OpenAI is beginning to build the infrastructure for a formal advertising business around ChatGPT — but early performance signals suggest the company still has work to do to match established search platforms.
What’s happening. OpenAI started testing an Ads Manager dashboard with a small group of partners, according to confirmation shared with ADWEEK. The tool allows marketers to launch, monitor, and optimise campaigns in real time, similar to the campaign management platforms used across digital advertising.
Why we care. OpenAI is beginning to build a self-serve ads ecosystem around ChatGPT with a dedicated Ads Manager, as they prepare for AI assistants becoming a scalable channel. As conversational search grows, paid media marketers may need to think about visibility inside AI responses, not just traditional platforms like Google Search.
Early testing also means advertisers who participate now could gain first-mover insights into performance, formats, and optimisation strategies in a new advertising environment.
How it works today. Early testers currently receive weekly CSV performance reports that include metrics such as impressions and clicks. The reporting indicates the ads product is still evolving, with more advanced analytics and tooling likely to follow as the program develops.
The challenge: Early tests suggest click-through rates on ChatGPT ads trail those seen on Google Search, highlighting a key hurdle for OpenAI as it tries to prove the value of advertising inside conversational AI.
The cost of entry. Some early advertisers have reportedly been asked to commit at least $200,000 in spend, raising the stakes for OpenAI to demonstrate measurable performance and ROI.
Between the lines. Building an ad ecosystem requires more than ad inventory. Marketers expect robust reporting, optimisation tools, and predictable performance — areas where mature platforms like Google have years of advantage.
Google appears to be testing a new “Sponsored Shops” format in Google Shopping results that highlights entire stores instead of individual products — a potential shift in how brands compete in Shopping ads.
What’s happening. Instead of displaying only single product listings, the new block groups multiple products from the same retailer into one sponsored unit. The format features the store name, several products from that shop, and signals such as ratings and brand presence, effectively creating a mini storefront directly inside the Shopping results.
Why we care. The new “Sponsored Shops” format in Google Shopping could shift competition from individual products to entire stores. Instead of winning visibility with a single SKU, brands may need stronger product feeds, better ratings, and broader assortments to appear in these store-level placements.
It also introduces multiple click paths within one ad unit, which could change how traffic flows between product pages and store pages. If the format scales, it may reshape how advertisers optimise campaigns across Google Shopping — prioritising brand presence and feed quality, not just product-level bids.
The big picture. The test suggests a move slightly up the funnel for Shopping ads. Rather than focusing solely on a single SKU, brands can showcase a broader product assortment and reinforce their store identity within one placement.
Why it’s notable. Store-level visibility means advertisers can highlight multiple products at once, increasing exposure per impression. It also strengthens brand presence by combining store name, ratings, and product range in one block.
For users, it makes discovery easier by allowing them to browse several items from the same retailer without navigating away from results.
Between the lines. If the format rolls out widely, it could reward brands with strong product feeds, high seller ratings, and clear brand trust signals. Merchants with well-structured feeds and competitive assortments may gain more visibility compared with those relying on a few individual product listings.
What to watch. One open question is how users will interact with the different clickable elements inside the ad unit. Marketing Operating Lead, Stephanie Pratt commented on this and what measurement split we may expect:
“It’ll be interesting to see the split of clicks on each part of the ad unit, and how much is on the brand name vs product and if that will confuse some consumers
The bottom line. If “Sponsored Shops” expands beyond testing, it could push Google Shopping toward more store-level competition — shifting strategy from purely product-level optimisation to building stronger brand presence within the Shopping ecosystem.
Fist seen. This update was spotted by PPC Specialist Arpan Banerjee who shared a screenshot of the update on LinkedIn.
Chloe Varnfield, a digital marketing specialist at Atelier Studios with nearly eight years in PPC, joined me to share the mistakes that shaped her career — and the lessons every advertiser should take from them.
When Google sneaks settings past you
Chloe’s first story centers on Google’s account-level automated assets setting — a feature so well hidden that many advertisers don’t know it exists until a client sends a screenshot asking why their headline looks completely wrong. The setting, buried behind a three-dot menu, defaults to “on”, meaning Google can automatically generate and serve headlines advertisers never wrote or approved. The takeaway: always audit your account-level settings, and treat every Google update as a potential default you’ll need to turn off.
Why you should never make changes on a Friday
A client asked Chloe to narrow their campaign’s location targeting mid-call. She made the change quickly — and accidentally excluded the UK entirely while targeting only the desired regions. Campaigns stopped delivering. It took three days of head-scratching before she audited the full campaign and found the culprit. The lesson she now swears by: never make significant changes on a Friday, and when something stops working, go straight to a full audit rather than waiting for the algorithm to “fix itself.”
