Google is pushing advertisers toward a more modern, scalable infrastructure for Shopping integrations—bringing new capabilities (including AI tools) directly into scripting workflows.
What’s happening. Google Ads scripts will begin supporting the Merchant API starting April 22nd, as Google prepares to retire the Content API for Shopping on August 18th. The new API will be available as an Advanced API in the scripts editor, while the existing Content API remains usable until its official sunset.
What’s new: The Merchant API introduces a modular architecture, breaking functionality into sub-APIs that allow for faster updates, easier maintenance, and fewer disruptions. It also expands capabilities with features like the Google Product Studio API for generative AI, dedicated APIs for managing product and store reviews, and a Notifications API for real-time updates.
In addition, advertisers gain more control over data management, including supplemental product data, local and regional inventory, and promotions—all within a system designed for omnichannel use while still supporting legacy setups.
Why we care. The Merchant API gives advertisers more a more flexible way to manage product data at scale, especially for complex or omnichannel setups. It also introduces new capabilities—like AI-driven content tools and improved data handling—that can enhance feed quality and performance. Just as importantly, with the Content API being retired, adopting the new system is essential to avoid disruption and stay competitive.
Yes, but. Migration will require some adjustment—especially for advertisers with custom scripts or complex feed setups tied to the legacy API.
Bottom line. For advertisers using scripts, this is an opportunity to upgrade to a more powerful and scalable integration, unlocking new features while future-proofing Shopping workflows before the cutoff.
Google is removing complexity from one of its most important measurement tools. By merging enhanced conversions for web and leads—and allowing multiple data inputs at once—advertisers get more accurate tracking with less setup friction.
What’s happening. Google Ads is consolidating its enhanced conversions features into a unified system with a single on/off toggle. At the same time, it’s eliminating the need to choose a single implementation method.
Advertisers will be able to send user-provided data through multiple channels simultaneously—including website tags, Data Manager, and API integrations. The current split between “enhanced conversions for web” and “enhanced conversions for leads” will disappear.
What’s changing and when: Google Ads is currently accepting user-provided data from website tags (e.g., Google tag, Google Tag Manager), Data Manager, and API connections. This multi-source approach is designed to improve conversion accuracy and bidding performance.
Starting June 2026, enhanced conversions become a single feature with a simple toggle, and method selection (tag vs API, etc.) is removed from the interface.
Why we care. This update makes conversion tracking more accurate and resilient at a time when signals are disappearing. By allowing multiple data sources at once, Google Ads can better match conversions, which can directly improve bidding efficiency and campaign performance. Just as importantly, it removes technical friction—so you get better data without having to choose or maintain a single integration method.
Impact on advertisers. Existing users require no action and will be automatically migrated if customer data terms have already been accepted. New users can enable enhanced conversions at either the account level or individual conversion action level. Opt-out remains available at the conversion action level.
How to enable it (quick take). At the account level, go to Goals → Settings, enable enhanced conversions under Customer data use, and accept data terms. At the conversion level, create or edit a conversion action, enable enhanced conversions during setup, and accept data terms.
Yes, but. To use enhanced conversions, advertisers must agree to Google’s Data Processing Terms and confirm compliance with its policies—an increasingly important step as platforms expand their use of first-party data.
Bottom line. Google is streamlining setup while quietly encouraging broader adoption of user-provided data. For advertisers, this means better performance with less manual setup. You get more complete conversion data feeding into bidding and optimization, without having to manage multiple tracking methods—helping you drive stronger results while simplifying your measurement strategy.
Alibaba’s Qwen models are nearing 1 billion downloads, dominating open-source AI globally
Qwen holds over 50% of total downloads, far ahead of global competitors
Growth is driven by aggressive pricing, strong performance, and an open-source strategy
Alibaba’s Qwen AI models are rapidly emerging as the global leader in open-source artificial intelligence. As of March 2026, the Qwen model family has reached around 942 million total downloads, with a massive 153.6 million downloads recorded in February alone. This puts Qwen significantly ahead of competitors, with more than double the combined downloads of the next eight players.
A major boost came from the launch of Qwen 3.5 in February 2026. The latest version delivers major improvements in both speed and efficiency. It is reported to be up to eight times faster and around 60% cheaper than its predecessor, making it highly attractive for developers and businesses.
