campaign-analytics

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Campaign Analytics

营销活动分析

Production-grade campaign performance analysis with multi-touch attribution modeling, funnel conversion analysis, and ROI calculation. Three Python CLI tools provide deterministic, repeatable analytics using standard library only -- no external dependencies, no API calls, no ML models.

生产级别的营销活动表现分析工具，支持多触点归因建模、漏斗转化分析和ROI计算。三款Python CLI工具仅使用标准库即可提供可确定、可重复的分析结果——无需外部依赖、无需API调用、无需机器学习模型。

Capabilities

功能特性

Multi-Touch Attribution: Five attribution models (first-touch, last-touch, linear, time-decay, position-based) with configurable parameters
Funnel Conversion Analysis: Stage-by-stage conversion rates, drop-off identification, bottleneck detection, and segment comparison
Campaign ROI Calculation: ROI, ROAS, CPA, CPL, CAC metrics with industry benchmarking and underperformance flagging
A/B Test Support: Templates for structured A/B test documentation and analysis
Channel Comparison: Cross-channel performance comparison with normalized metrics
Executive Reporting: Ready-to-use templates for campaign performance reports

多触点归因：五种归因模型（首次触点、末次触点、线性、时间衰减、位置加权），支持参数配置
漏斗转化分析：分阶段转化率、流失点识别、瓶颈检测和细分群体对比
营销活动ROI计算：ROI、ROAS、CPA、CPL、CAC等指标，支持行业基准对比和表现不佳活动标记
A/B测试支持：结构化A/B测试文档和分析模板
渠道对比：跨渠道表现对比，指标已标准化
高管报告：可直接使用的营销活动表现报告模板

Input Requirements

输入要求

All scripts accept a JSON file as positional input argument. See

assets/sample_campaign_data.json

for complete examples.

所有脚本均接受JSON文件作为位置输入参数。完整示例请查看

assets/sample_campaign_data.json

。

Attribution Analyzer

归因分析器

json

{
  "journeys": [
    {
      "journey_id": "j1",
      "touchpoints": [
        {"channel": "organic_search", "timestamp": "2025-10-01T10:00:00", "interaction": "click"},
        {"channel": "email", "timestamp": "2025-10-05T14:30:00", "interaction": "open"},
        {"channel": "paid_search", "timestamp": "2025-10-08T09:15:00", "interaction": "click"}
      ],
      "converted": true,
      "revenue": 500.00
    }
  ]
}

json

{
  "journeys": [
    {
      "journey_id": "j1",
      "touchpoints": [
        {"channel": "organic_search", "timestamp": "2025-10-01T10:00:00", "interaction": "click"},
        {"channel": "email", "timestamp": "2025-10-05T14:30:00", "interaction": "open"},
        {"channel": "paid_search", "timestamp": "2025-10-08T09:15:00", "interaction": "click"}
      ],
      "converted": true,
      "revenue": 500.00
    }
  ]
}

Funnel Analyzer

漏斗分析器

json

{
  "funnel": {
    "stages": ["Awareness", "Interest", "Consideration", "Intent", "Purchase"],
    "counts": [10000, 5200, 2800, 1400, 420]
  }
}

json

{
  "funnel": {
    "stages": ["Awareness", "Interest", "Consideration", "Intent", "Purchase"],
    "counts": [10000, 5200, 2800, 1400, 420]
  }
}

Campaign ROI Calculator

营销活动ROI计算器

json

{
  "campaigns": [
    {
      "name": "Spring Email Campaign",
      "channel": "email",
      "spend": 5000.00,
      "revenue": 25000.00,
      "impressions": 50000,
      "clicks": 2500,
      "leads": 300,
      "customers": 45
    }
  ]
}

json

{
  "campaigns": [
    {
      "name": "Spring Email Campaign",
      "channel": "email",
      "spend": 5000.00,
      "revenue": 25000.00,
      "impressions": 50000,
      "clicks": 2500,
      "leads": 300,
      "customers": 45
    }
  ]
}

Output Formats

输出格式

All scripts support two output formats via the

--format

flag:

```
--format text
```
(default): Human-readable tables and summaries for review
```
--format json
```
: Machine-readable JSON for integrations and pipelines

所有脚本通过

--format

标志支持两种输出格式：

```
--format text
```
（默认）：便于查看的人类可读表格和摘要
```
--format json
```
：便于集成和流水线处理的机器可读JSON格式

