Google is now promoting its own AI features inside Google Ads — a rare move that inserts marketing directly into advertisers’ workflow.
What’s happening. Users are seeing promotional messages for AI Max for Search campaigns when they open campaign settings panels.
The notifications appear during routine account audits and updates.
It essentially serves as an internal advertisement for Google’s own tooling.
Why we care. The in-platform placement signals Google is pushing to accelerate AI adoption among advertisers, moving from optional rollouts to active promotion. While Google often introduces AI-driven features, promoting them directly within existing workflows marks a more aggressive adoption strategy.
What to watch. Whether this promotional approach expands to other Google Ads features — and how advertisers respond to marketing within their management interface.
First seen. Julie Bacchini, president and founder of Neptune Moon, spotted the notification and shared it on LinkedIn. She wrote: “Nothing like Google Ads essentially running an ad for AI Max in the settings area of a campaign.”
B2B advertising faces a distinct challenge: most automation tools weren’t built for lead generation.
Ecommerce campaigns benefit from hundreds of conversions that fuel machine learning. B2B marketers don’t have that luxury. They deal with lower conversion volume, longer sales cycles, and no clear cart value to guide optimization.
The good news? Automation can still work.
Melissa Mackey, Head of Paid Search at Compound Growth Marketing, says the right strategy and signals can turn automation into a powerful driver of B2B leads. Below is a summary of the key insights and recommendations she shared at SMX Next.
The fundamental challenge: Why automation struggles with lead gen
Automation systems are built for ecommerce success, which creates three core obstacles for B2B marketers:
Customer journey length: Automation performs best with short journeys. A user visits, buys, and checks out within minutes. B2B journeys can last 18 to 24 months. Offline conversions only look back 90 days, leaving a large gap between early engagement and closed revenue.
Conversion volume requirements: Google’s automation works best with about 30 leads per campaign per month. Google says it can function with less, but performance is often inconsistent below that level. Ecommerce campaigns easily hit hundreds of monthly conversions. B2B lead gen rarely does.
The cart value problem: In ecommerce, value is instant and obvious. A $10 purchase tells the system something very different than a $100 purchase. Lead generation has no cart. True value often isn’t clear until prospects move through multiple funnel stages — sometimes months later.
The solution: Sending the right signals
Despite these challenges, proven strategies can make automation work for B2B lead generation.
Offline conversions: Your number one priority
Connecting your CRM to Google Ads or Microsoft Ads is essential for making automation work in lead generation. This isn’t optional. It’s the foundation. If you haven’t done this yet, stop and fix it first.
In Google Ads’ Data Manager, you’ll find hundreds of CRM integration options. The most common B2B setups include:
HubSpot and Salesforce: Both offer native, seamless integrations with Google Ads. Setup is simple. Once connected, customer stages and CRM data flow directly into the platform.
Other CRMs: If you don’t use HubSpot or Salesforce, you can build a custom data table with only the fields you want to share. Use connectors like Snowflake to send that data to Google Ads while protecting user privacy and still supplying strong automation signals.
Third-party integrations: If your CRM doesn’t integrate directly, tools like Zapier can connect almost anything to Google Ads. There’s a cost, but the performance gains typically pay for it many times over.
Embrace micro conversions with strategic values
Micro conversions signal intent. They show a “hand raiser” — someone engaged on your site who isn’t an MQL yet but clearly interested.
The key is assigning relative value to these actions, even when you don’t know their exact revenue impact. Use a simple hierarchy to train automation what matters most:
Video views (value: 1): Shows curiosity, but qualification is unclear.
Form fills (value: 100): Reflects meaningful commitment and willingness to share personal information.
Marketing qualified leads (value: 1,000): The highest-value signal and top optimization priority.
This value structure tells automation that one MQL matters more than 999 video views. Without these distinctions, campaigns chase impressive conversion rates driven by low-value actions — while real leads slip through the cracks.
