Signal Hierarchy for Cookie-Free Buying When the Only Data Is What Someone Just Typed

Buying ads inside AI assistant conversations requires access to conversational inventory and targeting based on the decision expressed in the chat. ChatGPT Ads Manager, Google Ads, and Microsoft Advertising provide buying routes for their respective AI surfaces. The targeting framework is a signal hierarchy that uses explicit purchase intent, multi-turn escalation, and semantic context in place of guesses about audiences based on identity.

Why the cookie-based signal stack broke — and what prompt data fills in

Google shut down the Privacy Sandbox ad APIs in October 2025 after six years with few adopters, and nothing took their place across the industry. More than 40% of web and mobile traffic now occurs in environments where traditional identifiers are unavailable. The structural loss is the behavioral and identity layer that connected a current impression to a record of past activity.

Conversational interfaces supply a different kind of input. What search reconstructed from a three-word query, a conversational system can receive directly, including the problem, constraints, and decision under consideration. The signal gap closes because the interface changed. Prompt data replaces historical inference with a declared situation rather than rebuilding the tracking infrastructure that supported it.

What a prompt actually contains as a data object

A prompt contains several dimensions that require separate interpretation, including topic, intent stage, specificity, constraints, and conversational position. Topic identifies the broad domain, but it does not establish purchase readiness. Specificity begins to separate research from a decision. The difference between “what's a good CRM?” and “compare HubSpot and Salesforce pricing for a ten-person team” is not simply the number of words.

The second prompt names the alternatives, asks how their pricing compares, and specifies the team size that governs the comparison. Budget, geography, integration requirements, and timeline can further narrow the match. Those constraints are targeting inputs in their own right. When follow-up questions are available, the trajectory adds another dimension by showing which uncertainties remain and where the decision appears to be heading. A flat topic label discards those distinctions before buying logic can use them.

Tier one: explicit purchase intent signals in the prompt

Explicit purchase intent is the most legible tier because the user states a commercial decision in plain language. This tier includes comparisons that name vendors, talk of budget and pricing, integration requirements that suggest a purchase, and phrases that ask about ROI or whether a product is worth its price. “Product A vs Product B for a specific use case” identifies both the competitive set and the job against which the alternatives will be judged.

These patterns are like the long-tail exact-match keywords that search marketers have used to reach people who want to buy. The surrounding language, however, clarifies which constraint makes the offer relevant. An integration requirement can be more decisive than a broad category match because it defines whether the product can fit the proposed use.

Query specificity is therefore a practical proxy for tier placement when it concerns a purchase decision. This tier replaces the behavioral “in-market audience” segment, which infers readiness from site visits accumulated over days or weeks, by prioritizing purchase-specific requests over broad exploration and locating readiness in the exchange itself.

Tier two: multi-turn escalation as the signal unique to conversational environments

Multi-turn escalation describes a change in the decision. When a user asks for a recommendation and then asks about pricing, contracts, or onboarding, they are moving toward a purchase. The sequence supplies the distinctive signal here. A cookie-based system observes visits across sessions, while a conversational system can observe how one question gives rise to the next within an unbroken context.

Microsoft Copilot uses the entire session context, including the latest query, to judge how well an advertiser fits. Microsoft calls the explanatory summary before the ad block “ad voice,” which describes how the ad relates to the conversation. This connects relevance to the developing exchange.

Users usually go roughly six prompts deep before they move to the open web to convert. The progression within those turns helps distinguish continued research from an approaching decision. That signal also has a short useful life. Some users convert within days, particularly for flights and electronics, so an in-session opportunity may reach someone who will not return to the channel before purchasing.

Tier three: contextual and semantic signals when explicit intent is absent

Even when early-stage prompts have no vendor name or pricing question, they still describe problems an advertiser can help solve. More than one in five pre-purchase digital journeys now begin with AI chat, and initial prompts are typically unbranded. Topic, problem narrative, and phrasing can establish relevance without supporting a claim that the user is ready to convert.

ChatGPT's context hint operationalizes this distinction through a natural-language description of the conversations where an ad belongs. It describes what the person is trying to accomplish. OpenAI lists no official character limit and lets teams use more than one hint per ad group. A comma-separated topic list does not express the conversational situation that the hint needs to capture.

Topic and stage therefore work together. A travel research prompt warrants different creative and a different CPM limit from a comparison prompt in the same domain. Ads appear across roughly 26% of commercial prompts, clustering around comparison, recommendation, and purchase-oriented conversations. Contextual matching expands the framework, but its usefulness depends on awareness-level creative and realistic conversion windows rather than treating exploration as suppressed purchase intent.

How the auction translates the signal hierarchy into bid logic

ChatGPT's relevance-weighted, second-price auction makes signal precision commercially consequential. An advertiser pays just above the next competing bid, which is less than the full amount they bid. A tightly matched ad can beat a higher bid, allowing a precise advertiser with a smaller budget to compete against a larger spender using broad contextual categories.

The auction reads the context hint, ad copy, and landing page as relevance inputs. Their coherence is a structural requirement. OpenAI reads the landing page when it reviews the ad, so that page is part of targeting itself. A page must help with the decision described in the conversation and keep its product pitch tied to that decision.

