Google is bringing generative AI directly into Google Ad Manager with the launch of Ask Ad Manager, a new Gemini-powered assistant designed to help publishers analyze performance, troubleshoot issues and navigate the platform using natural language.
The beta launches this month as Google pushes deeper into AI-powered ad operations.
What’s happening. Ask Ad Manager is a conversational AI agent built specifically for publishers using Google Ad Manager.
Unlike traditional reporting tools, publishers can ask questions in plain language and receive personalized answers, recommendations and reports based on their own Ad Manager data.
Google says the tool is designed to help users move from analysis to action faster by reducing the time spent generating reports, diagnosing problems and navigating the platform.
What it can do:
Troubleshoot delivery issues.
Instead of manually pulling reports to investigate underperforming line items, publishers can ask the AI agent questions and receive guidance on potential causes and next steps.
Generate reports on demand.
Users can request custom metrics, benchmarks and performance reports through a simple prompt rather than building multiple reports manually.
Navigate Ad Manager faster.
Ask Ad Manager can direct users to relevant pages within the platform and automatically apply the appropriate filters and settings based on the conversation.
Why we care. For publishers managing large inventories and complex campaigns, the ability to quickly surface insights and diagnose issues could reduce operational workload and accelerate decision-making.
The feature also reflects a growing shift across ad tech toward AI agents that can perform tasks and streamline workflows instead of simply generating information.
Looking ahead. Google says Ask Ad Manager is just the beginning of a broader move toward what it calls a more “agentic” future for advertising operations.
The company plans to introduce additional AI capabilities throughout the year, including developer tools such as REST APIs and an MCP server to support workflow automation and integrations.
Google is also developing specialized agents that could help publishers and agencies discover inventory, negotiate deals and execute campaigns more efficiently.
Bottom line. Ask Ad Manager brings Gemini-powered assistance directly into Google Ad Manager, giving publishers a new way to access insights, resolve issues and manage advertising operations through natural language prompts.
Google Ads is rolling out a beta that allows advertisers to connect additional data sources directly to website conversion actions, giving marketers a new way to supplement tag-based measurement with backend conversion data.
The feature enables advertisers to combine conversion signals collected through Google tags with transaction data from systems such as CRMs, order databases and ecommerce platforms.
What’s new. Advertisers can now attach an additional data source to an existing website conversion action through Google Ads Data Manager or the Data Manager API.
The beta is designed to supplement — not replace — website tagging by allowing advertisers to send conversion data from backend systems into the same conversion action used for campaign measurement and optimization.
Why we care. The new beta helps fill conversion measurement gaps by combining Google tag data with first-party data from backend systems like CRMs and order databases. This can recover conversions that may be missed due to browser restrictions, privacy settings, or ad blockers, giving advertisers a more complete view of campaign performance.
Why Google launched it. According to Google, combining tag-based measurement with backend conversion data can help advertisers create a more complete picture of conversions and improve campaign performance.
The company says the feature can help:
Recover conversions that may not be captured by website tags.
Improve measurement resilience.
Provide more comprehensive data for automated bidding.
Simplify data integration through Data Manager.
How it works. The system combines website conversion data collected through Google tags with conversion records uploaded from an advertiser’s backend systems.
To prevent duplicate reporting, Google uses transaction IDs to identify and deduplicate conversions between the tag and the additional data source within the same conversion action.
What advertisers need to know. The beta is currently limited to website conversion actions that use Google tag or Google Tag Manager implementations.
It is not available for:
Google Analytics imported conversions.
URL-based conversion actions.
Google recommends adding an additional data source to an existing conversion action rather than creating a new one to avoid potential double-counting across campaign goals.
Data requirements. Every upload must include:
Transaction ID.
Conversion date and time.
Advertisers must also provide at least one attribution identifier, such as hashed customer information or a Google click identifier.
Google recommends uploading conversion data as quickly as possible and ensuring uploaded conversion values match the same currency format used by website tags.
Bottom line. The beta marks Google’s latest effort to strengthen conversion measurement by bringing backend transaction data directly into Google Ads. As advertisers look for more complete performance data, the new capability offers a streamlined way to supplement website measurement with first-party business data.
PPC budgeting in 2026 isn’t just about setting spend levels. It’s about knowing when to adjust budgets, when to scale campaigns, and how the data feeding Google’s automation influences those decisions.
Google’s automation systems have always followed the signals you give them. In 2026, they follow them faster and with more confidence than before, which means clean signal architecture matters more than ever.
The fundamentals of budget management haven’t changed. What has changed is how quickly a poorly architected account can waste budget.
Two budget mechanics you need to understand right now
Before you adjust targets, audiences, or bid strategies, make sure you understand how these two budget controls work.
The ad scheduling pacing change
Google now paces all campaigns with ad scheduling toward the full 30.4x monthly billing cap, regardless of how many days your ads actually run. Before this change, a $100 daily budget on a weekday-only campaign targeted roughly $2,200 in monthly spend across 22 active days.
Now it targets $3,040, compressed into those same weekdays. The billing ceiling hasn’t changed. The system pursues it more aggressively within your active windows.
If your campaigns use ad scheduling, recalculate your daily budget based on your intended monthly spend rather than active days: divide your monthly target by 30.4 and set that as your daily limit. A $2,200 monthly target becomes a $72 daily budget. Campaigns running 24/7 aren’t affected.
Available for Demand Gen, Search, Standard Shopping, Performance Max, and YouTube campaigns, campaign total budgets let you set a fixed spend ceiling for a defined period rather than managing a daily limit.
For Search, Standard Shopping, and PMax, the window is three to 90 days. For Demand Gen and YouTube, it can run up to a year.
Unlike daily budgets, there’s no daily spending cap. The system can front-load or back-load spend within the flight to hit the total, which makes these useful for promotions and product launches, but worth monitoring closely when run alongside always-on campaigns.
Budget type can’t be changed after campaign creation, so the decision is final at setup.
What actually controls how Google Ads spends your budget
Efficiency targets usually constrain spend before budgets do
Smart Bidding treats your efficiency target as the primary constraint and your daily budget as the secondary one.
If you set a $50 tCPA and market conditions are returning leads at $80,the system restricts bids rather than generating conversions above your target. The daily budget cap never gets hit because the efficiency target is stopping spend first. What looks like a budget problem is usually a target problem.
