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Tag: Performance Max

How Expanded PMax Reporting Changes Performance Analysis
Google’s product-level reporting now captures a wider share of the activity associated with Performance Max and several other campaign types. That added visibility can help advertisers understand product results across more of Google’s inventory, but it also creates an abrupt break in reporting continuity.

The practical challenge is interpretation: a chart may rise because more activity is being counted, not because ads suddenly became more effective. Advertisers therefore need to distinguish a measurement expansion from a true performance change.

What changed in product-level reporting

Search Engine Land reports that, as of June 15, Google expanded Performance Max product reporting beyond Search network activity. Previously, reported metrics such as cost and conversions covered products served through Search networks and Standard Shopping campaigns.

The expanded scope includes product performance data from the following eligible campaign inventory:
- All Performance Max networks
- Video campaigns
- App campaigns
- Demand Gen campaigns where product data is available through Google Merchant Center
This is primarily a reporting change. It gives advertisers a broader view of where product interactions occur, but the source does not indicate that the campaigns themselves were altered by the update.

Why performance charts may show a sudden jump

When a report begins counting activity from additional networks, its totals can increase even when underlying campaign behavior remains stable. Search Engine Land says advertisers may see higher impressions, clicks and other metrics as a one-time consequence of the wider reporting scope.

That distinction matters because a larger reported total is not automatically evidence of improved targeting, stronger creative or better bidding. Performance should be judged only after determining whether the apparent change came from campaign results, measurement coverage or a combination of both.

Organic visibility, paid media and artificial intelligence merge into one connected search ecosystem, where vivid data streams link a creative website with a powerful analytics dashboard.

Key takeaways for advertisers
- Product reports now include more eligible Google Ads inventory than they did before the June 15 change.
- Sudden increases in reported activity may reflect newly included networks rather than genuine growth.
- Results from before and after the reporting expansion are not directly comparable without qualification.
- Network-level filtering and clear report annotations can reduce the risk of misreading the change.
How to handle historical comparisons

The reporting boundary creates a discontinuity in time-series analysis. A month-over-month comparison that crosses June 15 may combine two different measurement scopes, so the percentage change alone cannot explain what happened.

Advertisers can make those reports more useful by marking the date of the methodology change and explaining it in client or stakeholder summaries. Where possible, periods measured under the same scope should be compared with one another. If a report must cross the boundary, any observed lift should be presented as potentially influenced by expanded coverage.

This caveat also applies to internal benchmarks, forecasts and automated dashboards that rely on historical trends. The underlying data may still be valuable, but the change in scope needs to remain visible to anyone using it for decisions.

A practical review workflow for affected accounts

A disciplined review can prevent a measurement change from being mistaken for a campaign win or loss:
1. Identify reports and dashboards that use Performance Max product-level data.
2. Check whether the analysis period spans the June 15 reporting change.
3. Use the Network (with search partners) filter to examine where the newly reported activity originated.
4. Review impressions, clicks, cost and conversions in context instead of treating any single increase as proof of improvement.
5. Add a concise methodology note to recurring reports and explain the change to stakeholders.
Google Ads specialist Bia Camargo highlighted the notice, according to the source, and cautioned that clients should be prepared for apparent gains caused by expanded measurement. That communication step is important because broader reporting is useful only when decision-makers understand what changed.

As future reporting periods accumulate under the new scope, comparisons should become easier. Until then, advertisers should treat June 15 as a measurement boundary and require network-level evidence before crediting a spike to campaign optimization.

Inspired by this post on Search Engine Land.
July 16, 2026
Google Shopping Bidding Update Gives Me More Control

I’m seeing an important shift for Standard Shopping campaigns: Google is bringing Maximize Conversion Value bidding to these campaigns without requiring a Target ROAS. That gives advertisers more room to pursue value-based optimization without immediately being locked into a specific return target.

What’s happening. Google is rolling out Maximize Conversion Value bidding for Standard Shopping campaigns, and advertisers no longer have to set a Target ROAS to use it.

Before this update, if I wanted to optimize around conversion value in Standard Shopping, I generally had to use a Target ROAS bidding strategy. Now, this new option lets campaigns focus on maximizing conversion value while giving Google’s bidding system more flexibility to find the highest-value opportunities.

