active on TikTok both this year and last year spent 22% more year over year during the period between Thanksgiving and Cyber Monday. This marks a significant rebound from the spend growth observed in recent quarters, as advertisers were hesitant to spend big on the platform earlier in 2025 amid concerns about its future in the US.

Notably, CPM fell just 2% year over year over the course of the Cyber Five, a significant rebound from the 22% decline observed in both the second and third quarters.

TikTok advertisers are increasingly using Smart+ campaigns, similar to Meta’s Advantage+ campaigns, to drive performance on the platform. In the third quarter, Smart+ accounted for 42% of all performance TikTok investment for Tinuiti advertisers.

Why Did Reddit Spend Surge From Thanksgiving through Cyber Monday?

The median same-store Reddit advertiser increased spend on the platform by 248% over the course of the Cyber Five, with a 206% increase in impressions and 14% increase in CPM. This represents a massive acceleration from the already strong 55% and 40% spend growth rates observed in the second and third quarters of 2025.

Advertiser interest in Reddit is strong not only for the incremental reach it has with audiences that may not be active on other social platforms, but also for its inclusion in AI citations. Tinuiti’s 2025 Holiday Shopping Trends study found that 58% of holiday shoppers expect to use at least one AI-powered tool for holiday-shopping-related tasks.

Conclusion

While Black Friday and Cyber Monday are officially over, there are still plenty of opportunities left for marketers looking to make the most of the holiday shopping season. See how your brand stacks up to the trends outlined above and use those learnings to end 2025 on a high note while setting the stage for success in 2026. If you’re looking for more customized advice for your brand, don’t hesitate to contact us today.

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Paid Media Updates

Amazon vs. AI – Why the Perplexity Lawsuit is a Warning Shot for Agentic Commerce

By Tinuiti Innovation & Growth Team Dec 03 2025

What’s in store

1. Featured story: Old Guards, New Gods – the Carve-Up of WBD
2. Our Take On the News
3. Helpful Links & Resources

Old Guards, New Gods – the Carve-Up of WBD

Warner Bros. Discovery putting itself up for sale is less about one company and more about a phase change in the entire media ecosystem. A few years ago, WBD was the consolidator – the roll-up of major legacy players WarnerMedia and Discovery meant to stand against Netflix and Big Tech. Now it’s the asset on the auction block, with suitors that neatly capture the old-to-new spectrum: NBCUniversal, Skydance Paramount, and Netflix.

From broadcast plumbing to software stacks

The old TV world was built on scarcity and carriage: limited channels, bundled into cable packages, with affiliate fees and upfronts as the economic engine. Distribution was physical (via antennas or cables) and local. Measurement was panel-based. “Owning the pipe” mattered.

Streaming flipped that. The new game is global software platforms with infinite shelf space, personalized feeds, and direct relationships with hundreds of millions of users. Recommendation algorithms, identity graphs, and cloud infrastructure matter as much as writers’ rooms and soundstages.

That’s why WBD’s potential buyers care most about studios and streaming (HBO Max), not the cable networks. Studios are IP factories that can feed any platform. Streaming apps are software front ends for attention and data. Cable channels are now more like declining annuities: still cash-generative, but clearly in runoff.

The innovator’s dilemma, scripted in real time

Legacy media didn’t ignore streaming, they were simply unable to execute an extremely high-degree-of-difficulty pivot.

The linear business threw off reliable cash: affiliate fees, high-margin ad sales, and predictable audience reach. Every dollar aggressively moved into streaming risked cannibalizing that. So the big companies hedged – launching streaming products but protecting linear economics as long as possible.

Meanwhile, Netflix (and later YouTube, Amazon, etc.) had no legacy assets to protect. They could lean fully into:

• Global scale from day one
• All-in on subscriptions, then ad tiers
• Absence of any distribution conflicts
• Relentless experimentation on product and personalization

Now we’re in the predictable endgame of that dilemma: legacy players are being forced to consolidate just to reach the same scale the disruptors have already built organically. WBD as a seller just a couple of years after merging is a perfect illustration of how fast that logic culminated.

