Daily-Campaign-Agent/.agents/skills/daily-campaign-triage/references/decision-framework.md

Daily campaign triage decision framework

Read this reference before producing a hierarchical intervention and scale queue.

1. Construct comparable windows

For each KPI, let D be that KPI's latest mature date:

• analysis day: D;
• recent 7D: D-6..D using seven mature reporting days;
• previous 7D: the seven mature reporting days immediately before recent 7D;
• 14D context: both complete windows together.

If dates are missing, do not quietly substitute seven non-contiguous rows for seven calendar/reporting days. Explain the coverage and downgrade confidence. Compare only the same metric definition, currency, timezone, attribution window, entity scope, and target regime.

For ratio KPIs, aggregate components:

• CTR = total clicks / total impressions
• CVR = total conversions / configured denominator
• CPI/CPA = total spend / total attributed outcomes
• ROAS = total attributed revenue / total spend
• retention = total retained eligible users / total eligible cohort

Use the user's confirmed formula when it differs.

2. Benchmark hierarchy

Use only confirmed business targets for pass/fail decisions. Apply the narrowest confirmed target whose scope matches the entity, for example:

1. Ad- or Ad Group-specific target or hard guardrail;
2. Campaign-specific target or hard guardrail;
3. confirmed target for the matching product/objective/geo/account;
4. confirmed account-wide target.

Historical Campaign, peer, or account performance may explain direction and context, but is not a business target. Without a confirmed target, label findings 无业务 KPI，仅分析趋势 and do not place an entity in intervention or scale buckets solely from relative performance.

3. Diagnose the hierarchy before choosing an action

Use this sequence when the report contains the dimensions:

1. Portfolio/account: quantify daily spend attainment, total risk/opportunity, and which Campaigns concentrate it.
2. Campaign: identify weak, mixed, and strong Campaign aggregates, then measure how performance is distributed across their Ad Groups and Ads.
3. Ad Group: decide whether the issue is shared across material child Ads or concentrated in a few Ads.
4. Ad: classify concrete losers, uncertain Ads, and durable winners.

Campaign averages are routing evidence. They are not sufficient for a Campaign-pause recommendation when material child Ads disagree. Quantify concentration with spend, outcome, and retained/revenue shares where possible.

Prefer the lowest entity whose evidence supports the proposed lever. If the platform cannot change budget or status at Ad level, keep the Ad diagnosis but express the human action at the lowest supported parent level and explain the constraint.

4. Classify evidence at each relevant entity

Start with the primary KPI and then apply secondary KPI, delivery, and data-quality modifiers. Apply these classifications independently to Campaign, Ad Group, and Ad; do not inherit a parent's verdict without computing the child's evidence.

Underperformance

• Likely one-day noise: D misses target, but recent 7D still meets it or remains stable, same-direction misses are not a majority, and no material risk is worsening.
• Mixed/emerging risk: D and recent 7D disagree, multiple KPIs conflict, or the recent window deteriorates without enough consistent evidence.
• Persistent underperformance: recent 7D misses target, at least 4 of 7 mature days miss in the same direction, and previous 7D does not show credible recovery.
• Sharp deterioration: recent 7D materially worsens versus previous 7D while spend is stable or rising, even when the 14D aggregate hides the turn.

4 of 7 is an evidence-consistency default, not a KPI. Replace it when the user has configured a different persistence rule.

Good performance

• One-day spike: D looks strong but recent 7D, sample, or concentration does not support it.
• Improving candidate: recent 7D improves and approaches or clears target, but consistency, maturity, or delivery headroom is not proven.
• Durable winner: recent 7D meets target, at least 4 of 7 mature days support it, previous 7D is not contradictory, sample is sufficient, no hard guardrail is breached, and results are not dominated by one event or segment.

5. Handle daily spend and pacing

At the configured scope calculate:

• attainment = actual spend / target spend;
• shortfall = max(target spend - actual spend, 0);
• overage = max(actual spend - target spend, 0).

Label whether the spend target is a minimum, desired level, cap, or range. When spend is below a minimum/desired target, identify how much credible capacity exists among durable winners. Never solve a pacing gap by scaling entities that fail efficiency, quality, compliance, or data guardrails. If verified winner capacity is smaller than the gap, report the unfilled remainder and, when appropriate, propose bounded exploration rather than forced scale.

When spend meets or exceeds target, scaling one winner may require reallocating from losers rather than increasing portfolio spend. State that distinction.

