Dec 2 2025
YouTube Launches First Annual Recap Feature For All Users via @sejournal, @MattGSouthern

YouTube launched its first annual Recap feature, providing users with a personalized summary of their viewing activity throughout the year.

The feature is available starting today for users in North America and will roll out globally throughout the week, according to the YouTube Blog.

What’s New

YouTube Recap is accessible from the homepage or under the “You” tab on mobile and desktop. The feature generates up to 12 cards based on watch history, displaying top channels, interests, and viewing patterns over the year.

The cards also assign users a personality type based on viewing habits. Types include Adventurer, Skill Builder, Creative Spirit, Sunshiner, Wonder Seeker, Connector, Philosopher, and Dreamer.

YouTube said the most common personality types were Sunshiner, Wonder Seeker, and Connector. Philosopher and Dreamer were the rarest.

Users who listened to music through the platform will see Top Artists and Top Songs cards within their Recap. Additional music data, including genres, podcasts, and international listening, is available in the YouTube Music app.

YouTube said it conducted nine rounds of feedback testing and evaluated more than 50 concepts before finalizing the feature. In an accompanying video, YouTube representatives said the team used Gemini to analyze watch history patterns, which enabled them to create a structured recap from YouTube’s unstructured video library.

Why This Matters

YouTube Recap gives the platform a year-end engagement feature comparable to Spotify Wrapped.

For creators, the feature surfaces which channels appear in users’ top viewing lists. People can save and share their Recap cards, which could boost channels’ social media visibility during the holiday period.

Looking Ahead

Users in North America can access their Recap starting today. Those outside North America should see the feature become available throughout the week.

For more details, see the video below:

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Dec 2 2025
OpenAI Declares ‘Code Red’ To Improve ChatGPT Amid Google Competition via @sejournal, @MattGSouthern

OpenAI CEO Sam Altman has declared a “code red” to focus company resources on improving ChatGPT, according to an internal memo reported by The Wall Street Journal and The Information.

The memo signals OpenAI’s response to growing competition from Google, whose Gemini 3 model has outperformed ChatGPT in several benchmark tests since launching last month, according to Google’s own evaluation data and third party leaderboards.

What’s New

Altman told employees that ChatGPT’s day to day experience needs improvement. Specific areas include personalization features, response speed and reliability, and the chatbot’s ability to answer a wider range of questions.

The company uses a color-coded system to indicate priority levels. This effort has been elevated to “code red,” above the previous “code orange” designation for ChatGPT improvements.

A new reasoning model is expected to launch next week, according to the memo, though OpenAI hasn’t publicly announced it.

Delayed Products

Several product initiatives are being postponed as a result.

Advertising integration, which OpenAI had been testing in beta versions of the ChatGPT app, is now on hold, according to The Information. AI agents designed for shopping and healthcare are also delayed, along with improvements to ChatGPT Pulse.

Altman has encouraged temporary team transfers to support ChatGPT development and established daily calls for those responsible for improvements.

Competitive Context

On the technical side, Google’s Gemini 3 and related models have posted strong scores on reasoning benchmarks. Google says Gemini 3 Deep Think outperforms earlier versions on Humanity’s Last Exam, a frontier level benchmark created by AI safety researchers, and other difficult tests. Those results are reflected on Google’s own Gemini 3 Pro benchmark page and on independent leaderboards that track model performance.

OpenAI hasn’t released comparable public benchmark data for its next reasoning model yet, so comparisons rely on current GPT 5 results rather than the upcoming system referenced in the memo.

Google is also continuing to invest in generative image tools like its Nano Banana and Nano Banana Pro image generators, which sit alongside Gemini 3 as part of a broader AI product lineup.

Benchmark Context

Humanity’s Last Exam is intended to be a harder successor to saturated benchmarks like MMLU. It’s maintained by the Center for AI Safety and Scale AI, with an overview available on the project site and results tracked by multiple leaderboards, including Scale’s official leaderboard and third party dashboards such as Artificial Analysis.

Google’s Gemini 3 Pro benchmark documentation lists a higher score on Humanity’s Last Exam than several competing models, including GPT 5. That’s the basis for reporting that Gemini 3 has “outperformed” ChatGPT on that specific benchmark.

OpenAI has published strong results on other reasoning benchmarks for its GPT 5 series, but the memo appears to be reacting to this recent wave of Gemini 3 performance data rather than a single test.