Qwen’s rise also reflects a broader shift in the global AI landscape. Since mid-2024, Chinese open-source models have overtaken their US counterparts in total downloads. Earlier releases like Qwen 2.5 played a key role in this transition by accelerating adoption worldwide.
The company’s strategy focuses on offering powerful yet low-cost models to drive mass adoption. This approach helps build a large developer ecosystem, which is later monetized through cloud services. As a result, Qwen is not just gaining popularity but also strengthening its position in the global AI race.
Competitors: Still Far Behind Qwen
Qwen’s closest rival, Meta’s Llama, is now significantly behind, with estimates suggesting a gap of hundreds of millions of downloads. Fast-growing players like DeepSeek have seen sharp spikes in popularity, but still lag far in total usage. Meanwhile, other models from Nvidia and smaller AI firms show steady but limited adoption. Overall, no competitor currently matches Qwen’s scale, making its lead both clear and rapidly widening.
We’re being pushed harder than ever — expected to hit bigger revenue targets with the same or smaller PPC budgets. Even with flat budgets, rising platform costs mean we’re effectively facing a budget cut.
Average CPCs have risen by as much as 40%, with an average of 3.74%, per Wordstream. Certain periods, such as Black Friday, see much higher increases.
Teams are experiencing budget cuts, with average marketing budgets flatlining at 7.7%, according to Gartner.
Our own account audits show that 20-30% of most accounts’ spend is quietly underperforming.
This is the reality of paid media in 2026. But it isn’t all bad news. Efficiency isn’t just about spending less, it’s about spending smarter. Here’s how to find the waste, fix the fundamentals, and get maximum return from every dollar you invest.
Why efficiency has become the priority
Paid media has shifted dramatically over the last few years, with a greater focus on automation, which has led to hidden data. In parallel, businesses are freezing or reducing budgets while expanding revenue targets, and we’re seeing inflation hit CPCs across most industries, with accounts across our portfolio averaging 10% increases year on year, depending on the industry.
With the expansion into AI-driven automation, this has pushed us further into smart bidding strategies, meaning that where CPCs are rising, you have to be clever with the levers you pull to curtail or minimize these increases.
Meanwhile, customers are spreading their attention across more platforms than ever before, switching between screens and devices, and frequently double-screening.
The question for many businesses is no longer “how do we spend more?” but “how do we get maximum return from every dollar we spend?” Getting that answer right starts with an honest look at where money is being lost.
One of the most important (and uncomfortable) truths in paid media is that aggregate metrics hide wasted spend in plain sight.
A campaign with a 600% ROAS average might have a single product consuming 20% of the budget at just 300%.
An untouched search term report can contain dozens of irrelevant queries burning through spend, especially when broad match keywords or Performance Max campaigns are in play.
Settings or targeting that made sense when you first launched your campaigns may not do so now. Consumer behavior shifts, and business objectives develop and change over time. Are your ROAS targets still reflective, for example?
Common waste zones to investigate include:
Zero-conversion products or keywords.
Low ROAS/CPL outliers.
High spend, low ROAS/CPL.
Zero-conversion products or keywords
Products or keywords that receive spend but generate no conversions are generally loss-making. Before drawing this conclusion, apply impression, click, and spend thresholds to ensure sufficient data.
If a product or keyword has surpassed your target, look to stop spend in these areas. You also want to assess for seasonality and review other contributing factors such as:
Search term relevance.
Checkout funnels.
Competitive advantage.
Low ROAS/CPL outliers
Products consistently below your viable ROAS/CPL threshold are often hidden within blended campaign performance. Use performance bucketing, and set more aggressive targets to control spend and CPCs for these areas.
High spend, low ROAS/CPL
High visibility with low return is a common and costly pattern. Optimize your product feed, and apply more aggressive targets to bring these in line. Again, these products will benefit from implementing product bucketing.
Beyond products, a thorough audit should cover:
Account-level settings (such as content suitability, scheduling, landing page quality, and device performance).
Campaign-level detail (including search term reports, cannibalization, negative keyword coverage, bid strategy alignment, and asset performance).
AI tools can significantly accelerate this analysis. Feeding your data into a well-prompted model can surface patterns that would take hours to identify manually. AI can also help visualize data more clearly and break it down into manageable, easy-to-understand segments.
