I recently came across some important news from Google that I felt compelled to share with you. As of May 7, 2026, Google will no longer support FAQ rich results. This change means that these helpful snippets will no longer appear in Google Search results.
Additionally, Google Search Console will cease reporting on FAQ structured data, impacting how we track and analyze our content’s performance in search engines.
What Google said: Google has posted a notice on the FAQ structured data developer documentation. They state: FAQ rich results are no longer appearing in Google Search. By June 2026, Google plans to fully drop the search appearance, rich result report, and support in the Rich results test. To provide some adjustment time, support for the FAQ rich result in the Search Console API will be removed by August 2026.
Remove code: You might be wondering what to do with your existing FAQ structured data. The choice is yours—you can remove it from your code, but leaving it might still benefit you if other search engines use it for their own purposes.
Why we care: For me and many others, rich results have been instrumental in increasing web pages’ click-through rates and attracting additional traffic. The discontinuation of FAQ rich results could impact this dynamic.
To gauge the effect on your website, monitor pages with FAQ structured data closely and pay attention to any shifts in your traffic from Google.
These days, simply fixing technical SEO issues on my site isn’t enough to make a significant impact.
When my site achieves technical parity with competitors, the ranking focus shifts from infrastructure to relevance. Google evaluates relevance based on how well my content aligns with search intent.
Let’s explore how I can make my site more relevant.
Why an intent mismatch may be suppressing my site’s performance
An intent mismatch happens when the content on my page doesn’t meet user expectations. If the page isn’t relevant or the signals sent are mixed, it results in poor behavior signals, like users bouncing off the page without finding answers.
These signals suggest to Google that my page doesn’t satisfy the query, causing ranking drops, fewer users viewing the page, and worsening behavior signals. It’s a situation that technical SEO alone won’t solve.
Technical SEO improvements may no longer make a difference
Initially, when I start an SEO strategy, improvements come quickly. If my website lags in technical standards, resolving crawl errors, addressing duplicate content, boosting page speed, and adding schema can result in significant gains.
However, once these changes place my site on par with competitors, Google evaluates sites based on user query satisfaction. Now, my technical foundation is solid, but the rules have changed.
Intent alignment becomes the primary improvement focus here.
Signals that reinforce search intent
Various elements affect a page’s intent and Google’s decision on whether it matches. These include:
Click-through rate.
Engagement signals.
Core Web Vitals.
Schema type.
Internal linking anchor texts.
URL structure.
Click-through rate (CTR)
My CTR can be influenced by factors like my title tag, meta description, URL structure, and schema, all measured against intent.
If my title tag is well-optimized yet mismatched with user queries, CTR will drop. Google sees low CTR as a relevance signal and adjusts rankings.
Engagement rate
Intent misalignment can harm time-on-page, scroll depth, and interaction rates. A user searching to purchase something might exit immediately if they land on a how-to guide. Similarly, a user seeking an emergency plumber might bounce from a page lacking contact details.
Core Web Vitals (CWV)
LCP, INP, and CLS measure page load speed. A slow transactional page frustrates users ready to buy, whereas informational article readers are more patient.
While CWV thresholds matter everywhere, they heavily impact conversion and behavior on high-intent pages.
Schema type
Schema markup explicitly tells Google the page content type. Contradictory content and schema signals send Google a wrong intent signal, affecting traffic.
Internal linking anchor texts
Internal link anchor text informs Google about the linked page’s intent. If a transactional page’s links use informational text like “learn more about X,” intent signals get diluted.
URL structure
Google uses URL patterns to infer page type. For instance, URLs in /blog/ are seen as informational. A product page in a blog path may struggle with ranking expectations.
Cannibalization and canonicalization
Multiple pages targeting the same keyword with different intents dilute Google’s signal, hindering ranking. Using canonical tags can emphasize the preferred page for a keyword, consolidating or redirecting when necessary.
How to fix intent misalignment
Let’s consider a common intent mismatch and steps I can take to audit and fix it.
What an intent mismatch looks like
If someone searches for “financial analysis software,” they intend to purchase software, a highly transactional query. Targeting this keyword with an informational blog post explaining DIY analysis creates a mismatch.
These users want to compare features and pricing or book a demo. Therefore, targeting the keyword with a dedicated page outlining features and pricing is optimal, aligning with user needs and boosting conversions.
Identify the intent of my pages
To remedy intent mismatches, I start by compiling top-performing keywords and manually checking their Google rankings. This research shows what type of page and content best suits these keywords.
