I’m seeing Google make recipe results in AI Mode more publisher friendly with a new visual treatment that gives recipe creators more visibility. For some recipe responses, Google is now showing details such as the creator name, recipe ratings, and the number of ingredients.
What is new. Google’s Robby Stein said AI Mode now includes “prominent links at the top of responses with useful details and images,” including creator names, ratings, and ingredient counts. From my view, the key shift is that Google is trying to make recipe sources easier to recognize and visit directly from AI Mode.
What it looks like. The new treatment places recipe links, images, and useful recipe details more prominently in the AI Mode experience, giving users a clearer path from the AI-generated response back to the original recipe page.
Previously. Back in March, Robby Stein announced earlier changes to recipe results in AI Mode. At the time, he said Google had heard feedback and was making updates to better connect people with recipe creators across the web.
I see this latest update as part of Google’s effort to address concerns around AI recipe slop and to make original recipe content more visible when people search for cooking ideas through AI-powered results.
Why I care. Recipe bloggers, and content creators more broadly, have been frustrated that Google’s AI experiences often send less traffic than traditional search results. This update suggests Google is trying to encourage more searchers to click through from AI Mode to the publishers and creators behind the recipes.
If Google continues adding more clickable link units into AI search experiences, I think it could help ease some of the tension between publishers and Google. The bigger question is whether these changes will drive enough meaningful traffic back to recipe sites and other content creators.
I see Google rolling out new Agency Admin and Standard roles in Merchant Center for Agencies, giving agencies a more centralized way to control client access while improving security and day-to-day efficiency.
What is new: I can now look at client access differently because clients are linked directly to an agency instead of being tied to individual users. That makes it easier to manage permissions from one place, especially when team members join, move roles, or leave.
I also see custom labels becoming a useful part of this update. Agency Admins can organize client accounts by brand, business vertical, internal team, or another structure that fits how the agency works.
Those labels can then be used to give Standard users access to groups of accounts in bulk. For me, that is the practical improvement: agencies no longer need to configure access one account at a time when the same permission logic applies across multiple clients.
Why I care: Agencies managing several Merchant Center accounts have often had to depend on user-level permissions, which can make onboarding, offboarding, and account management more cumbersome than they need to be. This role-based structure moves client management to the agency level, which should reduce administrative work and strengthen access controls.
How it works: Agency Admins get full administrative privileges inside the Merchant Center agency account. In that role, I can link and unlink clients’ Merchant Center accounts, add or remove Standard users, modify Standard users, manage their access to client accounts, and create custom labels for organizing clients.
Standard users receive more limited permissions, which helps agencies follow stronger security practices. I see this as a way to make sure team members only access the client accounts they actually need.
Bottom line: For agencies managing large client portfolios, I expect centralized client linking, bulk access management, and customizable account labels to reduce manual work while making Merchant Center administration more secure and scalable.
I am seeing Google Search Console’s page indexing report running more than two weeks behind, with the latest visible timestamp still showing June 11, 2026. That means I cannot get a fresh view of page indexing data for the pages on my site right now.
When I check the Google Search Console page indexing report, I would expect to see that June 11 date instead of a more recent update. The delay is inconvenient, especially when I am trying to understand whether Google has recently found, crawled, or indexed important pages.
This report matters because it shows me which pages Google can find and index on a website. It also helps me spot indexing problems Google may have encountered while crawling the site.
I can access the report in Search Console over here, or I can open Search Console, go to the Indexing section, and then select Pages.
Inside the report, I usually see a chart with indexed pages in green and not indexed pages in gray. I can also overlay impressions on the chart, which makes it easier to connect indexing patterns with search visibility.
Below that chart, Google lists the reasons pages on the website are not being indexed. That section is often where I look first when I need to understand whether the issue is related to crawling, duplication, redirects, noindex signals, canonical choices, or another indexing reason.
For more details about how the page indexing report works, I can refer to Google’s help document.
Why I care: if I am trying to debug why Google has not indexed specific pages over the past couple of weeks, this delay leaves me with limited visibility. Until Google updates the report again, I would need to rely on my own SEO analysis or use the URL inspection tool to investigate indexing issues one page at a time.
The delay is frustrating, but I do not see it as especially uncommon. Search Console reports can lag from time to time, so for now I would treat the page indexing report as stale and avoid making major conclusions from that delayed data alone.