Full-funnel thinking: Where should your budget sit?
When budgets are tight, funnel prioritization becomes critical. Not all spend is equal, and the hierarchy matters.
This is where the highest intent and highest return live. Protect this budget as much as you can, but also assess whether other channels can pick up some of this slack. For example:
Do you need to spend on brand searches, or can you capture this organically?
Can you re-engage better through email?
Consideration (generic search, shopping, social)
For established brands, this is where the majority of the budget will sit, supporting the pipeline. These users have an active need for your product, and you should prioritize appearing for these searches/users. Again, consider the need for paid ads.
If you are strong organically, with low competition, can you cut back?
Which keywords and products is your budget best spent promoting?
Awareness (social, display, video, audio)
Valuable for long-term brand building, but is usually the first area to be trimmed when budgets are under pressure.
You should try to maintain a level of branding, or you end up passing the issues down the road, as you are unable to build a future pipeline. In Google Ads, campaign types like Performance Max allow full-funnel targeting.
Creative is no longer just a brand awareness nice-to-have. It’s directly correlated to campaign success.
Google and Meta campaigns rely heavily on creative variation to test and optimize. Without sufficient variants, the system runs out of testing capability, and performance plateaus over time as frequency increases.
Campaign types such as Performance Max (Google Ads), GMV Max (TikTok), and Advantage+ (Meta) are heavily restricted without sufficient creative. This results in inefficient spending.
Variety is a system requirement: Platforms need multiple creative variations to identify what works for each auction, audience, and placement. If you don’t supply enough variety, you risk performance decline.
Fatigue is accelerating: With AI-generated content flooding the digital landscape, audiences are tiring of ads faster than ever. For most categories, refreshing creative at least every four to six weeks is now the baseline.
Quality beats quantity: Variation is valuable, but one clear, well-crafted message will outperform ten low-quality. Know the purpose of each ad, and who it’s for before.
AI can support creative production, but strong messaging and strategic clarity still matter most.
Attribution and measurement: Getting honest about what works
Platform attribution has become more fragmented and broken over the years, but many advertisers are unsure how to address this and move forward.
Elements such as cross-device behaviors, iOS privacy changes, consent mode, and GDPR, modeled data, plus the platform’s bias toward claiming conversion credit mean that in-platform numbers should be treated as optimization signals, and not sources of truth.
Using blended metrics gives a cleaner picture of actual efficiency, and can help you establish how your paid media efforts are working:
Marketing efficiency ratio (MER): Total revenue divided by total ad spend. A single, honest view of overall paid media efficiency.
New customer acquisition cost (nCAC): Total spend divided by the number of new customers acquired. Shifts focus from retention to business growth.
CLV:CAC ratio: Sets a strategic ceiling on customer acquisition costs. A ratio of 3:1 or above is the benchmark to aim for.
Building a reliable measurement framework follows a clear sequence: fix your base tracking first, build a blended view of performance, use in-platform data for optimization signals only, and apply incrementality testing when making significant budget decisions.
Incrementality testing allows you to use treatment and holdout groups to clearly establish whether a new campaign or platform launch, for example, has added incremental value.
Automation and AI: Efficiency with guardrails
AI and automation offer real efficiency gains, but only when applied with thought and control. The biggest mistake is automating decisions that require strategic judgment, or removing human oversight from areas where context matters.
Safe to automate:
Bidding strategies.
Budget pacing alerts.
Data-backed budget adjustments.
Product labeling and exclusions.
Scheduled reporting and data visualization.
Competitor ad monitoring.
Keep human oversight:
Channel strategy.
Audience targeting.
Creative strategy.
Targets and KPIs.
Campaign launches.
Interpreting significant performance changes.
Scripts for product bucketing are a particularly high-value area of automation. Automatically labeling products based on performance criteria allows for continuous, data-driven management without manual intervention.
Performance Max: When to use it (and when not to)
PMax works well when you have a strong product feed, sufficient conversion volume, high-quality assets, clear audience signals, an appropriate budget, and effective conversion measurement in place.
Without these conditions, the risks can be high, and can hide troublesome metrics among the averages. This can include:
Cannibalization of brand search.
Over-indexing on existing customers.
Loss of product-level control.