See what my competitors are doing
By researching competitors’ pages targeting my keywords, I note elements they include, such as tables, comparisons, or videos, which can inform improvements on my pages.
Measure my page’s performance based on intent metrics
After making page improvements, I track performance indicators like clicks, rankings, and time on page to evaluate the effectiveness of changes.
Technical SEO and intent need to work together
Technical SEO is vital; it lays the groundwork. Pages that aren’t properly crawled won’t rank to their full potential, regardless of intent alignment.
Intent alignment, however, dictates how high a technically sound page can rank and its conversion rate. Every page should have clearly defined intent supported by technical signals for reinforcement.
I’ve been navigating the rapidly evolving world of AI-driven search, and I’ve realized that search visibility now means more than just rankings. AI has redefined where discovery takes place, reaching across platforms like Google, ChatGPT, and Perplexity.
<!–<!–>Generative engine optimization (GEO) is my way of adapting how my brand is retrieved and represented in these systems.
/wp:paragraph –>
I’ve noticed traditional <!–<!–>SEO metrics aren’t capturing the full picture of visibility anymore. AI-generated summaries mean that users are clicking traditional search results far less often — only <!- 8% of the time –><!–>8% of the time, according to some studies.
/wp:paragraph –>
This realization highlighted a gap in measurement that GEO metrics can fill for me.
What Visibility Means in Generative Search
For me, GEO focuses on whether AI can find and use my content to generate answers. It’s not just about being indexed; it’s about how my content is utilized—cited or summarized in AI responses.
With GEO, I’m shifting my focus from rankings to ensuring my content is clear and trusted in context.
In practice, I’m optimizing for extractability, credibility, and relevance—key aspects that make GEO metrics valuable.
I find tracking GEO performance through these eight metrics essential because they highlight presence, influence, and downstream impact.
1. AI Citation Frequency
This metric tells me how often my brand or content is cited in AI-generated answers—a clear sign my content is valuable enough to be referenced by generative systems.
I track this across platforms like Google AI Overviews, ChatGPT search, and others, focusing on citation at the topic level.
2. Share of Model Voice (SOMV)
For me, SOMV is a measure of my brand’s presence in AI-generated answers, comparing visibility to competitors.
This metric is useful especially in competitive categories, where share matters more than visibility due to compressed consideration sets in AI answers.
3. Answer Inclusion Rate
Answer inclusion rate helps me see how often my content contributes to AI-generated answers, providing insight beyond just citation frequency.
I track inclusion for a range of prompts to see which content formats AI prefers to retrieve and summarize.
4. Entity Recognition and Authority
To ensure AI systems understand my brand, I focus on entity recognition—making sure AI correctly connects my brand to its key details and associations.
This involves consistently managing the signals AI systems use, like structured data and corroborating signals.
5. Sentiment in AI Responses
Understanding how AI describes my brand is crucial. I track sentiment in AI-generated responses to manage perception before users reach my site.
I focus on ensuring positive framing and correcting any misconceptions or outdated information.
6. Prompt Coverage
Prompt coverage shows me how well my brand surfaces across conversational and intent-rich prompts, which are crucial in AI search contexts.
For instance, I look at a variety of prompt types, including informational and decision-stage, to gauge comprehensive visibility.
7. Content Retrieval Success Rate
This metric evaluates how often AI systems pull from my content. If content isn’t easily parsed or updated, it may not appear in AI outputs.
I check various technical factors to enhance content retrieval, from crawlability to schema use.
8. Conversion Influence After AI Interaction
This involves measuring how AI visibility impacts business outcomes, tracing the journey from AI interaction to conversion.
Even with fewer sessions, AI-driven visits tend to be high-intent, so I track conversion quality and influence closely.
Tools and Methods for Tracking GEO Metrics
I find GEO measurement requires a combination of tools, audits, and tests, as no single platform currently captures the entire picture.
Emerging GEO Analytics Platforms
Using tools from both SEO giants and GEO-native products, I track brand visibility across AI-driven search.
Platforms like Semrush and SE Ranking provide visibility trends tied to AI, which are invaluable in aligning strategies.
Prompt Testing Frameworks
Manually testing prompts is still vital. I create a controlled prompt set and consistently observe how my brand is included across AI platforms.
By tracking over time, I identify patterns and adjust my strategies accordingly.