I’m seeing a sharp disconnect in B2B search visibility: many brands still rank for thousands of Google keywords, but they appear in only about 3% of AI-generated answers, according to Walker Sands’ B2B AI Search Visibility Benchmark of 828 enterprise companies. (Disclosure: I’m the director of SEO and GEO at Walker Sands.)
For this benchmark, I looked at more than 45 million search queries from March across 828 enterprise B2B companies in 14 industries. The analysis evaluated each domain across four core metrics: keyword coverage, keywords with AI Overviews, AI Overview incidence, and citation inclusion rate.
Keyword coverage measures how many keywords a company ranks for in Google. Keywords with AI Overviews shows how many of those ranking keywords trigger AI-generated responses. AI Overview incidence captures the percentage of ranking keywords where AI Overviews appear. Citation inclusion rate measures how often a company’s domain is cited inside those AI-generated answers.
Together, these metrics give me a baseline for understanding how often AI Overviews show up and how often B2B brands actually earn visibility within them.
A baseline for B2B AI search visibility
The benchmark shows a meaningful gap between traditional ranking visibility and AI citation visibility. AI Overviews appear in about 50% of search results where enterprise B2B brands rank, yet the median enterprise B2B brand is cited in just 3% of relevant AI Overviews.
I also found that 4.6% of enterprise B2B companies are not cited in AI Overviews for any of their relevant keywords. That may sound like a small share of the market, but it points to a serious visibility problem for brands that still appear in Google’s organic results while disappearing from the AI-generated answers buyers increasingly see first.
The typical enterprise B2B company ranks organically for about 9,700 search queries, and AI Overviews appear in nearly half of those searches. But across all those opportunities, the median brand earns citations in only 3% of AI Overviews.
In other words, I’m seeing B2B brands present in the search results that AI Overviews summarize, but largely absent from the summaries themselves.
When a brand has few or no citations, I often see deeper issues underneath: limited topical authority, unstructured or inaccessible content, and too little content that directly answers the questions buyers are asking.
Addressing those gaps is becoming essential for visibility in AI-driven search experiences.
The narrowing funnel from ranking to citation
I think of AI search performance as a funnel with four layers, and the value lost at each step is where the story gets clearer.
It starts with keyword coverage, or the number of keywords where a brand ranks in Google’s top 100 organic results. On that measure, many leaders still look strong. The median company ranks for about 9,700 keywords, while top-quartile brands rank for more than 37,000.
The next layer is keywords with AI Overviews. These are ranking keywords that trigger an AI Overview. The median company has roughly 4,500 of them, which is already less than half of its ranking footprint.
The third layer is AI Overview incidence, which measures how often AI-generated answers appear across a brand’s relevant searches. The median is 48.8%, meaning AI now intercepts roughly half the queries where these companies compete. Top-quartile brands operate in even more AI-heavy environments, with an incidence rate of 61.7%.
The final layer is the one that matters most, and it is where almost everyone loses ground: citation inclusion rate. This measures how often a brand is cited as a source within an AI Overview. The median is 3.0%. Even the top quartile reaches only 4.5%, while the bottom quartile sits at 1.7%.
Viewed from top to bottom, the funnel is unforgiving. Tens of thousands of ranking keywords compress into a single-digit share of AI citations. Much of the visibility B2B brands have built through organic search does not carry into the layer of search that is shaping buyers’ first impressions of a category.
Ranking breadth does not guarantee AI citations
The most important takeaway is also the most counterintuitive: ranking breadth alone does not predict AI citation rates.
I found that some companies rank for thousands of keywords but rarely surface in AI-generated answers. The strengths that helped brands win traditional SERP visibility, including page volume, broad keyword targeting, and years of accumulated domain authority, do not automatically make a brand the source an AI system chooses to cite.
That creates a real challenge for B2B SEO teams. If a dashboard only tracks ranking keywords and estimated organic traffic, it may tell a flattering story about a layer of search that is losing influence while saying little about the AI layer that is gaining it.
The brands that are consistently cited in AI-generated answers tend to share three traits: deep topical authority across related content areas, clear and structured explanations that directly answer buyer questions, and consistent coverage across multiple relevant pages.
The common thread is specificity. Generative systems appear to reward content that resolves a buyer’s question clearly and demonstrates sustained expertise on a topic, instead of content that simply ranks for a query.
That changes the work. Optimizing for AI citations looks less like chasing keyword volume and more like building genuine, well-structured subject-matter depth.
