ChatGPT 광고의 모든 것: 완벽한 어트리뷰션 추적 원리

OpenAI's ad platform has two halves. On the ChatGPT side, the backend injects structured single_advertiser_ad_unit objects into the conversation SSE stream while the model is responding. On the merchant side, a tracking SDK called OAIQ runs in the visitor's browser and reports product views back to OpenAI. The two are tied together by Fernet-encrypted click tokens, four of them per ad. I captured both halves on a consented mobile-traffic research fleet. Everything below comes from observed traffic. How an ad gets into a conversation When you send a message to ChatGPT, the backend opens an SSE response at chatgpt.com/backend-api/f/conversation . Most events in that stream are model-output. Some are ad units. They look like this: event: delta data: { "type": "single_advertiser_ad_unit", "ads_request_id": "069e89b3-c038-7764-8000-6e5a193e5f69", "ads_spam_integrity_payload": "gAAAAABp6Js_<...redacted...>", "preamble": "", "advertiser_brand": { "name": "Grubhub", "url": "www.grubhub.com", "favicon_url": "https://bzrcdn.openai.com/cabfae7ead26b03d.png", "id": "adacct_6984ed0ba55481a29894bb192f7773b4" }, "carousel_cards": [{ "title": "Get Chinese Food Delivered", "body": "Satisfy Your Cravings with Grubhub Delivery.", "image_url": "https://bzrcdn.openai.com/cabfae7ead26b03d.png", "target": { "type": "url", "value": "https://www.grubhub.com/?utm_source=chatgptpilot&utm_medium=paid&utm_campaign=diner_gh_search_chatgpt_kw_traffic_nb_x_nat_x&utm_content=nbchinese&oppref=gAAAA<...>&olref=gAAAA<...>", "open_externally": false }, "ad_data_token": "eyJwYXlsb2<...>" }] } Notes: single_advertiser_ad_unit is a typed schema. The naming implies siblings (multi-advertiser, etc.). advertiser_brand.id is adacct_<32-hex> — a stable per-merchant account identifier. Brand favicon and ad image both load from bzrcdn.openai.com . OpenAI hosts the advertiser's creative, not the merchant. target.open_externally: false opens the link in ChatGPT's in-app webview, so OpenAI observes the post-click navigation on top of any pixel signal. Four Fernet tokens per ad: ads_spam_integrity_payload , oppref , olref , and a base64-wrapped ad_data_token . Each is AES-128-CBC under a server-only key with HMAC-SHA256 integrity. How ads get selected A single account in the panel received six different ads across six conversations on six different topics. The targeting is contextual to the chat: Conversation topic Advertiser delivered Beijing trip planning (Great Wall, Forbidden City) Grubhub — "Get Chinese Food Delivered" Beijing tour bookings GetYourGuide — Great Wall tour, ad_id=beijing003 Beijing flights Axel — utm_term=vflight_beijing_03 NBA playoffs Gametime — utm_campaign=nba&utm_content=playoffs Spring fashion/trends Aritzia — utm_campaign=chatgptpilot_trav3 Productivity / slides Canva — utm_campaign=…link-clicks_products Same account, different topic, different brand. I didn't find evidence one way or the other on whether targeting also incorporates prior conversation history. The four-token attribution chain Every ad ships with four distinct Fernet-encrypted blobs. Their roles, based on where they appear: ads_spam_integrity_payload sent inside the SSE data, never on the click URL. Server-side integrity check against forged ad clicks. oppref present on the click URL and copied verbatim by the OAIQ pixel into the cookie __oppref (TTL 720 hours / 30 days). The forward attribution token. Travels with every subsequent merchant pixel event. olref paired with oppref on the click URL but not stored by the SDK we observed. Likely impression-side / outbound-link-reference logging on OpenAI's servers. ad_data_token base64-wrapped JSON containing yet another Fernet token. Carried in the SSE payload, presumably reconciled server-side at click time. Fernet's first nine bytes are public: version byte 0x80 plus an 8-byte big-endian Unix timestamp. So the mint time of any of these tokens is recoverable without OpenAI's key: import base64, struct, datetime b = base64.urlsafe_b64decode("gAAAAABp7fdA" + "==") print(datetime.datetime.utcfromtimestamp(struct.unpack(">Q", b[1:9])[0])) # → 2026-04-26 11:30:08 UTC The Home Depot click URL we captured was minted at 11:30:08; the browser fetched the merchant page at 11:31:43. Click latency: 95 seconds. How the loop closes on the merchant side User taps the card. Browser opens: https://www.grubhub.com/?utm_source=chatgptpilot&... &oppref=gAAAA<...> &olref=gAAAA<...> The merchant page loads the OAIQ SDK: <script src="https://bzrcdn.openai.com/sdk/oaiq.min.js"></script> <script> oaiq('init', { pid: '<merchant pixel ID>' }); oaiq('measure', 'contents_viewed', { ... }); </script> oaiq.min.js is at version 0.1.3. On init it reads ?oppref= from window.location , writes it into the first-party cookie __oppref with a 720-hour TTL, and sets a probe cookie __oaiq_domain_probe . Every subsequent measure call POSTs JSON to: POST https://bzr.openai.com/v1/sdk/events?pid=<merchant>&st=oaiq-web&sv=0.1.3 Two domains to add to your filter list if you want to block ChatGPT ad events: bzrcdn.openai.com , bzr.openai.com . Two cookie names to inspect after any ChatGPT-recommended click: __oppref , __oaiq_domain_probe .