Making Performance Max work for lead generation
You might dismiss Performance Max (PMax) for lead generation — and for good reason. Run it on a basic maximize conversions strategy, and it usually produces junk leads and wastes budget.
But PMax can deliver exceptional results when you combine conversion values and offline conversion data with a Target ROAS bid strategy.
One real client example shows what’s possible. They tracked three offline conversion actions — leads, opportunities, and customers — and valued customers at 50 times a lead. The results were dramatic:
Leads increased 150%
Opportunities increased 350%
Closed deals increased 200%
Closed deals became the campaign’s top-performing metric because they reflected real, paying customers. The key difference? Using conversion values with a Target ROAS strategy instead of basic maximize conversions.
Campaign-specific goals: An underutilized feature
Campaign-specific goals let you optimize campaigns for different conversion actions, giving you far more control and flexibility.
You can set conversion goals at the account level or make them campaign-specific. With campaign-specific goals, you can:
Run a mid-funnel campaign optimized only for lead form submissions using informational keywords.
Build audiences from those form fills to capture engaged prospects.
Launch a separate campaign optimized for qualified leads, targeting that warm audience with higher-value offers like demos or trials.
This approach avoids asking someone to “marry you on the first date.” It also keeps campaigns from competing against themselves by trying to optimize for conflicting goals.
Portfolio bidding: Reaching the data threshold faster
Portfolio bidding groups similar campaigns so you can reach the critical 30-conversions-per-month threshold faster.
For example, four separate campaigns might generate 12, 11, 0, and 15 conversions. On their own, none qualify. Grouped into a single portfolio, they total 38 conversions — giving automation far more data to optimize against.
You may still need separate campaigns for valid reasons — regional reporting, distinct budgets, or operational constraints. Portfolio bidding lets you keep that structure while still feeding the system enough volume to perform.
Bonus benefit: Portfolio bidding lets you set maximum CPCs. This prevents runaway bids when automation aggressively targets high-propensity users. This level of control is otherwise only available through tools like SA360.
First-party audiences: Powerful targeting signals
First-party audiences send strong signals about who you want to reach, which is critical for AI-powered campaigns.
If HubSpot or Salesforce is connected to Google Ads, you can import audiences and use them strategically:
Customer lists: Use them as exclusions to avoid paying for existing customers, or as lookalikes in Demand Gen campaigns.
Contact lists: Use them for observation to signal ideal audience traits, or for targeting to retarget engaged users.
Audiences make it much easier to trust broad match keywords and AI-driven campaign types like PMax or AI Max — approaches that often feel too loose for B2B without strong audience signals in place.
Leveraging AI for B2B lead generation
AI tools can significantly improve B2B advertising efficiency when you use them with intent. The key is remembering that most AI is trained on consumer behavior, not B2B buying patterns.
The essential B2B prompt addition
Always tell the AI you’re selling to other businesses. Start prompts with clear context, like: “You’re a SaaS company that sells to other businesses.” That single line shifts the AI’s lens away from consumer assumptions and toward B2B realities.
Client onboarding and profile creation
Use AI to build detailed client profiles by feeding it clear inputs, including:
What you sell and your core value.
Your unique selling propositions.
Target personas.
Ideal customer profiles.
Create a master template or a custom GPT for each client. This foundation sharpens every downstream AI task and dramatically improves accuracy and relevance.
Competitor research in minutes, not hours
Competitive analysis that once took 20–30 hours can now be done in 10–15 minutes. Ask AI to analyze your competitors and break down:
Current offers
Positioning and messaging
Value propositions
Customer sentiment
Social proof
Pricing strategies
AI delivers clean, well-structured tables you can screenshot for client decks or drop straight into Google Sheets for sorting and filtering. Use this insight to spot gaps, uncover opportunities, and identify clear strategic advantages.
