I’m excited to share that I can now effortlessly integrate Google Search Console data directly into any of my Profound Agents. This powerful combination, uniting Search Console insights with Profound’s answer engine data, is transforming how I handle reporting, content creation, monitoring, and optimization.
Staying on the Profound platform makes the entire process seamless, allowing me to focus on what truly matters—building and optimizing my digital strategies without the hassle of platform switching.
When I first heard about Walmart’s experiment with ChatGPT’s Instant Checkout, I was intrigued. But after testing 200,000 items, Walmart discovered that conversions through this method were three times lower compared to their website.
Why This Matters: This experiment highlights an important point: traditional shopping environments still hold the crown when it comes to conversions. Even in a world dominated by AI, guiding users to owned environments proves more effective.
The Experiment Details: Starting last November, Walmart introduced around 200,000 products available for purchase directly inside ChatGPT through OpenAI’s Instant Checkout. The goal was to let users buy items without ever leaving ChatGPT.
Daniel Danker, Walmart’s EVP of Product and Design, revealed that these purchases had a conversion rate one-third lower than similar transactions on their website. He described the experience as “unsatisfying,” which prompted Walmart to reconsider their approach.
Farewell to Instant Checkout: Originally, Instant Checkout aimed to complete transactions within ChatGPT. However, OpenAI recently confirmed plans to phase it out, leaning towards merchant-handled app checkouts.
Changes on the Horizon: Walmart plans to integrate its own chatbot, Sparky, within ChatGPT. This will allow users to log into Walmart’s system, sync their carts across platforms, and finalize purchases seamlessly.
A similar integration with Google Gemini is expected next month, broadening Walmart’s technological reach.
The WIRED Report: For those interested in the comprehensive story, WIRED provides further insights into how Walmart and OpenAI are revolutionizing agentic shopping (subscription required).
I’ve recently discovered Perplexity’s innovative Comet browser for iOS, which defaults to Google Search. It makes perfect sense, given that mobile users typically focus on navigating, finding local results, and completing transactions. As Perplexity CEO Aravind Srinivas points out, “Google does a much better job … than anyone else … including Perplexity.”
Comet for iOS. This browser integrates Perplexity’s AI assistant directly, providing a seamless experience. It cleverly merges AI-generated answers with standard search outcomes, so for numerous queries, you won’t miss the familiar results page.
While browsing, I can query using my voice, which is incredibly convenient. The assistant’s capabilities include summarizing entire pages, answering questions, and even drafting emails on my behalf.
One feature I find particularly useful is Deep Research, which generates cited summaries and prepares materials tailored for serious inquiry.
What Comet does. The assistant can take action on my behalf. Among other things, it excels at summarizing articles and sharing outputs, researching people or topics across tabs, and assisting with bookings or filling out forms. It’s like having a digital personal assistant ready at all times.
What Perplexity is saying.
“The search experience in Comet iOS provides traditional search result pages for fast, local, and high-intent queries that are more common on mobile. Meanwhile, the Comet Assistant easily allows for more advanced knowledge and intelligence powered by the Perplexity answer engine. The intention is for users to have the smoothest browsing experience possible for the real use cases of iOS.”
Why we care. As search continues to evolve towards hybrid models, optimizing for both traditional Google results and AI-generated responses becomes crucial. This shift underscores Google’s stronghold in commercial and local search, while driving the competition into the AI domain.
As I delve into the latest updates from Google, it’s clear that the company is advancing its Universal Commerce Protocol (UCP) to revolutionize AI-driven shopping experiences.
The UCP update is not just about ads anymore; it’s about the rich product data that will shape visibility and drive sales.
Google is making significant strides in supporting ‘agentic commerce’ by enhancing its infrastructure with new UCP capabilities. These updates will simplify retailers’ integration processes.
Google highlights how the UCP, an open standard aimed at connecting retailers to AI-driven shopping experiences, is evolving. This transformation seeks to emulate the feel of traditional storefronts even when purchases are done through automated agents.
What’s New: The focus is on creating more functional and flexible shopping experiences via AI agents.
The new cart feature allows AI agents to compile multiple products from a single retailer into one basket, making it resemble the typical shopping experience.
