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attribution-analysis-modeling归因分析建模

Agent Skill

attribution-analysis-modeling 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

588

周安装

24

GitHub Stars

183

下载量

190
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:attribution-analysis-modeling(归因分析建模)
来源仓库:https://github.com/liangdabiao/claude-data-analysis-ultra-main
仓库路径:skills/attribution-analysis-modeling
安装命令:
npx skills add https://github.com/liangdabiao/claude-data-analysis-ultra-main --skill attribution-analysis-modeling
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/liangdabiao/claude-data-analysis-ultra-main --skill attribution-analysis-modeling

简介

attribution-analysis-modeling 提供多渠道营销归因建模与效果分析全套解决方案。

  • 适用于电商、SaaS 等多触点客户旅程追踪与预算分配优化场景。
  • 支持 CSV/JSON/Excel 格式输入,自动解析时间序列路径与转化节点关联关系。
  • 内置线性、首次接触、末次接触及 Shapley 值等多种算法对比输出决策建议。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

Marketing Attribution Analysis & Modeling

A comprehensive attribution analysis skill that evaluates marketing channel effectiveness using advanced statistical models, helping optimize marketing spend and understand customer journey patterns.

Instructions

1. Data Loading and Preparation

When users provide marketing touchpoint data:

  • Load and validate channel interaction data
  • Parse customer journey paths and touchpoint sequences
  • Handle different data formats (CSV, JSON, Excel)
  • Support both user-level and session-level attribution analysis
  • Process timestamp data for chronological path analysis

2. Customer Journey Analysis

  • Reconstruct customer journey paths from touchpoint data
  • Calculate path lengths and conversion patterns
  • Identify common conversion paths and bottlenecks
  • Analyze channel sequencing and order effects
  • Support both online and offline channel attribution

3. Attribution Model Implementation

  • Markov Chain Attribution: Build transition probability matrices and calculate removal effects
  • Shapley Value Attribution: Calculate fair channel contributions using game theory
  • First-Touch Attribution: Assign full credit to the first channel in the path
  • Last-Touch Attribution: Assign full credit to the last channel before conversion
  • Linear Attribution: Distribute credit equally across all channels
  • Time-Decay Attribution: Weight channels based on recency
  • Position-Based Attribution: Weight first and last touches more heavily

4. Channel Performance Analysis

  • Calculate conversion rates by channel and channel combinations
  • Compute ROI and cost-per-acquisition (CPA) for each channel
  • Analyze channel synergy and interaction effects
  • Identify underperforming and overperforming channels
  • Generate channel contribution percentages

5. Visualization and Reporting

  • Create attribution weight distribution charts
  • Generate customer journey path visualizations
  • Build channel transition heatmaps and network graphs
  • Produce ROI analysis and budget allocation recommendations
  • Generate comprehensive attribution reports

Usage Examples

Marketing Channel Attribution

Analyze the effectiveness of our marketing channels:
[CSV with columns: user_id, timestamp, channel, conversion_status, conversion_value]

Digital Campaign Attribution

Calculate attribution for our digital marketing campaigns:
[Marketing touchpoint data with campaign, channel, timestamp, and conversion data]

E-commerce Conversion Attribution

Perform attribution analysis for e-commerce customer journeys:
[Customer path data showing touchpoints before purchase]

Budget Optimization

Help optimize our marketing budget based on attribution results:
[Channel performance data with spend and conversion metrics]

Key Features

Advanced Attribution Models

  • Markov Chain Analysis: Probabilistic model for channel transition analysis
  • Shapley Values: Game theory-based fair attribution calculation
  • Custom Models: Flexible framework for custom attribution logic
  • Model Comparison: Compare different attribution models side-by-side

Customer Journey Analysis

  • Path Reconstruction: Automatically build conversion paths from raw data
  • Touchpoint Sequencing: Analyze order and timing effects
  • Conversion Funnels: Identify drop-off points in customer journeys
  • Multi-path Analysis: Handle customers with multiple conversion paths

Channel Performance Metrics

  • Attribution Weights: Calculate each channel's contribution to conversions
  • ROI Analysis: Compute return on investment for each channel
  • Synergy Effects: Measure how channels work together
  • Incremental Impact: Estimate additional value from channel combinations

Business Intelligence

  • Budget Optimization: Recommend optimal budget allocation
  • Channel Recommendations: Suggest best channel combinations
  • Performance Benchmarks: Compare channel performance against baselines
  • Trend Analysis: Track attribution changes over time

