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Tests message variants on synthetic audience panels from CRM data to predict response rates, sentiment, objections, and top performers before live A/B tests.
npx claudepluginhub indranilbanerjee/digital-marketing-proHow this skill is triggered — by the user, by Claude, or both
Slash command
/digital-marketing-pro:message-testThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Test message variants against synthetic audience panels before real-world deployment. Predict which variant will perform best overall and per segment, identify potential objections, and narrow down variants for real A/B testing. This command eliminates wasted ad spend and testing cycles by pre-screening message variants through AI-simulated audience segments grounded in real CRM behavioral data...
Simulates focus groups with AI-generated personas from CRM data to test messaging, pricing, positioning, and creative concepts before real research spend.
Generates diverse AI persona panels, runs simulated interviews, and analyzes results with synthesis and charts for product concept testing and customer research.
Define audiences from tensions, mine customer reviews for real language, or analyze competitive creative for strategic signals. Covers tension-based audience mapping, 5-bucket review extraction, and competitive inspo research.
Share bugs, ideas, or general feedback.
Test message variants against synthetic audience panels before real-world deployment. Predict which variant will perform best overall and per segment, identify potential objections, and narrow down variants for real A/B testing. This command eliminates wasted ad spend and testing cycles by pre-screening message variants through AI-simulated audience segments grounded in real CRM behavioral data. Instead of testing six variants live and burning budget on underperformers, run them through synthetic panels first to identify the top two or three candidates worth real investment. Each variant is scored on five evaluation criteria — resonance, clarity, credibility, urgency, and differentiation — with per-segment breakdowns that reveal personalization opportunities where different segments prefer different messages.
The user must provide (or will be prompted for):
/digital-marketing-pro:focus-group or /digital-marketing-pro:message-test session, or new segment definitions to build from CRM data. New panels require segment criteria — demographic, behavioral, psychographic, or value-based attributes. Panels with 3-5 segments give the best balance of cross-segment insight and output manageability~/.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 messaging guidelines. 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.audience-simulator.py load-panel --panel-id {id}, or create a new panel via audience-simulator.py create-panel with CRM data grounding if new segment definitions were provided. Verify the panel has sufficient segment diversity for meaningful cross-segment comparison.audience-simulator.py test-message for each variant-segment combination. Score each variant on every evaluation criterion (resonance, clarity, credibility, urgency, differentiation) from the perspective of each segment's behavioral profile. Generate predicted response sentiment, key reactions, and specific objections for each combination.A structured message test report containing: