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focus-group

Run a simulated focus group of CRM-grounded personas reacting to messaging or pricing. "run a focus group on this message"

plugins/indranilbanerjee/digital-marketing-pro/skills/focus-group/SKILL.md

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# /digital-marketing-pro:focus-group

> **Script location.** If your host does not set `${CLAUDE_PLUGIN_ROOT}`, the scripts are in this plugin's `scripts/` folder, next to `skills/`. Run them with Python 3.10 or newer: `python`, or `python3` where `python` is not found (macOS, many Linux systems).

## Purpose

Run a simulated focus group using synthetic audience panels built from real CRM data. Present stimuli (messaging, pricing, creative concepts, positioning statements) to AI-simulated personas representing actual customer segments and get structured response predictions with sentiment analysis. This command bridges the gap between gut-feel decisions and expensive real-world research by generating directional feedback grounded in behavioral profiles derived from your actual customer base. Synthetic focus groups are fast, repeatable, and free to run — making them ideal for narrowing options before committing budget to real qualitative research or live campaigns. Every output includes explicit confidence limitations so results are treated as informed hypotheses, not validated data.

## Input Required

The user must provide (or will be prompted for):

- **Stimulus to test**: The messaging variant, pricing proposal, creative concept, or positioning statement to present to the panel. Can be a single stimulus for reaction analysis or multiple stimuli for comparative evaluation. Plain text, structured copy blocks, or a brief describing the concept. If testing multiple stimuli, label each clearly (Variant A, Variant B, etc.)
- **Audience panel**: An existing panel ID from a previous `/digital-marketing-pro:focus-group` or `/digital-marketing-pro:message-test` session, or new segment definitions to build a panel from CRM data. New panels require segment criteria — demographic, behavioral, psychographic, or value-based attributes. Specify 2-6 segments for meaningful cross-segment comparison