The time she listened to a Google rep — and tanked performance for two months
Chloe’s most costly story involves a campaign that was performing at its best in years. A Google rep recommended switching bid strategy from Maximise Conversions to Maximise Conversion Value. She made the switch — and performance collapsed. For small to medium-sized businesses that already struggle to hit the conversion volume thresholds needed for smart bidding to work effectively, changing bid strategy is a high-stakes decision that shouldn’t be made on the spot. It took two months to recover, with the pressure of a major seasonal sale looming. She fixed it — but the lesson stuck: don’t let enthusiasm or a rep’s insistence override your judgment. Sit on big decisions. Trust your gut.
The account mistakes that still happen in 2026
When auditing inherited accounts, Chloe consistently sees the same three problems: broken or absent conversion tracking (sometimes still pulling from Universal Analytics), broad match applied to brand campaigns — which makes it impossible to know whether results are genuinely driven by non-brand keywords — and accounts with zero negative keywords. These aren’t minor structural issues. They directly distort performance data and waste budget.
On honesty, client relationships, and not spiralling
Across all three of her own stories, Chloe’s client relationships survived because she communicated transparently — explaining what had gone wrong, what she was doing to fix it, and what the next step would be if that didn’t work. Her advice to anyone mid-crisis: breathe, be kind to yourself, stay calm, and remember that no one has died. The ability to fix problems under pressure is what builds expertise — and fixing something difficult often becomes your proudest professional moment.
The AI mistake too many marketers are making
On AI, Chloe is clear: using it to generate ad copy or proposals without reviewing or editing the output is lazy and obvious. AI should make you faster, not replace your judgment. Always put your own voice and review back into whatever it produces.
Chloe Varnfield shares sneaky Google settings, Friday mistakes, and Google rep advice that tanked her campaigns — and the hard-won lessons that came after.
The days of building campaigns around long lists of keywords are fading. Today, AI-powered Google campaigns and features like Performance Max (PMax) and AI Max are changing the rules.
These keywordless campaigns lean on automation, audience signals, and machine learning to find new opportunities, often faster and at greater scale than humans can.
At SMX Next, three PPC pros — Nikki Kuhlman, VP of search at Jumpfly; Brad Geddes, founder of Adalysis; and Christine Zirnheld, director of lead gen at Cypress North — explained where PMax and AI Max fit into your broader campaign strategy, where humans still make the difference, and how to strike the right balance between automation and control.
AI Max for Search: Best practices and what not to do
AI Max for Search is not a new campaign type. It’s a one-click opt-in setting within existing Search campaigns.
Without requiring you to switch to broad match, it expands your keywords — similar to broad match or Dynamic Search Ads — using your landing pages and other site assets. It then personalizes the ad copy and landing page the searcher sees.
The evolution from traditional setup
In the old setup, you might have used a keyword like “skincare for dry sensitive skin” that sent users to a moisturizer page with generic ad copy because you couldn’t capture every variation. With Google’s current matching, a specific ad group no longer guarantees that keyword will trigger that ad group.
AI Max for Search addresses this by generating ad copy based on the search query, making it more relevant and directing users to a landing page that better matches their needs.
Success with blog content
One area where AI Max for Search is seeing success beyond the norm is blog content. While DSA campaigns traditionally excluded blogs, AI Max for Search can now serve blogs as landing pages—and they’re converting. The key is that these blogs guide readers to specific products, not just general content.
The generated headlines are compelling and longer than what traditional RSAs allow, creating a more engaging user experience.
Best Practices for AI Max for Search
Do:
Use it on existing campaigns with history and data, not brand new campaigns
Test it as a 50/50 experiment instead of an outright change
Use it on brand campaigns with brand inclusion capabilities
Apply it to campaigns not hitting budget that could use more volume
Review landing pages and utilize URL exclusions (individual or rule-based)
Use landing page inclusions at the ad group level
Review search queries regularly and add negative search terms
Enable both text customization and final URL expansion for maximum value
Turn off AI Max at the ad group level when specific ad groups drive poor traffic
Don’t:
Use it on brand new campaigns without data
Change all campaigns at once without testing
Use it on brand campaigns without name recognition or brand inclusion ability
Apply it to budget-constrained campaigns
Turn off both URL expansion and text customization — if you’re not using both features, stick with broad match and smart bidding
Assume it works universally — test on individual campaigns
Your action plan
Week 1: Pick a search campaign to test (brand with brand inclusion, with budget capability, needing more volume). Review landing page URLs and add inclusions or exclusions.