Additionally, the catalog capability enables agents to access real-time data about products, including pricing, inventory, and variants, ensuring accuracy and responsiveness in shopping interactions.
Significantly, the identity linking feature preserves benefits such as member pricing and free shipping across platforms linked by UCP, enhancing the shopper’s experience beyond the retailer’s native site.
Why I Care: With this update, the shift toward AI-driven, agent-led shopping becomes more pronounced. Services like Search and the Google Gemini app might choose and purchase products on users’ behalf, making the quality of product data critical for visibility. Simplified onboarding and support from major platforms could mean quick adoption and an advantage for early adopters.
Zooming Out: UCP is a modular system, allowing retailers and platforms to adopt capabilities selectively rather than all at once, offering flexibility as the industry gauges the extent of control to cede to AI shopping.
Google’s Strategy: Google is set to integrate these capabilities into its ecosystem, including AI-enhanced experiences in Search and the Google Gemini app. To encourage wider adoption, Google plans to simplify the onboarding process within Merchant Center soon.
The Bottom Line: Google’s UCP is evolving from a concept into a broad ecosystem, enhancing capabilities while easing adoption. By doing so, Google is positioning agent-driven commerce as a compelling choice.
I’ve got some exciting news to share about Google’s latest developments! They’re expanding their innovative Personal Intelligence feature across AI Mode in Search, the Gemini app, and in Chrome—specifically for U.S. users.
Google’s Personal Intelligence now moves beyond its beta phase, reaching more everyday users. It’s an exhilarating step toward a truly personalized search experience, thanks to clever use of first-party data like Gmail and Photos. This shift makes search outcomes more personalized and unique, especially in AI Mode, where results adapt to individual user behaviors, previous purchases, and search histories.
Why I care
Google’s push into personalized search fascinates me. It’s creating a landscape where search results become increasingly individualized, but consequently harder to predict or replicate.
The details
Personal Intelligence will now function across:
AI Mode in Google Search (available now in the U.S.)
Gemini app (currently rolling out to free users)
Gemini integrated in Chrome (ongoing rollout)
How it works
I can connect applications such as Gmail and Google Photos, allowing Google to give me personalized responses. Some of the cool examples I’ve come across include:
Shopping suggestions rooted in my buying habits and favorite brands.
Tech troubleshooting aided by receipt details for the exact devices.
Travel tips tailored to my flight schedules and past getaways.
Custom itineraries and local recommendations.
Hobby proposals based on my interests.
Availability
It’s worth noting that these features are reserved for personal Google accounts and won’t extend to Workspace users—for now, at least.
Want to know more?
You can check out the details on the ad-free promise Google made for AI Mode users here.
Catch-up quick
Originally, Google introduced Personal Intelligence for Gemini subscribers in January with limited access to AI Pro and Ultra users. At that point, it hadn’t been integrated with Search—something they’ve since rectified.
Initially, the feature was optional and off by default.
New updates deliver on Google’s plan by making it part of Search AI Mode.
They’re rapidly expanding access to more users, even for free accounts.
Plus, it’s now merging into Chrome.
Privacy and control
Google emphasizes user choice:
Opt-in is required to connect apps like Gmail.
Users can enable or disable connections whenever they choose.
Importantly, Gmail and Photos content isn’t directly used to train AI models.
However, Google may use limited data like prompts and responses to enhance their systems.
For further reading, check out Google’s blog post on this impressive expansion of Personal Intelligence here.
When I learned about Google’s latest protocol, I realized how significant this new development could be for those of us in ecommerce. Google’s Universal Commerce Protocol (UCP) is here to revolutionize how purchases are made within the Gemini and AI search environments. It allows users to make purchases without ever leaving Google’s interfaces, which changes the game for search conversions.
As Google introduces AI Overviews, AI Mode in Search, and the Gemini ecosystem, a new challenge presents itself: how do users get answers and complete purchases seamlessly within Google’s spaces? That’s where UCP comes in, currently in its beta phase.
