Spillover Estimator
Estimate cross-channel spillover without pretending to prove perfect attribution.
Skill Card
- - Category: Measurement
- Core problem: Did growth in one channel also lift another channel?
- Best for: Operators comparing TikTok, Amazon, DTC, creator, paid, and marketplace channel effects
- Expected input: Source channel data + downstream channel data + timing context
- Expected output: Directional spillover estimate + confidence note + action recommendation
- Creatop handoff: Feed findings into budget allocation and channel planning
Before you run
Ask the user to clarify:
- - source channel to evaluate
- downstream channel(s) to check for spillover
- date range
- major campaign or promo dates
- whether they have exports, screenshots, or CSV data
If structured data is missing, say the result will be directional, not causal proof.
Optional tools / APIs
Useful but not required:
- - Shopify / WooCommerce export
- Amazon sales export
- TikTok Shop export
- ad platform export
- Google Sheets / CSV
If the user does not have APIs connected, ask for manual exports first instead of blocking the workflow.
Workflow
- 1. Confirm channel scope and time window.
- Collect source-channel change signals.
- Collect downstream-channel change signals.
- Align timing around campaigns, creator drops, content bursts, or promo windows.
- Judge whether the downstream lift looks:
- likely related
- weak / mixed
- insufficient evidence
- 6. Explain the estimate with honest caveats.
Output format
Return in this order:
- 1. Executive summary
- Spillover estimate
- Evidence blocks
- Confidence and caveats
- Recommended next step
Fallback mode
If the user only has weekly snapshots, rough screenshots, or partial exports:
- - use simple directional comparison
- do not claim causal attribution
- clearly label missing data and confidence limits
Quality rules
- - Never overclaim causality from timing alone.
- Prefer directional clarity over fake precision.
- Separate channel correlation from verified lift.
- Make the user’s next measurement step obvious.
License
Copyright (c) 2026 Razestar.
This skill is provided under CC BY-NC-SA 4.0 for non-commercial use.
You may reuse and adapt it with attribution to Razestar, and share derivatives
under the same license.
Commercial use requires a separate paid commercial license from Razestar.
No trademark rights are granted.
溢出效应估算器
在不假装能证明完美归因的前提下,估算跨渠道溢出效应。
技能卡片
- - 类别: 衡量
- 核心问题: 一个渠道的增长是否也带动了另一个渠道?
- 最佳适用对象: 比较TikTok、亚马逊、DTC、创作者、付费渠道及电商平台渠道效果的运营人员
- 预期输入: 来源渠道数据 + 下游渠道数据 + 时间背景信息
- 预期输出: 方向性溢出效应估算 + 置信度说明 + 行动建议
- Creatop交接: 将发现结果反馈至预算分配与渠道规划
运行前准备
请用户明确以下信息:
- - 待评估的来源渠道
- 需检查溢出效应的下游渠道
- 日期范围
- 主要营销活动或促销日期
- 是否有导出数据、截图或CSV文件
若缺少结构化数据,需说明结果仅为方向性参考,并非因果证明。
可选工具/API
以下工具非必需但有用:
- - Shopify / WooCommerce 导出
- 亚马逊销售导出
- TikTok Shop 导出
- 广告平台导出
- Google Sheets / CSV
若用户未连接API,请先要求手动导出,而非阻断工作流程。
工作流程
- 1. 确认渠道范围与时间窗口。
- 收集来源渠道变化信号。
- 收集下游渠道变化信号。
- 围绕营销活动、创作者发布、内容爆发或促销窗口进行时间对齐。
- 判断下游增长是否表现为:
- 可能相关
- 较弱/混杂
- 证据不足
- 6. 以诚实的前提说明解释估算结果。
输出格式
按以下顺序返回:
- 1. 执行摘要
- 溢出效应估算
- 证据模块
- 置信度与注意事项
- 建议的下一步行动
降级模式
若用户仅有周度快照、粗略截图或部分导出数据:
- - 使用简单的方向性比较
- 不得声称因果归因
- 清晰标注缺失数据与置信度限制
质量规则
- - 切勿仅凭时间顺序过度声称因果关系。
- 方向性清晰优于虚假精确。
- 区分渠道相关性与已验证的增长。
- 让用户的下一步衡量步骤清晰可见。
许可协议
版权所有 © 2026 Razestar。
本技能根据 CC BY-NC-SA 4.0 协议提供,仅供非商业用途使用。
您可在注明出处为Razestar的前提下重用和改编本技能,并以相同许可协议分享衍生作品。
商业用途需从Razestar另行获取付费商业许可。
不授予任何商标权利。