Product results in ChatGPT: the impact of ChatGPT Shopping.

A few days have passed since OpenAI announced and began public testing of selling products directly in search results, and after the initial hype and reactions, we want to share a reflection and invite discussion on the impact this will have from a growth perspective. To begin, we need to talk about the customer, the value we can bring, and the usage context.

Customer perspective

The customer perspective is the most complex, given the varying expectations people have when interacting with these models.

Casual or information-gathering queries are more likely to accept a transactional model (after all, they’re recommendations), but beyond the use cases we frequently see on LinkedIn—technical functions, workflows, queries, and education—there is a growing trend of using ChatGPT as a reference for emotional and mental health support. Without a clear separation within the conversational model, we could be creating ethically problematic situations. I trust that, just as the market eventually regulated SERP ads, product placements, or social media reels, this will be addressed through consensus.

A second consequence of this transactional model in ChatGPT is the shift in the subscription model. Today, we pay for access to information, but once it becomes the new Amazon, we may see different approaches, from freemium to premium tiers with no ads. Let’s not forget that all our interactions, geolocation, and search intent are assets that may be shared with brands, which will affect pricing for end users.

The third major customer impact concerns “walled gardens.” Until now, only three major companies had cross-device visibility of users: Google, Meta, and Amazon (Apple has it but doesn’t commercialize it). ChatGPT is becoming the fourth, shifting the value from social validation to automatic recommendations.

This customer impact (pricing, ethics, and potential) sets the framework for how growth teams can approach it, since the first rule of any sustainable growth strategy is to consistently deliver value.

The growth perspective

OpenAI’s move is not just a new acquisition channel. If we had to compare it to recent disruptions, it would be closer to the iPhone launch and how it changed how we interacted with information.

Concepts like Mobile first, ROPO, multi-device attribution, and the App Store mask the paradigm shift created by the iPhone:

• We went from connecting via computer to taking our phones to bed.
• Purpose-driven queries gave way to constant micro-interactions.
• We became always connected.

Today, we see mobile sales as a thing of the past, and just like we became “mobile first,” the next challenge is to be “conversational first.”

So, what challenges lie ahead?

As mentioned earlier, this is more than just a sales channel. The first challenge is the fragmentation of the customer journey. The market doesn’t yet have standardized tools, agreed-upon metrics, or validated processes for marketing teams to effectively analyze and project impact. That’s going to be OpenAI’s big challenge: creating transparency in the process to enable decision-making and build trust in the channel.

The second big challenge is in the reach of the models and the bias often introduced by marketing, growth, and sales teams. Platforms like N8N or no-code tools using OpenAI offer “custom instructions” that can push specific products or services without checking for underlying bias.

Additionally, these models—when promoting products and answering customer questions—need to clearly flag when information is hallucinated, inaccurate, or has legal implications.

The third challenge is the impact on attribution and consequently, on CAC. Companies investing in this new opportunity may see major shifts in customer cohorts. Conversational models provide faster context, reduce Time To First Key Action (TTFKA), and respond more strongly to branding campaigns, while decreasing the relevance of last-click campaigns. In the short and medium term, customer activation and conversion timing may shift depending on how much weight this channel gains.

Reflections and next steps

Beyond the challenges, the first reflection I’d like to share is that this shift rewards those already doing things right. With a solid foundation in semantic markup and indicators like EEAT (Experience, Expertise, Authoritativeness, and Trust), the market favors companies building strong brands with clear content, good UX, and simple structures for accessing information.

It allows us to embed our brand values in the purchase process. The model can learn about the types of products, quality, or origin I care about and provide comparisons to help me decide.

Ultimately, this shift will drive companies to focus more on UX and building relationships with their users, thereby reducing dependence on the new “sandbox.” A good example is brands selling on Amazon that include coupons, memberships, or incentives to establish a direct relationship with customers.

Future considerations:

• Closely analyze your competition and test open-ended queries. Understanding ranking and indexing models will shape your channel strategy.
• It’s an amplified channel—not an isolated one, with strong ties to content, branding, and PR. Treat it as a holistic initiative. Create human-centered content.
• Fail fast: create your own testing sandbox. Try conversational capture landings and learn how user search behavior is changing (tools like ChatBase can help with minimal dev work).
• What you don’t measure, doesn’t scale. Design your own benchmarking process based on your content, queries, and product types. It won’t be perfect, but it will help you communicate impact across the organization.

The impact of OpenAI will be measured by the experience we provide, not by the technology we use. Let’s position and communicate our products as the solutions they truly are, and highlight the value they bring.