When the gap between target and market reality is that wide, set your initial target closer to where the market is actually converting. Let the system accumulate conversion data and establish what efficiency looks like for your account, then gradually tighten toward your real goal.
The 10%-20% margin above target is a fine-tuning tool. It gives Smart Bidding enough room to find conversion opportunities when you’re already close to where you want to be, not when you’re $30 away.
Performance Max decides where your budget goes
Performance Max automatically distributes budget across Search, Shopping, Display, YouTube, and Discover. You set the total. Google decides the split.
Without brand exclusions, PMax will serve branded queries that would have converted through Search campaigns at a lower cost, which inflates its apparent efficiency while increasing your overall costs.
Campaign-level negative keyword lists for PMax have been available since January 2025, with the per-campaign limit expanded to 10,000 in March 2025. If your PMax campaigns predate that rollout, audit whether you have categorical exclusion lists built at the campaign level.
Jobs, salary, free, login, reviews, and any vertical-specific non-customer queries should be in there before the campaign launches, not added reactively from the search term report.
AI Max expands where your ads can appear
AI Max for Search, generally available since April, expands query matching beyond your keyword list, generates ad copy from your existing assets, and adjusts landing page targeting dynamically.
The budget risk is query drift: spend that was concentrated on your defined keywords now competes with AI-generated matches. AI Max provides search term reporting, which makes monitoring tractable. Review it closely during the first 60 days and proactively build categorical negatives.
The signal problem that makes budget allocation fail
An insurance broker running Smart Bidding toward form completions saw conversion volume rise 416% year over year while revenue stayed flat. The conversion action was firing on form starts, not form submissions.
The system had found the most efficient path to form page interactions and was scaling it confidently. A significant portion of those interactions were Cyrillic-language spam submissions from outside the service area. The dashboard was green. The pipeline was empty.
This is the core mechanism behind most budget waste in lead generation: identical conversion values across all form fills leave Smart Bidding with no basis to distinguish a qualified lead from a bounced session.
The system optimizes for volume and finds the cheapest path to completions. It follows its instructions precisely. The instructions are the problem.
Primary conversions should be high-intent, high-value actions that directly train Smart Bidding. Secondary conversions, such as newsletter signups, page views, and soft engagement, belong in reporting but should not influence bidding. Getting this distinction right is more consequential for budget efficiency than any adjustment to bid strategy.
Journey-aware bidding, currently in beta for Search campaigns on Target CPA, addresses the delayed-conversion problem that compounds this issue for B2B accounts.
Instead of optimizing only toward front-end actions, the system learns from the full lead-to-sale funnel — form submissions through closed deals — using intermediate stages as learning signals without counting them as biddable conversions.
The feature requires first-party CRM data, connected via Offline Conversion Import or Enhanced Conversions for Leads, to function. Without that pipeline data, there’s nothing for the system to learn beyond the form fills it was already optimizing toward.
For accounts not yet in the beta, extending your conversion window to 90 days and evaluating performance over 60- to 90-day periods is the right workaround.
First-party data as budget guidance
Customer Match is the most direct way to tell automation what valuable traffic looks like. Google enforces a 540-day maximum membership duration for Customer Match lists, effective April 2025. Any record not refreshed within that window expires, which shrinks your list over time without regular uploads or a continuous CRM sync.
The most effective use of Customer Match for budget allocation is to exclude before expanding.
Apply your existing customer list as an exclusion on acquisition campaigns so the acquisition budget reaches new customers rather than people who are already buying from you.
Run retention separately, with its own budget, targets, and messaging. Mixing both in the same campaign with identical conversion goals produces a blended signal. Smart Bidding typically settles on the segment that converts most cheaply, which is rarely the most valuable one.
Note that using Customer Match for targeting and bid adjustments requires at least 90 days of account history and $50,000 in lifetime spend. Exclusions are available to all compliant accounts regardless of spend history.
For always-on daily budget campaigns, the 10-20% weekly increase guidance still applies. For campaigns using ad scheduling, work in monthly targets and divide by 30.4 rather than scaling daily limits.
Smart Bidding Exploration is now in open beta for Performance Max, with Shopping expansion announced at GML 2026. On Search campaigns, it generates, on average, 27% more unique converting users by pursuing queries the account wasn’t previously winning, temporarily relaxing efficiency targets to test new conversion sources. Short-term CPA or ROAS fluctuations during the exploration phase are expected. Evaluate on a 60-day window before drawing conclusions.
Demand-led pacing, announced at GML 2026 and rolling out for Search and Shopping campaigns, dynamically shifts daily spend toward periods of predicted higher consumer demand within your existing budget parameters. It’s a complement to daily budget management, not a replacement. Monitor your account for rollout availability.
For B2B accounts, scale on 60- to 90-day evaluation windows, not 30-day ones. Short windows systematically undervalue campaigns with long sales cycles by cutting spend before the attribution data has time to accumulate.
Meta is introducing new commerce features across Facebook and Instagram as it looks to turn more AI-driven product discovery into completed purchases.
What’s happening. Meta is expanding Live Video Ads globally on Facebook and bringing them to Instagram, allowing businesses to promote livestreams to larger audiences and drive sales directly from live shopping experiences.
In the U.S., Meta is working with live commerce providers including CommentSold, Firework, LiveMeUp, Sprii and TalkShopLive, enabling sellers to convert eligible livestreams into ads that can reach potential customers beyond their existing audiences.
To support live commerce, Facebook’s Live Shopping Tools let viewers browse products, view pricing and discover items to buy without leaving the livestream.
New checkout experience. Starting this summer, Meta will roll out a virtual card payment option on Facebook and Instagram in partnership with Mastercard and Visa.
The feature generates temporary, one-time card numbers linked to a shopper’s existing payment card, allowing users to complete purchases without sharing their actual card details with merchants. Meta says the move is designed to increase consumer confidence and improve transaction security.
For advertisers. Meta is also making product data a foundational element of all Sales campaigns. Instead of selecting from separate catalog and creative ad formats, advertisers will be able to provide both product feeds and creative assets, with Meta’s AI automatically assembling the most effective ad for each individual user.
Product information such as price, availability and descriptions will be used across more campaign formats, helping advertisers create richer shopping experiences while maintaining performance.
Why we care. Meta is giving brands more ways to convert product discovery into sales without users leaving its apps. The expansion of Live Video Ads could help advertisers reach larger audiences with livestream shopping content, while AI-powered Sales campaigns will automatically combine product data and creative assets to serve the most relevant products to shoppers.
