Skill: no7dw/ads-performance-analysis (version 0.1.0)

# E-commerce Ad Performance Diagnostic Skill Comprehensive diagnostic framework for identifying and resolving e-commerce advertising performance anomalies through systematic funnel analysis and cross-metric validation. --- ## Core Principles 1. **Funnel-first diagnosis** — Every anomaly must be traced through the complete conversion funnel: 曝光 → 点击 → 加购 → 下单 2. **Cross-metric validation** — Never diagnose based on a single metric. Always validate with correlated indicators 3. **Blocking factors first** — Check data validity and critical blockers (stockout, system errors) before nuanced optimization 4. **Priority-driven triage** — P0 (system failures) → P1 (performance issues) → P2 (efficiency optimization) --- ## Patterns with Signatures ### Pattern 1: High Marketing Cost Ratio 🟡 #### Signature A: Aggressive ROI Bidding **Metrics Pattern**: - ✓ 转化率: Normal - ✓ CTR: Normal - ✗ 营销占比: >50% - ✗ ROAS: Low but positive (1.1-2.9x) **Key Evidence**: - Conversion funnel is **healthy** — problem is purely cost-side - ROI bid settings are **too aggressive** for current market conditions **Likely Cause**: ROI出价设置过激进，成本端问题 **Recommended Action**: 直接降低ROI目标出价，无需改动产品或详情页 **关联诊断**: - 若转化率同时偏低 → see Pattern 2 (转化问题) - 若ROAS接近0 → see Pattern 3 (漏斗断裂) ### Pattern 2: Conversion Funnel Issues 🔴🟠 #### Signature A: Complete Funnel Breakdown (Stockout/System Error) **Metrics Pattern**: - ✓ CTR: Normal (2-4%) - ✓ 加购率: Normal (5-20%) - ✗ 转化率: Zero (0%) - ✗ 库存: Zero or critical low **Key Evidence**: - Traffic and engagement are **healthy** until final conversion step - **Sudden drop** to zero conversion (not gradual decline) - Funnel breaks at 加购 → 下单 stage **Likely Cause**: 商品下架/缺货/价格错误/链接异常 **Recommended Action**: 1. 检查商品是否下架 2. 检查库存是否清零 3. 检查价格是否异常 4. 检查活动设置错误 #### Signature B: Detail Page Blocking (Zero Add-to-Cart) **Metrics Pattern**: - ✓ CTR: Normal - ✗ 加购率: Zero (0%) - ✗ 转化率: Zero - ✓ 曝光/点击: Normal volume **Key Evidence**: - Users reach detail page but **cannot or will not** add to cart - Complete failure at 点击 → 加购 stage - Detail page has **technical or content failure** **Likely Cause**: 详情页内容无法触发加购行为 **Recommended Action**: 1. 检查详情页图片是否加载失败 2. 检查SKU选项是否可选 3. 检查价格是否显示异常 **关联诊断**: - 若库存正常但加购率=0% → 详情页技术问题 - 若CTR也偏低 → see Pattern 4 (定向问题) ### Pattern 3: Low Overall Conversion Rate 🟠 #### Signature A: Detail Page Conversion Weakness **Metrics Pattern**: - ✓ CTR: Normal (3-5%) - ✗ CVR: <2% - ✓ 曝光量: Adequate - ✓ 库存: Available **Key Evidence**: - **主图吸引力OK** (proven by healthy CTR) - Users enter detail page but are **not convinced** to purchase - Problem at 详情页说服 stage **Likely Cause**: 落地页转化弱，产品竞争力不足 **Recommended Action**: 1. 检查详情页卖点是否清晰 2. 检查评价是否有负面积累 3. 检查产品价格与竞品对比是否失去优势 **关联诊断**: - 若CTR同时偏低 → see Pattern 4 Signature B (双重问题) - 若加购率正常但CVR低 → see Pattern 2 Signature A (系统问题) ### Pattern 4: Traffic Quality Issues 🔴🟠 #### Signature A: Poor Ad Targeting (Very Low CTR) **Metrics Pattern**: - ✗ CTR: <2% (基准≥3%) - ✓ 曝光数: Adequate volume - ✗ 点击成本: High due to low relevance - ~ CVR: May be normal or low **Key Evidence**: - **广告曝光充足** but users don't click - Targeting mismatch between ad content and audience - Problem at 曝光 → 点击 stage **Likely Cause**: 主图/定向关键词与受众不匹配 **Recommended Action**: 1. 检查主图与目标受众不符 2. 检查关键词匹配模式过宽 3. 检查广告定向标签错位 4. 