I’ve discovered some fantastic insights on how to effectively submit and optimize product feeds for ChatGPT’s agentic commerce system. This is crucial for keeping your products visible, enhancing ranking, and minimizing conversion loss.
Let me guide you through the process, so you can stay ahead of the competition and ensure your feeds are optimized to meet the latest standards. It’s essential for any business aiming to leverage the full potential of ChatGPT in boosting their ecommerce success.
This past Black Friday and Cyber Monday, I delved into the fascinating insights from our Black Friday Index, crafted from a vast pool of 400 million genuine conversations. It was enlightening to see which brands stood out as AI’s top recommendations, especially as so many of us relied on Answer Engines to hunt down the best deals.
As I explored the data, the impact of AI on shopping trends became crystal clear. The technology not only streamlined how we search for deals but also influenced brand visibility and consumer choices. The excitement of seeing how AI is reshaping shopping habits made this year’s Black Friday and Cyber Monday particularly intriguing for me.
The findings from the Black Friday Index are a testament to the growing importance of AI in retail, showing us how indispensable it has become for both consumers and brands. Being part of this evolution makes me look forward to what future shopping events will bring, especially as technology continues to advance.
I’ve realized that not every Shopify integration delivers the value we expect. Let me share how I organize and prioritize checkout, re-engagement, and optimization tools to make a real revenue impact.
Developers have the freedom to create apps for almost any function imaginable.
Yet, with countless options available, ecommerce teams often waste time on shiny add-ons that promise gains but fail to deliver.
Having been involved in numerous Shopify setups, I’ve seen firsthand which integrations truly enhance checkout completion and cart recovery while boosting revenue.
From my experience, I’ve structured the most impactful integrations into three tiers. This helps prioritize essentials before advancing to sophisticated optimization.
Thus, every Shopify store should integrate two key components into its storefront:
Compatibility with digital wallets.
A ‘buy now, pay later’ (BNPL) option.
Without these integrations, customers may face unnecessary friction and turn to competitors for a smoother transaction experience.
The great news is that both of these features integrate easily with Shopify without requiring custom development.
Digital wallets, like Apple Pay, Google Pay, and PayPal, streamline the payment process by autofilling necessary details, reducing friction on small screens.
This efficiency reduces the purchase process to just a few clicks from a social ad to checkout.
Up to 64% of Americans now use digital wallets as much as traditional methods, with 54% preferring them more often.
Beyond convenience, customers seek payment flexibility. Providers like Klarna and Afterpay offer BNPL options that mitigate price objections at checkout.
Last year, these options contributed $18.2 billion to online revenues.
Combining digital wallets with BNPL functionality forms a robust foundation for a mobile-first checkout experience. With these in place, Shopify sellers can focus on re-engagement tools that drive customers back to complete their purchases.
The second tier centers on re-engagement strategies. These tools are designed to entice back customers who have already shown interest.
They enhance abandoned-cart recovery, boost repeat purchases, and build trust through social proof.
Email remains a powerful channel for re-engaging customers across their journey. For Shopify users, platforms like Klaviyo and Attentive offer deep integrations with minimal setup.
These platforms also extend to SMS, enabling automated texts to shoppers’ mobile devices.
SMS consistently outperforms email in terms of open, click-through, and conversion rates, making it particularly effective for re-engagement needs such as recovering abandoned carts.
However, navigating CAN-SPAM and TCPA regulations means explicit opt-ins are required for email and SMS marketing, respectively.
While Klaviyo and Attentive excel at targeting opted-in customers, CartConvert helps merchants engage with the 50% to 60% who haven’t subscribed.
CartConvert uses real agents to reach out via SMS, bypassing automated restrictions and engaging customers in real-time conversations.
By combining CartConvert with platforms like Klaviyo, sellers can ensure comprehensive re-engagement strategies for both opted-in and non-opted customers.
Human-centered marketing also enhances buyer confidence. Modern online shoppers depend on reviews heavily when deciding on purchases.
Incorporating reviews directly into the shopping experience bolsters trust and legitimacy, boosting conversion rates.