The resulting budget logic assigns higher CPM limits and conversion-focused creative to explicit intent and multi-turn escalation. Exploratory contexts warrant awareness-level creative and longer attribution windows. Google AI Mode and AI Overviews work differently, with eligible campaigns serving automatically and no opt-in or opt-out. Smart bidding is strongly recommended and required for AI Max for Search, so teams should audit existing delivery before allocating new budget. Microsoft similarly opts eligible campaigns into Copilot automatically, where session-level matching requires assets that work across a conversational arc.

Why interpreting conversational signals requires no persistent identity

Conversational targeting does not need to establish who a person is to interpret the situation they have described. The current exchange gives teams the input they need for targeting. Its zero-party character is the mechanism. The hierarchy ranks evidence about the decision a person is making now in place of evidence drawn from their past activity.

Clean-room and identity-graph approaches try to approximate what cookies knew about an individual. Conversational targeting reads what the person says they need. First-party data can still supplement that process. ChatGPT custom audiences allow customer and prospect lists to control campaign eligibility through inclusion, exclusion, and bid adjustments, layered over OpenAI's internal conversational matching.

Audience segments, match rates, and cookie pools give way to context hints, coherent landing pages, and creative matched to conversational stage. The new capability is semantic judgment applied to commercial decisions. Teams need to tell when a problem is worth helping with and when a purchase decision is worth a high bid to reach.

Where the signal hierarchy breaks down: the attribution gap

The hierarchy is actionable as a targeting framework, but its feedback loop is impaired. A user can escalate across several turns, leave the chat, and purchase days later without leaving the standard click and session trail attribution expects. Last-click models systematically miss the conversational contribution to that journey.

Zéa Hirsh, performance media supervisor at Collective Measures, identifies limited visibility as the obstacle. “Our biggest concern right now is visibility. We don't have search- or prompt-level reporting, so we can't see exactly what users are asking when our ads are served beyond the contextual targeting we've set up”.

OpenAI expanded measurement infrastructure on October 5, 2026, with Pixel and Conversions API, an Event Quality Score, and a one-day view-through window. The expansion included attribution partners, full-funnel measurement partners, and incrementality experiments. It did not provide prompt-level reporting. Advertisers can measure outcomes without seeing the full conversational context in which an ad appeared.

That leaves a fundamental objection unresolved. If advertisers cannot see which tier received an impression, they cannot directly optimize performance across observed tiers. The practical holding position is to use incrementality experiments and view-through attribution to assess channel contribution while treating the hierarchy as a creative and bidding framework. Ahrefs research found that AI search traffic generates a disproportionately large share of signups relative to its small share of visits, supporting measurement beyond last-click without resolving the visibility gap.

How the signal hierarchy applies differently by category

Travel combines extensive AI discovery with decisions that naturally develop over several turns. More than a third of travel queries begin in an LLM, making travel the category with the highest share of AI-originated discovery traffic. Trip planning creates a clear escalation path. Itinerary building and comparison shopping can move into flight selection within a short conversion window, increasing the time value of tier-two signals.

Retail also offers chances to reach people who will buy soon, especially in electronics, where some users convert within days. That makes the distinction between research and comparison commercially useful. A broad category question belongs in a different buying posture from a constrained purchase comparison. The framework does not require every retail conversation to follow the same schedule, only that creative and bids reflect the decision actually expressed.

ChatGPT restricts ads for regulated medical products and services and claims related to prevention, diagnosis, or treatment, while health-service ads outside the US are generally prohibited. OpenAI removed categorical blocking of medical, legal, and financial advice contexts in April 2026, then clarified eligible categories and markets in July 2026. Sensitive conversations remain ineligible under those policies. Three in five US adults have ever used AI tools for medical information, placing substantial demand on a surface with constrained advertiser access. Eligible adjacent contexts therefore give tier three greater weight, and platform policy must be mapped before tiers become bid rules.

Building the targeting stack around conversational signal tiers rather than identity layers

A conversational targeting stack needs five connected functions, including placement buying, context targeting, tier-specific creative, matched landing pages, and cross-channel attribution. Placement access comes first, but access is not targeting capability. As of October 2026, teams can buy through ChatGPT Ads Manager, Google Ads for AI Mode and AI Overviews, and Microsoft Advertising for Copilot. ChatGPT's self-serve manager was live in 89 countries as of October 21, 2026.

For a DSP buying ads inside AI chat interfaces, the defining criteria are conversational interpretation and access across AI surfaces. Those are separate capabilities. A purpose-built conversational DSP that combines both occupies a different position from a generalist DSP with reach but no conversational interpretation, or a single-surface network with context matching but limited reach. The platform category alone does not establish which placements a buyer can access.

Above the buying layer, teams must connect context descriptions to creative and destinations that reflect the same decision stage. Platforms can draft first ads and suggest copy within their own channels. Teams still need to keep the brand consistent, test a range of creative angles, and measure results across channels. View-through attribution and incrementality experiments provide the holding framework where post-click reporting undercounts contribution.

The infrastructure shift is therefore from audience reconstruction to decision interpretation. Its central operational test is whether the stack can turn a declared need into a relevant placement, preserve that relevance through the landing page, and measure contribution without pretending the conversational reporting gap has closed.