Competitor keyword analysis
Use tools like Semrush or SpyFu to pull competitor keyword lists, then let AI do the heavy lifting. Create a spreadsheet with columns for each competitor’s keywords alongside your client’s keywords. Then ask the AI to:
Identify keywords competitors rank for that you don’t to uncover gaps to fill.
Identify keywords you own that competitors don’t to surface unique advantages.
Group keywords by theme to reveal patterns and inform campaign structure.
What once took hours of pivot tables, filtering, and manual cleanup now takes AI about five minutes.
Automating routine tasks
Negative keyword review: Create an AI artifact that learns your filtering rules and decision logic. Feed it search query reports, and it returns clear add-or-ignore recommendations. You spend time reviewing decisions instead of doing first-pass analysis, which makes SQR reviews faster and easier to run more often.
Ad copy generation: Tools like RSA generators can produce headlines and descriptions from sample keywords and destination URLs. Pair them with your custom client GPT for even stronger starting points. Always review AI-generated copy, but refining solid drafts is far faster than writing from scratch.
Experiments: testing what works
The Experiments feature is widely underused. Put it to work by testing:
Different bid strategies, including portfolio vs. standard
Match types
Landing pages
Campaign structures
Google Ads automatically reports performance, so there’s no manual math. It even includes insight summaries that tell you what to do next — apply the changes, end the experiment, or run a follow-up test.
Solutions: Pre-built scripts made easy
Solutions are prebuilt Google Ads scripts that automate common tasks, including:
Reporting and dashboards
Anomaly detection
Link checking
Flexible budgeting
Negative keyword list creation
Instead of hunting down scripts and pasting code, you answer a few setup questions and the solution runs automatically. Use caution with complex enterprise accounts, but for simpler structures, these tools can save a significant amount of time.
Key takeaways
Automation wasn’t built for lead generation, but with the right strategy, you can still make it work for B2B.
Send the right signals: Offline conversions with assigned values aren’t optional. First-party audiences add critical targeting context. Together, these signals make AI-driven campaigns work for B2B.
AI is your friend: Use AI to automate repetitive work — not to replace people. Take 50 search query reports off your team’s plate so they can focus on strategy instead of tedious analysis.
Leverage platform tools: Experiments, Solutions, campaign-specific goals, and portfolio bidding are powerful features many advertisers ignore. Use what’s already built into your ad platforms to get more out of every campaign.
Watch: It’s time to embrace automation for B2B lead gen
OpenAI confirmed today that it’s rolling out its first live test of ads in ChatGPT, showing sponsored messages directly inside the app for select users.
The details. The ads will appear in a clearly labeled section beneath the chat interface, not inside responses, keeping them visually separate from ChatGPT’s answers.
OpenAI will show ads to logged-in users on the free tier and its lower-cost Go subscription.
Advertisers won’t see user conversations or influence ChatGPT’s responses, even though ads will be tailored based on what OpenAI believes will be helpful to each user, the company said.
How ads are selected. During the test, OpenAI matches ads to conversation topics, past chats, and prior ad interactions.
For example: A user researching recipes might see ads for meal kits or grocery delivery. If multiple advertisers qualify, OpenAI shows the most relevant option first.
User controls. Users get granular controls over the experience. They can dismiss ads, view and delete separate ad history and interest data, and toggle personalization on or off.
Turning personalization off limits ads to the current chat.
Free users can also opt out of ads in exchange for fewer daily messages or upgrade to a paid plan.
Why we care. ChatGPT is one of the world’s largest consumer AI platforms. Even a limited ad rollout could mark a major shift in how conversational AI gets monetized — and how brands reach users.
Bottom line. OpenAI is officially moving into ads inside ChatGPT, testing how sponsored content can coexist with conversational AI at massive scale.
A newly discovered settings panel offers a first detailed look at how ads could work inside ChatGPT, including how personalization and privacy controls are designed.
Driving the news. Entrepreneur Juozas Kaziukėnas found a way to trigger ChatGPT’s upcoming ad settings interface. The panel repeatedly stresses that advertisers won’t see user chats, history, memories, personal details, or IP addresses.