Consolidation and the rebundling of everything

Whether the buyer is NBCU, Skydance Paramount, or Netflix, the strategic throughline is the same: more scale, more library, more leverage.

• A Paramount–WBD combo would look like a classic legacy super-stack: two major studios, multiple marquee brands, and a shot at a more compelling single streaming offering (plus a giant cost-synergy hunt).
• NBCU + WBD would create another mega-portfolio that can rebundle linear, streaming, and sports rights into something closer to the old cable experience – but delivered via apps instead of set-top boxes.
• Netflix + WBD would be the purest “new world” play: a dominant global streaming OS absorbing one of Hollywood’s deepest libraries.

For consumers, this all trends toward a new version of the cable bundle – fewer, bigger platforms packaging entertainment, sports, and maybe even gaming in tiered bundles. The names change, but the logic rhymes: simplify choice, lock in households, and smooth churn.

For advertisers, consolidation means fewer, larger partners with richer data – and less negotiating power on the buy side. Big platforms will sell against cross-portfolio reach, advanced targeting, and clean-room measurement, not just individual networks or shows.

Broadcast vs streaming: different worlds for viewers and brands

In broadcast, viewers got:

• Appointment viewing and mass cultural moments
• A finite number of choices, with clear “prime time”
• Mostly one-size-fits-all ad breaks, with broad demo targeting

In streaming, viewers get:

• On-demand, algorithmically sequenced feeds
• An infinite array of choices, on-demand and spanning niches and formats
• Personally targeted advertising much more akin to modern digital platforms

For advertisers, the trade has shifted from reach first to identity and outcomes first:

• GRPs and dayparts are giving way to audience segments, modeled signals, and performance KPIs
• Instead of buying “Thursday at 9 on a top network,” brands are buying incremental reach against a precise audience, often optimized by machine learning in real time
• Measurement is migrating from mixed-model and panel-only to multi-signal, multi-touch views stitched through clean rooms and partner APIs – with privacy rules and signal loss adding complexity at every step

The WBD sale is really about who can best operate in that software-and-signals paradigm, not who owns the most cable networks.

Where this is heading

Zooming out, a few things feel likely:

• We end up with a small handful of global video “operating systems” (Netflix, YouTube, Amazon, plus one or two legacy-born hybrids) and a long tail of niche and regional services.
• Traditional media companies that survive at scale will either bulk up through deals (like a WBD sale) or lean into being IP engines and production houses (like Sony), selling into whichever platforms win distribution.
• For advertisers, the center of gravity keeps shifting toward data-rich, commerce-adjacent environments – think shoppable streaming, retail media overlays, and closed-loop attribution – rather than pure awareness plays in linear.
  

Warner Bros. Discovery started this chapter as a would-be consolidator and is likely to exit it as raw material in someone else’s platform strategy. That’s the story in a nutshell: in the new era, content is still king, but the crown is now worn by whoever owns the software, the data, and the customer relationship.  |  WSJ 

What We’re Tracking

The news stories we’re tracking that are likely to impact advertisers in the month ahead.

U.S. Economy & Consumers

1. US holiday shoppers are still showing up — just a bit more cautiously. Black Friday sales rose 4.1% YoY (ex-auto) according to Mastercard, with in-store up 1.7% and online up 10.4%. Consumers are clearly still willing to spend, but they’re trading down and chasing value, responding to broad (if slightly less aggressive) discounting and cheaper gift options like $10 throws and $5 toys. Teen and young adult retailers, big-box promos, and home categories (decor, tools) were notable winners.