UCP is a tool designed to help brands reach customers directly within the Gemini or Language Learning Model (LLM) environments. It allows consumers to finalize transactions, earning reward points, and completing checkouts, all within the LLM. Imagine telling Gemini, “Find me a highly rated, waterproof hiking boot in size 10 under $200 and buy it,” and watching as UCP makes that transaction happen smoothly.
At its heart, UCP standardizes the communication between consumer AI interfaces and merchant checkout systems. Although Google’s developer documentation might mention terms like “Model Context Protocol (MCP)” and “Agent2Agent (A2A) interoperability,” the process is actually user-friendly:
UCP leverages your existing Google Merchant Center shopping feeds. It ensures you remain the merchant of record, thus preserving your customer relationships and data. Plus, by integrating checkout within Google’s AI ecosystem, it minimizes cart abandonment and boosts conversions.
Implementing UCP involves enhancing your shopping feed management and staying updated on best practices. Google’s guidelines suggest focusing on feed data hygiene, conveying trust signals, and upgrading your technical infrastructure.
To excel in this new system, it’s crucial to detail your product listings accurately and ensure comprehensive descriptions. Trust and convenience become paramount as AI-driven decisions heighten consumer’s purchasing confidence. Providing data on free shipping, return policies, and reliable pricing can make a difference.
Finally, preparing for UCP means keeping pace with technological updates and future tools. Venture into Google’s pilot programs and explore features like Business Agents or Direct Offers to stay ahead in this evolving landscape.
The evolution of search into a transactional engine within LLMs is undeniable. UCP offers a clearer path from search discovery to purchase conversion, and it’s up to us to adapt and thrive in this shift by ensuring our product data is impeccable.
When I think about how often I scroll through LinkedIn, I’m excited to share that the platform is launching a cutting-edge AI-powered feed ranking system. It’s designed to analyze what we post, read, and engage with, thanks to large language models and advanced GPUs. This innovation aims to provide more personalized content updates for its vast user base of 1.3 billion.
Why this matters to me. Understanding LinkedIn’s content surfacing process can be a game-changer for anyone wanting their posts—or their brand’s—to gain visibility. The focus is on what’s relevant and engaging within our network. As LinkedIn Tweaked their system, posts that show expertise and contribute to trending professional topics have a better chance to go viral, regardless of our existing connections.
What’s under the hood. LinkedIn has revamped its feed recommendation mechanism using large language models and sophisticated transformer models, all powered by GPU infrastructure. The overhaul targets two key functions: the retrieval and ranking of relevant posts in our feeds.
Unified retrieval system. One of the most intriguing aspects for me is how LinkedIn has consolidated its discovery processes into a single model powered by LLMs (large language models). Previously, posts could come from various sources such as network activity and trending topics. Now, LinkedIn uses LLM-generated embeddings to interpret post content and align it with our professional interests.
For instance, by engaging with posts about small modular reactors, I might see content linked to renewable energy or other related fields, even if they use different terminology.
Ranked by your interests. Once posts are retrieved, LinkedIn ranks them utilizing a transformer-based sequential model. Instead of looking at posts individually, the model examines patterns in my past interactions, including likes, comments, and the time I spent viewing content.
This helps LinkedIn adapt to my evolving professional interests and recommend content that aligns with these shifts.
System performance and architecture. Powered by a GPU infrastructure that processes millions of posts, this system keeps our feeds fresh.
LinkedIn reports that this system can refresh content embeddings in mere minutes and retrieve suitable candidates in under 50 milliseconds.
Enhancing feed quality and authenticity. LinkedIn has also announced updates aimed at boosting content quality:
Addressing automated engagement. They’ve started cracking down on tools that automate comments or use engagement pods to fake discussions. LinkedIn clarifies these violate platform policies and devalue genuine interactions.
Cutting down on engagement bait and generic content. The platform will deprioritize content designed solely to provoke comments or clicks—such as posts begging for comments to inflate reach, irrelevant video-text pairings, and regurgitated thought-leadership content.
Helping newcomers customize their feeds faster. New users can now utilize the “Interest Picker” during signup to select topics of interest, whether it be leadership, career growth, or job-seeking skills, ensuring relevance from day one.