Get the foundations right before leaning into automation.
Getting the most from AI bidding strategies
Choosing the right bidding strategy matters as much as setting it up correctly:
Strategy
When to use
Watch out for
Target ROAS
30+ conversions/month with a clear ROAS target
Too high throttles spend; too low creates wasted traffic
Target CPA
Lead generation, where dynamic revenue isn’t tracked
Works best with consistent CPA; wrong targets cause delivery to spiral
Maximize Conversion Value
When you lack sufficient data to set a ROAS target
No bid ceiling, monitor CPCs and budget closely
Maximize Clicks
Upper funnel only, where traffic volume is the goal
The highest-leverage moves for paid media efficiency
If your paid media budget is under pressure, the highest-leverage moves are:
Run a waste audit: Find the 20-30% that’s underperforming.
Protect lower-funnel spend: Conversion-focused campaigns should be the last to be cut.
Refresh creative more frequently: Creative fatigue is costing performance in ways that aren’t always visible in the numbers.
Move to blended measurement: Get honest about what’s working across channels, not just within platform dashboards.
Automate selectively: Use AI for what it does well, and keep human judgment where it counts.
Done well, efficiency can give you a competitive advantage, and it’s available to any team willing to look honestly at where their spend is actually going.
Reports have surfaced of YouTube 90-second ads landing on Samsung smart TVs and other connected televisions, unskippable for the full duration, and viewers are furious.
This isn’t the 30-second bumper YouTube quietly made official last month. That was bad enough, but this is three times that, locked to your screen, no exit button in sight.
One viewer caught the ads in a 40-minute video. Another saw them on something under 20 minutes. The ad block itself reportedly runs longer than 90 seconds total, with skipping only unlocking after that initial window.
Virtually every reply across both threads was hostile. People are recommending third-party YouTube clients, hunting for ad blockers that still work on TV platforms, or just venting about what the platform used to be.
Google’s logic here isn’t mysterious. The company wants traditional TV advertisers on YouTube, the kind accustomed to buying 60 and 90-second cable spots. Samsung TV owners are collateral damage in that negotiation.
YouTube Premium Lite launched in the US less than two months ago at $8 a month, offering ad-free playback on most content. Google seems to be making the free tier painful enough that paying feels like relief.
Google may be making local search ads more interactive, potentially changing how advertisers showcase multiple locations and capture nearby demand.
What’s happening. Google Ads appears to be testing a new format that displays multiple business locations in a swipeable carousel within search ads, allowing users to browse options directly in the ad unit.
How it works. Instead of listing locations separately, the new format groups them into a horizontal carousel with business details like ratings and proximity, enabling users to swipe through locations without leaving the search results page.
Zoom in. Early comparisons show a shift from static, stacked location assets to a more dynamic experience, where multiple listings are consolidated into a single, scrollable unit.
Why we care. Advertisers with multiple locations could gain more visibility within a single ad, while users get a quicker way to compare nearby options.
Between the lines. This format could increase engagement with location-based ads, but may also intensify competition within the carousel itself as businesses vie for attention.
What to watch. Whether the feature rolls out more broadly and how it impacts click-through rates and local ad performance.
First spotted. This update was spotted by Founder of Adsquire Anthony Higman who shared spotting this ad type on LinkedIn.
Google is laying the groundwork for “agentic commerce,” where users can complete purchases directly inside AI-driven search experiences.
What’s happening. Google has published a new onboarding guide for its Universal Commerce Protocol (UCP) in Merchant Center, outlining how merchants can integrate with the system and enable checkout directly from product listings in AI Mode and Gemini.
The big picture. As AI search evolves from discovery to transaction, Google is pushing to keep users within its ecosystem by embedding shopping and checkout into conversational experiences.
How it works. Merchants must first complete a technical integration, then submit an interest form and wait for approval before gaining access to onboarding tools in Google Merchant Center, including a sandbox environment to test integration, identity linking and checkout APIs.
Why we care. Google is moving search closer to transaction, meaning users may complete purchases directly inside AI experiences instead of visiting your website. This shifts where conversions happen and could change how performance is measured, attributed and optimized. Early adopters of the Universal Commerce Protocol may gain a competitive advantage as shopping becomes more integrated into tools like Gemini.