Cyber Monday remained the digital tentpole, but with a twist. US online growth lagged Europe, as tariff worries and macro jitters weighed on Americans while EU shoppers benefited from rate cuts. Salesforce saw mid-day US sales up 2.6% vs 5.3% globally, though Adobe tracked a stronger +4.5% YoY to $9.1B by early evening. Shoppers who waited were rewarded: Cyber Monday discount depth (31%) beat Black Friday (28%), especially on big-ticket items — except gaming consoles, where tariffs kept deals scarce.  |  Bloomberg, Bloomberg  

2. US consumers are heading into the holidays in a decidedly wary mood. Shutdown-delayed data show retail sales in September up just 0.2%, with tariffs cooling demand for vehicles, electronics, and apparel even as spending held up in bars, restaurants, and value channels. At the same time, consumer confidence dropped sharply in November: the Conference Board index fell to 88.7, while the University of Michigan gauge slid to one of its lowest readings on record, with views on personal finances the weakest since 2009.

Under the hood, the story is bifurcated. Higher-income households, buoyed by asset markets, are still spending, but lower earners are clearly under strain, trading down and flocking to off-price and discount retailers. Labor market signals are flashing yellow — layoffs are rising, jobless claims are at a four-year high, and fewer people see jobs as ‘plentiful.’ Add in sticky inflation and fading income expectations, and you get a holiday shopper who’s still buying, but more cautious and promotion-driven.  |  WSJ, Bloomberg, Bloomberg   

3. The US labor market is cooling, but not collapsing. Shutdown-delayed data show September payrolls up 119,000, more than double forecasts, but the jobless rate ticked up to 4.4%, a four-year high as nearly half-a-million people re-entered the labor force.

The catch: job growth is increasingly narrow. Healthcare, education, and leisure & hospitality are doing most of the hiring, while sectors like transportation, warehousing, and temp work are shedding jobs.

Under the surface, stress is building. Continuing jobless claims hit their highest level since 2021, WARN layoff notices surged to near-record territory, and private data show companies cutting thousands of roles per week. Job security worries are spreading, with more than half of workers concerned about losing their job.

For the Fed, it’s a Rorschach test: hawks see resilient job gains; doves see rising unemployment and weakening breadth—all based on stale data heading into the December meeting. Prediction markets expect a quarter-point rate cut at the next Fed meeting.  |  WSJ, NYT, Bloomberg, Bloomberg, Bloomberg     

Tech Giants & Platforms

1. Google is rolling Nano Banana Pro, its upgraded image-generation model, directly into Google Ads, including Performance Max and the broader display network. The big unlock for marketers: far better text fidelity in images—so copy on posters, product labels, and UI mockups no longer melts into AI gibberish after a few prompt tweaks.

The model also supports higher-resolution output (2K and 4K), making it more viable for premium digital placements and even some print use cases. Creatives can expect cleaner visuals and easier prompting, reducing the number of iterations needed to get client-ready assets.

Nano Banana has already built a reputation in the creator community for slick, on-brand imagery, competing with tools like Midjourney, DALL·E, Stable Diffusion, and Firefly. Now, tied into Google’s Gemini 3 ecosystem and native to its ads stack, it’s positioned to speed up asset production while raising the floor on AI creative quality—funny name, serious upgrade.  |  AdAge 

2. Walmart is quietly testing ads inside its AI shopping agent, Sparky, signaling how agentic commerce and chat UX could become ad-supported surfaces. The new unit, ‘Sponsored Prompt,’ appears on Walmart.com; when a user clicks (e.g., “What energy drink has the most caffeine?”), Sparky answers and a click-to-buy product ad renders directly beneath.

While engagement volumes in the test have been low, advertiser interest is high as brands race to understand what ad experiences will look like in conversational environments. As my Tinuiti colleague Simon Poulton notes, marketers are eager to see how agentic shopping will influence purchase behavior and performance.

Strategically, this fits Walmart’s broader AI and retail media push, including Sparky in the app, internal agents for associates/suppliers, and a new OpenAI tie-in that will let ChatGPT users buy Walmart products. With Amazon’s Rufus already live with sponsored prompts, AI assistants are fast becoming the next battleground for retail media dollars.  |  WSJ 

3. Reddit posted a breakout Q3, with revenue up 68% YoY to $585M and ad revenue up 74%, driven by both 19% DAU growth and ARPU up 41% globally / 54% in the US. The company beat on both top and bottom line and guided Q4 revenue to $655–$665M, signaling confidence in continued momentum.