The addition of virtual card checkout could also reduce purchase friction and increase consumer trust, potentially improving conversion rates.
The bigger picture. Meta says AI is fundamentally changing how people discover products, with recommendations increasingly occurring within content feeds, creator videos and conversations rather than through traditional search.
The company is positioning product catalogs as a key signal powering these experiences, helping products appear across shopping surfaces such as creator content, business recommendations and Meta AI-powered shopping experiences.
Google is removing a layer of advertiser control over Customer Match audience classification, automatically assigning customer types to conversion-based lists starting in August 2026.
Advertisers will no longer be able to leave eligible lists unclassified.
What’s changing. Beginning in August 2026, Google Ads will automatically assign conversion-based customer lists to one of several customer types, including:
Existing customers
New customers
Other customer segments
Google is encouraging advertisers to review and update their audience classifications in Audience Manager before the change takes effect.
Why Google is making the change. The move appears aimed at improving audience consistency across Google’s growing suite of customer acquisition and retention tools.
By standardizing customer lifecycle classifications, Google can more accurately distinguish between prospecting and retention audiences, helping automated bidding and targeting systems make better optimization decisions.
Why we care. For advertisers using customer acquisition goals, new customer bidding, or retention-focused strategies, the accuracy of customer classifications could have a direct impact on campaign performance.
Misclassified audiences could affect how Google’s systems evaluate and optimize users throughout the customer lifecycle.
What advertisers should do. Advertisers using Customer Match lists derived from conversion data should use audience manager to audit their audiences before August.
Key questions include:
Are customer lists currently categorized correctly?
Which lists represent existing customers versus acquisition audiences?
Will automatic classification align with internal customer definitions?
Reviewing audience settings now may help avoid unexpected changes once Google’s classifications become mandatory.
The bottom line. Google is taking a more active role in audience management, automatically assigning customer lifecycle labels to conversion-based customer lists and further standardizing the signals that power its automated advertising systems.
First spotted. This update was spotted by Google Ads expert Bia Camargo, who shared seeing the alert on LinkedIn.
Google is changing how it charges for certain Demand Gen campaigns on Discover, signaling a closer link between billing models and campaign optimization goals.
What happened. Google Ads has notified advertisers that Demand Gen campaigns using view-through conversion (VTC) optimization on Discover will move from cost-per-click (CPC) billing to cost-per-thousand impressions (CPM) beginning July 15th.
The change affects a limited number of advertisers and applies only to campaigns with VTC optimization enabled. Advertisers not using VTC optimization will see no change.
The transition will happen automatically, with no action required from advertisers.
Why we care. The change could alter how advertisers evaluate efficiency within Demand Gen campaigns. Campaigns optimized for view-through conversions may see differences in spend pacing, impression volume, and reporting metrics once billing transitions from clicks to impressions.
Advertisers focused primarily on click-driven performance may want to reassess whether VTC optimization remains the right fit for their objectives.
Why Google is making the change. According to Google, the update is designed to better align billing with campaign objectives.
View-through conversions measure actions taken after a user sees an ad but does not click it. Because impressions play a central role in generating those conversions, Google argues that CPM billing more accurately reflects the value being delivered.
The company also says the change will allow its systems to optimize more effectively for view-through conversion goals.
Opt-out option. Advertisers who do not want to transition to CPM billing can opt out by disabling view-through conversion optimization in campaign settings.Doing so will prevent the billing change from taking effect for those campaigns.
The bottom line. Google is tying payment more closely to the behavior its Demand Gen campaigns are designed to optimize for. For advertisers using view-through conversions, impressions—not clicks—will soon become the basis for both optimization and billing on Discover.
First spotted. The update was shared by Adsquire founder, Anthony Higman, who shared the comms he received on X.
Advertisers using Microsoft Ads can now target users based on job seniority, adding another layer of B2B audience precision powered by LinkedIn data.
What’s happening. Microsoft Advertising expanded its LinkedIn Profile targeting capabilities to include job seniority targeting across Search and Audience campaigns, according to Product Liaison Navah Hopkins.
The update allows advertisers to target or observe users based on 10 standardized seniority levels: CXO, VP, Director, Manager, Senior, Entry, Owner, Partner, Training, and Volunteer.
The feature is available at both the campaign and ad group level, giving advertisers more flexibility when segmenting audiences.
Why we care. B2B marketers have long struggled to distinguish between decision-makers and practitioners within search campaigns. The addition of job seniority targeting gives advertisers a way to better align messaging, bidding strategies, and reporting with specific audience segments.
For organizations with longer sales cycles or multiple stakeholders involved in purchasing decisions, understanding who is engaging with ads can be as important as the conversion itself.
Between the lines. Unlike many audience targeting options available across advertising platforms, Microsoft’s integration with LinkedIn data offers a professional identity layer that can help advertisers better understand who is behind a click.
The new seniority filters can be applied directly within campaign settings or used in observation mode to gather performance insights without restricting reach.
How marketers can use it:
Tailor messaging by seniority
Advertisers can create separate ad groups for executives, managers, and individual contributors, adapting tone and messaging based on audience expectations.
An executive-focused campaign might emphasize strategic outcomes and business growth, while messaging aimed at practitioners could focus on workflows, implementation, or efficiency gains.
Identify who is actually converting
Observation mode allows marketers to analyze conversion performance across seniority levels without narrowing targeting.
This can help answer questions such as:
Are conversions coming from decision-makers or influencers?
Is budget being spent on audiences that rarely close?
Which seniority levels generate the highest-quality leads?
Improve audience testing
The additional reporting layer provides another signal for optimization and expansion decisions.
Advertisers importing campaigns from other platforms may find performance patterns differ on Microsoft Ads, making seniority reporting a useful source of testing and audience discovery.
Availability. The feature is currently available in selected markets across the Americas, EMEA, and APAC regions.
Americas: Argentina, Brazil, Canada, Chile, Colombia, Ecuador, Mexico, Peru, and the United States.
EMEA: Egypt, Nigeria, Saudi Arabia, and South Africa.
APAC: Australia, India, Indonesia, Japan, Malaysia, Philippines, Singapore, Taiwan, Thailand, and Vietnam.
The bottom line. Microsoft Ads continues to lean into its LinkedIn integration as a differentiator in the B2B advertising market. The addition of job seniority targeting gives advertisers another way to connect search intent with professional identity, helping them better understand not just what audiences are searching for, but who is doing the searching.
