Message Test

/digital-marketing-pro:message-test

Purpose

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.

Input Required

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

• Message variants: 2-6 variants to test, each containing a headline, body copy, and call-to-action. Variants can be full ad creatives, email subject lines with preview text, landing page hero sections, social media posts, or any message format. Label each variant clearly (Variant A, B, C, etc.). Variants should test meaningfully different approaches — different value propositions, emotional appeals, proof points, or framing — rather than minor word swaps that synthetic testing cannot reliably distinguish
• Target 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 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
• Evaluation criteria: The dimensions to score each variant on. Default criteria are resonance (emotional connection and relevance), clarity (ease of understanding the message and desired action), credibility (believability of claims and proof points), urgency (motivation to act now rather than later), and differentiation (distinctiveness from competitor messaging). Custom criteria can be added or defaults can be narrowed to focus the analysis

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 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.
2. Load audience panel: Reference the existing panel by its ID (list available panels with python "${CLAUDE_PLUGIN_ROOT}/scripts/audience-simulator.py" --brand {slug} --action list-panels), or create a new panel via python "${CLAUDE_PLUGIN_ROOT}/scripts/audience-simulator.py" --brand {slug} --action create-panel --panel-name {name} --segments '[...]' with CRM data grounding if new segment definitions were provided. Verify the panel has sufficient segment diversity for meaningful cross-segment comparison.
3. Test each variant against each segment: Run python "${CLAUDE_PLUGIN_ROOT}/scripts/audience-simulator.py" --brand {slug} --action test-message --panel-id {id} --variants '[...]' for the variant set. 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.
4. Aggregate scores: Calculate overall variant rankings by averaging scores across all segments weighted by segment size. Identify the overall winner and per-segment winners. Flag cases where the overall winner is not the per-segment winner — these represent personalization opportunities.
5. Identify segment preferences: Map which segments prefer which variant and why. Highlight cases where a single variant wins across all segments (universal appeal) versus cases where different segments strongly prefer different variants (personalization-required). Calculate preference strength to distinguish strong preferences from marginal differences.
6. Extract objection patterns per variant: Catalog all objections raised across segments for each variant. Identify recurring objections (cross-segment issues to fix), segment-specific objections (addressable through targeting), and objections unique to the weakest variants (reasons to eliminate them).
7. Recommend top variants for real A/B testing: Based on overall ranking, segment preference patterns, and objection severity, recommend the top 2-3 variants worth investing in for real A/B testing. Include specific suggestions for minor improvements that could strengthen each recommended variant based on the objection analysis.