VERIFIED TECHNICAL DOSSIERSources checked

Christopher S. Penn

Co-founder & Chief Data Scientist, Trust Insights | Keynote AI Educator

2 Verified ArtifactsSource Checked & Attributed

Back to Profile & Contact Inquire with Christopher

Verified Proof of Work Artifacts

2 items cataloged

Each artifact below represents an authenticated research publication, production code repository, or technical architectural framework directly authored or co-created by Christopher S. Penn. Every entry undergoes editorial source verification.

IMPLEMENTATION Checked Sep 23, 2026

Predictive Marketing Analytics & Customer Lifetime Value Forecasting Pipeline

Architected an end-to-end open pipeline combining statistical regressors with LLM narrative generation to translate raw Google Analytics 4 data into actionable executive summaries with identified churn risk signals.

Model & Execution Context:Google Analytics 4 Data API, Python, scikit-learn, OpenAI API narrative engine.

Scope & Limitations

Sampling thresholds in GA4 can skew small-dataset cohort attribution.

View Enriched Study & Permalinks Primary Source

EXPLANATION Checked Sep 23, 2026

The PARE Prompt Architecture for Complex Business Analytics

Engineered the PARE framework (Prime, Augment, Refresh, Evaluate) to systematically eliminate model drift and hallucination during multi-step financial and marketing reporting over raw tabular data.

Model & Execution Context:Code Interpreter / Advanced Data Analysis environments, Python execution sandboxes, Claude Artifacts.

Scope & Limitations

Requires structured schema validation on source tabular inputs prior to prompt passing.

View Enriched Study & Permalinks Primary Source