Creator Attribution Lite
Skill Card
- - Category: Measurement
- Core problem: Which content actions actually move outcomes?
- Best for: Small-team performance review
- Expected input: Content list + campaign metadata + simple outcome fields
- Expected output: Lightweight attribution view + insight notes
- Creatop handoff: Feed winning patterns back to Creatop templates
What this does
Show which content likely moved business outcomes, not just vanity metrics.
Workflow
1) Validate data availability
Minimum fields per content item:
- - content ID + date + platform
- views/watch metric
- click metric
- downstream metric (lead/signup/sale/GMV)
If real data is missing, run simulation mode and label outputs clearly as demo/synthetic.
2) Map funnel stage
Classify each content item as:
- - awareness
- consideration
- conversion
3) Compute lightweight impact score
Use transparent weighted components:
- - engagement quality
- click intent
- conversion signal
Explain formula and normalization assumptions.
4) Output action decisions
Return:
- - ranked performers
- pause/optimize list
- next 3 content bets with rationale
- confidence note (sample size/data quality)
Quality rules
- - Keep model explainable and auditable.
- Avoid fake precision on tiny samples.
- Do not overclaim causality; treat as directional evidence.
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.
创作者归因精简版
技能卡片
- - 类别: 衡量
- 核心问题: 哪些内容行为真正推动了业务成果?
- 最佳适用场景: 小型团队绩效评估
- 预期输入: 内容列表 + 活动元数据 + 简单成果字段
- 预期输出: 轻量级归因视图 + 洞察笔记
- Creatop交接: 将获胜模式反馈至Creatop模板
功能说明
展示哪些内容可能推动了业务成果,而不仅仅是虚荣指标。
工作流程
1) 验证数据可用性
每条内容项的最低字段要求:
- - 内容ID + 日期 + 平台
- 观看/浏览指标
- 点击指标
- 下游指标(线索/注册/销售/商品交易总额)
如果缺少真实数据,则运行模拟模式,并明确标注输出结果为演示/合成数据。
2) 映射漏斗阶段
将每条内容项分类为:
3) 计算轻量级影响力评分
使用透明的加权组件:
说明公式和归一化假设。
4) 输出行动决策
返回:
- - 排名表现者
- 暂停/优化列表
- 下一步3个内容押注及理由
- 置信度说明(样本量/数据质量)
质量规则
- - 保持模型可解释和可审计。
- 避免对小样本进行虚假精确计算。
- 不过度声称因果关系;将其视为方向性证据。
许可协议
版权 © 2026 Razestar。
本技能根据 CC BY-NC-SA 4.0 提供,仅限非商业用途。
您可以在注明Razestar出处的前提下重用和改编本技能,并在相同许可协议下分享衍生作品。
商业用途需要从Razestar另行购买付费商业许可。
不授予任何商标权。