The idea that AI is killing advertising misses the bigger shift. As AI expands across search, assistants, productivity tools, and transactions, advertising is moving with it.
Ad density may be changing within AI experiences, but advertising opportunities are expanding across a growing number of surfaces.
At the same time, paid and organic are becoming harder to separate. The same AI systems increasingly power ad campaigns, search experiences, and brand visibility across Google’s ecosystem.
That changes how brands should think about visibility.
Paid and organic are no longer separate channels competing for the same click. They are increasingly different ways of influencing the same AI systems, which means the signals shaping organic visibility may also affect paid performance.
The old model: Paid and organic on one finite SERP
Google’s SERP was a finite surface: 10 organic blue links, a few ad slots, and a knowledge panel on the right. The user landed, scanned, and clicked.
Paid and organic teams operated on separate budgets, separate tools, and separate quarterly reports, and rarely talked to each other because manual Google Ads kept the paid specialist busy full time. Titles, descriptions, bids, and campaign structure were all chosen by hand and required constant attention, which is why the organic team had no part in any of it.
DSA changed that for me. It read my organic pages to decide which ads to run, who to show them to, when, at what bid, and what title to use. I controlled the descriptions. The engine decided everything else, and it did it better than I would’ve done manually because it was reading the same signals the organic side was already optimizing for.
When someone at Google in Singapore explained how PMax worked, I thought, “That’s exactly what I was doing.”
PMax took the DSA logic and extended it across every Google surface simultaneously: Search, YouTube, Gmail, Display, Maps, and Shopping, all in one campaign, with the engine making every placement decision from your assets and audience signals.
AI Max brought the same intelligence into Search campaigns, specifically, with Gemini underneath instead of rules. PMax and AI Max run on the same Gemini brain: one focused on Search, the other spread across every surface, applying the same funnel logic to different contexts with different signal layers on top.
And if Gemini’s understanding of your brand is thin, it fills those decisions with whatever it thinks will work, which isn’t necessarily your brand narrative, and you have no direct way to override it. You train it, or you lose control of your own ads.
The new model: Gemini sits inside every surface, and it carries ads with it
Gemini now sits inside every layer of the Google ecosystem:
Discovery (Search, Maps, YouTube, Lens, News, Discover, and Shopping), productivity (Gmail, Docs, Drive, Photos, and Calendar).
Distribution (Android, Chrome, Google Play, Pixel, Wear OS, Google TV, and Nest).
Transaction (Google Pay, Wallet, Flights, Hotels, and Travel).
Assistive surfaces themselves (AI Mode, AI Overviews, Assistant, NotebookLM, and the Gemini app).
That’s how many connected consumers spend most of their workday, and most of those surfaces either carry ads now or have the infrastructure to start carrying them.
Microsoft Advertising sits inside Copilot across Bing, Edge, Windows Consumer, Office Consumer, Teams Free, and GitHub.
OpenAI Ads launched in February for logged-in users on Free and Go tiers in the U.S., placing ads below ChatGPT responses and clearly labeling them as sponsored. By May, OpenAI had opened a self-serve Ads Manager and was expanding internationally.
The ads layer travels with the engine, the engine is everywhere, and ads therefore have the potential to be everywhere. Most brands still treat paid as a separate channel run by a separate team on a separate dashboard, which is a search-era inheritance that was never ideal but now needs to be dropped.
Performance Max already runs the auction across YouTube, Display, Search, Discover, Gmail, and Maps as one campaign type. Search is one surface among many, and the “ads are dying in AI search” narrative is measuring the wrong thing. It sees ad slots compress inside the assistive interface while ignoring that the surface base has multiplied by an order of magnitude.
Ad density follows the delegation the user has made to the machine
The dominant narrative in 2026 is that ads are dying because AI is replacing search, and ads inside AI are a problem nobody has fully solved yet. That’s partially correct: Ad density per session drops as AI takes more control, and nobody – including Google – has yet figured out how to insert ads into the AI response itself without killing the experience that makes the AI valuable in the first place.
But this is the part the analysis gets wrong: This doesn’t add up to fewer ads overall.
Search ads are Google’s goose with the golden egg, and the goose may be slowing down — though nobody outside Google actually knows, because Google doesn’t break out search ad revenue from YouTube, Display, and the rest. That ambiguity is doing a lot of work.
What we do know is that total ad revenue has kept growing even as AI has taken over more of the search experience, which proves the flock is already working.
Kodak invented the digital camera and then buried it to protect film-processing revenue, and we know how that ended. Google appears to be doing what Kodak didn’t: building the replacement while the original is still profitable.
Every surface Gemini sits inside is a new bird in the flock, each laying a smaller egg that grows over time, and when Google finally cracks ads inside the AI response itself, that’s one more goose. The surface base has expanded faster than density has dropped, and the ad-density problem in Search and AI is temporary.
The more the user delegates decisions to the machine, the less room the machine has to surface a paid option. Search keeps the user in charge, so the engine surfaces ads the user might pick. Assistive narrows the options, so a sponsored slot still has a chance. Agentic executes the decision, so the ad has nobody to persuade. Ad density follows that delegation, mode by mode, with AI deciding which brands win at each mode.
Ad density follows the delegation the user makes to the machine.
Google is running two moves at once, and it seems most people have noticed only the first one. Gemini is taking over the recommendation, targeting, and auction logic on surfaces that have carried ads for years. And Google is adding ads to surfaces where they were previously absent, with AI Overviews now eligible for ads above, below, and within the answer, and AI Mode testing conversational ad formats.
The first move is AI taking over the existing ad business. The second is the ad business expanding into surfaces it never occupied. The net effect is more AI-driven ads across more of the stack than ever before.
The freemium system still works, but the ad is becoming part of the surface
The monetization model that works at consumer internet scale is simple: pay with money, or pay with attention.
YouTube is Google’s clearest example — and proof that it works: free with ads, paid without, and the vast majority of users have always chosen ads.
Gmail draws the same line: Where the user pays directly, Google doesn’t insert ads. Where the user pays with attention, Google monetizes it.
I learned about freemium the hard way. When our children’s media company, Boowa & Kwala, survived the dot-com crash, we added a paid tier that removed the ads. Out of a million unique visitors a month, a few hundred paid. Almost nobody chose to pay.
The freemium contract — free access in exchange for ads — is the deal they actively prefer, and the numbers prove it. And for ad-driven businesses, pure volume makes the money. In Big Tech, Google has the clear advantage.