Analytics and Logs
I utilize analytics tools like GA4 to identify AI platform traffic and its influence on conversions.
These insights guide me in understanding AI’s business impact, including direct and branded search changes.
Search Console and Traditional SEO Tools
Despite declining clicks, Search Console remains vital, showing me where AI Overviews are impacting demand and where restructuring is needed.
Traditional SEO tools are also key for technical health and competitive research, laying the groundwork for comprehensive GEO measurement.
How to Build a GEO Measurement Framework
Starting with a baseline, I choose core topics that should be associated with my brand and map prompts accordingly.
By building a dashboard across visibility, accuracy, technical, and business impact categories, I lay out clear actions and align them with business goals.
Ultimately, my GEO strategy must adapt according to metrics and business objectives, ensuring dynamic business value.
<!–<!–>Generative engine optimization (GEO) is my way of adapting how my brand is retrieved and represented in these systems.
/wp:paragraph –>
I’ve noticed traditional <!–<!–>SEO metrics aren’t capturing the full picture of visibility anymore. AI-generated summaries mean that users are clicking traditional search results far less often — only <!- 8% of the time –><!–>8% of the time, according to some studies.
/wp:paragraph –>
This realization highlighted a gap in measurement that GEO metrics can fill for me.
What Visibility Means in Generative Search
For me, GEO focuses on whether AI can find and use my content to generate answers. It’s not just about being indexed; it’s about how my content is utilized—cited or summarized in AI responses.
With GEO, I’m shifting my focus from rankings to ensuring my content is clear and trusted in context.
In practice, I’m optimizing for extractability, credibility, and relevance—key aspects that make GEO metrics valuable.
I find tracking GEO performance through these eight metrics essential because they highlight presence, influence, and downstream impact.
1. AI Citation Frequency
This metric tells me how often my brand or content is cited in AI-generated answers—a clear sign my content is valuable enough to be referenced by generative systems.
I track this across platforms like Google AI Overviews, ChatGPT search, and others, focusing on citation at the topic level.
2. Share of Model Voice (SOMV)
For me, SOMV is a measure of my brand’s presence in AI-generated answers, comparing visibility to competitors.
This metric is useful especially in competitive categories, where share matters more than visibility due to compressed consideration sets in AI answers.
3. Answer Inclusion Rate
Answer inclusion rate helps me see how often my content contributes to AI-generated answers, providing insight beyond just citation frequency.
I track inclusion for a range of prompts to see which content formats AI prefers to retrieve and summarize.
4. Entity Recognition and Authority
To ensure AI systems understand my brand, I focus on entity recognition—making sure AI correctly connects my brand to its key details and associations.
This involves consistently managing the signals AI systems use, like structured data and corroborating signals.
5. Sentiment in AI Responses
Understanding how AI describes my brand is crucial. I track sentiment in AI-generated responses to manage perception before users reach my site.
I focus on ensuring positive framing and correcting any misconceptions or outdated information.
6. Prompt Coverage
Prompt coverage shows me how well my brand surfaces across conversational and intent-rich prompts, which are crucial in AI search contexts.
For instance, I look at a variety of prompt types, including informational and decision-stage, to gauge comprehensive visibility.
7. Content Retrieval Success Rate
This metric evaluates how often AI systems pull from my content. If content isn’t easily parsed or updated, it may not appear in AI outputs.
I check various technical factors to enhance content retrieval, from crawlability to schema use.
8. Conversion Influence After AI Interaction
This involves measuring how AI visibility impacts business outcomes, tracing the journey from AI interaction to conversion.
Even with fewer sessions, AI-driven visits tend to be high-intent, so I track conversion quality and influence closely.
Tools and Methods for Tracking GEO Metrics
I find GEO measurement requires a combination of tools, audits, and tests, as no single platform currently captures the entire picture.
Emerging GEO Analytics Platforms
Using tools from both SEO giants and GEO-native products, I track brand visibility across AI-driven search.
Platforms like Semrush and SE Ranking provide visibility trends tied to AI, which are invaluable in aligning strategies.
Prompt Testing Frameworks
Manually testing prompts is still vital. I create a controlled prompt set and consistently observe how my brand is included across AI platforms.
By tracking over time, I identify patterns and adjust my strategies accordingly.
Analytics and Logs
I utilize analytics tools like GA4 to identify AI platform traffic and its influence on conversions.
These insights guide me in understanding AI’s business impact, including direct and branded search changes.