Zoom in. The protocol acts as an open standard for connecting product data, user identity and payment flows, enabling seamless purchases without redirecting users to external sites.
What to watch: The rollout is gradual and currently limited to the U.S., with a dedicated UCP integration tab expected to appear in Merchant Center accounts over the coming months.
Bottom line. If widely adopted, the Universal Commerce Protocol could redefine how online shopping works — turning search into a full-funnel, AI-powered checkout experience.
Google is giving advertisers new visibility into whether its automated recommendations actually drive performance — a long-standing blind spot in the platform.
What’s happening. A new “Results” tab within Recommendations shows the incremental impact of bidding and budget changes after they’ve been applied, allowing marketers to evaluate outcomes instead of relying on assumptions.
How it works. The feature attributes performance changes to specific recommendations, helping advertisers understand what effect adjustments like budget increases or bid strategy shifts had on results.
Why we care. Marketers can now validate whether recommendations improved performance, making it easier to decide which automated suggestions are worth adopting in the future.
Between the lines. Google has a vested interest in encouraging adoption of its recommendations, so providing performance data could build trust — but it also raises questions about how that impact is measured.
The catch. Advertisers may question whether the reported results are fully objective or skewed toward showing positive outcomes, given Google’s incentives.
What to watch. How detailed and transparent the reporting becomes — and whether advertisers see mixed or negative results alongside wins.
Bottom line. Google is moving from “trust us” to “here’s the proof,” but advertisers will be watching closely to see how impartial that proof really is.
First seen. This update was first spotted by Arpan Banerjee who shared seeing the new tab on LinkedIn.
Google is giving advertisers more control over how AI generates ad copy, making it easier to scale campaigns without losing brand consistency.
What’s happening. Google Ads is rolling out a beta feature that allows marketers to copy text guidelines from existing campaigns and apply them to new ones, eliminating the need to rewrite brand rules from scratch.
How it works. Advertisers can replicate approved tone, style and messaging rules across campaigns in one click, ensuring AI-generated ads stay aligned with brand standards while reducing setup time.
Why we care. The feature helps teams launch campaigns faster by reusing what already works, while maintaining consistency across large accounts where multiple campaigns run simultaneously.
Between the lines. This shift reflects a growing demand from marketers to “train” AI systems rather than rely on them blindly, effectively turning brand guidelines into reusable inputs for automation.
Bottom line. AI is speeding up ad creation, but control is becoming the real differentiator — and Google is starting to hand more of it back to advertisers.
First spotted. This update was spotted by Paid Media expert Arpan Banerjee when he shared spotting the alert on LinkedIn.
Google has begun placing sponsored ad units directly inside the Images tab of mobile search results — a new placement that eligible campaigns can access without any changes to existing keyword targeting.
What’s happening. When a user navigates to the Images tab within Google Search on mobile, they may now see sponsored units appearing within the image grid. Each unit shows a full image creative as the primary visual alongside text, and is clearly labelled “Sponsored” — consistent with how Google labels ads elsewhere in search results.
How it works. Eligible campaigns can serve into the Images tab without any changes to keyword targeting or campaign structure. The placement draws from existing image assets, meaning advertisers running Search or Performance Max campaigns with strong visual creative are best positioned to benefit. No separate image-only campaign setup is required.
Why we care. This is a meaningful expansion of Google’s paid search real estate. For product-led and catalog-heavy advertisers, the Images tab is where purchase-intent discovery often starts — and now ads can appear right in that moment. If your campaigns already use strong image assets, you may be picking up incremental impressions without lifting a finger.
The big picture. Early indications suggest this placement behaves more like a visual discovery surface than classic paid search. Expect high impression volume but lower click-through rates — more in line with display or Shopping than traditional text ads. That said, the assist value in multi-touch conversion paths could be significant, particularly for retail and direct-to-consumer brands. Treat it as upper-funnel reach, not a last-click channel.
What to watch. Google has not made a formal announcement, and there is no dedicated reporting breakdown for Images tab placements yet. Monitor your impression share and segment data closely to understand whether this placement is contributing — and whether it’s eating into organic image visibility for competitors.
First seen. The placement was spotted by Google Ads Expert – Matteo Braghetta, who shared seeing this update on LinkedIn. No official documentation has been published by Google at the time of writing.
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