ChatGPT is already running ads on free tiers.
Gemini is ad-free without login, but that’s a launch state, not a permanent model.
Perplexity is blocking users instead of monetizing them, which is a different bet on the same problem — and a bet with a limited runway.
Every AI surface is in the process of landing on the same answer because there is no other answer.
What changes is how the ads appear. The classic SERP ad was clearly labeled and set off in a colored panel. The Gemini recommendation that surfaces a product inside a Gmail context, the Copilot suggestion that names a vendor inside a Word document, and the agent that picks a supplier on the user’s behalf are something else entirely.
The ad becomes ambient. It dissolves into the surface, and what advertising looks like becomes harder to identify as advertising. Gemini reads context and intent with enough precision that an ad placed in a meeting summary can feel useful rather than disruptive, which is a risk profile Google’s rules-based systems could never have accepted.
At Boowa & Kwala, when we scaled free ad-supported views from 100 million to 1 billion, revenue multiplied by roughly two, and costs rose by around 20%. Surface (a.k.a. pageviews) multiplied tenfold, revenue doubled, costs grew by a fifth, and we went from profitable to significantly more profitable.
The aim was never to push revenue up at the same rate as surface expansion. It was to keep expanding the surface, knowing the incremental delivery cost was negligible.
Google’s ratios at planetary scale differ from ours, but the structural shape almost certainly doesn’t: surface expansion plus near-zero incremental cost equals profit growth, regardless of whether revenue per surface keeps pace.
Cohort, intent, and profit drive both paid and organic
PMax, AI Max, AI Overviews, AI Mode — Gemini is driving all of them. The AI optimizing your paid campaigns is the same AI evaluating your organic content, reading the same user, in the same moment, with the same intent.
The engine reads three signals:
Cohort.
Intent.
Profit.
In paid, you declare all three explicitly when you structure your campaigns. In organic, the engine infers all three from behavior: clicks, dwell time, and return-to-search serve as proxies for the profit signal that is missing there. Google denied using behavioral signals for years. Its own court case documentation told a different story.
Which means the organic discipline the whole series has been building — the funnel query pathway, the entity home, and the corroboration stack — has always been pointing at one thing: engineer the page so precisely for the right cohort that the behavioral signal does the same job as a correctly structured PMax campaign. The user lands, stays, converts, and doesn’t go back and research the same thing again. Google reads that behavior and infers your profit tier.
My bet, and I want to be clear it’s a bet rather than a documented fact, is that Gemini can’t serve a paid ad in real time without grounding against current search results because the ad has to match the organic context it’s appearing in.
If it doesn’t ground, the ad is inconsistent with what the user sees organically, which breaks the experience and loses the click. So the grounding process for paid is the same process as for organic: same knowledge graph, same search index, same LLM.
That means training Gemini on your brand through organic improves your paid performance through the same mechanism. One training investment, two outputs. I’ll be proven right on this eventually, and this article is the timestamp.
The same AI runs your organic and your paid. Train it once, win twice.
You can’t directly target Gemini in AI surfaces. You can only train it.
Across AI-driven placements, Gemini decides everything: where to show your ad, what to show, how to show it, who to show it to, when, and at what bid. The advertiser feeds it information and sets the parameters, but Gemini makes every decision that matters.
What you’re buying when you spend on Google Ads in 2026 is the right to feed a recommendation system that analyzes your brand on its own terms. The explicit signals you declare in paid — cohort, intent, and profit — are a real advantage over organic, where the engine has to infer all three from behavior.
But your ability to dominate through pure campaign structure is vastly reduced when Gemini doesn’t understand or trust your brand. The control has shifted: you guide it through signal clarity, not through the settings dashboard, and that guidance works best when your organic foundation is solid.
Use paid to find the combinations that work, build organic pages around them
In a correctly structured PMax or AI Max campaign, you declare cohort, intent, and profit margin explicitly: this audience, this goal, this margin, in the same campaign. You don’t mix a luxury hotel and a budget guesthouse in the same ad group because the cohort is different, the profit margin is different, and handing the engine a mixed signal makes it spend your budget resolving a contradiction you created.
Organic doesn’t let you declare profit directly. The engine infers it from who landed, who stayed, who converted, and who never came back to search for the same thing. That behavioral signal is the only proxy it has for the profit tier, and it’s a thin signal compared to the explicit declaration you make in paid.
The smartest move for any brand running both is to treat them as a single loop. Run paid to find which cohort-intent-profit combinations actually convert. Build the organic pages around those combinations, designed so precisely for the right cohort that the behavior on the page sends the engine the same signal the paid campaign explicitly declared.
The paid side becomes cheaper because organic pages provide the behavioral confirmation the engine needs. The organic side gets stronger because the paid data tells you exactly which pages to build and for whom, and then feeds the engine the same signal the paid campaign declared explicitly, for free.
Most travel sites serve the same page template to a budget traveler looking for a €30 guesthouse in Bangkok and a wealthy traveler looking for a €3,000 suite at the Peninsula. Same layout, same fields, same photo grid, same review format.
The engine has to infer which cohort the page serves mostly from behavior because the differentiation of the pages is limited. Build the page for the person rather than the query, and you hand the engine the cohort signal it’s currently having to guess. That’s not a UX decision. That’s your profit margin declaration to an engine that can’t see your margins any other way.
And you win on all three fronts simultaneously. A page built precisely for the right person converts better because it works better for the human.
Better conversion behavior sends cleaner implicit signals to the engine, which improves your organic ranking for that cohort. And cleaner organic signals reduce your paid CPC because the engine has less to guess about. Better pages, more organic, cheaper paid – the same work produces all three.
When Gemini isn’t convinced about you, you pay on both sides simultaneously
The three revenue taxes — the doubt tax, the ghost tax, and the invisibility tax — operate on the organic side. Because the engine powering your organic results is the same one powering your paid placements, you pay all three on both sides simultaneously.
The doubt tax: When the engine hedges on basic facts about you organically, it rewrites your paid creative to soften the same claims.
The ghost tax: When the engine prefers competitors in organic comparisons, your paid creative gets passed over even when your bid is competitive.
The invisibility tax: When the engine doesn’t surface you organically, it doesn’t show your ad either. You’re not in the running.
Paid surfaces carry two additional taxes that don’t exist on the organic side, and one discount you earn when you get it right.