优化主图+收窄关键词至精确匹配 #### Signature B: Combined Targeting + Conversion Issues **Metrics Pattern**: - ✗ CTR: <3% - ✗ CVR: <2% - ✗ ROAS: Low (1.2-2.9x) - ✗ 整体效率: Poor across funnel **Key Evidence**: - **双重漏斗损耗** — problems at multiple stages - Both traffic quality AND conversion quality are subpar - Requires **parallel optimization** of targeting and landing page **Likely Cause**: 流量精准度差+详情页说服力弱 **Recommended Action**: 优先收窄广告定向（优化关键词/人群标签）同步改善详情页，二者都需要处理 **关联诊断**: - 若仅CTR低 → see Pattern 4 Signature A (单纯定向问题) - 若仅CVR低 → see Pattern 3 (单纯转化问题) ### Pattern 5: Cost Efficiency Degradation 🟡 #### Signature A: Bidding/Pricing Issue (Non-Funnel) **Metrics Pattern**: - ✓ CTR: Normal - ✓ CVR: Normal - ✗ ROAS: 1-3x (low but not zero) - ✓ 转化流程: Healthy **Key Evidence**: - **流量转化正常** — funnel is working - Problem is **unit economics**, not user behavior - Issue is competitive bidding or pricing strategy **Likely Cause**: 竞价或单价问题，非漏斗问题 **Recommended Action**: Review bidding strategy and competitive pricing position **关联诊断**: - 若转化率也偏低 → see Pattern 3 (复合问题) - 若营销占比>50% → see Pattern 1 (ROI出价问题) --- ## Decision Trees ### Tree 1: Zero Conversion Diagnosis ``` IF 转化率 = 0%: IF 数据量 < 最小样本要求: → 等待更多数据 ELIF 库存 ≤ 0 OR 商品状态 = 下架: → Pattern 2 Signature A (stockout/delisting) ELIF 加购率 = 0%: → Pattern 2 Signature B (detail page blocking) ELIF 加购率 > 0% AND CTR > 0%: → Pattern 2 Signature A (checkout/system error) ELSE: → 需要更多数据验证 ``` ### Tree 2: Low CTR Diagnosis ``` IF CTR < 2%: IF 曝光量 < 1000: → 数据不足，等待更多曝光 ELIF CVR 同时 < 2%: → Pattern 4 Signature B (targeting + conversion) ELIF CVR 正常: → Pattern 4 Signature A (pure targeting issue) ELSE: → 检查主图和关键词匹配度 ``` ### Tree 3: Normal Traffic, Low Conversion ``` IF CTR ≥ 3% AND CVR < 2%: IF 加购率 = 0%: → Pattern 2 Signature B (detail page blocking) ELIF 加购率 > 0% AND 最终转化率 = 0%: → Pattern 2 Signature A (checkout issue) ELIF 加购率 > 0% AND 转化率 > 0% but low: → Pattern 3 Signature A (conversion optimization needed) ELSE: → 深入分析详情页表现 ``` ### Tree 4: Cost Efficiency Issues ``` IF 转化率正常 AND CTR正常: IF 营销占比 > 50%: → Pattern 1 Signature A (ROI bidding too aggressive) ELIF ROAS 1-3x: → Pattern 5 Signature A (competitive/pricing issue) ELSE: → 检查其他成本因素 ``` --- ## Sample Size Validity Gates | Metric | Minimum for Valid Evaluation | |--------|------------------------------| | CTR | ≥1,000 impressions | | CVR | ≥100 clicks OR ≥500 page views | | 加购率 | ≥50 detail page visits | | ROAS | ≥10 conversions OR ≥¥500 spend | | 营销占比 | ≥7 days of data | --- ## Cross-Metric Validation Framework ### Primary Validation Chains 1. **CTR + CVR together** — never evaluate conversion rate without checking traffic quality 2. **库存 + 转化率** — always verify stock availability before diagnosing conversion issues 3. **曝光量 + CTR** — ensure sufficient impression volume before concluding targeting failure 4. **加购率 + 转化率** — distinguish detail page issues from checkout/system issues ### Correlation Patterns - **High CTR + Low CVR** → Detail page problem (Pattern 3) - **Low CTR + Low CVR** → Compound issue (Pattern 4B) - **Normal funnel + High cost** → Bidding issue (Pattern 1 or 5) - **Zero conversion + Normal engagement** → System/stock issue (Pattern 2A) --- ## Detection Checklist ✅ **Step 1: Data Validity Check** - Verify minimum sample sizes per metric - Confirm data freshness (within 24-48 hours) - Check for incomplete data collection periods ✅ **Step 2: Critical Blocker Scan** - Check inventory levels (库存 > 0) - Verify product listing status (not delisted) - Confirm pricing display is normal - Test checkout process functionality ✅ **Step 3: Primary Anomaly Identification** - Identify which metrics are outside normal ranges - Note severity: P0 (zero performance) vs P1 (degraded) vs P2 (suboptimal) ✅ **Step 4: Funnel Stage Localization** - Map anomaly to specific funnel stage: 曝光→点击→加购→下单 - Identify where the breakdown occurs ✅ **Step 5: Cross-Metric Validation** - Validate primary finding with correlated metrics - Rule out alternative explanations - Confirm pattern consistency ✅ **Step 6: Pattern/Signature Matching** - Match observed pattern to documented signatures - Verify all signature criteria are met - Check for mixed patterns requiring multiple actions ✅ **Step 7: Action Prioritization** - Assign priority: P0 (immediate) → P1 (this week) → P2 (optimization) - Confirm recommended actions align with root cause --- ## Common Misattribution Traps ### Trap 1: CTR vs CVR Confusion **Symptoms**: Low overall performance **Wrong Diagnosis**: "Conversion rate is low, need to optimize detail page" **Correct Diagnosis**: Check CTR first — if CTR <2%, the problem is targeting/creative, not detail page ### Trap 2: Cost vs Conversion Issues **Symptoms**: Low ROAS **Wrong Diagnosis**: "Need to improve conversion rate" **Correct Diagnosis**: If conversion rate is normal, problem is bidding strategy or pricing, not funnel optimization ### Trap 3: System Issues vs Performance Issues **Symptoms**: Zero conversions **Wrong Diagnosis**: "Detail page needs optimization" **Correct Diagnosis**: Check stock and system status first — zero conversion with normal engagement = system/stock issue ### Trap 4: Single Metric Diagnosis **Symptoms**: One metric is off **Wrong Diagnosis**: Jump to conclusion based on single metric **Correct Diagnosis**: Always cross-validate with related metrics before diagnosing ### Trap 5: Data Sufficiency Assumption **Symptoms**: Poor performance in early data **Wrong Diagnosis**: Immediate optimization action **Correct Diagnosis**: Verify minimum sample size requirements are met before concluding performance issues ### Trap 6: Composite Pattern Oversimplification **Symptoms**: Both CTR and CVR are low **Wrong Diagnosis**: "Just a general performance issue" **Correct Diagnosis**: Pattern 4B requires **parallel** optimization of both targeting AND conversion — treating as single issue will only solve half the problem --- ## Dimensional Coverage Check ✅ **Data validity gate**: Covered in Sample Size Validity Gates table and Detection Checklist Step 1 ✅ **Category/segment differentiation**: Implied by SKU-level analysis in data rows — different product categories (DT, YMJ, CBD, etc.) may have different baseline performance expectations ✅ **Time dimension**: Covered in validity gates (minimum 7 days for 营销占比) and diagnostic notes mentioning data freshness requirements ✅ **External factors**: Covered extensively in Pattern 2 Signatures (stockout, delisting, pricing errors) and Detection Checklist Step 2 (critical blockers) ⚠️ **SUGGESTED ADDITION**: **Currency/unit normalization** — The current framework assumes single currency analysis. For multi-currency operations, add standardization rules for ROAS calculations and cost comparisons across different markets.

path: README.md
content: # ads-performance-analysis

## description 
Comprehensive diagnostic framework for identifying and resolving e-commerce advertising performance anomalies. Analyzes conversion funnels (impression → click → add-to-cart → order), cross-validates metrics, and maps issues to actionable patterns including high cost ratio, funnel breakdown, traffic quality issues, and bidding inefficiencies.


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