Some industries are far more exposed than others
AI search visibility is not distributed evenly across B2B technology. The industry breakdown shows very different competitive dynamics depending on the category.
Cybersecurity leads on both fronts. AI Overviews appear in a median of 59.9% of cybersecurity-related searches, and cybersecurity brands earn the highest median citation rate in the study at 4.2%. Enterprise software, with 55.3% AI Overview incidence, and martech, with 56.3%, also see AI-generated answers in well over half of relevant queries.
At the other end, professional services and distribution and logistics trail in citations, both with a median rate of just 2.1%. Distribution and logistics also has the lowest AI Overview incidence at 29.6%, meaning buyers in that category encounter AI-generated summaries far less often than buyers in cybersecurity.
These differences create both risks and opportunities. In categories where AI-generated answers are already common, such as cybersecurity, the cost of being invisible is immediate. Buyers are forming impressions inside AI summaries right now.
In categories where citation rates are low and few brands have figured out the new mechanics, I see a real first-mover opportunity. Brands that learn how to earn citations before competitors do can help shape how an entire category is framed in AI-generated answers, much like early SEO adopters captured outsized organic visibility.
The brands that have gone completely dark
The most striking number in the report is that 4.6% of enterprise B2B companies are not cited at all in AI-generated answers for their relevant keywords.
These are not small, unknown operations. They are companies with $100 million or more in revenue that, in many cases, still rank well in traditional search. They are present in the index but absent from the answer.
Near-zero citation rates usually point to deeper structural issues: thin topical authority, content that is difficult for systems to parse, and a lack of material that directly answers the questions buyers are asking.
For a small but meaningful slice of the market, AI search is not just a place where they are losing share. It is a place where they barely exist.
What this means for B2B search teams
The benchmark gives me a baseline, but the strategic implications for SEO, GEO, and marketing teams are already clear.
First, measurement has to evolve. Citation inclusion rate is now a distinct KPI from ranking. Teams that cannot see whether their content is being cited in AI-generated answers are missing visibility into one of the fastest-growing parts of the funnel. Knowing your own citation rate, and comparing it with the 3% median and 4.5% top-quartile benchmarks, is a practical starting point.
Second, the content mandate is shifting from breadth to depth. The drivers point toward consolidating authority around the topics buyers care about, structuring content so machines can interpret it, and answering real questions directly instead of producing content volume for its own sake.
Third, the window is open but closing. Generative AI is expected to influence more than 75% of B2B search queries within the next one to two years. If that projection is even close, the median 3% citation rate is not a stable endpoint. It is a snapshot of an early, contested market that rewards brands that move now.
The uncomfortable truth is that much of the SEO equity B2B brands have built is being summarized by AI systems that do not cite the companies that created it. For most enterprise brands, I no longer see the central question as whether they rank. The question is whether they are in the answer at all.
I’m noting that Google has confirmed its June 2026 spam update is now fully rolled out. The update started on Wednesday, June 24, around noon ET, and finished on June 26 at 2 p.m. ET.
Google’s official status update was brief and direct: “The rollout was complete as of June 26, 2026.”
What stands out to me is that this was the second Google spam update announced in 2026. It appeared to feel somewhat bigger than the March 2026 spam update, but as with most updates, if my site was not affected, I would treat that as a good sign for now.
That said, I always keep in mind that spam updates can sometimes affect sites that are not intentionally trying to spam Google. Hopefully, that is not the case for your site, but it is still worth watching traffic, rankings, and Search Console data closely after a rollout like this.
As for the type of update, Google originally described it as a normal spam update that would roll out across all languages and locations, with completion expected to take a few days.
If I wanted more context on how these updates work, I would review Google’s official documentation on spam updates in this Google help document.
I see Google Discover’s “Tailor Your Feed,” now showing up as “Add topics to your feed,” as a meaningful shift in how people can shape what appears in their feed. Instead of relying only on Google’s inferred signals, such as clicks, dwell time, follows, and engagement history, I can now type what I want to see in natural language and let Google translate that request into feed instructions.
That matters because it creates a third visibility path for small and niche publishers. Until now, a smaller site usually needed either strong implicit affinity from a user or an explicit follow. With prompt-based tuning, a user can simply ask for a topic, creator, source, or type of content, and Google can retrieve matching material even when that content has barely appeared in Discover before.