How to Use

使用方法

Attribution Analysis

归因分析

bash

undefined

bash

undefined

Run all 5 attribution models

运行全部5种归因模型

python scripts/attribution_analyzer.py campaign_data.json

Run a specific model

运行特定模型

python scripts/attribution_analyzer.py campaign_data.json --model time-decay

JSON output for pipeline integration

输出JSON格式用于流水线集成

python scripts/attribution_analyzer.py campaign_data.json --format json

Custom time-decay half-life (default: 7 days)

自定义时间衰减半衰期（默认：7天）

python scripts/attribution_analyzer.py campaign_data.json --model time-decay --half-life 14

undefined

python scripts/attribution_analyzer.py campaign_data.json --model time-decay --half-life 14

undefined

Funnel Analysis

漏斗分析

bash

undefined

bash

undefined

Basic funnel analysis

基础漏斗分析

python scripts/funnel_analyzer.py funnel_data.json

JSON output

输出JSON格式

python scripts/funnel_analyzer.py funnel_data.json --format json

undefined

python scripts/funnel_analyzer.py funnel_data.json --format json

undefined

Campaign ROI Calculation

营销活动ROI计算

bash

undefined

bash

undefined

Calculate ROI metrics for all campaigns

计算所有营销活动的ROI指标

python scripts/campaign_roi_calculator.py campaign_data.json

JSON output

输出JSON格式

python scripts/campaign_roi_calculator.py campaign_data.json --format json

---

python scripts/campaign_roi_calculator.py campaign_data.json --format json

---

Scripts

脚本说明

1. attribution_analyzer.py

Implements five industry-standard attribution models to allocate conversion credit across marketing channels:

Model	Description	Best For
First-Touch	100% credit to first interaction	Brand awareness campaigns
Last-Touch	100% credit to last interaction	Direct response campaigns
Linear	Equal credit to all touchpoints	Balanced multi-channel evaluation
Time-Decay	More credit to recent touchpoints	Short sales cycles
Position-Based	40/20/40 split (first/middle/last)	Full-funnel marketing

实现五种行业标准归因模型，用于在营销渠道间分配转化功劳：

模型	描述	适用场景
First-Touch	100%功劳归于首次互动	品牌认知类营销活动
Last-Touch	100%功劳归于末次互动	直接响应类营销活动
Linear	所有触点功劳均等	平衡的多渠道评估
Time-Decay	最近的触点获得更多功劳	短销售周期场景
Position-Based	40/20/40分配（首次/中间/末次）	全漏斗营销场景

2. funnel_analyzer.py

Analyzes conversion funnels to identify bottlenecks and optimization opportunities:

Stage-to-stage conversion rates and drop-off percentages
Automatic bottleneck identification (largest absolute and relative drops)
Overall funnel conversion rate
Segment comparison when multiple segments are provided

分析转化漏斗以识别瓶颈和优化机会：

分阶段转化率和流失百分比
自动识别瓶颈（绝对和相对流失最大的环节）
整体漏斗转化率
提供多个细分群体时支持群体对比

3. campaign_roi_calculator.py

Calculates comprehensive ROI metrics with industry benchmarking:

ROI: Return on investment percentage
ROAS: Return on ad spend ratio
CPA: Cost per acquisition
CPL: Cost per lead
CAC: Customer acquisition cost
CTR: Click-through rate
CVR: Conversion rate (leads to customers)
Flags underperforming campaigns against industry benchmarks

计算全面的ROI指标并提供行业基准对比：

ROI：投资回报率百分比
ROAS：广告支出回报率
CPA：客户获取成本
CPL：线索获取成本
CAC：用户获取成本
CTR：点击率
CVR：转化率（线索到客户）
标记与行业基准相比表现不佳的营销活动

Reference Guides

参考指南

Guide	Location	Purpose
Attribution Models Guide	`references/attribution-models-guide.md`	Deep dive into 5 models with formulas, pros/cons, selection criteria
Campaign Metrics Benchmarks	`references/campaign-metrics-benchmarks.md`	Industry benchmarks by channel and vertical for CTR, CPC, CPM, CPA, ROAS
Funnel Optimization Framework	`references/funnel-optimization-framework.md`	Stage-by-stage optimization strategies, common bottlenecks, best practices