The taxes and discounts in AI-driven paid search include:
The mistrust tax: What you pay when the engine’s confidence in your brand is low. A CPC premium because Quality Score penalizes low entity trust, and message distortion because the Gemini Filter rewrites your creative away from your intended positioning. You can’t turn the filter off. The practical answer isn’t constraining it. It’s improving the entity confidence that the engine reads when deciding how to filter.
The intent tax: This is self-inflicted. Build an ad group with mixed intent, and you hand the engine a contradiction. Gemini will spend your money figuring out a mess you made. Each ad group should align on cohort, intent, and profit margin — any mix across those three, and Gemini is billing you to resolve the confusion.
The confidence discount: This is the blade cutting the other way. Every properly defined ad group is secretly doing two jobs: it buys you an efficient placement today, and it teaches the engine which cohort you serve tomorrow. When the engine trusts you, it stops second-guessing your ads, your CPC drops, and your creative lands cleaner. That’s worth more than any bid adjustment you make.
Google has a structural advantage that Microsoft and OpenAI can’t match
Google has all the cards: the model, the surfaces, and the ads platform, all owned and tuned together in absolute harmony. Microsoft has the surfaces but lacks the LLM to drive them at the same level.
OpenAI has the model and launched a real ads business in February 2026, but lacks the surfaces – no Gmail, no YouTube, no Maps, no Play – and without surfaces, an ads business can’t compound at scale. Only Google has all three working as one system.
Paid and organic are now inseparable. The goose is fading, but Google can afford to let it. They know it rises like a phoenix, and in the meantime, they’ve got the biggest gaggle.
While managing a major B2B SaaS account, Hallam PPC Lead, Simran Harichand tightened a target CPA to improve efficiency but failed to monitor the impact. The change dramatically reduced spend, leaving the account €30,000 short of its monthly budget target.
When underspending becomes a business problem
Underspending isn’t just a media issue — it can affect a client’s future budgets. In this case, unused funds had to be returned to finance, making it harder for the marketing team to justify similar investment levels in future planning cycles.
The hardest part wasn’t the mistake
The most difficult moment came when Simran had to explain the situation to the client. Rather than making excuses, she took full responsibility for the error and acknowledged the impact it had on their goals.
Trust is built after the mistake
Although the client was understanding, trust had been damaged. Simran rebuilt confidence by introducing weekly budget pacing updates, showing transparency and proving the issue wouldn’t happen again.
Why the “brilliant basics” matter
The experience reinforced the importance of fundamentals such as budget pacing, account monitoring and conversion tracking. No matter how advanced advertising platforms become, strong basics remain the foundation of good performance.
What she’d do differently today
Looking back, Simran says she underestimated how much influence a target CPA change could have on delivery. Today, she treats any spend-related adjustment as a significant account change that requires close monitoring.
The danger of relying on AI without oversight
Simran supports testing AI-powered tools but warns against blindly adopting every new feature. She believes advertisers should balance experimentation with human oversight and strategic thinking.
Why conversion tracking remains the industry’s biggest blind spot
One of the most common issues she sees in account audits is poor tracking implementation. Inaccurate conversion data can lead to flawed optimisation decisions, making reliable measurement more important than ever.
The human side of client relationships
Strong client relationships can help teams navigate difficult moments when mistakes happen. Building trust through communication and honesty often matters just as much as delivering strong performance.
The bottom line
Mistakes are inevitable in PPC, but accountability and learning from them are what matter most. For Simran, the experience was a reminder that long-term success is built on mastering the fundamentals and maintaining trust.
Microsoft Advertising launches Product Explorer, a new Merchant Center tool that helps advertisers analyze feed health, product status, and performance data.
A lower cost per lead and higher lead volume don’t necessarily translate into more signed cases. That’s because leads and signed cases are not the same thing.
Between the ad click and the signed retainer sit intake qualification, follow-up speed, and conversion. If you’re measuring PPC success based only on cost per lead, you’re making budget decisions with incomplete data.
I’ve managed more than 1,000 ad accounts across plaintiff-side law firms, and I see the same pattern repeatedly. The ads generate activity, but the system that turns leads into clients leaks at multiple points.
The firms that consistently grow signed cases connect their advertising data to intake performance, lead qualification, and retained clients. That requires a different approach to keywords, budget allocation, landing pages, and attribution.
Start with the right keywords (hint: they’re not Google’s suggestions!)
Most law firms build a campaign backward. They start with broad-match keywords like injury attorney, best lawyer, and legal advice. Those terms bring in volume, but also noise. You end up getting tons of unqualified, early-stage prospects, and this equates to low-intent traffic that burns through your budget.
We protect our budget and improve conversions by reverse-engineering a keyword strategy from actual signed-case data. Instead of relying on Google’s keyword suggestions as a starting point, we mine call transcripts, intake notes, and CRM records to identify the real language that precedes retained clients.
With practice, you can pinpoint the exact phrase-match terms that people may be using to find attorneys like you. Phrases like “truck accident lawyer near me,” “motorcycle injury attorney Houston,” or “wrongful death law firm Tampa.”
The core principle is to segment every keyword by funnel stage and intent. High-intent exact- and phrase-match terms get the budget. Low-intent terms get tested and throttled, or excluded.
The single most important ritual for effective law firm PPC management is integrating the search terms report into your workflow. This report shows you exactly what someone typed before clicking your ad and whether that click generated a quality lead or was a waste of money. Most law firms, or the advertising agencies they work with, either skip this entirely or check it once a quarter.
Keep your spend efficient by reviewing the report every week to identify and add negative keywords to your campaign. This weekly hygiene compounds over the long term.
Allocate budget by funnel stage, not by channel
Most law firms treat Google Ads like a monolith. You’ll get a better ROI if you separate campaigns by funnel stage, intent, budget allocation, and conversion goals.
My PPC strategy is based completely on the Pareto Principle (aka the 80/20 rule): About 80% of your spend goes to bottom-of-funnel direct response, and 20% goes to mid-funnel and retargeting. Here’s what that looks like in practice:
Bottom of funnel
This is where most of your signed cases will come from. These are high-intent search campaigns and Local Services Ads.
LSAs are the best-converting channel for personal injury firms, Pareto Legal’s “The State of Law Firm PPC” report found. They are pay-per-lead, review-driven, and require no landing page infrastructure from you. (Disclosure: I’m the CEO and co-founder of Pareto Legal.)