File Requirements

Standard Touchpoint Data Format

user_id,timestamp,channel,conversion_status,conversion_value,cost
USER001,2024-01-15T10:30:00Z,paid_search,0,0,50
USER001,2024-01-16T14:20:00Z,social_media,0,0,30
USER001,2024-01-18T09:15:00Z,email,1,1000,10

Required Fields:

  • user_id: Unique customer identifier
  • timestamp: Touchpoint timestamp (ISO format preferred)
  • channel: Marketing channel or touchpoint
  • conversion_status: Binary indicator of conversion (0/1)
  • conversion_value: Monetary value of conversion (optional)
  • cost: Marketing cost for touchpoint (optional, for ROI analysis)

Supported Channel Types:

  • Digital: paid_search, organic_search, social_media, email, display, video
  • Traditional: tv, radio, print, outdoor, direct_mail
  • E-commerce: marketplace, affiliate, referral
  • Custom: Any channel name can be used

Output Files Generated

  • attribution_results.csv: Complete attribution analysis with channel weights
  • channel_performance.csv: Channel metrics including ROI and CPA
  • customer_paths.csv: Reconstructed customer journey paths
  • transition_matrix.csv: Markov chain transition probability matrix
  • attribution_dashboard.png: Comprehensive visualization dashboard
  • attribution_report.md: Detailed analysis report and recommendations

Dependencies

  • Core Analytics: pandas, numpy, scipy
  • Visualization: matplotlib, seaborn, networkx (for path graphs)
  • Statistical Models: scikit-learn (optional, for advanced models)
  • Data Processing: Standard Python libraries for file operations

Attribution Models Explained

Markov Chain Attribution

Uses probability transition matrices to model customer journey behavior:

  • Calculates removal effect of each channel
  • Considers channel transition probabilities
  • Handles complex multi-path customer journeys
  • Provides incremental value assessment

Shapley Value Attribution

Applies cooperative game theory for fair attribution:

  • Calculates marginal contribution of each channel
  • Considers all possible channel combinations
  • Provides theoretically optimal attribution
  • Handles channel interaction effects

Custom Attribution Models

Flexible framework for business-specific attribution:

  • Configurable weighting rules
  • Time-based decay functions
  • Position-based weighting
  • Custom business logic integration

Business Applications

Marketing Budget Optimization

  • Allocate budget based on true channel contribution
  • Identify underutilized high-performing channels
  • Reduce spend on low-impact channels
  • Test new channel opportunities

Campaign Performance Analysis

  • Evaluate multi-channel campaign effectiveness
  • Understand channel synergy effects
  • Optimize campaign sequencing and timing
  • Measure incremental lift from channel combinations

Customer Journey Optimization

  • Identify optimal channel sequences
  • Remove friction points in conversion paths
  • Enhance high-performing channel combinations
  • Personalize channel selection by customer segment

Advanced Features

Real-time Attribution

  • Process streaming touchpoint data
  • Update attribution weights dynamically
  • Provide real-time channel performance insights
  • Support live campaign optimization

Multi-Conversion Analysis

  • Handle multiple conversion types
  • Analyze different conversion values separately
  • Compare attribution across conversion types
  • Optimize for specific conversion goals

Segmentation Analysis

  • Perform attribution by customer segment
  • Compare channel effectiveness across segments
  • Optimize channel mix by segment
  • Personalize marketing strategies

Best Practices

Data Quality

  • Ensure consistent user identification across touchpoints
  • Maintain accurate timestamp data
  • Include cost data for ROI analysis
  • Handle data gaps and missing values appropriately

Model Selection

  • Choose attribution model based on business goals
  • Compare multiple models for validation
  • Consider customer journey complexity
  • Validate results with business stakeholders

Implementation

  • Start with simpler models before advancing to complex ones
  • Test attribution results against known business outcomes
  • Implement gradual changes based on attribution insights
  • Monitor attribution model performance over time

*This skill transforms complex attribution analysis into actionable marketing insights, helping businesses optimize their marketing spend and understand true channel effectiveness.*

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Antigravity

27.42%
按下载量换算52

Claude Code

21.73%
按下载量换算41

Gemini CLI

19.39%
按下载量换算37

windsurf

13.26%
按下载量换算25

Codex

7.27%
按下载量换算14

OpenCode

3.82%
按下载量换算7

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安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。

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