Traffic And Usage Context

Despite the technical pressure, OpenAI still has a large lead in assistant usage.

In a recent post on LinkedIn, ChatGPT head Nick Turley said ChatGPT is the “#1 AI assistant worldwide,” accounting for “around 70% of assistant usage” and roughly “10% of search activity.” You can read his full comments here.

Separate reporting from outlets including the Financial Times indicates OpenAI has more than 800 million weekly users, with most on the free tier, while Gemini’s user base has been growing quickly from a lower starting point.

Altman’s memo acknowledges Google’s recent progress and warns of “temporary economic headwinds,” while also saying OpenAI is “catching up fast.”

A Familiar Playbook

The “code red” designation echoes Google’s own response to ChatGPT several years ago.

Google management declared a “code red” after ChatGPT’s viral launch. CEO Sundar Pichai redirected teams across Google Research, Trust and Safety, and other departments to focus on AI product development.

That urgency led to the accelerated development of Google’s AI products, culminating in Bard’s launch in early 2023 and its subsequent evolution into Gemini.

Now the roles have reversed. Google’s sustained investment in AI infrastructure has produced a model that scores higher than ChatGPT on several high profile benchmarks, prompting OpenAI to adopt a similar crisis response framework for its flagship product.

Company Response

Nick Turley, OpenAI’s head of ChatGPT, addressed the competitive landscape in recent posts on LinkedIn and X, where he described ChatGPT as the top AI assistant worldwide.

“New products are launching every week, which is great,” he wrote in one of the posts, saying that competition pushes OpenAI to move faster and continue improving ChatGPT.

He added that OpenAI’s focus is making ChatGPT “more capable” while expanding access and making it “more intuitive and personal.”

OpenAI hasn’t publicly commented on the leaked memo itself.

Looking Ahead

OpenAI’s new reasoning model launch will provide the first indication of how the company is executing on Altman’s directive. The delay of advertising and AI agents suggests ChatGPT quality has become the company’s singular near term priority, at least internally.

For marketers and SEO professionals, the more immediate impact is likely to be on how ChatGPT handles complex queries, research tasks, and follow up questions once the new model is live. Any measurable changes in answer quality, speed, or personalization will be important to watch alongside Google’s continued Gemini 3 rollouts.

Featured Image: Mijansk786/Shutterstock

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Dec 1 2025
Google Connects AI Overviews To AI Mode On Mobile via @sejournal, @MattGSouthern

Google is testing a new mobile search flow that connects AI Overviews to AI Mode.

Robby Stein, VP of Product for Google Search, announced the test on X. The feature lets you ask follow-up questions in AI Mode without leaving the search results page.

What’s New

Under the current setup, AI Overviews and AI Mode function as separate experiences. People who want AI Mode’s deeper conversational capabilities must navigate away from standard search results.

The test changes that workflow. You still receive an AI Overview as a starting point for a query. From there, you can ask conversational follow-up questions that open directly in AI Mode.

Stein says the update as part of a broader product vision, stating:

“This brings us closer to our vision for Search: just ask whatever’s on your mind, no matter how long or complex, and find exactly what you need. You shouldn’t have to think about where or how to ask your question.”

He described the result as “one seamless experience: a quick snapshot when you need it, and deeper conversation when you need it.”

Google says the test is running globally on mobile devices.

Why This Matters

This test shows how Google may eventually merge its AI search experiences into a single interface.

It also means more search sessions could happen within AI-generated responses rather than on the traditional results page.

If this flow becomes default, the path from query to AI Mode gets shorter, and that could lead to more searches that resolve without a click to your site.

Looking Ahead

Google hasn’t announced a timeline for expanding this test to general availability. The company typically runs experiments for several months before deciding to make them permanent.

Whether this specific test leads to a merged interface remains to be seen. But it follows Google’s pattern of making it easier to stay within AI-powered responses.

Featured Image: Tada Images/Shutterstock

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Dec 1 2025
Google Reports Search Console Page Indexing Report Delays via @sejournal, @MattGSouthern

Google announces delays in Search Console’s Page indexing report. The company confirms crawling, indexing, and ranking remain unaffected by the reporting issue.

