As a global leader in SEO, content marketing, and data analytics, Anastasia Braitsik has spent years navigating the volatile shifts of the digital advertising landscape. With OpenAI’s recent move to introduce cost-per-click (CPC) ads within ChatGPT, the industry is witnessing a seismic transition from passive brand awareness to performance-driven conversational marketing. In this discussion, we explore the nuances of user intent in AI sessions, the strategic necessity of shifting away from declining impression-based revenue, and how brands can safely navigate the “wild west” of early-stage AI ad platforms to secure a first-mover advantage.
With cost-per-impression rates for AI interfaces dropping from $60 to $25, how do cost-per-click models help stabilize revenue? What specific metrics should advertisers track to justify spending $3 to $5 per click in a conversational environment compared to traditional search results?
The sharp decline in CPMs from $60 down to a mere $25 signals a clear maturation of the market where “eyes on glass” is no longer a premium commodity. By shifting to a CPC model, OpenAI is effectively de-risking the investment for the advertiser; you are no longer paying for the mere possibility of being seen, but for a tangible, intentional action. To justify the $3 to $5 price point per click, advertisers must look beyond the click itself and obsess over post-click engagement depth and downstream conversion rates. In a conversational environment, the “intent-to-buy” signal is often more refined than a frantic three-word search query, so tracking the lifetime value of users acquired through these deep-chat sessions is vital. If a $5 click leads to a high-quality lead who has already been “pre-sold” by the AI’s contextual assistance, that cost becomes an absolute bargain compared to the high-churn traffic we often see on traditional platforms.
Moving from brand awareness to a performance-driven model suggests a fundamental shift in how AI interactions are valued. How does the intent of a conversational user differ from a search engine user, and what specific ad creative strategies work best for these high-intent AI sessions?
A search engine user is often looking for a quick exit—a link that solves their problem so they can leave the results page—whereas a ChatGPT user is engaged in a continuous, fluid dialogue. This conversational context means the user is in a “discovery and refinement” mindset, which is incredibly valuable for performance marketing. Your ad creative shouldn’t feel like a jarring interruption; it needs to feel like a helpful, native extension of the ongoing conversation. The strategies that win here are those that prioritize utility over flashiness, using clear, benefit-driven language that answers the “what next?” in the user’s journey. When the AI is helping a user plan a trip or research a complex product, a well-timed, performance-oriented ad can act as the final bridge to a transaction.
Established search platforms have long dominated performance marketing budgets. As AI platforms adopt similar pricing structures, what operational changes must marketing teams make to their bidding strategies, and how do you anticipate this will impact the cost of customer acquisition across the industry?
Marketing teams can no longer afford to run their AI ad spend on autopilot; they must treat ChatGPT as a distinct performance channel that requires its own unique bidding logic and attribution rules. Because the platform is rolling out a limited ads manager, teams need to be agile, shifting budgets in real-time as they benchmark these $3 to $5 clicks against their historical Google Search data. I anticipate that while initial customer acquisition costs might be higher due to the premium nature of the platform, the quality of those customers will be significantly superior. Over time, as OpenAI builds out more robust self-serve infrastructure and measurement tools, we will see a fierce bidding war that will likely drive up costs across the board, making early experimentation a strategic necessity for survival.
Proving that conversational context leads to valuable conversions is a significant hurdle for new platforms. What measurement tools are necessary to track ROI in this format, and how can advertisers ensure they are not simply paying for accidental clicks during a fluid chat session?
The fear of the “fat finger” or accidental click is real in a fluid interface, which is why standard click-tracking isn’t enough to prove ROI in this space. Advertisers need to implement sophisticated server-to-server tracking and look at “engaged visit” metrics—measuring how long a user stays on the landing page after the chat session ends. By integrating these clicks into a broader data analytics framework, you can see if the $3 investment actually moves the needle on your bottom line or if it’s just burning through budget. It’s also about the “quality of the conversation” preceding the click; if the AI has already vetted the user’s needs, the likelihood of that click being a mistake or a low-value bounce drops significantly.
Early adopters often find lower competition but face higher technical risks and limited management tools. What specific steps should a brand take to audit their current performance mix before reallocating budget to AI ads, and what are the primary risks of being an early tester?
Before moving a single dollar, a brand needs to perform a rigorous audit of their current “high-intent” keyword buckets to see which segments are most likely to benefit from a conversational deep-dive. You should start by reallocating a small “innovation” budget—perhaps 5% to 10% of your current search spend—rather than gutting your proven channels. The primary risk is the lack of granularity in current management tools, which can feel like flying a plane with half the instruments blacked out. You are also dealing with a platform that is still learning how to balance user experience with monetization, so there is always the sensory discomfort of unpredictable algorithm shifts that could tank your performance overnight.
What is your forecast for AI-integrated performance advertising?
I believe we are standing at the edge of a total reconfiguration of the digital economy where the “search box” is replaced by a “thought partner.” Within the next two years, we will see AI platforms move beyond simple CPC models into “Cost-Per-Action” or even “Cost-Per-Resolution” models, where advertisers pay only when a user’s complex problem is fully solved by a product or service. The veteran search platforms will be forced to cannibalize their own interfaces to stay relevant, but the real winners will be the brands that master the art of “conversational conversion” today. If you aren’t testing these $3 to $5 clicks now to understand the rhythm of this new medium, you will find yourself priced out of the market once the full-scale bidding wars begin.