According to the Spiegel Research Center, a product with just five reviews is 270% more likely to be purchased than one without any reviews.
Tools like Okendo, Yotpo, and Shopper Approved easily integrate with Shopify and sync with Google Merchant Center, enhancing Google Shopping ads’ performance.
The third tier involves advanced integrations that help optimize your sales funnel and performance for scale.
With GA4’s updates, tracking and attributing performance has become more challenging. Since 2023, Triple Whale has positioned itself as a robust alternative with third-party attribution tools integrating easily with Shopify.
It supports various attribution models and provides real-time data—something Google Analytics lacks—offering valuable insights, especially during high-stakes periods like Black Friday.
For improving conversion rates, custom landing pages are key. Replo allows Shopify users to design and A/B test landing pages on a large scale without coding risks.
These personalized pages typically convert at higher rates than standard templates by using site data to adapt to users’ browsing patterns.
Lastly, as TikTok grows as a paid media platform, its Shopify integration allows sellers to link ads directly to their sites, opening new opportunities for creative outreach and engagement.
Remember, you don’t need to adopt every tool at once. Start by auditing your current set-up, fill in the gaps, and prioritize tools that promise to enhance conversions and re-engagement.
Shopify’s greatest strength is its flexibility, empowering us to convert more visitors into loyal buyers.
Have you ever felt uneasy managing large catalogs in Google Performance Max, almost like you’re handing over your wallet to an algorithm? I sure have.
La Maison Simons faced a similar struggle. With too many products and not enough control, they decided to rebuild their segmentation using Channable Insights. This change turned their perplexing campaign into a revenue powerhouse.
Step 1: Stop segmenting by category
Initially, Simons divided campaigns by product category. It seemed like a good idea until their popular sweater consumed the entire budget, leaving less visible or new products unnoticed.
Static segmentation brought limited visibility and sluggish decision-making. Marketers were trapped with manual tweaks, while Google auto-focused on what’s already succeeding.
Step 2: Segment by performance
With Channable Insights, product-level data like ROAS and clicks now fuel dynamic grouping:
Products automatically transition between segments based on performance. As Etienne Jacques, Digital Campaign Manager at Simons, expressed:
“One super popular item no longer takes all the money.”
Step 3: Shorten your analysis window
Instead of the usual 30-day signals, Simons decided to use a rolling 14-day window. This means quicker reactions, more accurate decisions, and less wasted spend in a fast-paced catalog.
Step 4: Push the strategy across channels
Why limit the strategy to Google? Simons applied the same segmentation across:
Meta
Pinterest
TikTok
Criteo
This cross-channel consistency amplifies optimization.
Step 5: Watch the metrics climb
Simons unlocked impressive results without increasing ad spend:
ROAS growth: from ~800% to ~1500%
CPC decrease: $0.37 to $0.30
CTR lift: 1.45% to 1.86%
14% increase in average order value
1300% ROAS for New Arrivals campaigns
Faster workflows and fewer manual tweaks
Even previously invisible products turned into unexpected profit drivers with a spot in the limelight.
Step 6: Treat automation as control, not chaos
Automation has restored marketing control rather than taking it away. Now, teams can learn from data and actively influence product growth instead of leaving everything to PMax autopilot.
Your action plan
Classify products as Stars, Zombies, and New Arrivals.
Automate campaign reassignment based on real-time data.
Refresh product insights every 14 days.
Roll out segmentation logic to every paid channel.
Scale what wins – test what’s yet to succeed.
Aiming for Simons-style ROAS gains without raising ad spend? Start with a free feed and segmentation audit to enhance your product data quality.
Every year, Black Friday offers a unique glimpse into how consumers search, compare, and decide. This year, it added another layer: it became a real-world arena to see how AI models comprehend commerce amidst genuine demand.
I embarked on a journey to test major large language models (LLMs), analyzing 10,000 responses to understand how these systems perceive the retail landscape and the signals that shape their responses.
As I dissected the dataset, a pattern was unmistakable: Black Friday acts as a genuine stress test for AI-driven discovery.