One of the fastest lead-quality wins we consistently see with new clients is correcting their LSA category selections from broad practice areas to specific case types, such as personal injury or motor vehicle accidents.
Mid-funnel
This includes non-brand search, Dynamic Search Ads, and segmented Performance Max. Evaluate these on qualified lead rate, not raw volume. If a campaign generates 200 leads but only 10 are qualified, it’s a budget drain, even with a good-looking CPL.
Top of funnel
Meta and YouTube retargeting can target people who have already visited your website. Expand these campaigns to cold prospecting only after attribution proves incremental lift.
This framework is simple, but it can dramatically improve your PPC results. For one injury firm, we generated 273 signed cases from $765,000 in ad spend (3.57x ROI) without increasing the budget. All it required was a restructuring of the firm’s Google Ads.
Build landing pages that match intent, not just keywords
Sending paid traffic to your homepage or a generic practice area page that doesn’t match the ad you ran is a conversion-rate killer. This is common knowledge, yet effective landing pages are probably the most overlooked part of PPC management.
Many great PPC marketers know bidding strategy, campaign structure, and audience targeting inside and out. But when it comes to building a high-quality, compelling user experience, many marketers fall short.
What kind of landing pages work?
Your goal should be to match the intent of what your prospect was searching for. Here are the important components:
An intent-matched H1 headline.
Settlement amounts.
Client reviews as social proof.
Click-to-call and live chat options.
Your landing pages should also load quickly and be mobile-first.
You need one page per practice area, per intent. For example, a “car accident lawyer” landing page and a “truck accident attorney” landing page should be different. Each practice area and intent level deserves its own experience and unique design.
What kind of results will you see?
I split one client’s generic personal injury page into intent-segmented pages, added a stack of recent reviews and results, and reduced form fields from nine to four. Our team also optimized load time from 5.1 seconds to 2.1 seconds.
The page conversion rate went from 3.8% to 17.4%. The cost per signed case dropped by 42% with the same ad spend.
Most agencies won’t tell you the primary reason law firm advertising fails. It’s not the marketing process itself.
Surface-level numbers like cost per click (CPC) are often fine. What fails is the intake process. You need to focus on what happens after the form fill or phone call.
Here are the key intake KPIs to track alongside your ad metrics:
Answer rate: Target 90% or higher.
Speed to lead: Target under 60 seconds.
Signed rate (the percentage of qualified leads that become retained clients): A healthy benchmark is 25% to 40% of qualified leads.
The math is simple: If you’re spending $20,000 on Google Ads every month and your average cost per lead is $250, that’s 80 leads. If your intake team only contacts 70% of them and signs 30% of those contacts, you get about 17 cases.
If your team responds to 95% and converts 40%, that’s 30 cases from the same $20,000. You’ve essentially doubled your return on Google Ads, and it had nothing to do with your ad spend.
The key is for your marketing and intake teams to share KPIs. You can’t have the media buyer optimizing for lead volume while the intake team cherry-picks easy calls.
Connect the full attribution chain: Ad click to signed retainer
Most law firm reporting isn’t detailed enough to serve as a decision-making tool. It stops at platform metrics such as impressions, clicks, and cost per lead. These reports rarely reach the CRM, where signed cases actually live and where the most valuable intelligence resides.
Your attribution infrastructure needs these components:
UTMs and click IDs from ad platforms to capture the traffic source.
Call tracking through a tool like CallRail to handle phone leads.
A CRM, such as Lawmatics or Clio, that tracks post-conversion activity all the way through to the signed case.
The metric that ties everything together is marketing efficiency ratio (MER): total revenue divided by total marketing spend.
MER forces you to evaluate marketing as an ecosystem instead of looking at each channel through last-click attribution. For example, knowing you have a healthy MER can help you spot the hidden work a single channel is doing, even if it looks expensive on a last-click basis. MER can help you maintain confidence in your budget allocation.
Your ideal dashboard setup
Every law firm should build a one-page dashboard with these metrics: spend, leads, qualified leads, signed cases, cost per lead (CPL), and cost per acquisition (CPA), all broken down by channel and practice area.
If your reports can’t drill down to this level of granularity, they’re probably just a slideshow. Building this tracking infrastructure makes it easy to identify winning campaigns and reallocate budget intelligently. This is how you increase ROI without additional funding.
Google is rolling out a series of Smart Bidding and budgeting updates designed to help advertisers uncover new demand, capitalize on seasonal opportunities and maintain more predictable campaign performance.
What’s new. The updates include an expansion of Smart Bidding Exploration, a new Promotion Mode beta and changes to bidding target optimization for budget-constrained campaigns.
Driving discovery. Smart Bidding Exploration now allows advertisers to set a return on ad spend (ROAS) tolerance that enables campaigns to pursue additional conversion opportunities from search queries they may not currently be capturing.
Google says campaigns using the feature see, on average, an 18% increase in unique converting search query categories and a 19% increase in conversions.
The company is expanding the capability to Performance Max campaigns without product feeds and opening a beta for Shopping ads across both Performance Max and Standard Shopping campaigns.
Peak period bidding. Promotion Mode allows advertisers to temporarily adjust ROAS targets and allocate additional daily budget during high-demand periods such as seasonal events, product launches and flash sales.
What else is changing. Beginning Aug. 17, Google will update bidding target optimization for campaigns limited by budget, with the goal of delivering more consistent performance that better aligns with advertisers’ CPA and ROAS targets.
Starting July 6, advertisers will begin receiving notifications in Google Ads if campaign adjustments may be needed.
Why we care. These updates give Google’s AI bidding systems more freedom to find incremental conversions beyond existing keyword and audience patterns, potentially unlocking new demand that campaigns might otherwise miss.
The new Promotion Mode is particularly relevant for retailers and seasonal advertisers, as it allows temporary adjustments to ROAS targets and budgets during peak demand periods without requiring major campaign restructuring. Meanwhile, the bidding optimization changes aim to make performance more predictable for campaigns that are constrained by budget.
The bottom line. Google’s latest bidding updates are designed to help advertisers find new conversion opportunities, respond more aggressively during peak demand periods and maintain steadier performance as campaigns scale.
Google is broadening its Limited ad serving policy on Search, giving itself more authority to restrict impressions from advertisers it considers unqualified or potentially confusing to users.
The update could affect how frequently ads appear on certain searches, particularly for newer advertisers, brands with poor user feedback or advertisers whose identity is not clearly communicated in their ads.