Search Console and Traditional SEO Tools
Despite declining clicks, Search Console remains vital, showing me where AI Overviews are impacting demand and where restructuring is needed.
Traditional SEO tools are also key for technical health and competitive research, laying the groundwork for comprehensive GEO measurement.
How to Build a GEO Measurement Framework
Starting with a baseline, I choose core topics that should be associated with my brand and map prompts accordingly.
By building a dashboard across visibility, accuracy, technical, and business impact categories, I lay out clear actions and align them with business goals.
Ultimately, my GEO strategy must adapt according to metrics and business objectives, ensuring dynamic business value.
Have you noticed a change in how Google displays links and citations in its AI search features? I recently learned about five key updates that aim to enhance our experience with AI Mode and AI Overviews.
According to Hema Budaraju, VP, Product Management at Google, these upgrades are designed to help us connect with authentic voices and access valuable information across the web. She detailed these updates in a recent article.
Let’s dive into the updates rolling out:
(1) Suggested angles at the end of AI responses. Google now suggests further reading options at the end of AI responses. These link to unique articles or analyses that deepen our understanding of the topic. It’s like having a roadmap to satisfy our curiosity!
Here’s a preview of this feature:
(2) Easier access to your news subscriptions. With this update, Google displays links from our news subscriptions prominently. This means I can quickly access content I trust, maximizing the value of my subscriptions. During Google’s early tests, these subscription links significantly boosted click-through rates.
If you’re a publisher, check out the documentation to enable this feature.
Here’s what this looks like in action:
(3) Social media and online discussions now include creator details. When AI features cite social media, Google includes not only the website’s name but also the creator’s name, handle, and community name. This transparency helps me spot firsthand sources at a glance.
Here’s a glimpse of how this plays out:
(4) More links, next to relevant text. Google is increasing the number of links shown directly within AI responses, strategically placing them next to relevant text. This makes it tempting for me to explore these sources further.
Here’s what it looks like:
(5) Hover over inline links for a quick look. Now when I hover over an inline link in Google’s AI features, I get a sneak peek of the website. This could just be the nudge I need to click through and explore further. I remember seeing Google test this back in February and thought it was a brilliant idea.
Here’s an example of the feature:
Why this matters. Google is committed to ongoing testing and refinements, ensuring these features serve us better. I truly believe these changes will promote more engagement with the cited pages, presenting an exciting step forward for both users and the web ecosystem. The real question is, will they meet my expectations?
When I first looked at my SEO data, everything seemed perfectly fine. All metrics from Google Search Console, traffic, and indexing were normal without any red flags. But then, I decided to dig deeper using Scrunch, our AI citation monitoring tool, to examine the platform presence for searchinfluence.com over the past 30 days.
Here’s what I found: Google AI Mode showed a presence of 37.8%, Copilot at 22.2%, Google Gemini at 16.3%, ChatGPT at 9.6%, and Perplexity at 7.8%. Alarmingly, both Claude and Meta AI were at 0.0%.
Two platforms had zero presence. Given that every crawler reads the same site, differences in content quality or topical authority couldn’t explain this discrepancy. The only factor that varied was crawler access.
To understand this further, I analyzed seven days of Cloudflare logs and discovered 29,099 bot requests, with 65.8% involving AI bots. The requests rate-limited with HTTP 429, or “too many requests,” were interestingly varied by bot user-agent.
Training crawlers that make bulk requests are throttled, while user-facing crawlers that mimic human pacing during live queries aren’t. For example, ClaudeBot made 20,583 crawl requests for each referral returned.
My assumption was that the 429 errors originated from Cloudflare, perhaps due to a web application firewall (WAF) or security plugin interference. I went down a rabbit hole investigating multiple layers. It was time-consuming and ultimately unnecessary.
The truth emerged when I performed a reproduction test using curl requests, revealing that the block was based on user-agent, not path or rate. The realization hit when I discovered the x-powered-by header: WP Engine hosted our site, and the block came from their platform infrastructure.
I then tested other AI bot UAs and crafted a fingerprint for each, discovering that the blocklist was outdated. While some bots were blocked, others like Common Crawl passed through unaffected.
In conclusion, while WP Engine’s firewall, documented on their support page, was intended as a security measure, it wasn’t transparent to customers. Identifying these blocks requires specific diagnostic steps, and the process taught me much about managed hosting’s hidden layers.