6. Apply decision modifiers

Check every candidate for:

• spend exposure and estimated avoidable loss/opportunity;
• minimum sample and cohort maturity;
• launch age and recent budget, bid, creative, targeting, status, or tracking changes;
• pacing, budget fill, learning limits, and delivery headroom;
• funnel conflict, such as cheap acquisition with poor retention or weak ROAS;
• concentration in a day, creative, placement, geo, device, or large revenue event;
• possible creative fatigue supported by frequency/CPM/CTR/CVR evidence;
• portfolio effects, shared budgets, audience overlap, or cannibalization;
• data integrity and attribution lag;
• prior intervention outcomes when available.

Phrase unsupported causal explanations as hypotheses. A recent intervention normally needs time to mature; extreme ongoing exposure can still justify urgent review.

When a recent change, scale event, restructuring, former winner, or rescue question is material to the verdict, apply the phase-aware method in change-response.md before choosing the action.

For the daily brief, do not mix new and mature entities in one leaderboard. Apply the two-pool method in learning-stable-ranking.md.

7. Choose the intervention state

Use the smallest state that matches the evidence:

• Pause/reduce: persistent mature failure with adequate exposure and no credible recent-change or data-lag explanation.
• Rollback/recovery: deterioration follows a known intervention and a credible prior control can be restored without sacrificing protected winners.
• Hold/protect: evidence is promising or mixed, but changing variables now would destroy the ability to learn; keep the entity stable until the next mature check.
• Clean-control retest: structure or traffic variables are entangled; preserve a control and isolate one hypothesis per test cell.
• Scale candidate: durable winner evidence survives maturity, concentration, and downstream guardrail checks.

Rollback, hold, and retest are explicit actions, not vague observation. State which variables stay fixed, the mature observation window, and the exit condition.

Pause/reduction candidate

Require persistent mature Ad-level underperformance, meaningful spend exposure, adequate confirmed sample, and no unresolved attribution, tracking, or recent-change explanation. A single bad day or low-sample zero outcome belongs in observation.

When only some Ads fail, protect the good Ads and recommend intervention on the named losers. Move to Ad Group only when most material Ads share the problem or the lever is available only there.

Campaign-wide pause candidate

Treat this as exceptional. Require one of:

• broad persistent failure across the Campaign's material Ad Groups and Ads;
• a Campaign-level structural constraint such as wrong objective/targeting, compliance risk, broken tracking, or unsafe delivery;
• no durable child winner worth protecting and material ongoing loss.

Show the child-level evidence and spend coverage. Do not recommend Campaign pause from the aggregate alone.

Scale candidate

Require durable mature Ad-level KPI performance, adequate confirmed sample, at least the configured consistency rule, no hard downstream guardrail breach, no severe result concentration, and plausible delivery headroom. A good day or a good Campaign average does not qualify an Ad.

Keep the recommendation platform-neutral. If direct Ad budget scaling is unsupported, suggest the applicable controlled lever—parent allocation, bid/target adjustment, duplication/test, or creative expansion—without claiming execution. Do not invent a scale percentage when no growth rule is configured.

8. Prioritize the human queue

Use three explicit judgments instead of a fake precise score:

• Impact: avoidable loss/opportunity or share of spend affected.
• Confidence: target validity, maturity, sample, consistency, and data quality.
• Urgency: whether delay increases loss or blocks safe interpretation.

Suggested priorities:

• P0: tracking, data, or delivery fault affecting material live spend.
• P1: persistent/high-confidence Ad or Ad Group underperformance, rapid deterioration with material exposure, or broad child-level failure supporting a Campaign review.
• P2: mixed evidence, pending intervention, low sample, or a durable Ad winner that needs an operator decision.
• P3: no material action; summarize only.

Do not recommend a generic pause because one KPI missed. Match the human review to the entity and evidence: verify tracking, inspect a breakdown, protect a good Ad, cap/reduce/pause a named loser, restore a control, refresh creative, adjust targeting/bid, reallocate, or run a controlled scale test. Quantify how a proposed pause or rollback changes the pacing gap, and do not nominate an unstable early winner to compensate. These remain recommendations, not executed actions.

9. Recommended row shape

Priority	Entity level	Campaign > Ad Group > Ad	State	D vs target	Recent 7D	Previous 7D	Change / pre-post	Spend/sample	Evidence and risk	Human action	Recheck/exit condition

Each row should make clear what changed, against which confirmed target, over which exact mature window, with how much spend/sample, why this entity—not merely its parent—owns the action, and why the next review follows.