Campaign Analytics

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.

Table of Contents

Capabilities

Input Requirements

Output Formats

How to Use

Scripts

Reference Guides

Best Practices

Limitations

Capabilities

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

Input Requirements

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

assets/sample_campaign_data.json

for complete examples.

Attribution Analyzer

{

"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

{

"funnel"

:

{

"stages"

:

[

"Awareness"

,

"Interest"

,

"Consideration"

,

"Intent"

,

"Purchase"

]

,

"counts"

:

[

10000

,

5200

,

2800

,

1400

,

420

]

}

}

Campaign ROI Calculator

{

"campaigns"

:

[

{

"name"

:

"Spring Email Campaign"

,

"channel"

:

"email"

,

"spend"

:

5000.00

,

"revenue"

:

25000.00

,

"impressions"

:

50000

,

"clicks"

:

2500

,

"leads"

:

300

,

"customers"

:

45

}

]

}

Output Formats

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 How to Use Attribution Analysis

Run all 5 attribution models

python scripts/attribution_analyzer.py campaign_data.json

Run a specific model

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

JSON output for pipeline integration

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

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

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

Basic funnel analysis

python scripts/funnel_analyzer.py funnel_data.json

JSON output

python scripts/funnel_analyzer.py funnel_data.json --format json Campaign ROI Calculation

Calculate ROI metrics for all campaigns

python scripts/campaign_roi_calculator.py campaign_data.json

JSON output

python scripts/campaign_roi_calculator.py campaign_data.json

--format

json

Scripts

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

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

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 Reference Guides 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 Best Practices 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. Limitations 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.