In this report, I’m excited to share with you the average conversion rate data we gathered from B2B companies over the span of 2019 to 2026. We meticulously segmented this information by landing page type and industry. By analyzing data from 83 companies across 27 diverse industries, we provide a comprehensive insight into the world of B2B landing pages. Each of our clients in this study turned to us for an SEO campaign, and 38 of these organizations additionally took advantage of our content creation, email marketing, or LinkedIn marketing services.
When I mention conversion, I refer to actions like filling out a contact form, signing up for a demo, downloading a gated white paper, subscribing to a newsletter, making a purchase, or any other action that aligns with the page’s call-to-action. The conversion rate of a page is the percentage of visitors who perform one or more of these actions in a given timeframe (commonly measured quarterly).
Our analysis covered six different types of landing pages: Product Pages, Service Pages, Industry Pages, Location Pages, Customer Type Pages, and Application Pages. We intentionally excluded Home Pages, About Pages, and other general informational pages.
Customer-type pages are explicitly targeted to well-defined client profiles. Consequently, when this client lands on this page, the conversion rate is high compared to other landing pages.
Application
3.1%
Similar to service pages, application pages should demonstrate your experience in solving a problem related to the reader’s issue.
Product
2.9%
Product pages are typically direct and enjoy relatively high conversion rates since they target the most transactional search intents.
Service
2.7%
Service pages are akin to product pages, enjoying high conversion rates due to the customer journey stage visitors are in when they reach a service page.
Industry
1.8%
These pages serve a dual purpose: demonstrating your understanding and expertise in the industry.
Location
1.1%
Location landing pages should make it clear that you’re familiar with the specific geographic area’s nuances necessary for delivering the service/product. Many location pages suffer from poor conversion rates due to duplicate content.
The following landing page strategies have consistently improved conversion rates for our clients. Let me walk you through them:
Reduce Form Fields: For early-stage conversions, I recommend limiting fields to email, first name, and company (optional). For demo requests, include additional fields like job title or phone number.
Add Trust Signals: Ensure your landing page includes client logos, review scores, testimonials, and certification information if they’re not already there.
Include Product Features & Highlights: Present your product’s value clearly by addressing the reader’s pain points in a visually engaging manner.
Include Mid-Page Calls to Action: Anticipate that readers might not scroll to the bottom of the page. Provide opportunities for them to engage further with offers such as a free demo or newsletter signup.
Further Reading and Requesting a Copy of This Report
If you’re interested in learning more about conversion rates, I invite you to explore our other resources below:
As I dive into the world of B2B SaaS marketing for 2026, I’ve identified several pivotal channels worth your attention. Based on costs, expected ROI, and how swiftly they generate leads, I’ll guide you through making the best choice. Check out the comparison table below before we delve deeper into each channel’s details.
The Most Effective B2B SaaS Marketing Channels, Compared
The table offers an overview of costs, ROI, and the time needed to see results. Each channel is unique, bringing its own set of opportunities and challenges:
SEO
SEO stands out as a top contender, offering impressive ROI. It not only attracts leads but also nurtures them through your marketing funnel. The enduring benefits of a strategically executed SEO campaign never cease to amaze me, despite the initial slow pace compared to paid channels.
However, the complexity of SEO means investing in a skilled team adept at interpreting search intent and producing high-quality content consistently. Pairing SEO with PPC can alleviate some of the long wait times.
PPC / SEM
As a paid strategy, PPC offers rapid results and is excellent for short-term goals or testing new markets. I’ve observed that its high cost and pay-dependent nature can hinder long-term success, but for quick market insights, it’s invaluable.
LinkedIn Advertising
LinkedIn gives B2B marketers access to a professional audience with precision targeting capabilities. Despite its lower ROI than organic strategies, it remains an essential tool in my marketing arsenal for reaching decision-makers in our industry.
Account-Based Marketing (ABM)
ABM is all about focusing on a select group of valuable prospects. I’ve found it effective in industries where landing a single client can be transformational. The high risks are balanced by substantial rewards if executed correctly.
Email Marketing
Email marketing allows for cost-effective communication, particularly in nurturing leads. By leveraging existing content and maintaining relationships, I’ve managed to keep the engagement alive, even if building a quality email list took time.
Trade Shows
There’s nothing quite like the personal touch trade shows offer. Although costly, they provide a great opportunity to establish connections and gauge interest firsthand. However, standing out amid the competition is always a challenge.