What the settings reveal. The interface lays out a structured ad system with dedicated controls:
A history tab logs ads users have seen in ChatGPT.
An interests tab stores inferred preferences based on ad interactions and feedback.
Each ad includes options to hide or report it.
Users can delete ad history and interests separately from their general ChatGPT data.
Personalization options. Users can turn ad personalization on or off. When it’s on, ChatGPT uses saved ad history and interest signals to tailor ads. When it’s off, ads still appear but rely only on the current conversation for context.
There’s also an option to personalize ads using past conversations and memory features, though the interface stresses that chat content isn’t shared with advertisers. Accounts with memory disabled won’t see this option active.
Why we care. This settings panel offers the clearest view yet of how ad personalization and privacy controls could work with ChatGPT ads. It points to a system built on strict privacy boundaries. The controls suggest ads will rely on contextual signals and opt-in personalization, not deep user tracking. That shift makes creative relevance and in-conversation intent more important than traditional audience profiling for brands preparing for conversational ad environments.
The bigger picture. The discovery suggests OpenAI is building an ad system that mirrors familiar controls from major ad platforms while prioritizing clear privacy boundaries and user choice.
Bottom line. ChatGPT ads aren’t live yet, but the framework is coming into focus — pointing to a future where conversational ads come with granular privacy and personalization controls.
First seen. Kaziukėnas shared the preview of the platform on LinkedIn.
On episode 340 of PPC Live The Podcast, I speak to Amanda Farley, CMO of Aimclear and a multi-award-winning marketing leader, brings a mix of honesty and expertise to the PPC Live conversation. A self-described T-shaped marketer, she combines deep PPC knowledge with broad experience across social, programmatic, PR, and integrated strategy. Her journey — from owning an gallery and tattoo studio to leading award-winning global campaigns — reflects a career built on curiosity, resilience, and continuous learning.
Overcoming limiting beliefs and embracing creativity
Amanda once ran an gallery and tattoo parlor while believing she wasn’t an artist herself. Surrounded by creatives, she eventually realized her only barrier was a limiting belief. After embracing painting, she created hundreds of artworks and discovered a powerful outlet for expression.
This mindset shift mirrors marketing growth. Success isn’t just technical — it’s mental. By challenging internal doubts, marketers can unlock new skills and opportunities.
When campaign infrastructure breaks: A high-stakes lesson
Amanda recalls a global campaign where tracking infrastructure failed across every channel mid-flight. Pixels broke, data vanished, and campaigns were running blind. Multiple siloed teams and a third-party vendor slowed resolution while budgets continued to spend.
Instead of assigning blame, Amanda focused on collaboration. Her team helped rebuild tracking and uncovered deeper data architecture issues. The crisis led to stronger onboarding processes, earlier validation checks, and clearer expectations around data hygiene. In modern PPC, clean infrastructure is essential for machine learning success.
The hidden importance of PPC hygiene
Many account audits reveal the same problem: neglected fundamentals. Basic settings errors and poorly maintained audience data often hurt performance before strategy even begins.
Outdated lists and disconnected data systems weaken automation. In an machine-learning environment, strong data hygiene ensures campaigns have the quality signals they need to perform.
Why integrated marketing is no longer optional
Amanda’s background in psychology and SEO shaped her integrated approach. PPC touches landing pages, user experience, and sales processes. When conversions drop, the issue may lie outside the ad account.
Understanding the full customer journey allows marketers to diagnose problems holistically. For Amanda, integration is a practical necessity, not a buzzword.
AI, automation, and the human factor
While AI dominates industry conversations, Amanda stresses balance. Some tools are promising, but not all are ready for full deployment. Testing is essential, but human oversight remains critical.
Machines optimize patterns, but humans judge emotion, messaging, and brand fit. Marketers who study changing customer journeys can also find new opportunities to intercept audiences across channels.
