How to Win in ChatGPT Shopping: Data Density, Field Completion, and the New Product Description Playbook

Why “field completion” is the new on-page SEO

The screenshots make a strong point: there are no classic keyword fields or custom labels the way performance marketers are used to in shopping feeds, and you can’t assume a 1:1 transfer of Google Shopping SEO tactics.

Instead, your advantage comes from completeness and accuracy across the feed fields that the system uses to match and filter products. OpenAI’s feed spec outlines how merchants share structured product data so ChatGPT can surface products in shopping experiences.

The implication is straightforward:

• More complete fields = more ways to match real user prompts

• Better structured attributes = better “fit” when users ask multi-constraint questions

The “data density” framework

Think of your feed like a product knowledge graph. Every attribute you populate increases the number of valid queries you can satisfy.

Based on the post, here are the three tactics that matter most:

1) Aim for 100% field completion where relevant

Optional fields aren’t optional in competitive markets. They’re opportunities:

• Material

• Weight

• Dimensions

• Color / size

• Shipping and delivery estimate

• Return window

• Review count (where supported)

These fields help the system confidently match a user who asks for specifics (“lightweight,” “leather,” “fits carry-on,” “arrives by Friday”).

2) Treat the description as a knowledge base

The post calls out the description field as a “long-tail battleground.” The reason is simple: in conversational discovery, the description becomes a primary source of contextual match—use cases, constraints, compatibility, fit, care, and common questions.

A practical structure for product descriptions (especially if you have a high character limit) is:

• What it is: 1–2 sentence plain-language definition

• Who it’s for: the top 2–3 use cases

• Key specs: material, dimensions, compatibility, included items

• Fit / sizing: clear guidance (even for non-apparel: mounting, clearance, sizing standards)

• Care / maintenance: where applicable

• Shipping & returns summary: align with your official policies

• FAQs: 5–8 bullets answering the questions your support team gets weekly

This is not “keyword stuffing.” It’s context density.

3) Structure everything to build a “data moat”

Your competitors can copy an ad. They can’t quickly replicate operational excellence in product data.

A data moat looks like:

• Consistent attribute standards across SKUs

• Clean variant handling

• Controlled vocabularies (materials, finishes, sizes)

• A process to keep feeds updated as inventory and policies change

This is where feed optimization becomes an extension of your analytics and governance program—similar to how teams standardize measurement in a website and app analytics audit.

A practical “feed QA” checklist

Use this to pressure-test whether your feed is actually match-ready:

• Category: correct taxonomy, consistent across variants

• Brand: consistent formatting (no duplicates like “Acme Co” vs “ACME”)

• Price: accurate and up to date

• Availability: real-time or near real-time

• Shipping / delivery estimate: not vague; aligned to reality

• Returns: clearly defined return window and conditions

• Attributes: material, dimensions, weight, color, size filled whenever applicable

• Description: includes use cases + constraints + FAQs

If this sounds like “data ops,” that’s the point.