Why I care. This matters because I can now use Google’s value-based bidding in Standard Shopping without being constrained by a Target ROAS goal. That gives me more flexibility while preserving the control and transparency that many advertisers still prefer in Standard Shopping campaigns.

It may also reduce the need to run feed-only Performance Max campaigns just to access Maximize Conversion Value bidding. For advertisers who prefer tighter campaign control, that is a meaningful change.

Between the lines. I know many advertisers have continued to favour Standard Shopping because it offers more visibility and control than Performance Max. But when they wanted flexible value-based bidding, they often created feed-only Performance Max campaigns as a workaround.

With this update, that workaround may no longer be necessary for some accounts.

Why advertisers should care. I can now combine the structure and transparency of Standard Shopping with a more flexible automated bidding strategy. In practical terms, this could simplify campaign setups, reduce unnecessary Performance Max usage, and make account management cleaner.

The bottom line. Google is narrowing one of the biggest feature gaps between Standard Shopping and Performance Max. For me, this gives advertisers another reason to keep using Standard Shopping while still benefiting from automated value-based bidding.

First spotted. Performance marketer Yash Mandlesha spotted the update and shared the option on LinkedIn.

Inspired by this post on Search Engine Land.

July 3, 2026
Google Ads API Ending Smart Campaign Creation: My Take

I see Google’s latest Google Ads API change as another clear move away from legacy automation and toward newer AI-driven campaign types, especially Performance Max.

Beginning August 3, 2026, Google says developers will no longer be able to create new Smart Campaigns through the Google Ads API. For me, the key detail is that this change is about new campaign creation only.

Existing Smart Campaigns are not being shut down. They can keep serving ads, and advertisers and developers will still be able to update and manage those campaigns through the API.

What changes is the ability to create brand-new Smart Campaigns through API workflows. If I depend on automated campaign setup, that is the part I would review now.

I care about this because it signals where Google wants advertisers to go next. Smart Campaigns may continue running, but the path for new API-based campaign creation is moving toward newer products such as Performance Max, Search campaigns, and Demand Gen campaigns.

Google is specifically pointing advertisers toward Performance Max as the primary alternative. Since Performance Max runs across Google’s advertising inventory and uses AI to automate more of the campaign process, it fits the broader direction Google has been taking for years.

I also see this as part of a wider consolidation around automated campaign formats. Google has increasingly emphasized systems that handle bidding, targeting, and creative optimization across channels, and limiting new Smart Campaign creation reinforces that shift.

For developers, the practical next step is to audit any application that creates Smart Campaigns before the August 3, 2026 deadline. The affected requests are campaign creation operations where advertising_channel_type is set to SMART and advertising_channel_sub_type is set to SMART_CAMPAIGN.

After August 3, attempts to create new Smart Campaigns through the API will fail. In version 24 of the Google Ads API, developers will receive a SmartCampaignError.CREATION_FAILED error.

In version 23 and earlier, the same type of request will return an OperationAccessDeniedError.CREATE_OPERATION_NOT_PERMITTED error.

My main takeaway is that advertisers, agencies, and software providers should not treat this as a last-minute technical cleanup. If campaign creation is built into an internal tool, onboarding flow, or platform integration, I would start mapping the replacement path now.

Google is not ending existing Smart Campaigns, but it is removing a key creation path for new ones. To me, that is a strong signal that future campaign planning should center on Performance Max and other AI-driven Google Ads campaign types.

Dig deeper: Changes to Support for Smart Campaigns in the Google Ads API

Inspired by this post on Search Engine Land.

July 3, 2026
Effortless Google PMax Campaign Import with Microsoft Updates
I’m thrilled to share that Microsoft is simplifying the process of expanding Google PMax campaigns into Microsoft, allowing us to enjoy greater visibility and control over our campaign performance.

Microsoft Advertising is launching several updates to make managing, measuring, and migrating Performance Max campaigns more straightforward, especially for those of us already familiar with Google Ads.

Driving the news. Microsoft now allows us to import Google PMax campaigns with new customer acquisition (NCA) goals, a feature that’s been part of Microsoft since earlier this year.

