The E-commerce AI Visibility Gap
89%
Of B2B firms are 'invisible' in AI search
A vast majority of companies are not mentioned in AI answers for relevant commercial queries.
52/100
Average AI visibility score for e-commerce
E-commerce scored highest among industries, but still has significant room for improvement.
9%
Of B2B firms can track AI-driven traffic
The inability to measure AI's impact is a major blind spot for most marketing teams.
Optimizing a product feed for ChatGPT means writing natural, descriptive language for a conversational AI, a shift from simply stuffing keywords for a machine. It is the difference between being found and being forgotten.
The syntax is familiar, merchants submit product data in standard formats like CSV or JSON, but the strategy is entirely new. Where Google Shopping rewards structured, keyword-optimized titles, ChatGPT’s shopping agent digests narrative descriptions and rich attributes to hold a conversation with a buyer. Getting this right means appearing in answers for a user base that OpenAI reported as 1.2 billion weekly users in late September 2026, according to AI Business Weekly; this means a vast and growing audience now discovers products through conversation, not just keyword search.
Why Your Product Feed Needs a Rewrite for AI
The urgency to adapt comes from two converging trends: buyers are rapidly adopting AI for product research, and most businesses are completely unprepared. A comprehensive study of 720 B2B companies published by mojodojo.io on October 15, 2026, revealed that 89% are effectively invisible in AI search results. The opportunity is not just in being present, but in being more helpful than competitors who are still optimizing for last decade's search algorithms.
Unlike traditional search, where a user types “waterproof running jacket size M”, AI-powered queries are conversational: “What’s the best lightweight waterproof jacket for running in the city during spring?” To answer, ChatGPT needs more than a keyword-matched title. It needs context about use cases (city running), seasonality (spring), and qualitative attributes (lightweight). This material comes directly from your product feed.
A sparse, spec-sheet-style feed gives the AI almost nothing to work with, which is a bit like sending a salesperson into a meeting with no talking points. Your product, quite simply, gets ignored.
This represents a fundamental change in how products are discovered. The goal is no longer just to rank on a results page, but to become a cited source in a generated answer. It is a shift from discoverability by algorithm to recommendation by an artificial intelligence, and it requires a different kind of input. You can learn more about how AI shopping agents choose products to understand the mechanics behind the curtain.
Why Your Google Product Feed Won't Work for ChatGPT
The core distinction is that a Google feed is optimized for keyword matching, while a ChatGPT feed must be written for conversational synthesis. For years, marketers have perfected the art of optimizing for Google Merchant Centre, but that muscle memory is now a liability. A feed built for Google's keyword-matching logic is poorly suited for conversational AI, which demands narrative context over rigid structure.
The following table breaks down the strategic differences between the two feed types. While both share some basic fields, the emphasis on what makes a product visible has shifted dramatically from structured data to narrative context.
| Feature | Google Merchant Centre | ChatGPT Product Feed |
|---|---|---|
| Primary Goal | Keyword matching for SERPs | Conversational recommendation |
| Data Structure | Rigid, schema-enforced | Flexible, prioritizes descriptive text |
| Title Optimization | Keyword-led, structured | Natural language, question-answering |
| Description Focus | Feature lists, specifications | Narrative, use cases, benefits |
| Attribute Importance | Critical for faceted filtering | Fuel for generating conversational answers |
| Update Logic | Rewards stable, consistent data | Encourages frequent updates for accuracy |
This table highlights the pivot: what was once a data entry task is now a copywriting challenge. The fields that were secondary in a Google feed, long descriptions, nuanced attributes, and detailed product types, are now the primary drivers of visibility in ChatGPT. Your feed is no longer a catalog; it is the script for a sales conversation.
The Fields That Actually Get Your Products Recommended
Visibility in ChatGPT is driven by descriptive fields that provide conversational context, primarily the title, description, and custom attributes. These fields provide the narrative material the AI uses to match your product to a user's query, going beyond basic required fields like id, link, and price. Simply meeting the minimums will not win recommendations; richness is what gets you chosen.
Product Title: This is your first and best chance to provide context. Move beyond the rigid Brand - Model - Size - Color formula. Instead, write a title that describes the product's purpose or benefit in natural language. For example, instead of “BrandX Hiker Pro Jacket Blue L”, try “BrandX Hiker Pro: A Warm, Waterproof Jacket for Cold-Weather Trails”. The second title immediately answers unstated questions about the product's use and attributes.
