# Learning-period and stable-period ranking

Apply this two-pool method in every daily brief. The purpose is to prevent fresh tests
from being judged by mature-entity standards while still pruning obvious failures
quickly.

## 1. Define age and comparable peers

Use confirmed launch/effective dates when available. Otherwise infer launch from the
first eligible delivery date and label it as inferred.

- Use calendar age since confirmed launch to detect zero-delivery problems.
- Use delivered days and KPI-mature cohort days to judge performance.
- A lagged KPI may have fewer mature cohorts than the entity's calendar age; never
  count immature values as failures.
- A major reset to creative, targeting, bid, word pack, or structure may re-enter the
  affected entity into a learning cohort when it materially changes traffic.

Rank peers only within a comparable scope: same entity level, objective/KPI contract,
currency, attribution regime, and sufficiently similar launch cohort. Rank sibling Ads
launched together within their parent before comparing them portfolio-wide.

## 2. Rule one — learning-period pool

Treat a new or materially reset Ad as learning-period through the day-7 checkpoint
unless the operator configures another window.

### Day 3: early selection

Rank simultaneous tests by this ordered evidence, without inventing a composite score:

1. no hard-guardrail breach;
2. primary KPI result on mature data;
3. number of confirmed KPIs meeting target;
4. distance from target and direction of trend;
5. mature sample and result concentration;
6. delivery/budget fill and spend impact.

Actions:

- Confirmed launch with zero spend through three calendar days: label `无投放学习失败`;
  inspect review/status/bid/audience/data issues and recommend pause/rebuild if no
  correctable delivery block exists.
- Spend exists, every mature KPI fails, and sample is adequate: rank last and recommend
  pause or reduction at day 3. If sample is inadequate, state the exact day-5 sample or
  spend stop condition rather than treating missing outcomes as proof.
- Some KPIs meet target and delivery/budget is filling: keep stable through day 5–7;
  specify which KPI must mature and what failure would stop the test.
- A peer clearly leads on mature KPIs: label `学习期扩量素材候选`, but do not call it a
  durable winner yet.

### Day 5: confirmation

- Eliminate persistent bottom-ranked peers when mature evidence and sample support it.
- Continue mixed but improving or partially compliant entities to day 7 with variables
  fixed.
- Promote consistent leaders to a bounded scale-test candidate only when downstream
  guardrails are mature enough; otherwise keep the candidate label.

### Day 7: graduate or stop

- Graduate to the stable pool when the entity has adequate mature sample and an
  interpretable run.
- Mark as a scale candidate when multi-day KPI support, downstream guardrails, and
  headroom are credible.
- Pause/reduce or redesign persistent failures. Do not extend `learning` indefinitely
  to avoid a decision.

At each checkpoint, show rank movement where earlier checkpoints are available.

## 3. Rule two — stable-period pool

Classify entities that graduated from learning or have an established run into one of
four tiers:

### S-优质

Recent 7D exceeds the confirmed KPI targets with multi-day stability, adequate sample,
and no severe concentration or recent deterioration. Rank the strongest durable Ads
first.

### A-稳定

The confirmed KPIs meet target with adequate sample and acceptable multi-day
consistency. Protect or consider bounded scaling subject to creative age and headroom.

### B-波动待优化

Results fluctuate, only some KPIs meet target, or performance hovers near thresholds,
but there is still a specific optimization or controlled-retest path. Keep, cap,
reduce, or test one bounded improvement hypothesis with an exit condition.

### C-劣质

The primary KPI or multiple core KPIs seriously or persistently fail on mature data
with meaningful exposure, or performance sharply deteriorates without credible
recovery. Treat these as risk Ads and rank by avoidable loss and confidence for
pause/reduction review.

Within each tier use the same ordered evidence as the learning pool, then use spend
impact to prioritize operator attention. Every stable-pool row must include a concrete
`决定` column.

## 4. Historical winner deterioration and new-material alert

Flag a creative/material alert when an established Ad has a credible strong historical
control but recent mature performance deteriorates materially and persistently. Show:

- total run age and historically strong window;
- exact recent 7D versus prior 7D or pre-change evidence;
- which funnel stage worsened;
- whether spend, budget, targeting, placement, or audience mix changed;
- fatigue as a hypothesis only when frequency/CPM/CTR/CVR or creative-age evidence
  supports it.

Recommend preparing or testing new material when the decline is not explained by data
lag or an easily reversible traffic change. Protect any still-performing sibling Ads.

## 5. Ranking output invariants

- Show an ordinal rank and the decisive reason; do not present a fake precise score.
- Rank Ads only; do not output Campaign or Ad Group leaderboards.
- Show KPI pass count only alongside the primary KPI and hard guardrails; KPI counts
  alone do not determine quality.
- State whether budget fill uses configured budget or a delivery proxy.
- Recompute rank when mature cohorts arrive; do not retroactively treat immature zeros
  as valid evidence.