The update is live for all advertisers now, enabling us to transfer campaigns focused on first-time buyers more seamlessly, without having to start from scratch.

What’s new. Microsoft ensures that when we import Google PMax campaigns with NCA goals, they will be retained if they don’t already exist in our account. Our existing settings won’t be overwritten.

Regarding audience lists:
- Google website visitor segments transform into Microsoft remarketing lists.
- Google’s “all visitors” and “all converters” lists map to similar lists on Microsoft.
- For unsupported lists like Customer Match, we may need to use alternate options.
I’ve also noticed that Microsoft takes a cautious approach with “unknown” customers, categorizing them as existing customers to avoid inflating new customer conversion counts.

Why we care. This initiative could streamline cross-platform campaign expansion and reduce the hassle of rebuilding, making it simpler to test Microsoft’s PMax inventory. Plus, enhanced landing page reporting and search term insights offer a clearer picture of campaign performance, aiding our optimization and budget decisions.

More visibility for PMax. Microsoft is integrating landing page (Final URL) reporting for PMax campaigns, allowing us to review spend, clicks, impressions, conversion value, and ROAS by landing page.

We can also break this information down by campaign, asset group, and other dimensions.

Additionally, Microsoft stated that search term reporting will become more apparent by default, with more transparency updates such as auction insights and publisher URL metrics rolling out soon.

Other key updates:
- Seasonality adjustments now support portfolio bid strategies, aiding short-term promotions.
- Campaign name limits have increased, enabling up to 400 characters for easier management.
- Autogenerated assets are improving ad relevance and performance by filling in underused Responsive Search Ads.
- Merchant Center users can directly update store names and domains without needing support.
The bottom line. These updates simplify scaling across platforms, save time on campaign setups, and enhance our visibility into campaign performance, giving us greater control over efficiency and outcomes.

Inspired by this post on Search Engine Land.
April 16, 2026
Mastering Audience Engineering: Elevate Your Paid Media Strategy
Embrace audience engineering to influence AI decisions, manage ad spend wisely, and connect with high-value customers through creativity and data.

I’m witnessing a significant transformation in the paid media landscape as platforms shift from manual targeting to AI-driven audience discovery. This change is redefining how we approach advertising, with automation tools consolidating campaigns, obscuring data, and favoring prediction algorithms over manual selection.

This transition requires me to innovate by mastering the art of audience engineering. By doing so, I ensure I’m equipped with strategies to thrive in this evolving landscape.

The End of Manual Targeting as I Knew It

Previously, I depended on detailed keyword lists and demographic filters to pinpoint my ideal audience. I directed platforms about where to focus and paid to access the desired market.

However, these options are now outdated:
- Google has transitioned to Performance Max, which eliminates keyword-specific targeting in favor of more fluid groups and signals.
- Meta’s Advantage+ automates demographic focus, turning my role into that of a signal provider instead of an audience selector.
- Microsoft’s inclusion of this model confirms this is an industry-wide evolution.
While traditional targeting seems to have vanished, it has merely moved to the internal structures of the platforms where algorithms dictate the direction based on their indigenous data.

The Rise of Audience Engineering

My role shifts from targeting to engineering as it becomes more about guiding algorithms than manually selecting audiences.

From Targeting to Teaching

The distinction is crucial. Traditionally, targeting emphasized choosing audiences, but now it’s about educating AI with comprehensive conversion data, targeted creativity, and insightful first-party data.

Previously, I might have targeted CFOs with job filters, but now I feed the AI robust data (e.g., “deal closed” signals) to characterize valuable prospects and devise creative content tailored to their needs.

The New Competitive Discipline

Embracing this transformation gives me an edge. By finetuning conversion signals, honing creative content, and fortifying data systems, I ensure our performance remains robust.

The performance gap now relies on the quality of signals, making audience engineering pivotal for success.

The Three Levers that Now Drive Targeting

I focus on optimizing these three crucial AI inputs to ensure effective audience segmentation:

1. Conversion Signal Quality

By providing the algorithm with relevant business outcomes rather than superficial metrics, I encourage it to find results that truly matter.

