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marketing-science-academic-writing

Automate academic marketing science paper writing with LaTeX and Python.

8|Updated May 16, 2026
Search Tags:#data analysis#LaTeX#academic writing#paper writing#marketing science
One-click install
npx skills add https://github.com/Aradotso/marketing-skills --skill marketing-science-academic-writing
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: marketing-science-academic-writing
Source: https://github.com/Aradotso/marketing-skills/tree/main/skills/marketing-science-academic-writing
Command: npx skills add https://github.com/Aradotso/marketing-skills --skill marketing-science-academic-writing

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pyblp, pandas, numpy, scipy, matplotlib, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill automates the process of writing academic marketing science papers, guiding users through topic selection, structural modeling, identification, estimation, and full draft assembly.

Core Features & Use Cases

  • Topic Positioning: Provides gap analysis, journal selection, and contribution framing.
  • Consumer Utility Modeling: Assists with micro-founded demand systems, notation, and utility specification.
  • Identification & Estimation: Offers causal inference strategies and structural estimation (BLP/GMM).
  • Counterfactuals & Experiments: Guides through simulation design, conjoint analysis, and field experiments.
  • Full Draft Assembly: Assembles a complete LaTeX manuscript with journal-specific formatting.

Quick Start

Use the marketing-science-academic-writing skill to write a paper on 'Consumer Utility Modeling in Marketing Science'.

Frequently Asked Questions about marketing-science-academic-writing

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate structural estimation and LaTeX drafting for a marketing science paper?▼

Automate marketing science paper drafting by guiding structural estimation and LaTeX assembly through topic selection, consumer utility modeling, identification, and full manuscript generation.

What Python libraries do I need for BLP demand estimation and structural modeling?▼

Structural modeling and BLP demand estimation require Python libraries including pyblp, pandas, numpy, scipy, and matplotlib to execute data analysis and generate visual simulations.

How does counterfactual simulation work in academic marketing science research?▼

Counterfactual simulation in marketing science evaluates policy changes through conjoint analysis and field experiment design, leveraging Python libraries to model consumer utility and estimate causal impacts.

Can I generate a complete academic manuscript with journal-specific LaTeX formatting?▼

Generate a complete academic manuscript with journal-specific LaTeX formatting by assembling full drafts that integrate structural models, identification strategies, and counterfactual experiments.

What is the best way to position a new research topic in marketing science?▼

Position a marketing science research topic by performing gap analysis, selecting target journals, and framing contributions to clearly define the structural modeling approach and academic value.

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