On product, Reddit is leaning hard into search and AI: over 75M people search Reddit weekly, Reddit Answers is now integrated into core search (including non-English), and growth priorities center on app users, personalization, and international expansion.

For advertisers, the story is performance and automation. Reddit is scaling DPA, app-install optimization, CAPI adoption (3x YoY), auto-bidding and auto-targeting, with an end-to-end automation suite and SAN-style measurement on the roadmap. The platform is still mostly managed-service, but improving automation and shopping tools point toward more scalable lower-funnel performance — without simply cranking ad load.  |  Mobile Dev Memo 

4. Meta just got a major legal win at the exact moment its core product is driving some marketers up the wall. A federal court rejected the FTC’s antitrust case, ruling that Meta is not a social networking monopolist given “fierce competition” from TikTok and YouTube, especially in short-form video.

Yet on the ground, media buyers are living through continued challenges on Meta’s ad platform. Marketers report budget mispacing, erratic targeting, and glitchy AI creative, including warped visuals and auto-enabled promo features. For many, there’s no reliable path to human support, even for large-spend accounts, as automation and chatbots have replaced most reps.

The result is a sharp contrast: in court, Meta is framed as a disciplined competitor in a dynamic market; in Ads Manager, it can feel like an uncontested dependency on AI solutions. And despite the frustration, most advertisers still have little choice but to lean heavily on Meta in Q4 given the historic returns the platform drives for them.  |  CNBC, AdExchanger  

Media & Advertising

1. TV viewing numbers for October are out, and they underscore the power of the NFL in deciding the fates of media properties across platforms. Beginning at the high level, streaming took ~46% of October viewing while broadcast and cable TV each took 22% – 23%:

While overall TV usage increased by 1.3% from September, streaming outpaced at +2.4%. As we’ve detailed at length, streaming platforms no longer suffer reduced share when major sports are in season: most of the major streaming platforms are now home to those sports, and thus benefit from the trends that have traditionally boosted TV viewing. The streaming share picture remains quite stable, with Youtube at ~13% and Netflix at ~8% of the streaming market.  |  Nielsen Gauge 

2. We told you back in October that, beginning with the 2026 season, Formula One will have a new US home on Apple TV. The technology giant paid a premium for the broadcast rights, with plans to “amplify the sport across Apple News, Apple Maps, Apple Music, Apple Sports, and Apple Fitness+.” We expressed some caution when the deal was announced, reflecting on the arc of Major League Soccer:

There are two pieces of good news – first, Apple has learned its lesson and will not repeat its paywall mistake with F1: access to all live F1 content will be included with a standard Apple TV subscription, and select races and practices will be available for free within the Apple TV app. In even better news for F1 fanatics, the much-loved F1 TV app will be included with an Apple TV subscription. The second bit of good news is that Apple is eliminating MLS Season Pass next year, meaning MLS will no longer be behind a (secondary) paywall and will be available in full to all Apple TV subscribers.

When you decide to pay a lot for broadcast rights, it is unhelpful to limit the audience for the product. This is why there are so few cases of major sports existing on ad-free platforms. Now that Apple has taken this lesson, it just needs to introduce an ad-supported tier …  |  @JoePompliano, @MLSMoves

3. Speaking of Formula One, its media momentum in the US continues – the Las Vegas Grand Prix averaged ~1.5m viewers, a 70% increase from 2024. This year’s viewing was undoubtedly aided by a more viewer-friendly start time: the race began at 8pm Pacific, whereas the past two years saw the Vegas GP start at 10pm in order to make it more watchable for European audiences. (your humble correspondent was in attendance this year and in 2023, and can confirm 8pm is far better!)

