Focus Group

/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/.

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
• Questions to ask the panel: Specific questions to pose to the simulated personas — open-ended reaction questions ("What is your first impression?"), scaled evaluation questions ("Rate clarity from 1-10"), objection-surfacing questions ("What would stop you from buying?"), or comparative preference questions ("Which option do you prefer and why?"). If omitted, a default question set covering first impression, clarity, credibility, relevance, and purchase intent is used
• Number of segments to represent: How many distinct audience segments to include in the panel (2-6). More segments give richer cross-segment analysis but increase output length. If using an existing panel, this is inherited from the panel definition

Process

1. Load brand context: Read ~/.claude-marketing/brands/_active-brand.json for the active slug, then load ~/.claude-marketing/brands/{slug}/profile.json. Apply brand voice, positioning, competitive context, and target audience definitions. Also check for guidelines at ~/.claude-marketing/brands/{slug}/guidelines/_manifest.json — if present, load restrictions. Check for agency SOPs at ~/.claude-marketing/sops/. If no brand exists, ask: "Set up a brand first (/digital-marketing-pro:brand-setup)?" — or proceed with defaults.
2. Load or create synthetic panel from CRM data: If an existing panel ID was provided, reference it by --panel-id (list panels via python "${CLAUDE_PLUGIN_ROOT}/scripts/audience-simulator.py" --brand {slug} --action list-panels). If new segment definitions were given, create the panel via python "${CLAUDE_PLUGIN_ROOT}/scripts/audience-simulator.py" --brand {slug} --action create-panel --panel-name {name} --segments '[...]' with CRM data grounding — pulling behavioral patterns, purchase history distributions, engagement profiles, and demographic attributes from the CRM to build realistic persona archetypes for each segment. Then run the panel through python "${CLAUDE_PLUGIN_ROOT}/scripts/audience-simulator.py" --brand {slug} --action focus-group --panel-id {id} --stimulus "..." --questions '[...]' to gather structured per-segment reactions.
3. Present stimulus to each segment persona: For each segment in the panel, present the stimulus material along with the user's questions. Frame the presentation in the context of each persona's behavioral profile, preferences, pain points, and communication style derived from the CRM data grounding.
4. Generate predicted responses per segment: Based on behavioral profiles, generate structured responses for each segment — sentiment (positive, neutral, negative with intensity), key concerns raised, enthusiasm level (1-10), specific objections, improvement suggestions, and verbatim-style quotes that represent how each segment would likely articulate their reaction.
5. Analyze response patterns: Identify consensus themes where multiple segments agree (strong signals), divergence points where segments split (personalization opportunities or risk areas), and unexpected reactions that challenge assumptions. Calculate overall sentiment distribution and flag any segment with strongly negative reactions.
6. Generate recommendations based on synthetic feedback: Synthesize the cross-segment analysis into actionable recommendations — what to keep, what to change, which segments are most receptive, which need a different approach, and what follow-up testing would be most valuable.
7. Flag confidence limitations: Explicitly state that synthetic responses are hypotheses based on CRM-derived behavioral profiles, not real consumer data. Assign a confidence level (low, moderate, high) based on CRM data quality, segment sample sizes, and stimulus complexity. Recommend specific real-world validation steps — actual focus groups, surveys, or A/B tests — to confirm the most critical findings.