Using tools like Offline Conversion Imports (OCI) and the Conversions API (CAPI), I ensure our data highlights genuine sales by leveraging value-based bidding techniques.

2. Creative as a Targeting Mechanism

With no demographic filters, my creative content now acts as the primary targeting tool, filtering users through its message.

If my creative targets niche pain points, the AI connects with users aligned with that perspective, even without traditional filters.

3. First-Party Data as Competitive Moat

Our customer lists and engagement signals become core learning elements for the algorithm, replacing third-party signals and offering a competitive edge.

Essentially, I’m arming the AI with a guide to discover the most profitable audiences.

How This Plays Out in Real Campaigns

The journey to AI-led targeting isn’t just theoretical. Within our agency, managing over $215 million in media spend annually, we have evaluated this approach across different platforms, witnessing its power firsthand.

Advantage+ Audiences in Practice

One long-standing client had a specific perception of their audience based on a vast history of accurate data. Initially, our campaigns ran with tightly controlled targeting to maintain efficiency.

Transitioning to Advantage+ allowed for data-driven optimization, revealing an unexpectedly lucrative older demographic, improving their click-through rates by 37% and conversion rates immensely.

Broader AI-optimized targeting cut costs and raised revenue — outperforming past manual methods.

By aligning goals with data and creative, we found valuable segments conventional targeting schemes previously overlooked.

Microsoft PMax Placement Transparency and Advanced Audience Signal Targeting

Another client benefited from a Microsoft PMax test, effectively targeting high-intent prospects using internal data across several Microsoft networks, seeing notable increases in performance metrics each month.

This trial highlighted the importance of combining strategic oversight with smart AI deployment, enhancing the algorithm’s reach while maintaining disciplined campaign direction.

The balance between scale and strategic input preserved efficiency and bolstered overall performance.

The Risks Nobody is Talking Enough About

While automated targeting offers significant advantages, it’s essential to understand its limitations. Here’s what I strive to avoid:

Garbage In, Garbage Out

Poorly defined conversion objectives, weak data quality, or junk data hinder performance and mislead the algorithm. Feeding it quality information and focused outcomes is crucial.

An overly broad goal without distinct signals results in quantity over quality, which doesn’t necessarily translate to business success.

The Self-Reinforcement Trap

If the seed data has biases, the AI will continuously optimize for those biases, possibly neglecting valuable audience segments.

These underrecognized biases present inherent risks in leveraging automated systems without mindfulness.

Automation Without Oversight

Platforms promote broad automation, but I recognize the need for continued oversight to realign campaigns with business goals.

Constant monitoring is essential to ensure objectives are met, avoiding a passive management style.

Creative Complacency

As automation advances, creative strategy becomes a crucial differentiator and shouldn’t be neglected.

Crafting compelling creative that addresses core customer issues is vital in distinctively standing out.

How to Put Audience Engineering into Practice

Here’s how I integrate audience engineering into everyday operations:
- Audit Conversion Events: Ensure conversion signals mirror authentic business achievements, prioritizing revenues.
- Restructure Creative: Focus on intent signals, addressing what beliefs inspire conversion.
- Predefine Guardrails: Establish performance boundaries before unleashing the algorithm, allowing for better campaign control.
The Future Belongs to Audience Engineers

The era of manual targeting is closing, but precision remains crucial. Audience engineering acts as an invaluable skill, unlocking AI’s full potential to achieve maximum results in this dynamic landscape.

Inspired by this post on Search Engine Land.
April 8, 2026
Unveiling Google’s PMax Timeline: Boost Your Ad Strategy

Recently, I discovered that Google has launched an exciting new feature for Performance Max campaigns. As an advertiser, I’m always on the lookout for tools that provide clearer insights, and this new channel performance timeline view does just that. It offers a comprehensive breakdown of how different channels like Search, YouTube, and Display contribute to my campaign results over time.

What’s New

The latest update introduces a timeline graph that showcases channel-level contributions over a selected period, complete with investment and performance filters. This means I can quickly identify which channels are excelling and which ones might need a bit more attention.

The chart features helpful visual cues—like a yellow box highlighting channel performance evolution over time, and a pink box indicating different ad types, such as All Ads, Ads Using Product Lists, and Ads Using Video.