Fourteen of 22 races this season have set a new viewership high, and the season average of 1.3 million is on pace to surpass the record of 1.2 million set in 2022.  |  SMW

4. Continuing with the theme of people-enjoy-watching-sports, NBA viewership for the new season is up 30% over last year, reaching its largest opening-month audiences since 2017. The NBA is of course on more platforms this year with the beginning of a new broadcast rights cycle, meaning games are now spread across ESPN, NBCUniversal, and Amazon Prime. While distribution fracturing is a genuine annoyance to fans, and can have the effect of depressing viewership, that doesn’t appear to be happening in the NBA’s case. Expect other leagues to take notice when their broadcast rights come up for renewal.  |  SMW 

Helpful Links & Resources

• Paramount has secured the broadcast rights to the UEFA Champions League, the world’s premier soccer competition, from 2027 – 2031 
• Major League Baseball has signed a new broadcast rights deal spanning ESPN, NBCUniversal, and Netflix
• The US ad market expanded by 5.5% YoY in October
• Adobe has acquired Semrush for $2B in a play for the GEO (generative engine optimization) market
• The European Union is seriously considering reform of GDPR, a data protection law that has contributed to Europe being a laggard in internet services and, now, in artificial intelligence

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Ecommerce

How to Geotarget Audiences & Maximize Marketing Impact

By Jenn Wheatley Nov 26 2025

The Skinny: Geotargeting enables brands to align media investments with local consumer behaviors by serving ads based on specific geographic locations, such as zip codes, cities, or custom radii. By integrating location-based data with incremental testing and media mix modeling (MMM), marketers can identify high-performing markets and reallocate budget to maximize ROI while reducing wasted spend.

When it comes to your media, what works in Milwaukee might not work in Miami. That is not just a creative challenge, it is a measurement problem. Brands that plan, target, and measure by geography can see which markets are actually moving the needle, and reallocate budget before they lose momentum.

Geotargeting lets you line up media investment with how people actually live, search, and shop in each city, state, or zip code. When you combine location-based tactics with incremental testing and media mix modeling (MMM), you can see where to push, where to pull back, and where the next best opportunity lives.

What is Geotargeting?

Geotargeting is the practice of creating, targeting, and serving ads based on a user’s geographic location, often layered with other audience signals like demographics, interests, and behaviors. That location can be inferred from signals like IP address, GPS coordinates, Wi-Fi positioning data, or the country, region, or zip code a user shares in a form or profile.

For example, a consumer electronics brand might increase its budget for a specific age range within a tight radius around a convention center during Comic-Con in New York City. At the same time, a landscaping company might pause lawn care ads and shift to snow removal offers as soon as the weather changes in a given metro. In both cases, the brand is using geographic location plus context to match local conditions and intent.

Geofencing vs. geotargeting

Geofencing builds a virtual “fence” around a location—like a football stadium, a concert venue, a museum, or even a single brick-and-mortar store—and reaches people who enter that defined area, usually in real time. It is about capturing anyone who crosses the boundary, with less focus on who they are beyond that moment.

Geotargeting, by contrast, focuses on reaching people in broader areas, such as a neighborhood, city, DMA, or country region, often with additional audience layers like age, income, or interests. Think of geofencing as targeting “anyone who walked into this train station today,” while geotargeting reaches “homeowners in Beacon Hill, Boston who have shown interest in home improvement.”

Why Should Marketers Use Geotargeting Techniques?

Geotargeting gives marketers another way to segment audiences, understand performance, and reduce wasted spend. Instead of making straightforward national media buys, you can see which locations are saturated, which are still under-invested, and which are quietly becoming your highest-value markets.

Segmenting audiences by geography

Geotargeting transforms broad media campaigns into highly localized strategies, providing marketers with actionable visibility and control over market-by-market performance. The following steps highlight how segmenting audiences geographically leads to more precise measurement, smarter allocation, and meaningful business outcomes.

• Target campaigns at the level of zip codes, DMAs, states, or regions to measure the incremental impact of channels that lack user-level precision, such as linear TV or some streaming platforms.
• Use geo experiments and geo-based MMM models to reveal how different markets respond to the same media, creative, or offer without needing individual user IDs.
• Spot saturation and decay trends early; shift budget to emerging markets when a region’s response starts to flatten to avoid overpaying for diminishing returns.
• Over time, build a clear view of which markets and channels consistently deliver the best incremental return on ad spend (ROAS), rather than just counting impressions.