  • Google is experiencing longer than usual delays in the Page indexing report within Search Console.
  • The issue affects reporting only, not actual crawling, indexing, or ranking of websites.
  • Google will provide an update when the issue is resolved.
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Dec 1 2025
Pragmatic Approach To AI Search Visibility via @sejournal, @martinibuster

Bing published a blog post about how clicks from AI Search are improving conversion rates, explaining that the entire research part of the consumer journey has moved into conversational AI search, which means that content must follow that shift in order to stay relevant.

AI Repurposes Your Content

They write:

“Instead of sending users through multiple clicks and sources, the system embeds high-quality content within answers, summaries, and citations, highlighting key details like energy efficiency, noise level, and smart home compatibility. This creates clarity faster and builds confidence earlier in the journey, leading to stronger engagement with less friction.”

Bing sent me advance notice about their blog post and I read it multiple times. I had a hard time getting past the part about AI Search taking over the research phase of the consumer journey because it seemingly leaves informational publishers with zero clicks. Then I realized that’s not necessarily how it has to happen, as is explained further on.

Here’s what they say:

“It’s not that people are no longer clicking. They’re just clicking at later stages in the journey, and with far stronger intent.”

Search used to be the gateway to the Internet. Today the internet (lowercase) is seemingly the gateway to AI conversations. Nevertheless, people enjoy reading content and learning, so it’s not that the audience is going away.

While AI can synthesize content, it cannot delight, engage, and surprise on the same level that a human can. This is our strength and it’s up to us to keep that in mind moving forward in what is becoming a less confusing future.

Create High-Quality Content

Bing’s blog post says that the priority is to create high-quality content:

“The priority now is to understand user actions and guide people toward high-value outcomes, whether that is a subscription, an inquiry, a demo request, a purchase, or other meaningful engagement.”

But what’s the point in creating high-quality content for consumers if Bing is no longer “sending users through multiple clicks and sources” because AI Search is embedding that high-quality content in their answers?

The answer is that Bing is still linking out to sources. This provides an opportunity for brands to identify those sources to verify if they’re in there and if they’re missing they now know to do something about it. Informational sites need to review those sources and identify why they’re not in there, something that’s discussed below.

Conversion Signals In AI Search

Earlier this year at the Google Search Central Live event in New York City, a member of the audience told the assembled Googlers that their client’s clicks were declining due to AI Overviews and asked them, “what am I supposed to tell my clients?” The audience member expressed the frustration that many ecommerce stores, publishers, and SEOs are feeling.

Bing’s latest blog post attempts to answer that question by encouraging online publishers to focus on three signals.

  • Citations
  • Impressions
  • Placement in AI answers.

This is their explanation:

“…the most valuable signals are the ones connected to visibility. By tracking impressions, placement in AI answers, and citations, brands can see where content is being surfaced, trusted, and considered, even before a visit occurs. More importantly, these signals reveal where interest is forming and where optimization can create lift, helping teams double down on what works to improve visibility in the moments when decisions are being shaped.”

But what’s the point if people are no longer clicking except at the later stages of the consumer journey?  Bing makes it clear that the research stage happens “within one environment” but they are still linking out to websites. As will be shown a little further in this article, there are steps that publishers can take to ensure their articles are surfaced in the AI conversational environment.

They write:

“In fewer steps than ever, the customer reaches a confident decision, guided by intent-aligned, multi-source content that reflects brand and third-party perspectives. This behavior shift, where discovery, research, and decision happen continuously within one environment, is redefining how site owners understand conversion.

…As AI-powered search reshapes how people explore information, more of the journey now happens inside the experience itself.

…Users now spend more of the journey inside AI experiences, shaping visibility and engagement in new ways. As a result, engagement is shifting upstream (pre-click) within summaries, comparisons, and conversational refinements, rather than through multiple outbound clicks.”

The change in which discovery, research, and decision making all happen inside the AI Search explains why traditional click-focused metrics are losing relevance. The customer journey is happening within the conversational AI environment, so the signals that are beginning to matter most are the ones generated before a user ever reaches a website. Visibility now depends on how well a brand’s information contributes to the summaries, comparisons, and conversational refinements that form the new upstream engagement layer.

This is the reality of where we are at right now.

How To Adapt To The New Customer Journey

AI Search has enabled consumers to do deeper research and comparisons during the early and middle part of the buying cycle, a significant change in consumer behavior.