Why I Care

Managing Performance Max campaigns across multiple channels often left me guessing about where my budget was working best. This new view provides valuable insights into channel-level trends, allowing me to adjust strategies or budgets more efficiently. If I notice YouTube underperforming while Search is thriving, I can now make informed decisions without relying purely on guesswork or exported data.

The Big Picture

This new view empowers me to evaluate PMAX performance more effectively, without relying solely on Google’s automated decisions. Now, I can see consistent underperformance or excellence across channels, which guides my budget and asset strategies moving forward.

The Bottom Line

Though it’s not full transparency, this update is a significant move in the right direction. I now have a more structured way to detect trend anomalies in PMax campaigns early and make necessary adjustments to optimize performance.

First Spotted

This feature was first noticed by Axel Falck, Head of Search at Le Mage du SEA, who shared his insights on LinkedIn.

Inspired by this post on Search Engine Land.

April 2, 2026
Unleashing AI in B2B: Your Patient Path to Growth

B2B buyers start their journey long before they even search for us. I’ve learned that AI-powered Google Ads campaigns can ignite early demand and reward patience over time.

If I’m relying solely on brand and non-brand keywords in Google Ads, my growth becomes limited. A decline in performance isn’t due to the platform but the strategy behind it.

Discovering a brand doesn’t begin with a non-brand search. Buyers are researching on platforms like Reddit, ChatGPT, Facebook, LinkedIn, and YouTube. They watch demos, read testimonials, and become familiar long before actively searching for us.

For complex sales processes with lengthy customer journeys, this transformation is crucial, demanding a strategic shift. Here’s how I can make it effective in B2B.

AI-powered Campaigns: Your Growth Treasure

Over the years, Google has innovated with multi-channel, multi-asset campaigns like Performance Max and Demand Gen. These campaigns place my brand front and center as audiences research and evaluate options.

When my audience is ready to choose vendors, they’ve already built trust in my brand. They’ll search specifically for me because of the trust I’ve cultivated through consistent visibility.

A well-rounded Performance Max campaign includes diverse ad types, like image and video ads displaying demos or testimonials on YouTube. These ads also engage audiences across the web via the Display Network and retarget them as they continue their research. This process naturally leads to branded searches that ultimately convert.

Such campaigns are cost-effective, allowing me to leverage customer data alongside keywords as intelligent signals, not replacements. It’s about smarter keyword usage.

Dig deeper: Why B2B brands are shifting from keywords to Performance Max

Adapting to the Evolving Search Experience

As AI Overviews and AI Mode transform Google’s search results pages, it’s time I reconsider my ad strategies to align with these changes.

I’m fond of the 4S framework: search, scroll, stream, and shop.

Adding “ask” captures how people now engage with AI tools. They consult ChatGPT or Gemini, search on Google, scroll through LinkedIn, stream videos on YouTube, and shop across numerous platforms. If my strategy focuses on only a couple of these behaviors, I’m missing the full growth opportunity.

Solely targeting keywords means missing the larger narrative. Brand keywords undoubtedly convert better, but how do people arrive at searching my brand? Consistent visibility ensures they notice my brand in their feeds.

Embrace Testing and Learn with Patience

This strategy requires time, especially in B2B settings with protracted sales cycles.

For example, it took almost a year to appreciate how Performance Max contributed to one of my life science client’s success, whose deals typically take months to finalize. There was a moment where our account manager nearly paused the campaign because initial data wasn’t promising.

Integrating sales data changed the perspective. As revenue figures rolled in, the campaign’s value became transparent.

If I can sync beyond MQLs with data like Proposal Sent, it keeps Google well-informed and offers reassurance until the sales data solidifies our insights.

Patience is key when providing the system quality data. I must remain steadfast and avoid quitting prematurely, accepting the complexity of B2B cycles.

An event might draw 100 people, some catch a webinar email later, and months pass before they search for us and request a proposal, eventually becoming customers. With long sales cycles, phenomena like this unfold subtly.

Dig deeper: How to optimize B2B PPC spend when budgets and confidence are low

Start with Small Steps, Then Scale Success

If testing funds are limited, I can designate 5% to 10% for AI-forward campaigns. Strategic testing without major commitments at peak times allows room to maneuver while the system adjusts.

