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analyze-conjoint-results

Generate conjoint analysis reports with part-worth utilities and attribute importance charts.

Updated Feb 10, 2026
Search Tags:#data visualization#reporting#market research#conjoint analysis#choice modeling#consumer preferences
One-click install
npx skills add https://github.com/expectedparrot/ep-skills --skill analyze-conjoint-results
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: analyze-conjoint-results
Source: https://github.com/expectedparrot/ep-skills/tree/main/plugins/edsl-research/skills/analyze-conjoint-results
Command: npx skills add https://github.com/expectedparrot/ep-skills --skill analyze-conjoint-results

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill automates the generation of detailed analysis reports for conjoint studies, transforming raw choice data into actionable insights about consumer preferences.

Core Features & Use Cases

  • Automated Reporting: Creates self-contained HTML reports with executive summaries, methodology, results, and segment analysis.
  • Data Visualization: Generates charts for attribute importance, part-worth utilities, price sensitivity, and segment comparisons.
  • Use Case: After running a conjoint study on a new product, use this Skill to automatically produce a professional report detailing which features matter most to consumers and at what price points.

Quick Start

Run the analyze-conjoint-results skill on the study directory located at /path/to/your/conjoint/study.

Frequently Asked Questions about analyze-conjoint-results

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

FAQPage Schema
How do I generate a conjoint analysis report from raw choice data?▼

To generate a conjoint analysis report, you can use this Skill to automate the processing of raw choice data. It computes part-worth utilities and attribute importance, then visualizes the findings into a self-contained HTML report.

Can I visualize attribute importance and price sensitivity using pandas and matplotlib?▼

Yes, you can visualize attribute importance and price sensitivity using pandas and matplotlib. This Skill leverages those libraries to automatically generate charts for part-worth utilities, segment comparisons, and price sensitivity from your study artifacts.

Do I need Python 3.9 to compute part-worth utilities and segment-specific preferences?▼

Yes, you need Python 3.9 or higher to compute part-worth utilities and segment-specific preferences. The Skill requires this environment along with pandas, matplotlib, and numpy for data processing and visualization.

What is the best way to automate market research reporting for choice modeling studies?▼

The best way to automate market research reporting for choice modeling is to run this Skill on your study directory. It transforms design specifications and choice sets into professional reports with executive summaries and detailed textual analysis.

What inputs are required to produce consumer preference charts from a conjoint study?▼

To produce consumer preference charts from a conjoint study, you must provide study artifacts including design specifications, results, and choice sets. The Skill processes these inputs to calculate attribute importance and generate visual charts.

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