In a podcast from May of this year, Michael Bonfils (LinkedIn profile) touched on this change in consumer behavior and underlined the importance of obtaining the signals from the consideration stage of consumer purchases. Read: 30-Year SEO Pro Shows How To Adapt To Google’s Zero-Click Search

He observed:

“We have a funnel, …which is the awareness consideration phase …and then finally the purchase stage. The consideration stage is the critical side of our funnel. We’re not getting the data. How are we going to get the data?

But that’s very important information that I need because I need to know what that conversation is about. I need to know what two people are talking about… because my entire content strategy in the center of my funnel depends on that greatly.”

Michael suggested that the keyword paradigm is inappropriate for the reality of AI Search and that rather than optimize for keywords, marketers and business people should be optimizing for the range of questions and comparisons that AI Search will be surfacing.

He explained:

“So let’s take the whole question, and as many questions as possible, that come up to whatever your product is, that whole FAQ and the answers, the question, and the answers become the keyword that we all optimize on moving forward.

Because that’s going to be part of the conversation.”

Bing’s blog post confirmed this aspect of consumer research and purchases, confirming that the click is happening more often on the conversion part of the consumer journey.

Tracking AI Metrics

Bing recommends using their Webmaster Tools and Clarity services in order to gain more insights into how people are engaging in AI search.

They explain:

“Bing Webmaster Tools continues to evolve to help site owners, publishers, and SEOs understand how content is discovered and where it appears across traditional search results and emerging AI-driven experiences. Paired with Microsoft Clarity’s AI referral insights, these tools connect upstream visibility with on-site behavior, helping teams see how discovery inside summaries, answers, and comparisons translates into real engagement. As user journeys shift toward more conversational, zero-UI-style interactions, these combined signals give a clearer view of influence, readiness, and conversion potential.”

The Pragmatic Takeaway

The emphasis for brands is to show up in review sites, build relationships with them, and try as much as possible to get in front of consumers and build positive word of mouth.

For news and informational sites, Bing recommends providing high-quality content that engages readers and providing an experience that will encourage readers to return.

Bing writes:

“Rather than focusing on product-driven actions, success may depend on signals such as read depth, article completion, returning reader patterns, recirculation into related stories, and newsletter sign-ups or registrations.

AI search can surface authoritative reporting earlier in the journey, bringing in readers who are more inclined to engage deeply with coverage or return for follow-up stories. As these upstream interactions grow, publishers benefit from visibility into how their work appears across AI answers, summaries, and comparisons, even when user journeys are shorter or involve fewer clicks.”

I have been a part of the SEO community for over twenty-five years and I have never seen a more challenging period for publishers than what we’re faced with today. The challenge is to build a brand, generate brand loyalty, focus on the long-term.

Read Bing’s blog post:

How AI Search Is Changing the Way Conversions are Measured 

Featured Image by Shutterstock/ImageFlow

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Nov 28 2025
Google’s Mueller Says Sites In A ‘Bad State’ May Need To Start Over via @sejournal, @MattGSouthern

Google’s John Mueller says sites with low-quality AI content should rethink their purpose rather than manually rewrite pages. Starting fresh may be faster than recovering.

  • Manually rewriting AI content doesn’t automatically restore a site’s value or authenticity
  • Mueller recommends treating recovery as starting over with no content, not as a page-by-page editing task
  • Recovering from a “bad state” may take longer than launching on a new domain
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Nov 26 2025
Mueller: Background Video Loading Unlikely To Affect SEO via @sejournal, @MattGSouthern

Google Search Advocate John Mueller says large video files loading in the background are unlikely to have a noticeable SEO impact if page content loads first.

A site owner on Reddit’s r/SEO asked whether a 100MB video would hurt SEO if the page prioritizes loading a hero image and content before the video. The video continues loading in the background while users can already see the page.

Mueller responded:

“I don’t think you’d notice an SEO effect.”

Broader Context

The question addresses a common concern for sites using large hero videos or animated backgrounds.

The site owner described an implementation where content and images load within seconds, displaying a “full visual ready” state. The video then loads asynchronously and replaces the hero image once complete.

This method aligns with Google’s documentation on lazy loading, which recommends deferring non-critical content to improve page performance.

Google’s help documents state that lazy loading is “a common performance and UX best practice” for non-critical or non-visible content. The key requirement is ensuring content loads when visible in the viewport.