The sheer number of queries and the diversity of categories reveal the sources, structures, and behaviors LLMs rely on for reasoning about products, retailers, and consumer intent.
The outcomes offer a sneak peek into how AI search is transforming—and how this will impact the broader commerce ecosystem.
TLDR; LLMs lean heavily on a limited range of external domains with YouTube, large retailers, and U.S. review media leading the charge.
Generalist retailers dominantly capture nearly half of all retail citations, serving as the recurring funnel LLMs use to address shopping queries.
Social and user-generated content see an 8.1% surge during Black Friday, as conventional retail and media sites experience a decline.
Off-page signals like Reddit, YouTube, Amazon, and Consumer Reports are vital, equally important as on-page content for shaping LLM comparisons and recommendations.
Structured comparison content wields significant influence, far surpassing branded assets.
The behavior of LLMs differs not only from Google but also from each other, with each platform like Gemini, OpenAI, and Perplexity offering unique formats, lengths, and reasoning patterns.
Unlike traditional search, where the process begins with a query leading to a list of ranked results, AI search reverses this. It starts with a model’s internal web of relationships, sources, and signals to construct a response.
In our review of the top 50 most-cited domains across 10,000 LLM responses—all centered around deals, reviews, and product recommendations—the distribution was notably skewed:
YouTube led with 1,509 citations, followed by Best Buy with 950, Walmart with 885, Target with 477, TechRadar with 355, RTings with 342, and Consumer Reports with 325.
This cluster shapes much of the commercial “knowledge” from which LLMs draw. It gravitates towards large retailers, global media outlets, and platforms specializing in comparisons and reviews.
In analyzing 10,000 responses, I compared the week leading up to Black Friday with the event itself. Pre-Black Friday, responses reins focused on planning behavior.
Retail and brand domains: 59.6%
Media: 23.4%
Social and user-generated content: 17%
When Black Friday commenced, the mix rapidly evolved. Social and UGC content jumped to 25.1%, gaining significant share, while retail and media slightly retreated.
This shift within the models mirrors consumer behavior but also highlights the models’ reliance on conversation-driven content for in-the-moment decision cues.
One of the most transparent insights is the weight third-party domains carry on AI reasoning. Today’s LLMs thrive by absorbing as much human interest in products as possible. Huge volumes of consumer insights, reviews, product demos, sentiment, and structured data guide how models reason and decide.
An analysis revealed key off-page signals LLMs depend on:
Reddit: 34%
YouTube: 19.5%
Amazon: 15.5%
Business Insider: 9.2%
Walmart: 8.9%
Each domain influences different aspects of the model’s decision-making. Across the board, LLMs lean on content that captures human interest, organizes consumer options, and mitigates uncertainty through verifiable data.
While third-party domains reign supreme, brand websites still hold measurable sway. They are vital for any consumer brand aiming to excel in AI discovery.
A site’s architecture plays a crucial role in how a model interprets a brand. Homepages account for 40% and serve as the primary identity layer—establishing tone, positioning, and offering quick semantic signals to models.
Blogs and product pages clarify brand definitions and long-tail context, providing the factual details models need.
Brands that rely too heavily on promotional copy, weak hierarchies, or thin product content risk sacrificing major visibility.
Across the entire dataset, certain retailer categories led the charge in model responses.
Generalist retailers hold 48% of the conversation. Walmart, Target, and Best Buy capture almost half of all retail citations. Their range, familiarity, and content depth make them central figures in LLM commerce reasoning.
Electronics specialists grasp 23% of the share. Best Buy leads, trailed by Newegg and Micro Center, with tech-focused queries often directing models toward these sources.
Other verticals lag behind. Despite strong category leaders, sectors like fashion, beauty, and home capture smaller portions due to the content volume disparity compared with generalist retailers.
Reviewing the platforms uncovered another pattern: major LLMs not only offer different answers but exhibit distinct thinking styles. Each platform has its own rhythm, structures, and styles for presenting commercial information.
Gemini provides the most detailed responses, with essays averaging 606 words, using lists and headings extensively.
OpenAI stands in the middle, averaging 401 words per response, with high list usage and balanced headings.