指南	位置	用途
归因模型指南	`references/attribution-models-guide.md`	深入讲解5种模型的公式、优缺点和选择标准
营销活动指标基准	`references/campaign-metrics-benchmarks.md`	按渠道和垂直领域划分的行业基准，包括CTR、CPC、CPM、CPA、ROAS等
漏斗优化框架	`references/funnel-optimization-framework.md`	分阶段优化策略、常见瓶颈和最佳实践

Best Practices

最佳实践

Use multiple attribution models -- No single model tells the full story. Compare at least 3 models to triangulate channel value.
Set appropriate lookback windows -- Match your time-decay half-life to your average sales cycle length.
Segment your funnels -- Always compare segments (channel, cohort, geography) to identify what drives best performance.
Benchmark against your own history first -- Industry benchmarks provide context, but your own historical data is the most relevant comparison.
Run ROI analysis at regular intervals -- Weekly for active campaigns, monthly for strategic review.
Include all costs -- Factor in creative, tooling, and labor costs alongside media spend for accurate ROI.
Document A/B tests rigorously -- Use the provided template to ensure statistical validity and clear decision criteria.

使用多种归因模型——单一模型无法完整呈现全貌。至少对比3种模型以精准评估渠道价值。
设置合适的回溯窗口——将时间衰减半衰期与平均销售周期长度匹配。
细分漏斗——始终对比不同细分群体（渠道、同期群、地域）以识别驱动最佳表现的因素。
先与自身历史数据对比——行业基准提供参考，但自身历史数据是最相关的对比依据。
定期进行ROI分析——活跃营销活动每周分析一次，战略回顾每月一次。
包含所有成本——除媒体支出外，还需计入创意、工具和人力成本以获得准确的ROI。
严谨记录A/B测试——使用提供的模板确保统计有效性和清晰的决策标准。

Limitations

局限性

No statistical significance testing -- A/B test analysis requires external tools for p-value calculations. Scripts provide descriptive metrics only.
Standard library only -- No advanced statistical or data processing libraries. Suitable for most campaign sizes but not optimized for datasets exceeding 100K journeys.
Offline analysis -- Scripts analyze static JSON snapshots. No real-time data connections or API integrations.
Single-currency -- All monetary values assumed to be in the same currency. No currency conversion support.
Simplified time-decay -- Uses exponential decay based on configurable half-life. Does not account for weekday/weekend or seasonal patterns.
No cross-device tracking -- Attribution operates on provided journey data as-is. Cross-device identity resolution must be handled upstream.

无统计显著性测试——A/B测试分析需要外部工具进行p值计算。脚本仅提供描述性指标。
仅使用标准库——无高级统计或数据处理库。适用于大多数规模的营销活动，但未针对超过10万条用户旅程的数据集优化。
离线分析——脚本分析静态JSON快照。无实时数据连接或API集成。
单一货币——所有货币值假定为同一币种。不支持货币转换。
简化的时间衰减——基于可配置半衰期的指数衰减。未考虑工作日/周末或季节性模式。
无跨设备追踪——归因基于提供的用户旅程数据进行。跨设备身份解析需在前置环节处理。

campaign-analytics

Original

Translation

Campaign Analytics

营销活动分析

Table of Contents

目录

Capabilities

功能特性

Input Requirements

输入要求

Attribution Analyzer

归因分析器

Funnel Analyzer

漏斗分析器

Campaign ROI Calculator

营销活动ROI计算器

Output Formats

输出格式

How to Use

使用方法

Attribution Analysis

归因分析

Run all 5 attribution models

运行全部5种归因模型

Run a specific model

运行特定模型

JSON output for pipeline integration

输出JSON格式用于流水线集成

Custom time-decay half-life (default: 7 days)

自定义时间衰减半衰期（默认：7天）

Funnel Analysis

漏斗分析

Basic funnel analysis

基础漏斗分析

JSON output

输出JSON格式

Campaign ROI Calculation

营销活动ROI计算

Calculate ROI metrics for all campaigns

计算所有营销活动的ROI指标

JSON output

输出JSON格式

Scripts

脚本说明

1. attribution_analyzer.py

1. attribution_analyzer.py

2. funnel_analyzer.py

2. funnel_analyzer.py

3. campaign_roi_calculator.py

3. campaign_roi_calculator.py

Reference Guides

参考指南

Best Practices

最佳实践

Limitations

局限性