Public Speaking
Public speaking can dramatically enhance both brand recognition and authority. When I engage audiences directly, the warm leads generated are unmatched. Yet, the need for a seasoned speaker and considerable travel expenses are factors to consider.
Webinars
Webinars offer a cost-effective alternative to in-person events, creating connections with prospects remotely. Crafting engaging presentations demands time and a charismatic host, but the trust built through educational content is well worth the effort.
Getting Help With B2B SaaS Marketing
In my experience, combining multiple marketing channels yields the best results. Midsize companies often find managing them daunting, but partnering with an experienced marketing agency can make all the difference. Our team excels in marrying B2B SaaS SEO with thought leadership for outstanding lead generation.
If you’re interested in exploring how we can collaborate, feel free to reach out. Together, we can strategize the best approach for your unique needs.
I’ve witnessed firsthand how ChatGPT ads are evolving with self-serve buying options, enhanced measurement features, and a vision to create a scalable advertising platform.
OpenAI is stepping up its game with the ChatGPT ads platform by introducing self-serve buying, CPC bidding, and improved measurement methods to invite more advertisers into its ecosystem.
What’s happening. The ChatGPT ads initiative is shifting from a limited pilot to a broader rollout, providing businesses new methods to purchase and manage their campaigns. Advertisers can now access inventory through agency and tech partners or directly via the new beta Ads Manager, which is currently rolling out in the U.S.
This marks a significant move from a controlled test phase to a promising, scalable ad platform.
Why we care. In the past, access to ChatGPT ads was restricted and costly, limiting it to major advertisers. These updates are lowering the entry barriers, allowing SMBs, startups, and diverse brands to experiment with this channel.
By introducing CPC bidding, ChatGPT aligns more closely with established performance platforms, enabling advertisers to optimize for actions rather than just impressions.
Self-serve Ads Manager. With the new Ads Manager, advertisers gain direct control over campaigns, including budgeting, bidding, creative uploads, and performance tracking.
Even though it’s still in beta, it demonstrates OpenAI’s commitment to building a full-service ad platform, beyond a mere partner-led ecosystem.
Between the lines. This approach is not new. Typically, platforms start with high-touch, partner-led campaigns before transitioning to self-serve tools that enhance scalability. ChatGPT is entering this second phase.
CPC bidding arrives. Originally, ChatGPT ads were sold on a CPM basis. The inclusion of CPC enables advertisers to align expenditures with user actions—a critical evolution for performance marketers.
The nature of ChatGPT queries—often exploratory, comparative, and decision-driven—means that clicks could become an effective indicator of user intent.
Measurement catches up. OpenAI is also introducing pixel-based tracking and a Conversions API, allowing advertisers to measure actions like purchases, sign-ups, and leads.
Notably, this data is aggregated, ensuring no access to individual conversations, emphasizing OpenAI’s commitment to privacy.
Why this is a big deal. Measurement was a major gap in early ChatGPT ads. Without it, justifying ad spend was challenging for advertisers. These updates help bridge that gap, making optimization more feasible.
The ecosystem grows. OpenAI is expanding its network by partnering with agencies like WPP and Publicis Groupe, along with tech platforms such as Criteo and Adobe.
This allows advertisers to buy ChatGPT ads through tools and workflows they are already familiar with.
What to watch:
How quickly self-serve adoption scales
Whether CPC performance holds as competition increases
How measurement evolves to match advertiser expectations
I find it fascinating that Google’s Universal Commerce Protocol (UCP), which was initially limited to AI Mode, is now expanding into regular search results. It’s not just a fleeting trend; some retailers have already begun integrating this technology into their listing pages, making our online shopping experience even more intuitive.
Earlier this year, Google rolled out UCP for AI-agents to facilitate direct purchases from search results. It first launched exclusively within Google’s AI Mode but now, we’re seeing it implemented in Google’s main search results for retailers who support UCP.
Discovering what the UCP checkout looks like was made easier thanks to a post by Brodie Clark. He shared a screenshot showing how Wayfair’s listings on Google Search now feature a UCP-powered ‘Buy’ button. This button is a game-changer because it allows purchases directly from Google’s interface without navigating to Wayfair’s website.
The UCP protocol is paving the way for seamless transactions by establishing a common language for AI agents and commerce systems. No longer do we have to worry about bespoke integrations across different platforms.
Collaboratively developed with big names like Shopify, Etsy, Wayfair, and Target, UCP aligns with existing standards, such as Agent2Agent and Agent Payments Protocols, creating a more cohesive digital commerce space.
