128 lines
5.5 KiB
Markdown
128 lines
5.5 KiB
Markdown
# Learning-period and stable-period ranking
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Apply this two-pool method in every daily brief. The purpose is to prevent fresh tests
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from being judged by mature-entity standards while still pruning obvious failures
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quickly.
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## 1. Define age and comparable peers
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Use confirmed launch/effective dates when available. Otherwise infer launch from the
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first eligible delivery date and label it as inferred.
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- Use calendar age since confirmed launch to detect zero-delivery problems.
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- Use delivered days and KPI-mature cohort days to judge performance.
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- A lagged KPI may have fewer mature cohorts than the entity's calendar age; never
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count immature values as failures.
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- A major reset to creative, targeting, bid, word pack, or structure may re-enter the
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affected entity into a learning cohort when it materially changes traffic.
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Rank peers only within a comparable scope: same entity level, objective/KPI contract,
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currency, attribution regime, and sufficiently similar launch cohort. Rank sibling Ads
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launched together within their parent before comparing them portfolio-wide.
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## 2. Rule one — learning-period pool
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Treat a new or materially reset Ad as learning-period through the day-7 checkpoint
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unless the operator configures another window.
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### Day 3: early selection
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Rank simultaneous tests by this ordered evidence, without inventing a composite score:
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1. no hard-guardrail breach;
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2. primary KPI result on mature data;
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3. number of confirmed KPIs meeting target;
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4. distance from target and direction of trend;
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5. mature sample and result concentration;
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6. delivery/budget fill and spend impact.
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Actions:
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- Confirmed launch with zero spend through three calendar days: label `无投放学习失败`;
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inspect review/status/bid/audience/data issues and recommend pause/rebuild if no
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correctable delivery block exists.
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- Spend exists, every mature KPI fails, and sample is adequate: rank last and recommend
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pause or reduction at day 3. If sample is inadequate, state the exact day-5 sample or
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spend stop condition rather than treating missing outcomes as proof.
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- Some KPIs meet target and delivery/budget is filling: keep stable through day 5–7;
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specify which KPI must mature and what failure would stop the test.
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- A peer clearly leads on mature KPIs: label `学习期扩量素材候选`, but do not call it a
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durable winner yet.
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### Day 5: confirmation
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- Eliminate persistent bottom-ranked peers when mature evidence and sample support it.
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- Continue mixed but improving or partially compliant entities to day 7 with variables
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fixed.
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- Promote consistent leaders to a bounded scale-test candidate only when downstream
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guardrails are mature enough; otherwise keep the candidate label.
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### Day 7: graduate or stop
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- Graduate to the stable pool when the entity has adequate mature sample and an
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interpretable run.
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- Mark as a scale candidate when multi-day KPI support, downstream guardrails, and
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headroom are credible.
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- Pause/reduce or redesign persistent failures. Do not extend `learning` indefinitely
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to avoid a decision.
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At each checkpoint, show rank movement where earlier checkpoints are available.
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## 3. Rule two — stable-period pool
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Classify entities that graduated from learning or have an established run into one of
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four tiers:
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### S-优质
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Recent 7D exceeds the confirmed KPI targets with multi-day stability, adequate sample,
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and no severe concentration or recent deterioration. Rank the strongest durable Ads
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first.
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### A-稳定
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The confirmed KPIs meet target with adequate sample and acceptable multi-day
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consistency. Protect or consider bounded scaling subject to creative age and headroom.
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### B-波动待优化
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Results fluctuate, only some KPIs meet target, or performance hovers near thresholds,
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but there is still a specific optimization or controlled-retest path. Keep, cap,
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reduce, or test one bounded improvement hypothesis with an exit condition.
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### C-劣质
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The primary KPI or multiple core KPIs seriously or persistently fail on mature data
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with meaningful exposure, or performance sharply deteriorates without credible
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recovery. Treat these as risk Ads and rank by avoidable loss and confidence for
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pause/reduction review.
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Within each tier use the same ordered evidence as the learning pool, then use spend
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impact to prioritize operator attention. Every stable-pool row must include a concrete
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`决定` column.
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## 4. Historical winner deterioration and new-material alert
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Flag a creative/material alert when an established Ad has a credible strong historical
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control but recent mature performance deteriorates materially and persistently. Show:
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- total run age and historically strong window;
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- exact recent 7D versus prior 7D or pre-change evidence;
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- which funnel stage worsened;
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- whether spend, budget, targeting, placement, or audience mix changed;
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- fatigue as a hypothesis only when frequency/CPM/CTR/CVR or creative-age evidence
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supports it.
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Recommend preparing or testing new material when the decline is not explained by data
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lag or an easily reversible traffic change. Protect any still-performing sibling Ads.
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## 5. Ranking output invariants
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- Show an ordinal rank and the decisive reason; do not present a fake precise score.
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- Rank Ads only; do not output Campaign or Ad Group leaderboards.
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- Show KPI pass count only alongside the primary KPI and hard guardrails; KPI counts
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alone do not determine quality.
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- State whether budget fill uses configured budget or a delivery proxy.
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- Recompute rank when mature cohorts arrive; do not retroactively treat immature zeros
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as valid evidence.
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