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Description: This is the single most important field for AI optimization. A sparse, bullet-point list of features is a missed opportunity. Write a full, narrative paragraph (or two) as if you were explaining the product to a customer. Describe who it is for, the problem it solves, how it feels to use it, and what makes it unique. This prose is what ChatGPT will parse, synthesise, and re-present to users in its own words.
Product Type & Custom Attributes: Go deeper than the standard Google Product Taxonomy. Use the product_type field and custom attributes (custom_label_0 through 4) to pre-emptively answer questions. Instead of just Apparel > Outerwear > Jackets, add attributes like Style: Bomber, Weather Resistance: Water-resistant, Best For: Autumn city walks. Each attribute is another potential match for a conversational query.
The importance of these details is growing. With OpenAI's introduction of features like virtual try-on for clothing, as announced in its October 2, 2026, ads update, structured data about fit, style, and material becomes even more critical. While structured data like Schema markup helps with AI visibility on your website, the product feed is your most direct line to the shopping agent.
How to Format and Submit Your Feed
Third-party guides suggest the submission process is straightforward, with merchants sending a structured product feed to OpenAI as a CSV or JSON file. This flexibility allows brands to adapt existing export processes. More importantly, the system is built for freshness. According to a post from an OpenAI team member, merchants can refresh their feeds as often as every 15 minutes, ensuring price and stock accuracy.
Below is a simplified JSON example for a single product, demonstrating how to structure the data with the rich, descriptive details that AI models favor. Note the natural language title and the narrative description.
{
"id": "hiker-jacket-fg-m",
"title": "The All-Weather Explorer Jacket in Forest Green",
"description": "Designed for serious hikers and city explorers alike, this jacket offers complete protection from wind and rain without sacrificing breathability. Its recycled polyester shell features a durable water-repellent finish, while the lightweight down insulation provides warmth on the coldest days. With multiple secure pockets and an adjustable hood, it's the only jacket you'll need from autumn through spring.",
"link": "https://www.yourstore.com/products/explorer-jacket-green",
"image_link": "https://www.yourstore.com/images/explorer-jacket-green.jpg",
"price": "249.99 USD",
"availability": "in stock",
"brand": "YourBrand",
"product_type": "Apparel & Accessories > Clothing > Outerwear > Coats & Jackets",
"color": "Forest Green",
"material": "Recycled Polyester",
"custom_label_0": "Best for: Winter Hiking",
"custom_label_1": "Insulation: 700-fill Down"
}This example serves as a template. The key is to populate the title and description fields with the kind of helpful, persuasive copy you would use on your product page, not the abbreviated jargon of a database.
Measuring Success: Is Your Feed Working?
The most direct way to measure success is by adding UTM tracking parameters to your product links, though full visibility also requires monitoring mentions directly in AI answers, a challenge Ahrefs notes makes AI search ROI messy to measure. The same mojodojo.io study that found most companies were invisible in AI also noted that only 9% of them could segment AI-driven traffic in their analytics. This makes it difficult to prove the return on investment for optimizing your feed.
The most direct method is to use UTM parameters. When formatting your feed, append tracking tags to every URL in the link field. A standard structure would be utm_source=chatgpt&utm_medium=referral&utm_campaign=product_feed. When a user clicks a link from a ChatGPT answer, this will show up clearly in your analytics software, allowing you to attribute sessions and conversions directly to this channel.
However, tracking clicks is only half the story. You also need to know if you are being mentioned in the first place, with or without a link. This requires active monitoring. AI visibility platforms, like our own at Honeyb, are designed to run your key commercial prompts against AI engines at scale and report back on which brands are mentioned, how often, and in what context.
This kind of monitoring is critical because the answers themselves are volatile. In a Honeyb measurement, for example, ChatGPT changed its top recommended brand 35% of the time between back-to-back identical runs (Honeyb measurement). The answer one customer sees may not be the one the next customer sees.
This provides the visibility data that analytics platforms currently miss. While you can track AI Overview traffic with proxy methods, direct monitoring is essential for chat engines.
The first step is to establish a baseline. Before you start optimizing, find out if you are being mentioned at all: run a free AI visibility check and see where your brand stands today.
Sources cited in this report