I recently had an enlightening chat with Chloe Varnfield, a seasoned digital marketer from Atelier Studios with nearly eight years of PPC experience. She shared invaluable insights on avoiding hidden Google Ads settings, steering clear of Friday mishaps, and the dangers of following Google rep advice blindly. These hard-learned lessons resonated with me deeply.
One of Chloe’s early eye-openers involved Google’s elusive account-level automated assets setting. It’s tucked away so deeply that I didn’t even realize it existed until I got an unexpected client message questioning a bizarre headline in their ad. It turns out Google had generated it automatically. This experience taught me the importance of auditing account-level settings and being proactive about Google updates.
Another lesson Chloe swears by is to never implement significant changes on a Friday. Once, she adjusted a campaign’s geographic targeting mid-conversation, only to accidentally exclude the UK. Recovery took three bewildering days. The rule I learned? Avoid major changes on a Friday and promptly audit your campaigns when things go awry.
Chloe’s most costly mistake unfolded when she followed a Google rep’s suggestion to switch bid strategies. What seemed like solid advice plummeted her campaign’s performance. It was a stark reminder of the high stakes involved in altering bid strategies, especially for businesses not hitting conversion volume thresholds. Patience and trusting my judgment emerged as crucial takeaways.
While auditing inherited accounts, Chloe often finds recurring issues like broken conversion tracking and brand-broad match campaigns—challenges that skew performance data and waste precious budget. These insights made me acutely aware of consistently vigilant account management.
Transparency in client relationships plays a pivotal role in Chloe’s success. Honest communication—explaining issues, solutions, and next steps—has shielded her from losing client trust. Her advice? Stay calm, be kind to yourself, and remember every problem offers a chance for growth.
Lastly, Chloe emphatically warns against over-relying on AI for generating ad copy without thorough review. AI should be a tool to enhance speed, not replace meaningful human oversight. It reinforced my commitment to always infuse my unique voice and critical review into AI outputs.
I find it fascinating how AI is transforming the world of Google campaigns, particularly through tools like Performance Max (PMax) and AI Max. The reliance is shifting from long keyword lists to automation, audience insights, and machine learning, presenting new opportunities with a speed and scale beyond human capabilities.
At a recent SMX Next event, PPC experts Nikki Kuhlman from Jumpfly, Brad Geddes of Adalysis, and Christine Zirnheld from Cypress North shared insights on integrating PMax and AI Max within our broader campaign strategies. They explored how to balance automation with human input, showing where personal strategy still trumps AI.
AI Max for Search is an opt-in setting that extends keywords without needing a broad match, utilizing site resources to craft personalized ad content. This approach ensures more relevant ads and landing pages that meet user expectations.
I’ve noticed remarkable results with AI Max when used in blog content, a departure from traditional Digital Search Ads (DSA) approaches. These campaigns now guide users toward specific products, not just general reading, resulting in higher conversions.
When testing AI Max for Search, experts recommend using it on established campaigns with data, starting with A/B tests rather than full-scale changes. It’s essential to monitor landing page quality and search queries, incorporating negative terms where necessary.
Initial experiments in match type performance suggest exact match tends to deliver the best conversion rates, especially in campaigns with robust data volumes. However, broad match can be surprisingly effective when data is scarce, thanks to its ability to leverage previous user search history.
For those working within ecommerce, broad match might yield higher average order values from shoppers still exploring their options, even if conversion rates dip.
PMax has shown its potential in lead generation, contrary to common belief that it suits only ecommerce. The key is aligning campaign goals with true bottom-of-funnel conversions rather than mere form submissions.
With increased control options, PMax is now viable even in regulated industries. Device control features, for instance, are a strategic advantage for B2B campaigns, allowing targeted CPA adjustments across different platforms.
AI Max for Search is showing early promise in financial services, where it outperforms standard search despite being in a highly competitive keyword environment. This showcases AI Max’s potential to deliver better quality leads throughout the conversion funnel.
Ultimately, the future of PPC lies in a strategic blend of AI-driven tools and human oversight, ensuring campaigns are optimized not just for immediate conversions but long-term success. By correctly applying automation, we can achieve unprecedented results in search campaigns.
