campaign-analytics

原文涵盖的主题

• Campaign Analytics
• Input Requirements
• Attribution Analyzer
• Funnel Analyzer
• Campaign ROI Calculator
• Input Validation
• Output Formats
• Typical Analysis Workflow

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. -- All scripts accept a JSON file as positional input argument. See assets/samplecampaigndata.json for complete examples. Before running scripts, verify your JSON is valid and matches the expected schema. Common errors: Missing required keys (e.g., journeys, funnel.stages, campaigns) → script exits with a descriptive KeyError Mismatched array lengths in funnel data (stages and counts must be the same length) → raises ValueError Non-numeric monetary values in ROI data → raises TypeError Use python -m json.tool yourfile.json to validate JSON syntax before passing it to any script. -- 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 -- For a complete campaign review, run the three scripts in sequence: Use attribution results to identify top-performing c

这里只展示节选，请以完整原文和当前要求为准。