This approach makes the narrative easier to scan and drives home each actionable point.

Better relevance and personalization

Location-based campaigns make it easier to tailor creative, offers, and landing pages to local context, which tends to improve engagement and brand affinity. Retailers and ticketing platforms, for example, often swap team colors, city names, or nearby venues into their content. Hence, the experience feels specific to each fan base.

If a business serves only a given state or country, or if regulations limit where it can sell, geotargeting helps ensure that only qualified audiences see performance-focused ads. This reduces clicks from people who cannot convert while still giving you room to experiment with new local concepts or micro-campaigns.

Better reach with minimal wasted spend

Sometimes, the most profitable customers live in places you haven’t invested in yet. When you add geographic location as a variable in your measurement framework, you can cast a wider net in a structured way: identify top-performing markets, look for adjacent zips or cities with similar profiles, and test controlled expansions.

That same approach works in reverse. Suppose certain locations have consistently high CPA and weak incremental sales. In that case, you can exclude or de-prioritize them, and reinvest those dollars into markets that are more responsive to your messaging and offers. Over time, this tightens the link between local media investment and local results.

How to Geotarget Audiences

Geotargeting works best when it is grounded in the realities of your business model: where you can legally and operationally serve customers, where demand exists today, and where your next growth markets are likely to be. Start by aligning those basics, then layer in channels, data, and measurement.

To get you started, we recommend asking yourself the following questions:

1. Which audiences are best to geotarget?

First, define the locations you can actually serve—by country, state, city, or radius from a business location—and where shipping, service coverage, or regulations might create hard boundaries. A brand that ships homemade cookie gift boxes within 100 miles of each retail store, for example, should build campaigns and exclusions around those specific radii rather than the entire country.

Geotargeting can also help with exclusion logic. Imagine a brewery that wants to retarget out-of-state visitors for online orders after a holiday weekend; it would need to remove states that do not allow alcohol delivery from its campaign targeting, even if those visitors were highly engaged, to avoid wasted impressions and compliance issues.

2. Which channels are best for geotargeting?

Most major ad platforms offer built-in geotargeting tools. Common options include:

• Search engines: Google Ads and Microsoft Ads provide location targeting by country, state, city, postal code, radius, and custom geographic areas. Brands can also target by physical location or places users show interest in.
• Social media platforms: Most social media platforms, including Meta and TikTok, allow you to target cities, zip codes, or regions, as well as use audience controls for behavioral and demographic segmentation. For the B2B marketers in the room, LinkedIn also enables precise location-based ad delivery down to city and zip code.
• Streaming TV and CTV platforms: Streaming services like Hulu, YouTube, and Netflix support geotargeting at the DMA or regional level for greater reach and local relevance. In some cases, streaming platforms can even target by zip code when bought programmatically.
• Out-of-home solutions: Billboards, place-based displays, and transit ads serve geotargeting by selecting locations based on business footprint, commuter routes, events, or high foot traffic.

These channels enable location-based strategies for everything from broad market scaling down to hyper-local engagement in specific neighborhoods or points of interest.

3. What data is needed for geotargeted campaigns?

Effective geotargeting depends on a mix of geographic identifiers and audience data. At minimum, you need to understand the country, state, region, city, DMA, or postal/zip codes you want to reach, along with any locations you need to exclude; that information often comes from your CRM, site analytics, and offline sales data.

From there, you can layer in audience attributes such as age, gender, income band, household type, or interest categories, along with indicators of whether someone likely lives in a place or is just visiting. Many brands also incorporate contextual data, such as weather, time of day, and nearby points of interest, to tailor offers and messaging to the moment.

