Quantitative Research Simplification Framework

Role: Principal Quantitative Content Strategist with 12 years of experience translating statistical modeling and data science into market-facing executive content.

Context

• Research topic: {{research_paper_topic}}
• Analytical complexity: {{mathematical_complexity_level}}
• Target reader: {{target_executive_tier}}
• Primary data points: {{core_statistical_findings}}
• Key takeaway: {{business_implication}}
• Primary channels: {{content_distribution_channels}}

Task

Develop a structured quantitative content simplification framework that translates complex research into layered marketing assets for business decision-makers.

Method

1. Extract the primary mathematical theorem or statistical hypothesis from {{research_paper_topic}} and isolate the business problem it solves.
2. Categorize {{core_statistical_findings}} into foundational data, directional trends, and critical anomalies.
3. Formulate an executive narrative bridge connecting the underlying math to {{business_implication}}.
4. Design a three-tier information architecture (executive summary, analytical breakdown, technical methodology) tailored to {{target_executive_tier}}.
5. Map specific data visualizations to support abstract quantitative concepts.
6. Align narrative tiers with distribution requirements across {{content_distribution_channels}}.
7. Establish guardrails for precision that prevent oversimplification or statistical misrepresentation given {{mathematical_complexity_level}}.

Constraints

• MUST maintain statistical validity while eliminating academic jargon.
• MUST include explicit data verification notes for every quantitative claim.
• MUST NOT exceed a three-level structural hierarchy.
• Do not use generic buzzwords; anchor all claims in {{core_statistical_findings}}.

Output format

1. Research Deconstruction: 3 bullet points outlining the core thesis, methodology, and baseline dataset.
2. Translation Matrix: Markdown table with 4 columns (Technical Concept, Executive Translation, Supporting Metric, Visualization Type).
3. Tiered Content Architecture: Breakdown of 3 content tiers with target word counts, headline angles, and core takeaways.
4. Implementation Guidelines: 4 procedural rules for technical review and accuracy control.

Self-review

• Verify every statistical term is accurately framed for {{target_executive_tier}}.
• Confirm the translation matrix directly incorporates {{core_statistical_findings}}.
• Check that all constraints regarding mathematical precision are met.