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.