Investing time in this strategy ensures sustainable growth. Those who master it gain an enduring competitive edge, unlike those focused on diminishing demand.

Inspired by this post on Search Engine Land.

March 27, 2026
Harnessing First-Party Data for AI-Enhanced Ad Success

I recently discovered how crucial first-party data has become in the evolving landscape of AI-powered advertising. It’s fascinating to see how it shapes the optimization and measurement of automated ad campaigns.

During a chat with Search Engine Land, I learned from Julie Warneke, CEO of Found Search Marketing, about the profound impact first-party data has on profitable advertising, regardless of potential changes to Google’s third-party cookie policies.

Embracing first-party data means tapping into customer information that I own, typically stored in a CRM, like lead details, purchase history, revenue, and customer value collected from various touchpoints.

This type of data is distinct from platform-owned or browser-based data, over which I have limited control.

Digital advertising has evolved over the years. The shift from focusing on impressions and clicks to outcomes emphasizes profitable conversions, according to Warneke. Advertisers who provide AI systems with quality customer data gain a significant edge.

Although rising cost-per-clicks (CPCs) are inevitable in paid media, first-party data enhances conversion quality, revenue, and return on ad spend, making higher costs justifiable with better results.

By leveraging first-party data tied to revenue and customer value, AI bidding systems can target users resembling high-value customers, even beyond usual demographic or geographic signals, leading to better conversions.

Among campaign types, Performance Max (PMax) thrives with first-party data activation. It performs best when I shift from manual optimizations to feeding it accurate data, allowing the system to learn, as Warneke highlighted.

Even small and mid-sized businesses can leverage first-party data, as seen in Warneke’s examples of success with small customer lists. The challenge lies in setting up proper infrastructure for tracking, consent management, and data flow.

Common mistakes include weak data capture, where brands rely on browser-side tracking that falters on platforms like iOS, and broken feedback loops from sporadic CRM data uploads. Continuous data streams are crucial.

Warneke advises taking a step back to audit how data is captured, stored, and relayed to platforms. Incremental improvements can pave the way for significant long-term gains, even starting with a small portion of a budget as a test.

Ultimately, AI optimization reflects the quality of signals received. By refining first-party data, I can influence outcomes favorably, avoiding inefficiency risks.

Inspired by this post on Search Engine Land.

February 5, 2026
Maximize Ecommerce Success with Demand Gen & Performance Max

When Google introduced Demand Gen campaigns in 2023, I saw them as a promising way to boost engagement across platforms like YouTube, Discover, and Gmail.

Initially, they felt experimental, straddling the line between awareness and performance, but they’ve come a long way since.

Now, the creative flexibility and enhanced audience control make Demand Gen a go-to campaign type for my ecommerce clients.

This strategy allows me to scale revenue in a controlled manner, maintaining brand consistency while testing creative approaches to drive conversions.

I’ve found that Demand Gen delivers the best results when strategically paired with Performance Max and Search campaigns.

Advertising with Demand Gen is ideal if you crave more control.

One major drawback of Performance Max is its lack of transparency and manual control.

If precise targeting, placement, or creative control is essential, Demand Gen stands out as the better option.

Performance Max auto-generates ads from your uploads, relying on Google’s AI to mix and match for the best performance.

This makes it crucial to provide top-notch creative assets.

For example, a fitness brand might create separate asset groups for products like leggings, shorts, and vests.

While this helps target relevant audiences, the control isn’t exhaustive.

However, Demand Gen offers far superior flexibility.

It allows me to upload, preview, and tweak ad combinations before launch, adapting each creative to its unique placement.

For instance, I can customize YouTube ads for in-feed, in-stream, and Shorts placements.

This control is perfect for ecommerce brands focusing on creative precision, message testing, and maintaining a strong visual identity.

Dig deeper: The Google Ads Demand Gen playbook

Using Demand Gen alongside Performance Max can be incredibly effective if you leverage their roles within the customer journey. They enhance each other rather than compete.

Demand Gen builds awareness and sparks interest by reaching higher-funnel audiences before they actively start product searching.