Perplexity shifts towards brevity with an average of 288 words, favoring short summaries akin to executive briefs.
These differences define unique retrieval and reasoning methods, shaping how each platform interprets brands, categories, and commercial intent.
The data presents a clear direction: AI search is forging its ecosystem, driven by familiar SEO inputs, source quality, content structure, and off-page signals, all interpreted to deliver precise answers.
If your content isn’t well-structured and present across the web, it risks becoming invisible to AI platforms delivering answers or product suggestions.
As this new environment evolves, it’s crucial for retailers and brands to rethink their communication strategies across the entire digital landscape.
On-page actions that matter:
Develop semantically coherent homepages that convey the brand, product categories, and relevance to core queries. LLMs prioritize clarity over cleverness.
Strengthen product pages with factual content, clear specifications, and Q&A sections aligned with user research intents.
Establish educational content clusters tied to core product themes, serving as reusable frameworks for AI models.
Off-page actions that matter:
Foster comprehensive review ecosystems and discussion forums to validate trust signals LLMs recognize with product quality.
Ensure visibility in media driven by comparisons and recommendations. Regularly appear in “best of” lists, product roundups, and influencer content.
Invest in rich media showcasing product value, particularly on YouTube and TikTok. Video content helps train LLMs on product use cases, reflecting sentiment, and experiential value.
Maintain accurate, indexable product data in marketplaces like Amazon, Walmart, and Etsy to enhance AI discovery pathways.
OpenAI’s Shopping Research announcement escalates the stakes. With ChatGPT, OpenAI tracks real-time consumer research behavior, turning preferences into a user-trained targeting engine for commerce.
This isn’t just AI learning about your product. It’s AI absorbing consumer shopping behavior, revolutionizing discovery through an active AI participation model.
Brands not infused into these AI systems risk invisibility during AI-driven consumer journeys.
What Black Friday revealed was more than top-selling products; it showed how LLMs operate under real demand, revealing their reasoning, referencing, and prioritizing patterns.
The advent of AI-native visibility requires structured, semantically rich content, adequately represented across the right off-page ecosystems to align with major AI models’ reasoning.
Black Friday might be the stress test, but the real transformation is only just beginning.
I’ve just come across some exciting news from Shopify. They’ve launched something called the Product Network, which essentially allows advertisers to connect with potential shoppers across various merchant sites using contextually relevant products. It’s a game-changer!
What’s amazing is that this system can suggest products from other merchants, even when I’m shopping at a store that doesn’t have what I’m looking for. For instance, if I search for “organic cleaning supplies” and the store doesn’t carry them, the Product Network might still offer me alternatives from different merchants. This means I can add everything to a single cart, without even realizing some items come from other merchants.
Here’s how Shopify is positioning themselves: It reminds me of ad platforms like Google Performance Max or Meta Advantage+ Shopping, where advertisers set a cost-per-acquisition goal, and the platform handles the rest. But Shopify is focusing more on the merchandising aspect rather than traditional advertising, which I find quite refreshing.
Amanda Engelman, who’s their advertising product director, summed it up nicely by saying, “It’s just a different approach to the world.”
Historically, Shopify has shied away from profiting heavily off advertising. Their Audiences program is a good example; it creates customer segments for various channels like Google and Meta, but doesn’t take a share of the ad spend.
For merchants, there’s an added incentive to join the network. They earn commissions on the sale of products from other merchants, either in cash or Shopify ad credits. It’s like getting extra ad budget support without the usual upfront investment.
In the early stages, placements in the Product Network are determined by context rather than being driven by revenue targets, though there’s potential for optimization in favor of higher commission items.
The reason this is relevant is that Shopify’s Product Network now allows brands to extend their reach with ease. Shoppers are introduced to relevant products seamlessly, as these can be featured on search results or even on different stores’ homepages.
Unlike typical ads, the focus here is on driving conversions through relevant, context-driven placements rather than simply filling ad space. This could mean better traffic quality and merchants benefiting from third-party sales commissions, thereby expanding the network’s reach and impact.