Measurement platforms can help you build these audiences from your highest-value customers, linking their physical locations to LTV and purchase behavior, and then finding similar audiences in new markets. Platforms like Bliss Point by Tinuiti can connect your first-party data with geographic performance, showing how your top LTV customers are distributed across regions and which markets are over- or under-invested. From there, Geo MMM can surface lookalike markets where your media is likely to work harder, so you can expand with more confidence. A restaurant group, for example, could use a geo-aware model to identify its top 10% of guests and then focus expansion media on neighborhoods that most closely match those profiles.

4. How can marketers test and measure geotargeted campaigns?

To make geotargeting truly performance-minded, it needs to sit inside a broader measurement plan. Brands can use forecasting, what‑if scenarios, and geo-based experiments to simulate how different budget levels or channel mixes are likely to perform across regions before pushing those changes live.

One proven approach is Geo Lift. Tinuiti’s Incrementality Playbook shows marketers how to use Geo Lift and measure incremental impact with precision, particularly when geographic segmentation allows controlled tests at the market level.

As campaigns run, monitor key metrics in every region:

• CTR (Click-through rate): Reveals if a local audience engages with your ad content.
• CPA (Cost per acquisition): Shows which locations are the most and least efficient to acquire customers.
• ROAS (Return on ad spend): Quantifies how much revenue is generated for every dollar spent in a geographic segment.
• Store visits: Tracks users who saw a mobile or online ad and then physically visited a brick and mortar location.
• Incremental sales: Measures additional sales directly attributed to geo-targeted marketing.
• Brand lift: Gauges the impact of campaigns on brand equity metrics, like awareness or perception in distinct regions.

These insights allow marketers to identify locations with low CPA and high incremental ROAS and flag areas of high CPA or declining brand lift for creative or budget changes.

5. How should marketers analyze geotargeted campaigns?

Once campaigns are in the market, reporting should focus on translating local performance into clear decisions. Look for locations with low CPA and high incremental ROAS impact; those are your best candidates for budget increases. Flag areas with high CPA or flat lift for deeper creative or offer testing.

If CTR is low in a specific geographic location, test new creative, offers, or landing page copy tuned to that market, or consider whether the audience targeting is too broad. When CPA is low, and ROAS is strong, scaling budgets within those zips, DMAs, or country regions can accelerate growth. In contrast, low or negative ROAS should prompt you to pull back and reallocate to more efficient markets.

Use Case: Parts Town & Geotargeted Paid Search

Parts Town, a distributor of OEM foodservice equipment parts, faced a fragmented market during COVID-19 as different states and cities shut down or reopened on very different timelines. Tinuiti’s team needed a way to keep sales strong while staying aligned to local conditions and rapidly shifting search demand.

Using paid search and geotargeting, the team adjusted bids and budgets by market based on local restrictions, search trends, and store demand, rather than treating the U.S. as a single, uniform market. This allowed Parts Town to prioritize areas where restaurants and service providers were still operating or reopening, while avoiding overspend in regions that were effectively paused.

Instead of a one-size-fits-all national approach, Tinuiti and Parts Town:

• Segmented campaigns by state, city, and DMA to mirror how local regulations and reopening policies differed.
• Shifted budgets daily toward locations showing stronger search volume and conversion rates, and away from markets where demand fell off.
• Tuned keyword coverage and ad copy to reflect local realities such as, prioritizing emergency repair intent where commercial kitchens were still active.
• Used geo-level performance reporting to understand which regions were driving the most efficient revenue and where diminishing returns were setting in.

By aligning geo-based bidding with real-time performance data, Parts Town was able to maintain strong revenue and continue growing paid search results even as the broader environment stayed unpredictable. The case shows how geographic segmentation can function as both a targeting strategy and a measurement lens when market conditions change quickly.

Read the full Parts Town case study to see the complete strategy and results.

Conclusion

Geotargeting is most powerful when it is treated as both a targeting strategy and a measurement framework. By planning media at the level of zip codes, DMAs, and regions, and then reading results through an incrementality lens, brands can see which locations truly drive incremental revenue, store visits, and brand lift.

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