In my tracking, the feature turns prompts into actions such as SEE_MORE and SEE_LESS. Those actions are applied after the user refreshes or updates the feed. The experience feels conversational, but underneath it appears to create persistent instructions that can affect both the current feed and future Discover sessions.
I also see signs of an LLM-style system behind the workflow. A user prompt is interpreted, converted into a readable assistant response, and returned with a structured result. In one observed example, the prompt “show me more content on seroundtable.com” produced an actionable SEE_MORE response and a persistent thread key, suggesting that feed tuning is treated as an ongoing conversation rather than a single isolated command.
The feature first appeared in Search Labs for US English accounts in December 2025. At that stage, the impact was subtle: after several refreshes, I could see a few on-topic cards, but the feed did not radically transform. By early 2026, Google started adding attribution, including labels such as “resulting from natural language tuning” and later “You asked to see,” making it easier to identify which cards were influenced by a prompt.
By spring 2026, “Tailor Your Feed” had effectively become “Add topics to your feed.” The interface moved toward a chat-style entry point with prompt starters such as “Show me content from…,” “I want videos about…,” and “Keep me updated…”. The same underlying verbs remained, but Google made them easier for everyday users to trigger.
The most important technical clue is the pipeline behind the feature. Discover cards influenced by these prompts can be associated with naturallanguagetuningcontent.f for current tuning and historicalnaturallanguagetuningcontent.f for older prompts that continue shaping the feed. I read that “historical” pipeline as evidence that these preferences are meant to last over time, not disappear after one refresh.
From the observed cards, I see two ways this content is selected. The first and dominant mode is entity or interest expansion. A prompt is mapped to related people, topics, publishers, or concepts, and Discover expands around that meaning. This is why asking for one source or creator may also surface related sources, related subjects, or nearby entities rather than only the exact name typed into the prompt box.
The second and more interesting mode is query-intent fan-out. In this mode, a prompt is decomposed into natural-language retrieval queries. A broad request about SEO, for example, can become query intents such as “SEO strategies algorithm changes,” “Google ranking system updates,” or “tips for getting content into google discover.” Those query intents then retrieve articles based on semantic relevance.
This is where the connection to Generative Engine Optimization becomes clear to me. The Discover fan-out behaves like the retrieval pattern we see in generative search: one user prompt becomes several more specific sub-queries, and content is selected because it answers one of those sub-queries well. Popularity can still matter in some cases, but it is not the only gatekeeper.
That distinction is what gives niche publishers a real opening. In the observed data, prompts surfaced examples such as vegan recipe creators, Mississippi Today, a LinkedIn post, niche Japanese-property blogs, and a gardening site tied to a seed-starting query. Some mainstream publishers still appeared, including Reuters and VentureBeat in certain contexts, but the pattern was not limited to the usual high-volume Discover winners.
In the most striking cases, the pipeline surfaced articles with no detectable prior Discover distribution in the tracking dataset. I am not using “distribution” here as an audience number or a Search Console metric. I mean that the article did not appear to have circulated previously in the Discover tracking data available for analysis.
That makes this pipeline different from classic Discover distribution. Traditional Discover systems often re-serve articles that already have engagement momentum. Prompt-based tuning can retrieve content because it matches what a user explicitly asked for, even if the article has not already built a Discover track record.
I would not treat this as a mass traffic channel yet. Google appears to promote these cards cautiously, and the pipeline does not seem to snowball the way broader Discover pipelines can. It serves the user who asked. It does not automatically broadcast the content to a much larger audience.
I would also be careful about false positives. In one Japanese-property cluster, relevant results such as guides to buying a home in Japan appeared alongside a video-game article about in-game home locations. That kind of loose match helps explain why Google may rank and distribute these cards conservatively.
For publishers, the practical implication is straightforward: I would optimize for both topical clarity and query-intent vocabulary. The entity-expansion mode rewards sites that are unmistakably about a topic users can name. The fan-out mode rewards titles, headings, and introductions that align with the natural-language questions and information needs Google derives from prompts.
That does not mean stuffing pages with raw keywords. The better move is to describe the content clearly in the language a real person would use when asking Discover for more of it. If a user might ask for “buying Japanese property guide,” “starting seeds indoors guide,” or “tips for getting content into google discover,” I want the page’s title, H1, and opening section to make that relevance obvious.
The strategic shift is that selection power moves closer to the user. In the classic feed, Google infers demand. In this model, the user declares it. Google then turns that declaration into entities, interests, and query intents that drive retrieval.
