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Ad Bidding Strategies

广告竞价策略

Overview

概述

Bidding strategies determine how much an advertiser pays per auction. Range from manual CPC (full control) to automated strategies (Target CPA, Target ROAS, Maximize Conversions) that use ML to optimize bids in real-time based on contextual signals.
竞价策略决定了广告主每次竞价的支付金额。涵盖从手动CPC（完全可控）到自动策略（Target CPA、Target ROAS、最大化转化）的多种类型，自动策略利用ML基于上下文信号实时优化竞价。

When to Use

适用场景

Trigger conditions:
  • Choosing between manual and automated bidding strategies
  • Setting up or troubleshooting Target CPA / Target ROAS campaigns
  • Analyzing bid strategy performance and making adjustments
When NOT to use:
  • When designing the auction mechanism itself (use GSP/VCG)
  • When building a CTR prediction model (use CTR prediction skill)
触发条件：
  • 在手动与自动竞价策略间做选择
  • 设置或排查Target CPA / Target ROAS广告系列
  • 分析竞价策略表现并进行调整
不适用场景：
  • 设计竞价机制本身时（使用GSP/VCG）
  • 构建CTR预测模型时（使用CTR预测技能）

Algorithm

算法

IRON LAW: Automated Bidding Requires SUFFICIENT Conversion Data
Below ~30 conversions/month, the algorithm lacks signal and performs
WORSE than manual bidding. Strategy selection depends on data volume:
- < 30 conv/month: Manual CPC or Maximize Clicks
- 30-50 conv/month: Maximize Conversions
- 50+ conv/month: Target CPA
- 50+ conv/month + revenue data: Target ROAS
IRON LAW: Automated Bidding Requires SUFFICIENT Conversion Data
Below ~30 conversions/month, the algorithm lacks signal and performs
WORSE than manual bidding. Strategy selection depends on data volume:
- < 30 conv/month: Manual CPC or Maximize Clicks
- 30-50 conv/month: Maximize Conversions
- 50+ conv/month: Target CPA
- 50+ conv/month + revenue data: Target ROAS

Phase 1: Input Validation

阶段1：输入验证

Assess: monthly conversion volume, conversion tracking accuracy, campaign budget, business goal (volume vs efficiency vs revenue). Gate: Conversion tracking verified, sufficient data for chosen strategy.
评估：月度转化量、转化追踪准确性、广告系列预算、业务目标（量 vs 效率 vs 收入）。 准入条件： 转化追踪已验证，所选策略具备充足数据。

Phase 2: Core Algorithm

阶段2：核心算法

Manual CPC: Set bid per keyword. Adjust based on: device, time, location, audience performance data.
Target CPA: 1. Set target cost-per-acquisition. 2. Algorithm predicts conversion probability per auction using contextual signals. 3. Bids up for high-probability conversions, down for low. 4. Aims to average at target CPA over time.
Target ROAS: Same as CPA but optimizes for return on ad spend = conversion_value / cost.
手动CPC： 为每个关键词设置竞价。基于设备、时间、地域、受众表现数据进行调整。
Target CPA： 1. 设置目标单次转化成本。2. 算法利用上下文信号预测每次竞价的转化概率。3. 为高概率转化提高竞价，为低概率转化降低竞价。4. 旨在长期达到目标CPA的平均值。
Target ROAS： 与CPA逻辑相同，但优化目标为广告支出回报率 = 转化价值 / 成本。

Phase 3: Verification

阶段3：验证

Monitor: actual CPA vs target, conversion volume stability, impression share changes, budget utilization. Gate: Actual CPA within 20% of target after learning period (2-4 weeks).
监控：实际CPA vs 目标值、转化量稳定性、展示份额变化、预算利用率。 准入条件： 学习期（2-4周）结束后，实际CPA与目标值偏差在20%以内。

Phase 4: Output

阶段4：输出

Return strategy recommendation with expected performance ranges.
返回策略建议及预期表现范围。

Output Format

输出格式

json
{
  "recommendation": {"strategy": "target_cpa", "target": 500, "currency": "TWD", "confidence": "high"},
  "expected_performance": {"cpa_range": [400, 600], "volume_change": "-10% to +15%"},
  "metadata": {"monthly_conversions": 85, "current_cpa": 550, "learning_period_days": 14}
}
json
{
  "recommendation": {"strategy": "target_cpa", "target": 500, "currency": "TWD", "confidence": "high"},
  "expected_performance": {"cpa_range": [400, 600], "volume_change": "-10% to +15%"},
  "metadata": {"monthly_conversions": 85, "current_cpa": 550, "learning_period_days": 14}
}

Examples

示例

Sample I/O

示例输入输出

Input: E-commerce campaign, 120 conversions/month, current CPA=NT$450, goal: maintain CPA, increase volume Expected: Target CPA at NT$450. Expected: volume +10-20% as algorithm finds efficient auctions.
输入： 电商广告系列，月度转化120次，当前CPA=新台币450元，目标：维持CPA，提升转化量 预期： 设置Target CPA为新台币450元。预期：转化量提升10-20%，因算法可找到更高效的竞价机会。

Edge Cases

边缘案例

InputExpectedWhy
10 conversions/monthManual CPCInsufficient data for automation
Target CPA too aggressiveVolume drops to near zeroAlgorithm can't find profitable auctions
Conversion tracking brokenAll strategies failGarbage data → garbage optimization
输入预期原因
月度转化10次手动CPC自动化所需数据不足
Target CPA设置过于激进转化量近乎归零算法无法找到盈利的竞价机会
转化追踪失效所有策略均失效错误数据→错误优化

Gotchas

注意事项

  • Learning period volatility: First 2 weeks after switching strategies show unstable performance. Don't change targets during this period.
  • Conversion delay: If conversions take days to attribute (e.g., B2B), the algorithm optimizes on stale data. Use conversion modeling or extend the attribution window.
  • Budget as a constraint: Target CPA won't spend if it can't hit the target. Setting an aggressive CPA with a large budget doesn't increase spend — it just saves money.
  • Micro-conversions: If training on a proxy conversion (add to cart) instead of final purchase, the algorithm optimizes for the proxy. Ensure the tracked conversion aligns with business value.
  • Seasonality shocks: Automated bidding learns from recent data. Black Friday, holidays, or competitive events can throw it off. Use seasonality adjustments.
  • 学习期波动： 切换策略后的前2周表现不稳定。此期间不要修改目标值。
  • 转化延迟： 如果转化需要数天才能归因（如B2B场景），算法会基于过时数据进行优化。使用转化建模或延长归因窗口。
  • 预算限制： 若无法达到目标值，Target CPA不会消耗预算。设置激进的CPA值搭配大额预算不会增加支出——只会节省资金。
  • 微转化： 如果基于替代转化（如加入购物车）而非最终购买进行训练，算法会针对替代转化进行优化。确保追踪的转化与业务价值对齐。
  • 季节性冲击： 自动竞价会从近期数据中学习。黑色星期五、节假日或竞争活动可能导致其失效。使用季节性调整。

References

参考资料

  • For bid strategy migration playbook, see 
    references/migration-playbook.md
  • For learning period best practices, see 
    references/learning-period.md
  • 竞价策略迁移手册，请查看
    references/migration-playbook.md
  • 学习期最佳实践，请查看
    references/learning-period.md