Six models and one anti-model. None is right. They are all approximations. The discipline is picking one, committing, and reading the others as sanity checks.
Last-click. Simple, reproducible, undercredits awareness. The conversion is fully credited to the last click before the conversion event. Easy to compute; easy to compare across channels; bad for understanding upper-funnel contribution.
First-click. Opposite bias. Fully credits the first touchpoint, undercredits closing channels. Useful as a sanity check against last-click; rarely the right primary view.
Linear. Equal credit across all touchpoints. Gives every channel something. Defensible; not informative. Most useful for board reporting where avoiding "Google gets 70% so we cut Meta" politics matters more than precision.
Time-decay. More credit to recent touchpoints. Reflects the intuition that recent ads are more influential. Hard to argue against; hard to verify.
U-shaped (position-based). Heavy on first and last (40% each), light on middle (20% distributed). Honors both opener and closer roles. The default in many MTA tools.
Data-driven attribution (DDA, Google). Machine-learning model that distributes credit based on observed conversion paths. Opaque; hard to audit. The closest to "right" for digital channels but a black box.
Marketing mix modeling (MMM). Regression-based, top-down. Uses spend and revenue time series across channels to estimate channel contributions. Requires 2+ years of data. The strongest defense against platform self-attribution because it does not rely on platform-reported conversions at all.
The anti-model: trusting platform-reported attribution. Each platform's "DDA" or "attributed conversions" is the platform's self-attribution. Sum across platforms exceeds reality. Use platform attribution for in-flight optimization within the platform; use a unified attribution model for cross-channel decisions.
Practical guidance.
- Early-stage. Use last-click plus a single guardrail metric (warehouse-attributed CAC). Sophisticated attribution requires data volume you do not have.
- Mid-stage. Data-driven attribution from Google plus GA4, with explicit awareness vs closing channel labeling.
- Mature. MMM as the canonical incremental reference. MTA for in-flight optimization. Last-click for channel-level decisions where ambiguity is acceptable.
Detail and a decision matrix in
references/attribution-model-comparison.md
.
六种模型和一种反模型。没有一种是绝对正确的，它们都是近似值。准则是选择一种模型并坚持使用，将其他模型作为 sanity check（合理性验证）。
最后点击归因：简单、可复现，但低估了品牌认知的贡献。转化的全部信用归于转化事件前的最后一次点击。计算简单，便于跨渠道对比，但不利于理解漏斗上层的贡献。
首次点击归因：与最后点击归因相反，将全部信用归于第一个触点，低估了收尾渠道的作用。可作为最后点击归因的合理性验证，但很少作为主要参考模型。
线性归因：所有触点平分信用。每个渠道都能获得一定认可，有说服力但缺乏信息量。最适合用于董事会汇报，此时避免“Google占70%所以砍掉Meta”这类争议比精度更重要。
时间衰减归因：越近期的触点获得越多信用，反映了“近期广告影响力更大”的直觉。难以反驳，但也难以验证。
U型（基于位置）归因：首尾触点各占40%，中间触点共占20%。兼顾引流和收尾角色，是许多MTA工具的默认模型。
数据驱动归因（DDA，Google）：基于观察到的转化路径分配信用的机器学习模型。不透明，难以审计。是最接近数字渠道“正确”结果的模型，但属于黑箱。
营销组合模型（MMM）：基于回归的自上而下模型，利用跨渠道的支出和收入时间序列估算渠道贡献。需要2年以上的数据。是对抗平台自我归因的最强手段，因为它完全不依赖平台报告的转化数据。
反模型：信任平台报告的归因：每个平台的“DDA”或“归因转化”都是平台的自我归因。跨平台数据相加会超过真实值。平台归因仅适用于平台内的实时优化；跨渠道决策需使用统一归因模型。
实用指南：
- 早期阶段：使用最后点击归因，搭配一个单一的保障指标（仓库归因的CAC）。复杂归因需要的数据量你暂时没有。
- 中期阶段：使用Google的数据驱动归因搭配GA4，明确标注品牌认知渠道和收尾渠道。
- 成熟阶段：将MMM作为标准的增量参考，MTA用于实时优化，最后点击归因用于可接受模糊性的渠道级决策。
详情和决策矩阵见
references/attribution-model-comparison.md
。