Why This Matters

If you’re running hero videos or animated backgrounds on landing pages, this suggests that background loading strategies are unlikely to harm your rankings. The critical factor is ensuring your primary content reaches users quickly.

Google measures page experience through Core Web Vitals metrics like Largest Contentful Paint. In many cases, a video that loads after visible content is ready shouldn’t block these measurements.

Implementation Best Practices

Google’s web.dev documentation recommends using preload=”none” on video elements to avoid unnecessary preloading of video data. Adding a poster attribute provides a placeholder image while the video loads.

For videos that autoplay, the documentation suggests using the Intersection Observer API to load video sources only when the element enters the viewport. This lets you maintain visual impact without affecting initial page load performance.

Looking Ahead

Site owners using background video can generally continue doing so without major SEO concerns, provided content loads first. Focus on Core Web Vitals metrics to verify your implementation meets performance thresholds.

Test your setup using Google Search Console’s URL Inspection Tool to confirm video elements appear correctly in rendered HTML.

Featured Image: Roman Samborskyi/Shutterstock

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Nov 26 2025
New Data: Top Factors Influencing ChatGPT Citations via @sejournal, @MattGSouthern

SE Ranking analyzed 129,000 unique domains across 216,524 pages in 20 niches to identify which factors correlate with ChatGPT citations.

The number of referring domains ranked as the single strongest predictor of citation likelihood.

What The Data Says

Backlinks And Trust Signals

Link diversity showed the clearest correlation with citations. Sites with up to 2,500 referring domains averaged 1.6 to 1.8 citations. Those with over 350,000 referring domains averaged 8.4 citations.

The researchers identified a threshold effect at 32,000 referring domains. At that point, citations nearly doubled from 2.9 to 5.6.

Domain Trust scores followed a similar pattern. Sites with Domain Trust below 43 averaged 1.6 citations. The benefits accelerated significantly at the top end: sites scoring 91–96 averaged 6 citations, while those scoring 97–100 averaged 8.4.

Page Trust mattered less than domain-level signals. Any page with a Page Trust score of 28 or above received roughly the same citation rate (8.3 average), suggesting ChatGPT weighs overall domain authority more heavily than individual page metrics .

One notable finding: .gov and .edu domains didn’t automatically outperform commercial sites. Government and educational domains averaged 3.2 citations, compared to 4.0 for sites without trusted zone designations.

The authors wrote:

“What ultimately matters is not the domain name itself, but the quality of the content and the value it provides.”

Traffic & Google Rankings

Domain traffic ranked as the second most important factor, though the correlation only appeared at high traffic levels.

Sites under 190,000 monthly visitors averaged 2 to 2.9 citations regardless of exact traffic volume. A site receiving 20 organic visitors performed similarly to one receiving 20,000.

Only after crossing 190,000 monthly visitors did traffic correlate with increased citations. Domains with over 10 million visitors averaged 8.5 citations.

Homepage traffic specifically mattered. Sites with at least 7,900 organic visitors to their main page showed the highest citation rates.

Average Google ranking position also tracked with ChatGPT citations. Pages ranking between positions 1 and 45 averaged 5 citations. Those ranking 64 to 75 averaged 3.1.

The authors noted:

“While this doesn’t prove that ChatGPT relies on Google’s index, it suggests both systems evaluate authority and content quality similarly.”

Content Depth & Structure

Content length showed consistent correlation. Articles under 800 words averaged 3.2 citations. Those over 2,900 words averaged 5.1.

Structure mattered beyond raw word count. Pages with section lengths of 120 to 180 words between headings performed best, averaging 4.6 citations. Extremely short sections under 50 words averaged 2.7 citations.

Pages with expert quotes averaged 4.1 citations versus 2.4 for those without. Content with 19 or more statistical data points averaged 5.4 citations, compared to 2.8 for pages with minimal data.

Content freshness produced one of the clearer findings. Pages updated within three months averaged 6 citations. Outdated content averaged 3.6.

Surprisingly, the raw data showed that pages with FAQ sections actually received fewer citations (3.8) than those without (4.1). However, the researchers noted that their predictive model viewed the absence of an FAQ section as a negative signal. They suggest this discrepancy exists because FAQs often appear on simpler support pages that naturally earn fewer citations.