For small publishers, that is the opportunity. If the feature graduates from Search Labs and users adopt it at scale, a focused site with clear topical authority could appear because it directly satisfies declared demand, not because it already won the popularity contest inside Discover.
There are still real limits. The feature has been US English and Search Labs focused, with French feeds showing essentially no presence in the observed data. Adoption also appears early. A powerful prompt-based personalization system changes little if users do not actually use it.
What I am watching next is whether Google expands this beyond Search Labs, whether the current and historical tuning pipelines become more visible, and whether this behavior converges with broader generative retrieval systems. A nascent generativeretrieval.f pipeline has already appeared in tracking data, but that broader connection still needs confirmation.
My read is that Discover is moving from observed personalization toward declared personalization. Google still infers plenty, but users are beginning to write part of their own interest profile. If that model becomes mainstream, niche publishers with clear focus, strong entity signals, and natural-language relevance may gain a new route into Discover visibility.
Notes: In this analysis, a Discover pipeline means the selection circuit that chooses and serves cards. The .f suffix in identifiers such as historicalnaturallanguagetuningcontent.f is an observed internal marker attached to Discover card metadata. “Fan-out” refers to a mechanism where one prompt is broken into several retrieval sub-queries. “GEO” means Generative Engine Optimization, or the practice of optimizing content for visibility in generative search and answer systems. “AIO” refers to AI Overviews, and “AI Mode” refers to Google Search’s conversational interface.
Field tracking referenced here covers Google app Search Labs US English accounts from December 2025 through June 2026. Pipeline behavior is based on close observation of Discover feed cards and 1492.vision tracking data. The internal mechanisms described are my interpretation of observed data and public research, and approximate dates are treated as approximate.
Recently, I delved into Google’s updated AI Max reporting guidance, which sheds new light on the AI-driven future of Search campaigns.
Google has revitalized its AI Max for Search reporting documentation, offering advertisers fresh insights into performance reporting, optimization best practices, and significant timelines for Dynamic Search Ads (DSA).
What’s happening? Google has expanded its help documentation for AI Max for Search campaigns, enriching the guidance on reporting and offering more details on campaign performance evaluation.
Though it doesn’t introduce new products, it clarifies how Google intends for us, as advertisers, to manage and interpret AI Max campaigns in the future.
Why this matters. This update offers insight into Google’s long-term vision for AI Max and the impending phaseout of DSA. With automatic DSA upgrades set for early 2027, it’s crucial for us to anticipate the necessary evolutions in our Search strategies.
The headline change: Google has officially outlined the transition from Dynamic Search Ads to AI Max in the help documentation.
Per the updated guidance, DSA campaigns will undergo automatic upgrades to AI Max beginning in February 2027, as Google aims to broaden the adoption of AI-powered Search campaign formats.
What’s new in reporting: Google introduced new reporting views that let us evaluate performance across several dimensions:
Search terms.
Search terms and landing pages from AI Max.
Search terms from Dynamic Search Ads.
Search terms and landing pages from Dynamic Search Ads.
They’ve clarified that search term reports reflect user destinations post-ad click and introduced options for excluding underperforming search terms or landing pages with negative keywords and URLs.
New guidance for travel advertisers: Google also introduced a section specifically for Search Campaigns related to Travel.
This documentation helps us consolidate performance data into a unified view, crucial for evaluating search terms, inventory performance, and conversion outcomes. Travel advertisers can further dissect reports by ad format to compare performance across different types of ads like Travel Promotion Ads, Booking Links, and Travel Feed-based ads.
A shift in optimization philosophy: The latest best practices emphasize targeting based on intent rather than focusing strictly on keyword matches.
Google now advises us to:
Prioritize conversion goals over mere keyword relevance.
Regularly review search term and item group performance every one to two weeks.
Use negative keywords judiciously.
Avoid over-filtering traffic to exploit AI-driven intent matching benefits.
Bottom line:Google’s documentation update serves as more than just a guide for reporting; it lays out a strategic path for us to navigate an AI Max-centric future as DSAs near their fadeout.
I’m excited to share that Google is expanding its financial services ad verification across 24 European countries. As of this summer, financial advertisers in these markets will face new compliance checks to continue running ads in the European Economic Area (EEA).
Here’s the scoop: Starting July 23rd, Google’s new requirements for financial services advertisers apply to 24 EEA countries, including Austria, Belgium, and Sweden, among others.
