Non-advertised e-commerce in the AI ​​search: Openai releases ranking factors for products in Chatgpt

Product recommendations 2.0: How Openai sets standards with Chatgpt

At the end of April 2025, Openai introduced a significant update for Chatgpt, which enables the AI ​​system to display product recommendations. In contrast to traditional e-commerce platforms, these recommendations are not influenced by advertising, but are based on a complex selection algorithm. Openai has now published the factors that determine which products appear in the new “visually appealing carousels” and how they are selected.

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• Openaai: Improved Shopping Results from Chatgpt Search
• Openaai: Help Chatgpt Discover Your Products

The basic selection algorithm

Chatgpt uses a new approach for product recommendations that fundamentally differs from conventional search engines and e-commerce platforms. "In contrast to Google or Amazon, Chatgpt's product results 'by Chatgpt are selected independently and are no advertisements'", as Openaai emphasizes in its documentation. This non-advertised approach represents a significant departure from traditional e-commerce platforms.

The product selection is made by an intention -based evaluation process. If a user makes an inquiry with an intention to buy (e.g. "I'm looking for costumes for my two dogs"), Chatgpt activates his shopping functions and shows relevant product options. The user experience is significantly more direct than with conventional search engines - while Google often shows numerous reports and blogs about products, Chatgpt immediately presents concrete products with relevant information.

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• Artificial intelligence (AI) creates more equal opportunities and competition between large and small companies in sales and marketing

The four key factors in the product selection

Openai has identified four primary factors that are taken into account in the product selection:

1. Structured metadata of third -party providers - technical specifications, price information and product descriptions flow into the evaluation
2. Contents of third -party providers -customer reviews and ratings help to assess product quality and customer satisfaction
3. Previously generated model answers - information that Chatgpt generates before the inclusion of new search results
4. Openai security standards-filter mechanisms that ensure that recommended products meet the company guidelines

The relative importance of these factors is dynamically depending on user requirements. For example, if a user indicates a budget, the system prioritizes price information. When specific product attributes are mentioned, these properties receive a greater weight in the selection algorithm.

Influence of the user context on the product selection

Chatgpt's product recommendations are strongly influenced by contextual signals from user interactions. The system processes:

• Explicit request information - direct product requirements in the search
• Memory data - when activated, Chatgpt takes into account earlier discussions on personalizing the recommendations
• User -defined instructions - user -defined preferences that remain across meetings

This contextual awareness creates a personalized shopping experience. For example, if a user had previously expressed an aversion to clowns, Chatgpt would include this information in the product selection and exclude clown costumes from the recommendations for dog costumes. This personalization goes far beyond what conventional search engines offer.

Presentation of product information

Openai has implemented several techniques to optimize the representation of product information:

• Standardized product descriptions -Chatgpt generates simplified, consistent product titles and descriptions that normalize the inconsistent terminology of various dealers for identical products
• AI-generated feature labels-products such as “inexpensive” or “most popular” can receive analysis of the available data based on chattgt, although these labels are “no guarantees or verified statements”
• Evaluation summaries - the system creates concise summaries that highlight frequent user opinions on products and use evaluations of public websites
• Evaluation aggregation - Star reviews displayed in the surface can represent aggregated reviews from several sources that "may not match the rating available on a specific website"

Selection methodology for dealers

The current selection of dealers mainly works as a presentation layer and not as a sophisticated ranking system. If users click on a product, Chatgpt can indicate a list of dealers who offer this article, whereby the placement "is mainly determined by these [third-party] provider" and not through performance metrics such as price competence or shipping guidelines.

Openaai expressly explains: "We do not reorganize retailers based on factors such as price, shipping or return guidelines." However, this could change because Openai notes: "We expect this to develop further while we further improve the shopping experience."

Technical basics of product selection

The technical implementation of Chatgpt's product recommendation system reveals several important aspects:

Request converting for product search

If users express purchase intentions, Chatgpt uses a complex mechanism for reorganization of inquiries to improve search precision. Instead of using the exact language of the users, the system transforms naturally language inquiries into structured search parameters.

Location awareness of the product selection

The product relevance is further improved by processing location data. The system “collects general location information based on your IP address and can share this general location with searches of third-party providers to improve the accuracy of your results”, according to the Openai documentation.

Timetals for dealer integration

Openaai has announced plans to build more direct relationships with dealers. The company “examines opportunities for dealers to provide our product feeds directly, which will guarantee more precise and up -to -date offers”. This indicates a future in which the product selection could be less based on third-party aggregators and more on direct dealer data.

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• The sales paradox-forget the Sales Funnel: The Customer Journey is dead-despite AI, Automation and CRM!

Effects on marketing and e-commerce

This non-advertising-financed approach represents a significant departure from conventional digital marketing. While search engines and e-commerce platforms traditionally generate revenue through sponsored placements, Chatgpts model relevance and user experience with independent selected results.

The shopping functionality is currently being rolled out for plus, pro, free and non-registered users worldwide, wherever chatt is available. With over a billion processed web searches in the first week after the introduction, this new approach could force established actors to rethink their dependence on advertising -based product recommendations.

Intention -based product recommendations: the end of paid placements?

Openai's publication of the ranking factors for products in Chatgpt marks a significant change in the product search and recommendation in digital space. The intention-based, non-advertising-financed approach offers users a personalized shopping experience that is based on relevance and context instead of paid placements.

For dealers and marketing experts, this means that traditional SEO strategies have to be adapted. Factors such as product quality, authentic reviews and accurate metadata are becoming more important, while paid placements take a back seat. With the planned direct integration of dealer data, this landscape could develop further and create new opportunities for dealers that offer high quality products and customer experiences.

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