The report also found that using question-style headings (e.g., as H1s or H2s) underperformed straightforward headings, earning 3.4 citations versus 4.3. This contradicts standard voice search optimization advice, suggesting AI models may prefer direct topical labeling over question formats.

Social Signals & Review Platforms

Brand mentions on discussion platforms showed strong correlation with citations.

Domains with minimal Quora presence (up to 33 mentions) averaged 1.7 citations. Heavy Quora presence (6.6 million mentions) corresponded to 7.0 citations.

Reddit showed similar patterns. Domains with over 10 million mentions averaged 7 citations, compared to 1.8 for those with minimal activity.

The authors positioned this as particularly relevant for smaller sites:

“For smaller, less-established websites, engaging on Quora and Reddit offers a way to build authority and earn trust from ChatGPT, similar to what larger domains achieve through backlinks and high traffic.”

Presence on review platforms like Trustpilot, G2, Capterra, Sitejabber, and Yelp also correlated with increased citations. Domains listed on multiple review platforms earned 4.6 to 6.3 citations on average. Those absent from such platforms averaged 1.8.

Technical Performance

Page speed metrics correlated with citation likelihood.

Pages with First Contentful Paint under 0.4 seconds averaged 6.7 citations. Slower pages (over 1.13 seconds) averaged 2.1.

Speed Index showed similar patterns. Sites with indices below 1.14 seconds performed reliably well. Those above 2.2 seconds experienced steep decline.

One counterintuitive finding: pages with the fastest Interaction to Next Paint scores (under 0.4 seconds) actually received fewer citations (1.6 average) than those with moderate INP scores (0.8 to 1.0 seconds, averaging 4.5 citations). The researchers suggested extremely simple or static pages may not signal the depth ChatGPT looks for in authoritative sources.

URL & Title Optimization

The report found that broad, topic-describing URLs outperformed keyword-optimized ones.

Pages with low semantic relevance between URL and target keyword (0.00 to 0.57 range) averaged 6.4 citations. Those with highest semantic relevance (0.84 to 1.00) averaged only 2.7 citations.

Titles followed the same pattern. Titles with low keyword matching averaged 5.9 citations. Highly keyword-optimized titles averaged 2.8.

The researchers concluded: “ChatGPT prefers URLs that clearly describe the overall topic rather than those strictly optimized for a single keyword.”

Factors That Underperformed

Several commonly recommended AI optimization tactics showed minimal or negative correlation with citations.

FAQ schema markup underperformed. Pages with FAQ schema averaged 3.6 citations. Pages without averaged 4.2.

LLMs.txt files showed negligible impact. Outbound links to high-authority sites also showed minimal effect on citation likelihood.

Why This Matters

The findings suggest your existing SEO strategy may already serve AI visibility goals. If you’re building referring domains, earning traffic, maintaining fast pages, and keeping content updated, you’re addressing the factors this report identified as most predictive.

For smaller sites without extensive backlink profiles, the research points to community engagement on Reddit and Quora as a viable path to building authority signals The data also suggests focusing on content depth over keyword density.

The researchers note that factors are interdependent. Optimizing one signal while ignoring others reduces overall effectiveness.

Looking Ahead

SE Ranking analyzed ChatGPT specifically. Other AI systems may weight factors differently.

SE Ranking doesn’t specify which ChatGPT version or timeframe the data represents, so these patterns should be treated as directional correlations rather than proof of how ChatGPT’s ranking algorithm works.

Featured Image: BongkarnGraphic/Shuttersrtock

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Nov 24 2025
ChatGPT Adds Shopping Research For Product Discovery via @sejournal, @MattGSouthern

OpenAI launched shopping research in ChatGPT, a feature that creates personalized buyer’s guides by researching products across the web. The tool is rolling out today on mobile and web for logged-in users on Free, Go, Plus, and Pro plans.

The company is offering nearly unlimited usage through the holidays.

What’s New

Shopping research works differently from standard ChatGPT responses. Users describe what they need, answer clarifying questions about budget and preferences, and receive a buyer’s guide after a few minutes.

The feature pulls information including price, availability, reviews, specs, and images from across the web. You can guide the research by marking products as “Not interested” or “More like this” as options appear.

OpenAI’s announcement states:

“Shopping research is built for that deeper kind of decision-making. It turns product discovery into a conversation: asking smart questions to understand what you care about, pulling accurate, up-to-date details from high-quality sources, and bringing options back to you to refine the results.”