Looking ahead, Shopify is planning to further enhance the personalization and monetization of this network, all while keeping users within their ecosystem. The whole aim is to support merchants in selling more, even if the products aren’t their own.
I recently came across some eye-opening data highlighting the distinct approaches Google AI and ChatGPT take in citing sources when it comes to retail information. While Google mentions retailers only 4% of the time, ChatGPT cites them 36% of the time. This significant gap of nearly nine times suggests that each platform guides shoppers in noticeably different directions, and this insight comes from the latest BrightEdge data.
Why is this important to us? Nowadays, millions of shoppers are relying on AI to discover deals and gift ideas. However, the process differs greatly between the top AI search platforms. Google tends to focus on what users are saying, while ChatGPT zeroes in on where you can actually purchase items.
Regarding what each AI prioritizes, Google AI Overviews are inclined to reference YouTube reviews, Reddit discussions, and various editorial sites. In contrast, ChatGPT frequently cites retail giants such as Amazon, Walmart, Target, and Best Buy.
Let’s break down the priorities further. Google AI Overviews tend to cite:
YouTube reviewers and unboxings.
Reddit threads and community consensus.
Editorial reviews and category experts.
Meanwhile, ChatGPT emphasizes:
Major retailer listings.
Brand and manufacturer product pages.
Editorial sources (secondary).
This citation divide is quite telling. On Google, retailers show up only about 4% of the time, as it leans more towards user-generated content and expert reviews—acting more as a research tool rather than a purchase assistant. Top reference sources include:
YouTube
Reddit
Quora
Editorial sites like CNET, The Spruce Eats, and Wirecutter
Conversely, ChatGPT features retailers about 36% of the time, functioning as both an explainer and a shopping assistant, hence why retailer links are far more prevalent. Key sources often cited include:
Amazon
Target
Walmart
Home Depot
Best Buy
About the data: BrightEdge scrutinized tens of thousands of e-commerce prompts across Google AI Overviews and ChatGPT during the 2025 holiday season, identifying and categorizing citation sources. Domains were sorted by type—retailer, UGC/social, editorial, and brand—and directly compared using identical prompts.
I’ve discovered incredible ways to optimize for Perplexity Shopping. Join me as I explore how AI-driven search is transforming the eCommerce landscape, giving brands like ours the opportunity to shine in ‘Shop Like a Pro’ outcomes.
I’ve delved deep into four key areas that shape how ecommerce PPC campaigns perform: mastering the essentials of Performance Max, leveraging Amazon’s conversion power, building social audiences, and crafting insightful dashboards.
PPC in ecommerce differs vastly from PPC for lead generation or SaaS. The mechanics of campaigns, the conversion data volume, and each platform’s unique role demand a specialized approach.
Entering the ecommerce realm helped me identify which fundamentals truly matter. Let’s look at how the core differences between ecommerce and non-ecommerce models influence PPC strategy and how to play to each platform’s strengths.
1. Performance Max is Built for Ecommerce
Google Ads is essential for ecommerce, primarily because of Performance Max campaigns, or PMax. It’s tailored for ecommerce, where data flows from high sales volumes and lower ticket sizes, allowing rapid learning and improvement.
To maximize PMax’s potential, optimizing your feed, segmenting your campaigns, and ensuring conversion tracking are crucial steps.
Feed Optimization
Optimizing your feed can dramatically enhance PMax performance. Ensure your product titles and descriptions are well-structured, utilize character limits, and incorporate keywords effectively.
Campaign Segmentation
By categorizing your feeds effectively, you can segment campaigns for better results. Utilize default and custom labels in Google Merchant Center to achieve precise targeting and higher ROAS.
Conversion Tracking
Accurate conversion tracking is critical. Integrating with tools like Shopify to sync data with Google Ads enables automated bidding strategies and campaign experiments for enhanced ROI.
2. Amazon Excels in Ecommerce Advertising
Amazon is an advertising powerhouse for ecommerce, offering transparency and deeper insights through its platform, which results in higher conversion rates compared to competitors.
Transparency
Amazon provides detailed reporting, enabling clear insights into conversion performance at both the keyword and market level, setting it apart from platforms like Google and Meta.
