ZARSUS Organic Growth & Business Conversion Framework: Synthetic Validation Benchmark

Digital Object Identifier (DOI): https://doi.org/10.5281/zenodo.20849024Open-Source License: CC BY 4.0 Abstract & Data Availability Statement This repository contains the reproducible analytical framework and synthetic validation benchmark for the ZARSUS Organic Growth System — a methodology for measuring and optimizing the business impact of organic short-form video content. To protect the commercial confidentiality and Non-Disclosure Agreements (NDAs) of participating digital micro-enterprises, this public repository provides mathematically standardized synthetic data. The primary purpose of this dataset is to provide an open-source, computationally reproducible environment. It allows independent researchers and data scientists to test and reproduce the proprietary Python algorithms — including Revenue per 1,000 Views (RPM), Lead-to-Sale Conversion Rate, Organic Customer Acquisition Cost (CAC), and Virality Velocity — that translate organic short-form content performance into measurable business outcomes. Real-world empirical data matching this architecture is maintained under strict access controls and is utilized exclusively within restricted evidentiary and research contexts. Theoretical Framework & Research Applications This computational framework is designed to serve as an empirical and analytical foundation for quantitative research in digital marketing and algorithmic communication. The dataset operationalizes qualitative theories into quantifiable, reproducible data points: Algorithmic Resilience: The dataset provides the hook_type, average_watch_time_sec, and platform-stratified view metrics necessary to mathematically model cross-platform dependency reduction and algorithmic stability. Transparent Communication Protocols: The hook_type and cta_type classification schema in content_raw.csv operationalizes qualitative communication variables into discrete, correlatable database fields, enabling quantitative analysis of the relationship between structural transparency and engagement metrics. Sustainable Audience Acquisition: The dataset facilitates the validation of multi-touch financial attribution models through the content_to_lead_conversion and organic_cac algorithms, demonstrating how organic visibility can compound into measurable outcomes. Repository Architecture The ZARSUS framework is built on a three-tier relational data architecture designed to serve both operational business intelligence and advanced Machine Learning (ML) integration needs. 1. The Raw Input Tier (/data) Standardized CSV files representing the manual data entry points for digital marketing agencies: accounts.csv — Anonymized business entity profiles (15 accounts across 15 industry niches). content_raw.csv — Core analytical table containing 120 video records with platform metrics, methodological content classifications (PRE/POST implementation), and financial attribution outcomes. evidence_index.csv — Verification registry linking financial outcomes to primary source documents (Stripe/CRM exports). All records marked synthetic_not_verified in this public benchmark. 2. The Analytical Core (/scripts) Executable Python logic representing the computational engine of the methodology: zarsus_metrics.py — Core conversion formulas (RPM, CAC, conversion rates) and dataset validation rules. build_dataset_workbook.py — Automated compiler that transforms raw CSVs into Excel and HDF5 analytical outputs. validate_dataset.py — Diagnostic tool for data integrity checks and relational consistency validation. create_empty_template.py — Utility to generate blank input templates for new agency onboarding. 3. The Compiled Outputs (/output) output_result.xlsx (Business Tier) — Automated intelligence dashboard designed for non-technical stakeholders, presenting PRE/POST impact analysis and account-level performance summaries. zarsus_ai_ready_dataset.h5 (Academic/ML Tier) — HDF5 file engineered for direct integration into predictive data science pipelines and AI training environments. Data Dictionary A complete variable-level description for all fields across all three tables is provided in CODEBOOK.md. The codebook documents data types, allowable values, units of measurement, and operational definitions for every variable, including all hook_type and cta_type classification values. Methodology The ZARSUS framework applies a Difference-in-Differences (DiD) quasi-experimental design. Each participating account provides a baseline measurement period (PRE, approximately 8 weeks prior to methodology implementation) and a post-implementation measurement period (POST, 8 weeks following implementation). This within-account design controls for account-level fixed effects including niche, audience size, and content production quality. Content is classified along two primary methodological axes: Hook Type — the structural pattern of the video's opening 3-second segment, classified from a taxonomy of 5 PRE-baseline types and 7 ZARSUS-compliant POST types. CTA Type — the call-to-action mechanism, classified from a taxonomy of 3 PRE-baseline types and 7 ZARSUS-compliant POST types. Financial outcomes are attributed using a single-touch, 30-day attribution window, verified against primary source documents indexed in evidence_index.csv. Installation & Usage Ensure Python 3.9+ is installed, then: 1. Install required dependencies: pip install -r requirements.txt 2. Compile raw CSVs into Excel and HDF5 outputs: python scripts/build_dataset_workbook.py --content data/content_raw.csv --accounts data/accounts.csv --evidence data/evidence_index.csv --output output/zarsus_dataset_analysis.xlsx 3. Run data integrity validation: python scripts/validate_dataset.py --content data/content_raw.csv 4. Generate a blank onboarding template: python scripts/create_empty_template.py --output templates/new_agency_template.xlsx Requirements pandas>=2.0 numpy>=1.24 openpyxl>=3.1 tables>=3.8 Dataset Summary Table Records Key Variables accounts.csv 15 15 niches, 5 countries, B2C & B2B content_raw.csv 120 60 PRE + 60 POST records; 3 platforms evidence_index.csv 60 4 source types; all synthetic_not_verified License This dataset and accompanying code are released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. You are free to share and adapt the material for any purpose, provided appropriate credit is given. Suggested citation: Yevhen Velychko. (2026). ZARSUS Organic Growth & Business Conversion Framework: Synthetic Validation Benchmark (Version 1.0) [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.20849024 Contact & Contributions For questions about methodology, variable definitions, or replication issues, please open an issue in this repository. Contributions to extend the synthetic benchmark (additional niches, platforms, or attribution models) are welcome via pull request.