Building a culture that welcomes mistakes
Amanda believes leaders act as emotional barometers. Calm investigation beats reactive blame when issues arise. Many PPC problems stem from external changes, not individual failure.
By acknowledging stress and focusing on solutions, leaders create psychological safety. This environment encourages experimentation and turns mistakes into learning opportunities.
Testing without fear in an changing landscape
Marketing is entering another experimental era with no clear rulebook. Amanda encourages teams to dedicate budget to testing and lean on professional communities for insight.
Not every experiment will succeed, but each provides data that informs smarter future decisions.
The tasmanian devil who practices yoga
Amanda describes her career as If the Tasmanian Devil Could Do Yoga — a blend of fast-paced chaos and intentional calm. It reflects modern marketing: demanding, unpredictable, and balanced by thoughtful leadership.
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Google is rolling out a beta feature that lets advertisers run structured A/B tests on creative assets within a single Performance Max asset group. Advertisers can split traffic between two asset sets and measure performance in a controlled experiment.
Why we care.Creative testing inside Performance Max has mostly relied on guesswork. Google’s new native A/B asset experiments bring controlled testing directly into PMax — without spinning up separate campaigns.
How it works. Advertisers choose one Performance Max campaign and asset group, then define a control asset set (existing creatives) and a treatment set (new alternatives). Shared assets can run across both versions. After setting a traffic split — such as 50/50 — the experiment runs for several weeks before advertisers apply the winning assets.
Why this helps. Running tests inside the same asset group isolates creative impact and reduces noise from structural campaign changes. The controlled split gives clearer reporting and helps teams make rollout decisions based on performance data rather than assumptions.
Early lessons. Initial testing suggests short experiments — especially under three weeks — often produce unstable results, particularly in lower-volume accounts. Longer runs and avoiding simultaneous campaign changes improve reliability.
Bottom line. Performance Max is becoming more testable. Advertisers can now validate creative decisions with built-in experiments instead of relying on trial and error.
First seen. Google Ads expert spotted the update and shared his view on LinkedIn.
Google Ads rolled out a new data source diagnostics feature in Data Manager that lets advertisers track the health of their data connections. The tool flags problems with offline conversions, CRM imports, and tagging mismatches.
How it works. A centralized dashboard assigns clear connection status labels — Excellent, Good, Needs attention, or Urgent — and surfaces actionable alerts. Advertisers can spot issues like refused credentials, formatting errors, and failed imports, alongside a run history that shows recent sync attempts and error counts.
Why we care. When conversion data breaks, campaign optimization breaks with it. Even small connection failures can quietly skew conversion tracking and weaken automated bidding. This diagnostic tool helps teams catch and fix issues early, protecting performance and reporting accuracy. If you rely on CRM imports or offline conversions, this provides a much-needed safety net.
Who benefits most. The feature is especially useful for advertisers running complex conversion pipelines, including Salesforce integrations and offline attribution setups, where small disruptions can quickly cascade into bidding and reporting issues.
The bigger picture. As automated bidding leans more heavily on accurate first-party data, visibility into data pipelines is becoming just as critical as campaign settings themselves.
Bottom line. Google Ads is giving advertisers an early warning system for data failures, helping teams fix broken connections before performance takes a hit.
First seen. The update was first spotted by digital marketer Georgi Zayakov, who shared the new option on LinkedIn.
Performance Max has come a long way since its rocky launch. Many advertisers once dismissed it as a half-baked product, but Google has spent the past 18 months fixing real issues around transparency and control. If you wrote Performance Max off before, it’s time to take another look.
Mike Ryan, head of ecommerce insights at Smarter Ecommerce, explained why at the latest SMX Next.
Taking a fresh look at Performance Max
Performance Max traces its roots to Smart Shopping campaigns, which Google rolled out with red carpet fanfare at Google Marketing Live in 2019.
Even then, industry experts warned that transparency and control would become serious issues. They were right — and only now has Google begun to address those concerns openly.