Week 2: Review search queries and add negatives.
Week 3: Continue optimization and turn off AI Max at the ad group level as needed.
Experiment checklist:
Ensure enough volume for a 50/50 experiment
Give the experiment 6 weeks to 2 months
Set up a custom experiment if you need to enable brand inclusion or update settings
For one-click experiments, change confidence level to medium and turn off auto-apply
Match type performance: What the data shows
A comprehensive study analyzing over 16,000 campaigns revealed surprising insights about match type performance across different bidding strategies.
Match type basics
Exact match: Should match only when the search term has the same intent as your keyword. Misspellings and word order haven’t mattered for years — focus on user intent.
Phrase match: The search intent should match your keyword, but could have additional information around it, whether modifiers, phone numbers, or websites.
Broad match: Shows for anything related to the search intent. The key difference is that broad match uses additional signals that exact and phrase don’t, such as content on the landing page, other keywords in the ad group, and, most powerfully, previous search history for that user.
Performance by bidding strategy
Max Conversion strategies (Max Conversions, Max Conversion Value):
Most campaigns using max bid strategies have under 30 conversions per month, giving machines limited data to work with. The findings:
Exact match has the best click-through rates and conversion rates
Broad match had the worst conversion rates but the best return on ad spend
Broad match also had lower CPA than phrase match
Phrase match performed worst overall
Recommendation: Start with exact match, then skip phrase match entirely and layer in broad match if you have more budget to spend.
Target Bid Strategies (Target CPA, Target ROAS):
Most campaigns using these strategies have over 30 conversions per month, with many at 50 or 100+, giving machines substantially more data. The findings:
Exact match is again the best match type
Phrase match comes second
Broad match is third
Phrase match performs better with more data
Recommendation: Start with exact match, layer in phrase match with more budget, then add broad match if additional budget is available.
The phrase match puzzle
Why does phrase match perform poorly with limited data but better with more data?
Broad match uses additional signals, particularly previous search queries, to determine bids. When conversion data is limited (under 30 conversions monthly), broad match’s ability to leverage previous search history makes it much stronger than phrase match.
However, with sufficient data (50–100+ conversions), Google can properly match phrase match keywords using machine-learning pattern matching.
Brand vs. non-brand considerations
When you combine brand and non-brand data, exact match becomes even more powerful, delivering significantly higher click-through rates, higher conversion rates, lower CPAs, and much higher return on ad spend. That’s why segmenting keywords by brand and non-brand is crucial when determining your match type strategy.
Ecommerce exception
For ecommerce companies, broad match (and sometimes phrase match) can produce higher average order values than exact match. When someone searches for a specific product, and you carry that exact item, conversion rates are high, but they’re usually buying a single product with a lower checkout value.
When shoppers haven’t decided on a product, they tend to match broader keywords and build larger carts — resulting in lower conversion rates but higher order values.
Performance Max for lead generation
There’s a common misconception that Performance Max only works for ecommerce and is too difficult for lead generation. That couldn’t be further from the truth.
The critical success factor
The biggest mistake you can make—one you should avoid entirely—is optimizing campaigns for form submissions alone. If you treat every form submission as your campaign goal, you’ll end up with spammy submissions and frustrated sales teams.
The solution: integrate your Google Ads account with your CRM and import bottom-of-funnel leads—sales-qualified leads (SQLs), marketing-qualified leads (MQLs), opportunities, or even customers if the sales cycle is short.
When you tell Google Ads what you actually want and set it as your campaign goal, Performance Max can cast a wide net while still bringing in qualified prospects.
Available controls for regulated industries
Performance Max has significantly more controls now than at launch, making it viable for highly regulated industries:
Brand exclusions: Exclude all brand traffic from Performance Max campaigns
Search term reports: See what’s triggering your ads and exclude accordingly
Channel reporting: View spending and performance across different networks
Page feeds: Control where you send traffic on your site
Final URL expansion toggle: Turn it off completely if needed
Text enhancement controls: Optional feature that can be disabled entirely
Text guidelines: Specify words to avoid (e.g., “discount” or “directory”)
Device control: The secret weapon for B2B
One of the most underutilized levers for B2B and regulated industries is device control, introduced at the beginning of 2025. You can turn off any device from your Performance Max campaign.
A B2B SaaS example demonstrates the impact: Before device segmentation in January, the account had 224 SQLs from desktop at an acceptable CPA, but 33 from mobile at $319 CPA (above goal). After creating separate mobile campaigns with more aggressive target CPAs, they achieved 190 desktop SQLs and 37 mobile SQLs in a shorter month, with mobile CPA dropping to $204 and overall Performance Max CPA declining from $238 to $204.