After conducting a thorough comparison of over 35 SEO agencies focusing on AI startups, I’ve ranked them based on five crucial factors. Each agency was evaluated to identify their capacity in rapidly evolving markets.
The criteria used in this assessment include:
Notable Clients (35%): Agencies were assessed based on their clientele, specifically those in AI and software startups, highlighting their proficiency in adaptable markets.
Leadership Experience Score (25%): A score from 1-5 that evaluates the leadership, focusing on their history in marketing and tech startups.
Average Reviews (25%): Agency performance was rated from 1-5, weighted more by reviews from AI firms.
Company Size and Year Founded (15%): While not as critical, company size and longevity are indicative of sustainable growth and enduring success.
The top agencies are displayed below, noting their rankings, headquarters, and SEO specializations.
Tech-focused marketing services centered on modern marketing channels like SEO, short-form video, and social media
First Page Sage
At First Page Sage, we’re leading the field with innovative SEO and generative engine optimization strategies tailored for AI companies. Our robust content production helps AI firms solidify their authority, with proven success on Google and AI platforms like ChatGPT.
“First Page Sage provides top quality content marketing with competent teams possessing specialized industry knowledge. Clients report measurable organic results within year one that significantly increased online leads.”
Clay Agency
Specializing in the technical side of SEO, Clay Agency excels in branding and UX/UI design, making them perfect for AI companies aiming to unveil products or services interactively and refresh their image in the AI realm.
“The Clay Agency worked as an extension of our own team, delivering an interface that clients are extremely proud of. Their tech-savvy teams are familiar with market trends, creatively tackling technical challenges.”
Marketing Eye
At Marketing Eye, we focus on technical SEO for tech firms, including website auditing and keyword analysis. Besides technical services, we also manage content and social media campaigns, particularly in the retail sector, while also supporting various tech companies.
One of the more established names here, our lean team thrives on blending marketing expertise with computing acumen, ensuring continued prominence in the field.
“Marketing Eye provides superior service, delivering measurable growth. Their teams are competent and professional but might require additional training.”
RNO1
RNO1 specializes in digital branding and product design for tech, AI, and commerce brands, offering technical SEO, market research, and services like AR/VR and Web3, distinguishing them from others.
Notable Clients: Prive, TakeUp, Fluxa
Leadership Experience: 3.5
Company Size: 51-100
Year Founded: 2018
Headquarters: Seattle, WA
Average Reviews: 4.2
Main Focus: Market research and UX/UI design for SaaS Companies
“RNO1 offers a redesigned website praised by users, but their teams sometimes rely too much on online management over direct communication.”
REQ
With REQ‘s expertise in branding, PR, and reputation management, we’re ideal for companies launching new products. While primarily focusing on branding and PR, our SEO services complement traditional marketing strategies effectively.
Notable Clients: Katabat, Verint, ActiveNav
Leadership Experience: 3.8
Company Size: 51-100
Year Founded: 2008
Headquarters: Washington, DC
Average Reviews: 4.3
Main Focus: Branding and UX focused SEO for tech companies
“REQ provides an excellent SEO analytics department that improves client reporting visibility and dramatically raises CTR, though improvements are needed in web development and response speed.”
Optimizely
Optimizely focuses on optimizing web pages through A/B testing, multivariate testing, and personalization, perfect for companies with solid content needing enhanced technical support.
Notable Clients: Google Cloud, Salesforce, New Era
Leadership Experience: 3.8
Company Size: 500+
Year Founded: 2010
Headquarters: New York, NY
Average Reviews: 4.0
Main Focus: A/B Testing, Mobile optimization, Conversion Rate Optimization
“Optimizely offers an intuitive UI that integrates easily, though lacking in extensive server-side testing capabilities.”
Directive Consulting
Directive Consulting excels in PPC and tech-focused marketing, offering performance-based campaigns that blend paid services with SEO to enhance visibility.
Notable Clients: Amazon, Snap Inc
Leadership Experience: 4.0
Company Size: 50-249
Year Founded: 2014
Headquarters: Irvine, CA
Average Reviews: 4.8
Main Focus: Tech-focused marketing services centered on modern marketing channels like SEO, short-form video, and social media