What really excites me is the potential for profit growth for retailers who embrace this technology. Although Wayfair might miss out on direct site traffic for specific searches, their affiliation with Google through UCP can still result in conversions.
While it’s clear that not everyone will bypass the traditional shopping journey, as many of us still prefer exploring products on the retailer’s site, the option to ‘Buy’ directly adds a layer of convenience. It’s definitely something worth monitoring as its prevalence in search results increases.
In February 2025, I watched a captivating display as humanoid robots graced the CCTV Chinese New Year stage. Although their steps were shaky, it was still delightful to witness.
A year later, these robots at the Spring Festival Gala had transformed, executing smooth moves, somersaults, and full kung fu routines. This rapid progression felt like a decade’s worth of technological advancement condensed into one year.
The technological leap wasn’t limited to robots. It raised a crucial question for digital marketers targeting the largest web population: How have China’s search trends evolved recently?
A parallel in the Chinese search landscape
We’re seeing early signs of a major shift. AI hasn’t replaced traditional search engines yet. Instead of a single breakthrough, change comes from consistent, subtle advancements.
New language models frequently emerge, each refined for a specific niche. Tech companies in China are increasingly sharing these developments openly, with players like Baidu integrating advanced models like DeepSeek into their platforms.
To understand the current search behaviors in China, we need to grasp the shift from simple link searches to more reasoning-based approaches and adjust our 2026 SEO strategies accordingly.
The great narrative fallacy: Is web search dead in China?
There’s a persistent narrative in marketing circles that traditional search, especially on Baidu, is obsolete — that everything is happening on platforms like WeChat. But how true is this?
The social supremacy argument
Indeed, China’s web is mobile-first and dominated by super-apps. While social media is pivotal, it’s not the sole avenue for B2C brands aiming to thrive amidst such a vast, versatile environment.
For instance, platforms like Xiaohongshu excel in lifestyle research, while Pinduoduo and Douyin are social commerce powerhouses. Meanwhile, WeChat is indispensable for everyday tasks.
The B2B reality check
For B2B sectors, dismissing Baidu is a mistake. Metrics show ongoing engagement and tangible results from Baidu SEO, often outshining Western counterparts in lead quality and conversion rates.
When B2B professionals seek industrial solutions, they prioritize verified websites over endless scrolling on social media apps, indicating an undying need for structured web searches.
Mapping the 2026 landscape: Intent-based specialization
As someone deeply integrated into the Chinese market, I’ve noticed that users select tools based on intent rather than defaulting to search engines. It’s an everyday occurrence here.
While optimizing for Baidu, others in my circle might be using Pinduoduo for deals or Xiaohongshu for travel plans. The right tool for the right task wins their clicks.
1. Traditional web search: The authority tier
Traditional search continues to dominate B2B and high-authority research areas. Baidu, despite narratives of its decline, remains central to mobile and web searches.
Baidu: Dominates mobile search with a vast user base. Though AI-driven, it remains a key player in web search.
Microsoft Bing: Offers a professional experience for a tech-focused audience.
Haosou (360 Search): Known for its security and enterprise-centric approach.
Sogou: Integrates with WeChat, bridging between app-based and traditional searches.
Google: Despite restrictions, it’s accessed by tech-savvy users via VPN for global insights.
2. Social discovery: The inspiration tier
Here, search turns into discovery. Users are led by interests rather than predefined keywords, making SEO a matter of being on the right social platforms at the right time.
WeChat (Weixin): For brand news and internal communications.
Xiaohongshu (RED): Essential for lifestyle and luxury brand discovery.
Douyin: Offers visual insights into product utility.
Kuaishou: Used predominantly in emerging markets for grassroots content.
Weibo: Ideal for real-time trends and news.
Bilibili: Focus on long-form video content and niche interests.
3. Ecommerce: The transactional tier
While Westerners often end their buying journeys on Amazon, Chinese users tend to both start and finish on the same platform, whether for variety or efficiency.
Taobao / Tmall: The prime destination for diverse product offerings.
JD.com: Favored for electronics and efficient logistics.
Pinduoduo: A leader in group-buy and value-driven purchases.
Douyin Mall: Capitalizes on impulse purchases through engaging content.
Xianyu (Goofish): Supports second-hand markets and niche hobbies.
4. Generative AI (LLMs): The reasoning tier
This emerging layer focuses on “thinking” searches where AI synthesizes data into insights rather than mere lists.
