We prepare and manage ChatGPT Ads for ecommerce from a commercial perspective: which catalogue deserves activation, how to structure the pilot, what the product experience needs to do after the click and how purchase, revenue and margin should be measured. The feed is a technical layer; strategy decides what is worth funding.
This page explains how we run ChatGPT Ads for ecommerce as a service: catalogue selection, commercial architecture, product experience, checkout, measurement and management. The technical catalogue layer — formats, expiry, filters, ads_metadata, templates and Products reporting — is covered separately in our Product Feeds technical guide.
Before building campaigns, we define the commercial question the pilot needs to answer. Products differ in margin, stock depth, repeat potential, seasonality, returns and the acquisition cost they can realistically absorb.
The first job is therefore to prioritise assortment and risk: which ranges to test, which to exclude, what order value we need and which signal would justify expanding investment. A full catalogue can be technically available and still be the wrong commercial starting point.
Campaign structure should not automatically copy the store taxonomy. We can separate by product line, margin, availability, intent, season or any other criterion that changes message, budget or scale decisions.
The feed layer makes that logic executable; the mechanics are documented in our Product Feeds guide. Here the question is commercial: can each group tell us whether one subset of the catalogue deserves more, less or no additional spend?
An ecommerce campaign is not solved by a technically valid feed. We review how each range is positioned, which benefit or difference deserves to appear in the message and whether image, price, availability and proposition remain coherent as products change.
When one category needs a materially different promise, proof point or explanation, we treat it as a separate hypothesis. The objective is to learn which product-context-message combinations create useful demand, not simply to generate ad volume.
After the click, performance depends on the ecommerce experience. We review consistency across title, image, price and availability, but also speed, variants, proof, delivery terms, returns, CTA and checkout friction.
If the product page cannot support the promise made by the ad, or checkout destroys purchase intent, increasing media spend only scales the post-click problem. That is why CRO and media need to be read together.
We implement the measurement needed to connect campaign activity with purchases and revenue, then reconcile that view against the ecommerce platform. Where appropriate, the setup can include OpenAI Pixel and Conversions API, preservation of oppref and browser/server deduplication using the same event ID.
Scale should not be decided by product count or CTR in isolation. The decision belongs to real economics: orders, revenue, margin, returns and any signal that determines whether growth is defensible. For the implementation layer, see our ChatGPT Ads Tracking service.
Feeds reduce manual campaign work and keep commercial data current, but structure should follow the economics of the business rather than only the technical taxonomy of the store.
Product feeds, catalogue eligibility, measurement and the current limits of the format.
Pilot definition, catalogue selection and structure, campaign architecture, product-page and checkout review, purchase/revenue measurement, QA and optimisation criteria. Exact scope depends on the state of the store and catalogue.
No. It often makes more sense to start with a subset that has enough margin, stock and commercial value to produce a clear read before expanding coverage.
Yes, provided the current source can be turned into reliable campaign data. Before spend starts, we review IDs, URLs, imagery, pricing, availability and the other fields needed to keep ads and the store consistent.
By differences that change a business decision: margin, stock, season, intent, proposition, destination, average order value or budget requirements. We do not copy the store taxonomy if it does not help manage investment.
Yes. A campaign can attract the right traffic and still lose it because of a weak product page, confusing variants, insufficient proof or checkout friction. That post-click layer is part of the ecommerce pilot.
Yes. We configure the required signal and reconcile Ads Manager against the ecommerce system so decisions can be based on orders and revenue rather than clicks alone. Technical scope can include Pixel, CAPI and deduplication.
Yes. We can run a bounded pilot first and move into ongoing management once there is enough evidence to justify continuous optimisation, catalogue expansion and new testing.
See our ChatGPT Ads Product Feeds technical guide for formats, expiry, filters, ads_metadata, templates, reporting and feed operations.
We can manage the complete channel or solve the feed, tracking, landing-page, CRO or account-structure layer that is limiting performance.
We review assortment, margin, stock, product experience and measurement before deciding what to activate. If the catalogue is not ready, we will tell you what needs fixing before spend begins.
What you sell, which markets you work in and what you want to learn from the first pilot. If the scope is not clear yet, we will define it with you.