What’s changing. Starting this month, Google expanded the policy to cover additional Search scenarios, with implementation rolling out gradually through 2028.
Under the updated rules, Google may limit ad impressions on searches that it believes have a higher risk of creating negative user experiences.
How Google decides. User feedback will play a larger role in determining whether an advertiser is qualified. Advertisers that receive persistent and disproportionate reports about misleading content, products or business practices may see their ads restricted on certain searches.
Google also says it may limit ads that make it difficult for users to identify who the advertiser actually is.
Why we care. Google is applying more discretion to limiting ad visibility, making it based on advertiser trust signals and branding clarity, not just policy compliance. That means advertisers with generic ad copy, unclear brand identity or a history of negative user feedback could see reduced reach on certain searches.
The change also reinforces the growing importance of brand transparency in Search ads. Advertisers may need to revisit ad copy, landing pages and branding elements to ensure users can immediately identify who is behind an ad and why they’re seeing it.
What advertisers should do. Google is encouraging advertisers to strengthen brand visibility across both ads and landing pages, avoid overly generic messaging and clearly communicate any affiliation with other brands.
The company also recommends pinning a domain headline in the first position of responsive search ads to make advertiser identity more obvious to users.
The bottom line. Google’s updated policy gives greater weight to advertiser trustworthiness and clear branding, potentially limiting visibility for advertisers whose identity or business practices create confusion for users.
First spotted. This update was spotted by Founder of Adsquire, Anthony Higman, who shared his displeasure of this update on LinkedIn.
Looking to take the next step in your search marketing career?
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Botify’s leading agentic AI search technology and seasoned experts ensure every brand has the power to be found, both in traditional and AI search. With one powerful platform, brands achieve visibility, relevance, and greater control across Google, Bing, ChatGPT, Perplexity, and more. Botify’s technology powers agentic workflows, AI-driven recommendations, and automated cross-platform indexation and deployment. […]
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Fanatics is building a leading global digital sports platform. We ignite the passions of global sports fans and maximize the presence and reach for our hundreds of sports partners globally by offering products and services across Fanatics Commerce, Fanatics Collectibles, and Fanatics Betting & Gaming, allowing sports fans to Buy, Collect, and Bet. Through the […]
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Lead organic strategy for a portfolio of clients, building annual and quarterly roadmaps that account for both traditional SEO performance and Generative Engine Optimization (GEO) visibility across AI-driven search environments.
Serve as the primary point of contact for clients, guiding strategic conversations around organic growth, AI Overviews, LLM-powered discovery tools, and how evolving search behavior impacts brand visibility and competitive positioning.
Act as the lead interpreter of search performance and visibility quality across both traditional and AI-led discovery experiences
Maintain a broad expertise across the evolving landscape of LLMs and generative engines, including Google Gemini, OpenAI GPT, Anthropic Claude, and other leading frontier models
Write and publish high-quality, search-optimized content on a consistent cadence, including (but not limited to) blogs, thought leadership, comparison pages, FAQs, infographics, and other digital content assets.
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Microsoft Ads is introducing Product Explorer, a new reporting tool designed to give advertisers a centralized view of product catalog health and performance, according to Microsoft Product Liaison Navah Hopkins.
Managing large product feeds can make it difficult to identify which items are eligible to serve, generating impressions or missing critical data. Product Explorer aims to simplify that process.
What’s new. Product Explorer provides a searchable view of an advertiser’s entire product catalog, allowing users to filter products by attributes such as SKU, title, GTIN and product ID.
Advertisers can quickly see which products are active, serving ads and driving performance.
What it does. The tool highlights eligibility issues, metadata gaps and other factors preventing products from serving. It also surfaces recommended actions and allows advertisers to export filtered product lists for further analysis.
Why we care. By combining feed diagnostics and performance reporting in a single interface, Microsoft is making it easier for advertisers to move more products into a servable state and identify underperforming inventory.
Advertisers will now get searchable catalog reporting, product-level performance data covering the previous 30 days, issue detection and actionable recommendations to improve feed quality.
The big picture. Retail advertisers are increasingly focused on feed quality as product-based advertising becomes more automated. Visibility into catalog issues can have a direct impact on campaign reach and performance.
Availability. Hopkins said it should be live in accounts already.
Google Analytics is introducing a new Source Group reporting dimension and hostname filtering controls aimed at improving attribution analysis and data quality.
The updates are designed to help advertisers clean up fragmented traffic source reporting, better analyze cross-channel performance and reduce noise in their analytics data.
What’s new. Source Group is a new reporting dimension that consolidates multiple variations of the same traffic source into a standardized category.
For example, traffic from Facebook can now be grouped under a single reporting value instead of appearing across multiple naming conventions such as “facebook,” “fb” or other variations.
At the same time, Google is updating its existing Source Platform field to align with the new grouping structure and provide more consistent classifications across advertising channels.
Why we care. Cleaner source classification means more accurate attribution and cross-channel reporting. Instead of traffic being fragmented across inconsistent labels, marketers can more easily understand which platforms are actually driving conversions and where budgets are performing best.
The inclusion of AI traffic sources like ChatGPT and Perplexity is particularly noteworthy, as it gives advertisers a standardized way to measure and compare emerging AI-driven referral traffic alongside traditional channels. The new hostname filters also help improve data quality by ensuring only traffic from approved domains is included in reporting.
The big picture. As advertisers manage campaigns across a growing number of platforms, inconsistent source naming can make attribution and budget analysis more difficult. The new reporting structure is intended to simplify performance comparisons across channels.
Between the lines. The update expands source standardization beyond Google’s own properties, creating consistent classifications for platforms including TikTok, Pinterest and Amazon while also introducing support for emerging AI-driven traffic sources such as ChatGPT and Perplexity.
Also new. Google is launching hostname filters within the Admin section, allowing advertisers to exclude events from unapproved domains before they enter reporting.
The feature is designed to help improve data accuracy by preventing unwanted traffic from influencing analysis.
What advertisers get. Standardized source reporting, retroactive access to historical source group data, cleaner attribution analysis and greater control over which domains contribute data to reporting.
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is seeking a Marketing Associate to help build PupGum, a new dog dental chew brand. This role requires a blend of creativity and analytics, focusing on paid and organic marketing efforts, influencer partnerships, and content strategy. The ideal candidate will bring 2-3 years of marketing experience and a Bachelor’s degree. You’ll collaborate with cross-functional […]