As advertisers in designated financial categories, we must undergo verification when prompted by Google. This initiative targets financial fraud and aims to ensure ads are from genuine and regulated providers.
Why it matters: If I don’t complete this verification process, my ads may no longer run in these markets. This policy impacts not just banks and insurers but also the agencies that manage their campaigns.
The big picture: This is part of Google’s efforts to improve transparency and protect consumers. If notified, I’ll receive alerts on the Google platform indicating that ad performance could be impacted unless verification is completed.
Failing to comply means I might lose the ability to serve financial services ads in these countries. It’s crucial for continued campaign success.
How does verification work? I need to complete two steps: First, I’ll verify through G2, a third-party compliance partner. Next, I’ll submit Google’s financial verification application using a code from G2.
During this process, I’ll provide details about the services offered, regulatory status, and necessary evidence of authorization or exemption from a relevant financial regulator.
Agencies, beware: These requirements also apply to agencies like mine managing campaigns for financial services clients. We’ll need to pass compliance checks before continuing operations.
A key point to note: Third-party advertisers don’t have the same freedom. If I promote services approved by a verified institution but lack direct authorization, I must rely on them to submit verification requests on my behalf.
Depending on the financial services being promoted, such as banking or credit products, I might need to undergo this verification. Google can update its list at any time, so staying informed is crucial.
Stay vigilant: As a financial brand targeting European customers, I must ensure compliance now to avoid disruptions later. This could affect agencies handling multiple clients due to administrative demands.
Dig deeper: For more details on the new requirements, I can visit Google’s support page.
I’ve noticed something exciting happening with Google Search Console lately. The AI performance reports are becoming accessible to a wider audience, and it’s a game-changer for those of us eager to see how our content performs in Google’s AI environments.
John Mueller from Google recently shared on Bluesky, “We’re just rolling these out incrementally to sites, and reviewing the feedback along the way. I know everyone wants the new shiny thing immediately… but first, patience.” It’s like waiting for a gift you’ve been longing for!
AI performance report. These reports offer insights into how well our content and websites are featured in AI-driven searches, showcasing metrics such as impressions, pages, countries, devices, and dates. Although it doesn’t yet track click data, it’s still a significant step forward.
Expanding access. Earlier today, I spotted several SEOs sharing that these reports are now available beyond the UK! They’re able to access reports for sites in the US, India, Switzerland, and more.
As John mentioned, Google is gradually rolling these updates out to more sites, listening to feedback, and hopefully moving towards a global release.
What it looks like. Here’s a snapshot of the report:
Why we care. As someone deeply invested in how content is presented, I find this development thrilling. Publishers and site owners like me have long wanted more control over Google’s AI features. The speed at which Google has rolled this out is impressive—just within 20 days of its initial release!
I’ve come across important news about Google Ads that could significantly impact how we manage our campaigns. Google is on the verge of altering its target-based bidding strategies, particularly for campaigns running on limited budgets.
Mark your calendar for August 17th when these changes will take full effect. But don’t worry, a Bid Target Adjustment Tool will be available as of July 6 to help us prepare and adjust our goals accordingly.
What’s going on? Google’s update aims to closely align target-based bidding strategies such as Target CPA with our set goals, even when budget constraints come into play.
They’re introducing a new tool that allows us to tweak our targets before the updates hit, which is crucial for maintaining our campaign performance.
Why should we care? If your campaigns are currently exceeding their target CPA or ROAS goals, they might not continue to do so post-update without adjustment. This update is meant to ensure budget-constrained campaigns stay true to their targets.
For example, if my campaign is achieving a $5 CPA against a $10 target, the performance might shift towards $10 unless I make some changes.
Thankfully, the new tool is there to help us proactively update our bidding goals before the changes roll out. If we don’t take advantage of this, we might end up paying more per conversion or see our performance realign with Google’s targets instead of our historical results.
Why is Google doing this? Google wants to reduce fluctuations and provide more predictable results when we tweak or adjust our budgets.
The tool is designed to help us synchronize our bidding targets more closely with actual business outcomes before the automatic implementation begins.
What should we do? It’s a good time for us to reevaluate campaigns using target-based strategies and verify if our current targets still align with desired results.
Notifications will be sent through Google Ads accounts before the update, and the Bid Target Adjustment Tool can highlight which campaigns might be affected.
Key takeaway: For those of us with campaigns that consistently outperform their targets, maintaining current performance might require tweaking target settings instead of leaving them unchanged.