The company says the tool performs best in categories like electronics, beauty, home and garden, kitchen and appliances, and sports and outdoor.

Technical Details

Shopping research is powered by a shopping-specialized GPT-5 mini variant post-trained on GPT-5-Thinking-mini.

OpenAI’s internal evaluation shows shopping research reached 52% product accuracy on multi-constraint queries, compared with 37% for ChatGPT Search.

Product accuracy measures how well responses meet user requirements for attributes like price, color, material, and specs. The company designed the system to update and refine results in real time based on user feedback.

Privacy & Data Sharing

OpenAI states that user chats are never shared with retailers. Results are organic and based on publicly available retail sites.

Merchants who want to appear in shopping research results can follow an allowlisting process through OpenAI.

Limitations

OpenAI acknowledges the feature isn’t perfect. The model may make mistakes about product details like price and availability. The company encourages users to visit merchant sites for the most accurate information.

Why This Matters

This feature pulls more of the product comparison journey into one place.

As shopping research handles more of the “which one should I buy?” work inside ChatGPT, some of that early-stage discovery could happen without a traditional search click.

For retailers and affiliate publishers, that raises the stakes for inclusion in these results. Visibility may depend on how well your products and pages are represented in OpenAI’s shopping system and allowlisting process.

Looking Ahead

Shopping research in ChatGPT is now available to logged-in users starting today. OpenAI plans to add direct purchasing through ChatGPT for merchants participating in Instant Checkout, though no timeline was provided.

Featured Image: Koshiro K/Shutterstock

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Nov 24 2025
What Optmyzr’s Three-Year Study Reveals About Seasonality Adjustments During BFCM via @sejournal, @brookeosmundson

Every Q4, the same message shows up in our accounts:

“Use seasonality adjustments to get ready for Black Friday and Cyber Monday.”

On paper, it sounds reasonable. You expect conversion rates to rise, so you give Smart Bidding a heads up and tell it to bid more aggressively during the peak.

Optmyzr’s latest study puts a pretty big dent in that narrative.

Over three BFCM cycles from 2022 through 2024, Fred Vallaeys and the Optmyzr team analyzed performance for up to 6,000 advertisers per year, split into two cohorts: those who used seasonality bid adjustments and those who did not.

The question was simple: do these adjustments actually help during Black Friday and Cyber Monday, or are we just making Google bid higher for no meaningful gain?

Based on the data, seasonality adjustments often hurt efficiency and rarely deliver the breakthrough many advertisers expect.

Below is a breakdown of the study and what it means for PPC managers heading into peak season.

Key Findings from Optmyzr’s BFCM Seasonality Study

The study compared performance across three BFCM periods (2022–2024), defined as the Wednesday before Black Friday through the Wednesday after Cyber Monday. Each year’s results were then measured against a pre-BFCM baseline.

The accounts were grouped into:

  • Advertisers who did not use seasonality bid adjustments
  • Advertisers who did apply them

Across all three years, consistent patterns emerged from their study.

#1: Smart Bidding already adjusts for BFCM without manual prompts

For advertisers who skipped seasonality adjustments, Smart Bidding still responded to the conversion rate spike:

  • 2022: Conversion rate up 17.5%
  • 2023: Conversion rate up 11.9%
  • 2024: Conversion rate up 7.5%

In other words, the algorithm did exactly what it was designed to do. It detected higher intent and increased bids without needing an external nudge.

#2: Seasonality adjustments inflated CPCs far more than necessary

Seasonality adjustments tell Google’s system to raise bids based on your predicted conversion rate increase.

Optmyzr notes that:

When you apply a seasonality adjustment, you are effectively telling Google: ‘I expect conversion rate to increase by X%. Raise bids immediately by X%.

And Smart Bidding acts as if you’re exactly right. It usually doesn’t soften that prediction or test into it.

The study showed that this is why CPCs climbed much faster for advertisers who used adjustments:

CPC inflation (no adjustment vs. with adjustment)

  • 2022: +17% vs. +36.7%
  • 2023: +16% vs. +32%
  • 2024: +17% vs. +34%

Adjustments consistently doubled CPC inflation, even though Smart Bidding was already raising bids based on real-time conversion signals.

#3: ROAS dropped for advertisers using seasonality adjustments

When CPC increases outpace conversion rate increases, ROAS inevitably suffers.