Doubao (ByteDance): Popular for casual queries.
DeepSeek (Domestic): Integrated with WeChat for deep logic queries.
Kimi (Moonshot AI): Specializes in handling lengthy documents.
Qwen (Alibaba): Plays a crucial role in business and coding tasks.
Tencent Yuanbao: Focuses on WeChat content.
Wen Xiaoyan (Baidu): Represents the next stage of Baidu’s AI search capabilities.
5. Hyper-local and logistics: The utility tier
This sector addresses immediate, location-driven demands, prioritizing services that cater to “now” and “near me” needs.
Meituan / Dianping: Leading platforms for food and leisure services.
Amap (Gaode) / Baidu Maps: Vital for navigation and local search optimization.
Ctrip (Trip.com) / Railway 12306: Key for travel and transportation booking.
From mapping to maneuvering: The Baidu specialist’s edge
Optimizing Baidu SEO extends beyond ranking web pages; it’s about mastering search landscape intricacies.
The ‘walled garden’ SERP: A decade of distraction
Focusing solely on Google-style tactics might overlook nuances like Baidu’s ad-heavy SERPs and content positions.
The ad-heavy layout: Ads can dominate substantial SERP real estate.
The Baidu monopoly: Prime organic positions often favor Baidu properties.
The portal giants: High-authority contributors also claim space within results.
Riding the Chinese SERP dragon
In this scenario, relying on long-tail strategies often proves more lucrative than targeting head keywords due to the complex Chinese language and diverse user base.
Whether leveraging platform authority or becoming a trusted contributor, it’s essential to adapt upcoming SEO tactics to sustain visibility.
What is changing in Baidu SEO?
The competition among AI models emphasizes versatility over loyalty, making Baidu SEO a nuanced challenge.
The AI-switching reality
Chinese users frequently shift between AI models, seeking superior intelligence or alternatives when certain models falter. This behavior means SEO must account for broader dynamics.
Brainstorming the wisdom platforms
Understanding the foundational platforms for AI development can greatly boost a brand’s presence in AI-dominated searches.
Tencent is invested in Sogou: Hence, Sogou Baike becomes integral for WeChat-based AI queries.
Bytedance owns Baike.com: Engaging here helps brands appear in Doubao’s results.
The neutral giants: Zhihu sits at the intersection of multiple investments, making it a balanced source for varied AI insights.
The new SEO commandment
SEO is now about optimizing for diverse data sources that fuel AI models, across various ecosystems.
In the B2B realm, Baidu remains central. Yet for ecommerce, branching into Alibaba or Doubao ecosystems will expand visibility across key AI systems.
The 2026 China SEO/GEO blueprint: From keywords to semantic saturation
Anticipating a specific SEO guide for AI like DeepSeek or Doubao misses the evolving landscape’s essence. The need is not for singular-model focus but a diversified approach that shifts with frequently changing user and model preferences.
Optimize for citations and not just clicks
Chinese SEO centers around fact density, aiming for content immediately recognizable by AI as authoritative.
The logic: AIs like Kimi and DeepSeek rank content based on factual reliability.
The tactic: Use clear, concise, data-backed writing, enabling rapid fact verification by AI.
Build an entity moat across wisdom platforms
Given that AI models distill and share intelligence, maintaining consistent brand representation across various platforms is crucial.
The goal: Ensure uniformity in brand presentation across Baidu, Sogou, and Baike.com.
The result: Consensus between AI models establishes your authority.
Leverage information gain
AI in China demonstrates a preference for recent data by about 25% compared to traditional search engines.
The tactic: Present unique, timely insights to stand out amidst common knowledge.
The era of the entity architect
We’ve moved past the initial robotic steps of 2025. In 2026, China’s search landscape is a dynamic entity, requiring an intricate understanding of intent fragmentation.
Despite the dominance of super-apps, the real revelation lies in this fractured landscape. My personal experiences echo this as my wife seeks deals on Pinduoduo, and my colleagues navigate Bing for professional resources. Meanwhile, AI enthusiasts cycle through LLMs for varied answers.
As a Baidu specialist, my role has evolved from targeting websites to designing robust entities. Building for the source, not just the bots, ensures your brand is consistently recognized and trusted, no matter which AI models deliver the solutions.
Imagine your brand becoming the celebrated go-to source, regardless of the search model. That’s the ultimate goal for today’s SEO specialists.
