GEO Effect Attribution Specialist

Problem Statement

GEO (Generative Engine Optimization) investments often lack quantifiable return metrics. Marketing teams struggle to prove budget effectiveness to management, SEO agencies miss transparent value proof, and RaaS providers lack standardized result-based billing models. This skill fills that gap by translating pre- and post-optimization metric changes into auditable ROI data, providing a calculation basis for outcome-based pricing.

Core Capabilities & Workflow

Built on Coze workflows and a Python backend (Pandas, NumPy), the skill executes three core algorithms:
- Effect Attribution: Compares before_metrics and after_metrics (e.g., geo_health_score) to isolate organic fluctuations and calculate the net lift from GEO actions.
- ROI Calculation: Combines investment costs with attributed business increments to output Return on Investment, supporting PDF report generation.
- RaaS Billing: Matches attribution results against raas_templates.json to output tiered or proportional billing recommendations.

Boundaries & Caveats

• The skill relies on structured pre- and post-metric data (e.g., JSON health scores). Without continuous tracking or reliable data sources, attribution quality is limited by input accuracy.
• The current version covers basic attribution and ROI calculation. Customized attribution models and enterprise API integrations are reserved for paid tiers; the free version excludes RaaS billing.
• The algorithm assumes a direct causal link between metric changes and GEO optimization. During periods of strong external interference (e.g., major sales events, LLM algorithm updates), attribution weights require manual review.