Conversely, Performance Max focuses on converting lower-funnel users who are primed to purchase.

For example, a fitness retailer might utilize Demand Gen for lifestyle videos and discovery ads promoting their latest activewear.

When a potential customer begins to research or exhibit purchase intent, Performance Max engages with tailored Shopping and Search ads to finalize the sale.

I’ve set up feed-only Performance Max campaigns, providing only a product feed within the asset group.

This restricts Performance Max activities to Shopping placements, focusing it sharply on direct conversions.

Meanwhile, Demand Gen operates across platforms like YouTube, Gmail, Discover, and Shorts, covering the upper and mid-funnel with more visual, creative content focused on awareness.

This configuration minimizes overlap between campaign types while ensuring user engagement throughout the funnel, from brand discovery to purchase.

For larger accounts with flexible budgets, this dual structure drives holistic performance and clearer attribution.

In contrast, smaller accounts seeking efficiency should prioritize mastering high-intent campaigns before layering in Demand Gen once the core conversions are stable.

The diverse campaign types now offer advertisers more flexibility than ever, yet it requires understanding Google’s restructuring of video and discovery products.

Dig deeper: Why Demand Gen is the most underrated campaign type in Google Ads

Since July 2025, Google’s Video Action Campaigns (VACs) have been replaced by Demand Gen.

It streamlines Google’s visual placements into one campaign type, including YouTube in-stream, Shorts, in-feed, Gmail, and Discover.

This change is significant. VAC was successful for ecommerce, particularly for conversion-centric video. Its removal underscores Google’s encouragement to embrace Demand Gen.

The advantage is that Demand Gen provides stronger creative control and diverse testing options across YouTube placements.

If you previously ran VAC campaigns, they are now under Demand Gen. Ensure your top-performing assets and audiences have migrated correctly, then use the new controls to optimize performance.

Audience control is a significant benefit of Demand Gen, and it’s a reason why I consistently use it for ecommerce.

Demand Gen allows precise audience creation, letting me decide who sees the ads.

I can select placements, merge audience types, and allocate the budget strategically.

It’s the only Google Ads campaign type supporting lookalike audiences, valuable for brands focused on acquiring quality leads.

While Performance Max utilizes audience signals over fixed targeting, Demand Gen excels for control, testing, and segmentation strategies.

In mid-2025, Google rolled out an open beta for advertisers to opt out of specific Demand Gen channels manually.

This means I can now control ad display, excluding Discover or YouTube Shorts if they don’t align with my objectives or creative format.

This small but significant update offers more control, a feature often lacking in many of Google’s automated campaign types.

Dig deeper: Google Ads rolls out channel control for Demand Gen campaigns

In early 2025, Google introduced product feed integration for Demand Gen campaigns. This change allows me to link the Google Merchant Center feed, incorporating live product data directly into visual ads.

This development bridges performance and branding for ecommerce, enabling storytelling through creative visuals while displaying actual products.

For instance, a fashion retailer can showcase a new collection in a video advert while featuring shoppable product cards below.

This update positions Demand Gen as a hybrid between Shopping and Display, a much-anticipated capability among ecommerce advertisers.

Demand Gen typically demands a larger budget than other campaign types.

Google recommends starting at about £100 per day per campaign or 20 times your target CPA/tROAS, whichever is higher.

Practically, the £100-per-day baseline is a viable starting point for effective data collection and optimization. Lower budgets restrict data flow and slow progress.

Demand Gen complements your broader Google Ads strategy, rather than replacing Search or Performance Max.

It’s a premium, visually led campaign type that boosts awareness leading to conversions, particularly effective when you have accurate measurement, a clean product feed, and clearly defined audiences.

The table compares Demand Gen and Performance Max on key aspects that matter to advertisers.

Dig deeper: Google pushes Demand Gen deeper into performance marketing

Performance Max excels in scale but can be opaque.

Demand Gen offers the control advertisers have demanded—genuine creative testing, audience precision, and placement visibility.

For sustainable ecommerce growth, I recommend using both. Performance Max captures demand, while Demand Gen creates it.

Together, they form a comprehensive framework for scalable and sustainable growth.

Inspired by this post on Search Engine Land.

January 22, 2026