Higher Conversion Rates
Amazon’s unified platform leads to seamless transactions, resulting in higher average conversion rates and more reliable attribution data, minimizing guesswork.
Rankings Philosophy
Amazon’s approach to linking ads and organic rankings provides clarity and allows advertisers to precisely strategize on improving offers and performance based on conversion metrics.
3. Social Media: Not the Conversion Leader
While social platforms are crucial for brand awareness and audience building, they typically aren’t optimal for direct conversions, making them secondary to platforms like Amazon Ads and PMax.
Building Customer Lists
Using social channels to host giveaways can substantially grow your customer lists, which are invaluable for targeted marketing efforts such as promotions and cross-selling.
Awareness
Utilize social media to build brand visibility with cost-effective campaigns, focusing on awareness over immediate sales for new-to-market products.
Remarketing
Social media excels in creating remarketing funnels that engage customers more deeply, enhancing overall campaign effectiveness.
4. Dashboarding for Clarity and Success
Effective dashboarding is vital for maintaining clarity across multiple platforms. A good dashboard distills complex data into actionable insights, critical for profitability and strategy alignment.
With tools like Sellerboard, you can connect revenue and costs down to the SKU, providing clarity and revealing which platforms and strategies are truly driving success.
Guide to Next Steps in Ecommerce PPC
Recognizing the nuances of ecommerce PPC is crucial for making informed decisions that result in campaign success. These insights continue to guide my strategy and I hope they do the same for you.
In my recent dialogue with Slava Kravchuk, the Founder and CEO of Atwix, we delved into the future of B2B eCommerce and the key elements businesses need to thrive beyond 2026. Drawing from Atwix’s 15+ years of industry experience with manufacturers, distributors, and wholesalers, Slava shared invaluable insights on the rapidly evolving marketplace.
Slava started his journey with Atwix back in 2006, and the transformation in the B2B eCommerce realm since then has been nothing short of remarkable. Initially, the majority of B2B firms lacked a digital presence, but today, eCommerce is indispensable. Slava highlighted how COVID-19 rapidly accelerated digital transformation, compressing a decade’s worth of changes into mere quarters. This urgency pushed countless businesses to embrace digital commerce as a means of survival and growth.
We discussed the ongoing debate about selecting the right eCommerce platform. Slava emphasized that it’s not about the ‘best’ platform but choosing one that meets a business’s specific needs. Atwix offers expertise across various platforms like Adobe Commerce, Magento, Shopify Plus, and Shopware, because of their diverse capabilities. For complex B2B operations, Adobe Commerce and Shopware are often ideal due to their flexible architecture suited for intricate business requirements.
Another crucial aspect of B2B eCommerce is effective ERP integration. Slava insists that seamless eCommerce and ERP connectivity is vital to avoid data errors and ensure customer satisfaction. Atwix’s proprietary tool, Sirius, allows businesses to integrate their ERPs with their eCommerce frontends smoothly. This tool has transformed customer experiences, enabling real-time order tracking and payment capabilities.
We also touched on the decision-making process between building a custom solution or utilizing a platform. Slava advised starting with a platform due to the efficiencies and flexibility they offer. He stressed the importance of customizing smartly to avoid technical debt and ensuring a platform can evolve with the business’s future needs.
Slava’s approach is one of partnership. He believes in engaging with clients to map out a strategic vision before any development begins. This foresight helps prevent costly setbacks and aligns the technology with long-term business goals. For example, Byrne Electrical’s rapid development during the pandemic was successful due to careful, phased planning upfront.
Looking ahead to 2026 and beyond, Slava predicts that AI, integrated experiences, and personalization will be the driving forces of change in B2B eCommerce. AI advancements are already shaping product discovery and customer interactions. Meanwhile, customers now expect integrated, personalized experiences akin to B2C interactions.
For businesses contemplating digital transformation, Slava’s advice is clear: start with a minimal viable product and continuously refine it based on feedback. Choosing the right partner who understands your industry is crucial for building lasting, adaptable eCommerce solutions. The time for B2B companies to embark on their digital journey is now.