Real Performance Max results for B2B SaaS
Despite lower conversion rates from Performance Max compared to search campaigns (due to broader reach), the results speak for themselves. In September 2025, one B2B SaaS account achieved:
Search Campaigns: 150 SQLs at $237 CPA
Performance Max: 204 SQLs at $220 CPA
Performance Max cast a wider net with cheaper CPCs, bringing in not just more leads but more sales-qualified leads at a lower cost.
How they did it:
Optimized for SQLs, not form submissions
Set lower target CPAs in Performance Max than search (to control spend while casting wider net)
Created separate campaigns for off-hours to control weekend spending
Turned off final URL expansion and text enhancements (client preference)
Implemented separate mobile and tablet campaigns with aggressive target CPAs
AI Max for Search in lead generation
AI Max for Search brings the power of Performance Max to the search network, where bottom-of-funnel intent is strongest. This is especially valuable for lead generation accounts that spend on other networks in Performance Max but don’t generate leads from them.
Early results: Higher ed financial services
A higher education financial client (loan products) showed promising early results:
Approved applications (primary KPI):
Standard Search: 86 approved applications at $660 CPA
AI Max: 70 approved applications at $579 CPA
AI Max brought in qualified leads cheaper despite the highly competitive keyword environment.
Down-funnel performance
Beyond the initial conversion action (soft credit check), AI Max showed superior performance throughout the funnel:
42% of AI Max form submissions resulted in soft pulls vs. 36% for standard search
9.9% of AI Max form submissions resulted in bookings vs. 5.58% for standard search
AI Max isn’t just bringing more qualified prospects at the top—lead quality remains higher throughout the entire funnel.
How they did it:
Optimized for approved applications, not form submissions
Set lower target CPAs in AI Max than standard search
Used high-performing bottom-of-funnel keywords with broad match types
Kept final URL expansion and text enhancements disabled (still worked well without them)
Win with AI without losing control
PPC success requires embracing AI-driven campaigns while maintaining strategic human oversight. Whether you use AI Max for Search, Performance Max for lead generation, or adjust match types based on bidding methods and data volume, the key is understanding how these tools work and applying best practices aligned with your business goals.
The data is clear: exact match remains powerful across scenarios, but phrase and broad match perform differently depending on bidding strategy and data volume. For lead generation, the game changer is optimizing for true bottom-of-funnel conversions rather than form submissions, combined with strategic device controls and proper campaign segmentation.
The future of PPC depends on knowing when — and how — to apply automation and control for maximum impact.
Google is leaving the door open to advertising in its Gemini AI app, with a senior executive telling WIRED the company is “not ruling them out” — a notable shift from the flat denials made just months ago.
The current strategy. Rather than rushing into Gemini, Google is using AI Mode — its Gemini-powered Search product — as a testing ground for ad formats in AI experiences.
Ads are kept separate from organic results and clearly labeled
Google says it only shows ads when they’re relevant — if nothing fits, nothing runs
The company is drawing on 20-plus years of Search ad experience to inform the approach
Why we care. Google’s entire business is built on advertising. How and if they bring ads into AI products will shape the future of the industry — and set the tone for every AI company trying to figure out how to monetize free users. The brands that figure out how to show up relevantly in conversational AI environments now — before the auction gets competitive — will have a significant first-mover advantage.
The bigger picture. Google is in a stronger position than its rivals to take its time. The company crossed $400 billion in revenue in 2025, giving it the luxury of patience. OpenAI, by contrast, is under pressure to more than double its $30 billion in revenue this year — and has already started testing ads in ChatGPT’s free tier.
Between the lines: Fox’s framing is careful but revealing. By positioning Gemini ads as a “prioritization question” rather than a values question, Google is signaling it’s a matter of when — not if.
What to watch: Personal Intelligence — Gemini’s feature that pulls from a user’s Gmail, Photos, and Calendar — is the sleeper story here. Fox called personalization his “holy grail” for Search, and hinted it could eventually roll into the broader Search experience. If it does, advertisers would gain access to an entirely new layer of contextual targeting — though Fox was quick to add that user data will not be sold or shared.
What’s next. Advertisers should start preparing now. As Google refines its AI ad formats in AI Mode, those learnings will eventually migrate to Gemini. Brands that understand how to show up relevantly in conversational, context-rich AI environments will have a significant head start when the floodgates open.
Looking to take the next step in your search marketing career?
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