Unlike Netflix, which struggles with attribution using clean rooms and IP matching, ChatGPT's ad platform can leverage direct clicks. This allows for high-fidelity measurement similar to Meta's CAPI and pixel, providing advertisers with much clearer, less probabilistic attribution for their ad spend.
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A novel way to measure ad effectiveness in LLMs is "attention shift"—analyzing how much an ad pivots the conversation's topic toward the brand. This metric, derived from vector analysis of messages before and after an ad, captures influence beyond traditional clicks or impressions, reflecting deeper engagement.
Unlike Meta or Google, OpenAI's early ad offering for ChatGPT will not provide detailed attribution data or conversion tracking. Advertisers will only receive high-level metrics like impressions and clicks, a significant step back from the granular performance measurement they are accustomed to.
To effectively sell ads, OpenAI must provide advertisers with targeting tools and performance data. This will inadvertently open up a treasure trove of analytics for all marketers, offering the first real glimpse into user behavior, popular topics, and prompt trends within ChatGPT.
New measurement tools are moving beyond probabilistic models (guessing based on IP/device) to deterministic view-through attribution. By using first-party data like platform logins, marketers can now directly match an ad impression to a purchase, solving a major measurement challenge.
Data from SimilarWeb indicates that users referred from ChatGPT show dramatically higher engagement and conversion. They spend 3x more time on site, view 25% more pages, and have a 7% conversion rate compared to 5% from Google. This suggests LLMs are a powerful platform for high-intent advertising.
AI conversations capture high-intent moments, allowing ads to target active decision-making rather than passive attention-grabbing like social media. This fundamental difference could lead to significantly higher average revenue per user (ARPU), making social media's ad performance a floor, not a ceiling for AI platforms.
Instead of traditional cost-per-click models, ChatGPT could pioneer a "verified outcome" system where advertisers pay only upon a completed transaction and user satisfaction. This would inherently favor advertisers with superior products that lead to actual conversions, improving ad quality and relevance for all users.
Meta's core moat is its ability to solve the classic advertiser's dilemma: knowing which half of their ad spend works. By providing granular data on impressions, conversions, and ROI, it created what Pat Dorsey called the perfect advertising platform.
The next major shift in ad tech is performance-based CTV. This merges the attention of linear TV with the accountability of digital media, allowing advertisers to tie ad spend directly to outcomes like sales—a revolutionary change from traditional television's limitations.
AI now enables the tracking of every customer touchpoint, including interactions outside of marketing-controlled channels. This provides a complete view from first contact to close, finally solving the long-standing challenge of accurate marketing attribution and ROI measurement.