Smart Shopping marked the low point of black-box advertising in Google Ads, at least for ecommerce. It stripped away nearly every control advertisers relied on in Standard Shopping:
Promotional controls.
Modifiers.
Negative keywords.
Search terms reporting.
Placement reporting.
Channel visibility.
Over the past 18 months, Performance Max has brought most of that functionality back, either partially or in full.
Understanding Performance Max search terms
Search terms are a core signal for understanding the traffic you’re actually buying. In Performance Max, most spend typically flows to the search network, which makes search term reporting essential for meaningful optimization.
Google even introduced a Performance Max match type — something few of us ever expected to see. That’s a big deal. It delivers properly reportable data that works with the API, should be scriptable, and finally includes cost and time dimensions that were completely missing before.
Search term insights vs. campaign search term view
Google’s first move to crack open the black box was search term insights. These insights group queries into search categories — essentially prebuilt n-grams — that roll up data at a mid-level and automatically account for typos, misspellings, and variants.
The problem? The metrics are thin. There’s no cost data, which means no CPC, no ROAS, and no real way to evaluate performance.
The real breakthrough is the new campaign-level search term view, now available in both the API and the UI.
Historically, search term reporting lived at the ad group level. Since Performance Max doesn’t use ad groups, that data had nowhere to go.
Google fixed this by anchoring search terms at the campaign level instead. The result is access to far more segments and metrics — and, finally, proper reporting we can actually use.
The main limitation: this data is available only at the search network level, without separating search from shopping. That means a single search term may reflect blended performance from both formats, rather than a clean view of how each one performed.
Search theme reporting
Search themes act as a form of positive targeting in Performance Max. You can evaluate how they’re performing through the search term insights report, which includes a Source column showing whether traffic came from your URLs, your assets, or the search themes you provided.
By totaling conversion value and conversions, you can see whether your search themes are actually driving results — or just sitting idle.
There’s more good news ahead. Google appears to be working on bringing Dynamic Search Ads and AI Max reports into Performance Max. That would unlock visibility into headlines, landing pages, and the search terms triggering ads.
Search term controls and optimization
Negative keywords
Negative keywords are now fully supported in Performance Max. At launch, Google capped campaigns at 100 negatives, offered no API access, and blocked negative keyword lists—clearly positioning the feature for brand safety, not performance.
These negatives apply across the entire search network, including both search and shopping. Brand exclusions are the exception — you can choose to apply those only to search campaigns if needed.
Brand exclusions
Performance Max doesn’t separate brand from generic traffic, and it often favors brand queries because they’re high intent and tend to perform well. Brand exclusions exist, but they can be leaky, with some brand traffic still slipping through. If you need strict control, negative keywords are the more reliable option.
Also, Performance Max — and AI Max — may aggressively bid on competitor terms. That makes brand and competitor exclusions important tools for protecting spend and shaping intent.
Optimization strategy
Here’s a simple heuristic for spotting search terms that need attention:
Calculate the average number of clicks it takes to generate a conversion.
Identify search terms with more clicks than that average but zero conversions.
Those terms have had a fair chance to perform and didn’t. They’re strong candidates for negative keywords.
That said, don’t overcorrect.
Long-tail dynamics mean a search term that doesn’t convert this month may matter next month. You’re also working with a finite set of negative keywords, so use them deliberately and prioritize the highest-impact exclusions.
Modern optimization approaches
It’s not 2018 anymore — you shouldn’t spend hours manually reviewing search terms. Automate the work instead.
Use the API for high-volume accounts, scripts for medium volume, and automated reports from the Report Editor for smaller accounts (though it still doesn’t support Performance Max).
Layer in AI for semantic review to flag irrelevant terms based on meaning and intent, then step in only for final approval. Search term reporting can be tedious, but with Google’s prebuilt n-grams and modern AI tools, there’s a smarter way to handle it.