ROAS change (no adjustment vs. with adjustment)

  • 2022: -2% vs. -17%
  • 2023: -1.5% vs. -10%
  • 2024: +5.7% vs. -15.7%

The “no adjustment” group maintained stable ROAS, even improving in 2024. The “with adjustment” group saw steep declines every year.

Why Do Seasonality Adjustments Struggle During BFCM?

Optmyzr explains this dynamic as a precision issue.

When you apply a seasonality adjustment, you are making a specific prediction about the conversion lift. If you estimate the lift at +40% and the real lift ends up being +32–35%, that gap translates directly into overbidding.

Fred Vallaeys writes:

Smart Bidding takes this literally. It does not hedge your bet. It assumes you have perfect foresight.

That’s the core problem.

Black Friday and Cyber Monday are also in the category of highly predictable retail events. Google has years of historical BFCM data to model expected shifts. As a result, Optmyzr concludes:

Seasonality adjustments work best when Google cannot anticipate the spike.

BFCM is not one of those situations. It’s practically encoded into Google’s models.

The Trade-Off: More Revenue, Lower Efficiency

The study did show that advertisers using seasonality adjustments often drove higher revenue growth:

Revenue growth (no adjustment vs. with adjustment)

  • 2022: +25% vs. +50.5%
  • 2023: +30.3% vs. +52.8%
  • 2024: +33.8% vs. 39.9%

In 2022 and 2023, the incremental revenue jump was significant. But again, those gains came with notable ROAS declines.

This supports a practical interpretation:

  • If your brand’s priority is aggressive market share capture, top-line revenue or inventory liquidation, seasonality adjustments can deliver more volume.
  • If your brand’s priority is profitable performance, adjustments tend to work against that goal during BFCM.

When Seasonality Adjustments Do Make Sense

In the study, Optmyzr made it very clear: seasonality adjustments themselves aren’t the problem. The misuse of them is.

They work well in scenarios where you genuinely have more insight into the spike than the platforms do, such as:

  • A short flash sale
  • A new one-time promotion with no historical precedent
  • A large, concentrated email push
  • Niche events with little global relevance

Situations where they may not make the most sense:

  • Black Friday and Cyber Monday (supported by their data study)
  • Christmas shopping windows
  • Valentine’s Day for gift categories

These events are already modeled extensively by Google’s bidding systems.

What Should PPC Managers Do With This Data?

If you’re looking to make some changes into your PPC accounts this holiday season, here’s a few ways to apply these findings in a practical way.

#1: Default to not using seasonality adjustments for BFCM

For the majority of advertisers, letting Smart Bidding handle the conversion rate spike naturally leads to steadier ROAS and fewer surprises.

The data supports this approach across three consecutive years.

#2: If leadership insists on volume, be explicit about the trade-off

You can lean on Optmzyr’s findings to set expectations, not just express an opinion.

For example:

  • “Optmyzr’s three-year analysis shows that seasonality adjustments can increase revenue but typically reduce ROAS by 10-17 percentage points.”
  • “We can use them if revenue volume is the priority, but we will need to prepare for much lower cost efficiency.”

These examples keep the conversation focused on the business, not just the tactical levers you pull.

#3: Spend your energy on guardrails, not the predictions

In the study, Optmzyr reminds advertisers that trusting the algorithm doesn’t mean blindly letting it run without any oversight.

Instead of guessing the exact uplift, your value during peak season come from:

  • Smart budget pacing
  • Hourly monitoring (with automated alerts, of course!)
  • Bid caps when necessary
  • Audience and device segmentation checks
  • Creative and offer readiness

These are some of the key areas where human judgment beats prediction.

Final Thoughts On Optmyzr’s Study

Optmyzr’s study doesn’t argue that seasonality bid adjustments are bad. What it does argue is that context is everything.

For predictable, high-volume retail events like BFCM, Google’s bidding systems already have the signal they need. Adding your own forecast often leads to overshooting, inflated CPCs, and unnecessary efficiency loss.

For unique or brand-specific spikes, adjustments remain valuable.

This research gives PPC managers something we rarely get during BFCM: solid data to support a more measured, less reactive approach. If nothing else, it gives you the backup you need the next time someone asks:

“Should we turn on seasonality adjustments this Black Friday?”

Your answer can be confident, data-driven, and clear.

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