Channels and placements reporting
Channel performance report
The channel performance report — not just for Performance Max — breaks performance out by network, including Discover, Display, Gmail, and more. It’s useful for channel visibility and understanding view-through versus click-through conversions, as well as how feed-based delivery compares to asset-driven performance.
The report includes a Sankey diagram, but it isn’t especially intuitive. The labeling is confusing and takes some decoding:
Google also announced that Search Partner Network data is coming, which should add another layer of useful performance visibility.
Channel and placement controls
Unlike Demand Gen, where you can choose exactly which channels to run on, Performance Max doesn’t give you that control. You can try to influence the channel mix through your ROAS target and budget, but it’s a blunt instrument — and a slippery one at best.
Placement exclusions
The strongest control you have is excluding specific placements. Placement data is now available through the API — limited to impressions and date segments — and can also be reviewed in the Report Editor. Use this data alongside the content suitability view to spot questionable domains and spammy placements.
For YouTube, pay close attention to political and children’s content. If a placement feels irrelevant or unsafe for your brand, there’s a good chance it isn’t driving meaningful performance either.
Tools for placement review
If you run into YouTube videos in languages you don’t speak, use Google Sheets’ built-in GOOGLETRANSLATE function. It’s faster and more reliable than AI for quick translation.
You can also use AI-powered formulas in Sheets to do semantic triage on placements, not just search terms. These tools are just formulas, which means this kind of analysis is accessible to anyone.
Search Partner Network
Unfortunately, there’s no way to opt out of the Search Partner Network in Performance Max. You can exclude individual search partners, but there are limits.
Prioritize exclusions based on how questionable the placement looks and how much volume it’s receiving. Also note that Google-owned properties like YouTube and Gmail can’t be excluded.
Based on Standard Shopping data, the Search Partner Network consistently performs meaningfully worse than the Google Search Network. Excluding poor performers is recommended.
Device reporting and targeting
Creating a device report is easy — just add device as a segment in the “when and where ads showed” view. The tricky part is making decisions.
Device analysis
For deeper insight, dig into item-level performance in the Report Editor. Add device as a segment alongside item ID and product titles to see how individual products behave across devices. Also, compare competitor performance by device — you may spot meaningful differences that inform your strategy.
For example, you may perform far better on desktop than on mobile compared to competitors like Amazon, signaling either an opportunity or a risk.
Device targeting considerations
Device targeting is available in Performance Max and is easy to use, much like channel targeting in Demand Gen. But when you split campaigns by device, you also split your conversion data and volume—and that can hurt results.
Before you separate campaigns by device, consider:
How competition differs by device
Performance at the item and retail category level
The impact on overall data volume
Performance Max performs best with more data. Campaigns with low monthly conversion volume often miss their targets and rarely stay on pace. As more data flows through a campaign, Performance Max gets better at hitting goals and less likely to fall short.
Any gains from splitting by device can disappear if the algorithm doesn’t have enough data to learn. Only split when both resulting campaigns have enough volume to support effective machine learning.
Conclusion
Performance Max has changed dramatically since launch. With search term reporting, negative keywords, channel visibility, placement controls, and device targeting now available, advertisers have far more transparency and control than ever before.
It’s still not perfect — channel targeting limits and data fragmentation remain — but Performance Max is fundamentally different and far more manageable.
Success comes down to knowing what data you have, how to access it efficiently using modern tools like AI and automation, and when to apply controls based on performance insights and data volume needs.
Watch: PMax reporting for ecommerce: What Google is (and isn’t) showing you
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Job Title: On-Page SEO Specialist 
Experience: 5+ Years 
Schedule: 8 AM – 5 PM CST 
Compensation: $10-$15/hour (based on experience) 
Fully Remote | Full-time Contract Position 
Job Overview We’re looking for a seasoned On-Page SEO Specialist to optimize and enhance our website’s on-page SEO performance while driving multi-location performance […]
delivers interoperable data where and when it’s needed, empowering data and analytics leaders with unparalleled […]
