PROMPT
# Emails Retention Strategy for AI Analytics Using BAB
## EXPERT ROLE
Act as a senior Emails specialist. Solve the exact task. Use supplied scenario details as inputs, separate facts from assumptions, and avoid generic advice.
## INTENT
**Job:** Improve the named business/performance outcome using a measurable, testable intervention.
**Audience:** Professionals or practitioners using AI for emails tasks.
**Context:** User-specified channel or workflow
**Success:** define 2–4 observable criteria tied to the requested outcome.
## WORKING BRIEF
Treat this as the authoritative source brief. Preserve useful specifics; do not invent facts or turn scenario values into verified claims.
> Act as Senior Emails Strategist for Writing (experienced, scaled AI Analytics to 8-figures, experienced technology/product background, generated substantial commercial experience with this system). CONTEXT: Business: AI Analytics - $12M ARR, team 48, targeting B2B founders, stage pre-seed Current: CAC $171, LTV $853, churn 11%, traffic 50496/mo, conversion 5.3% Assets: Next.js codebase + Stripe Goal: boost retention 85% in 90 days Constraint: Budget $3k/mo, team of 3 Tone: friendly expert FRAMEWORK: BAB + structured 4-step. TASK: Complete Emails system for Writing / AI Analytics. - common failure modes about emails for AI Analytics - 5 Whys root cause for boost retention 85% - Hidden cost of failure - Contrarian insight advanced practitioner insight Variant A (Us vs Them): Subject/headline 10 options with hypothesis, preview 3, body 250 words with {{FirstName}} tags PS PPS, visual brief, CTA, send logic delay trigger segment. Include placeholder [BRAND][AUDIENCE][DATA] + filled example for AI Analytics Variant B (Social Proof Avalanche): Same structure different angle Variant C (Referral Ask): Same structure third angle Each variant must be fundamentally different not reworded OUTPUT: Markdown H2/H3 tables copy blocks ready for Notion/Google Docs/Meta Ads, both placeholder and filled side by side No generic advice, every sentence actionable, specific numbers, decision tree If [condition] then A else B, no buzzwords without definition
## EXECUTION
Purpose → Audience → Message Strategy → Structure → Drafting → Editorial QA → Revision. Complete the task in the smallest complete workflow that solves it. Make inputs, decisions and deliverables explicit. Return the requested result, not a description of the workflow.
## FAILURE PREVENTION + QA
Watch for: unclear audience or purpose; generic structure; unsupported claims; tone mismatch. Apply only relevant prevention rules. Verify the result against intent, audience/context, constraints and output requirements.
## ITERATION
If weak, change the single highest-impact variable, preserve what works, revise, and rerun the relevant check.
## OUTPUT CONTRACT
Return a specific, immediately usable result. Include assumptions only when material; use concrete decisions and examples where useful; omit irrelevant boilerplate. Do not merely restate this prompt.
## MODEL-NEUTRALITY
Keep core reasoning model-agnostic unless model-specific behavior materially changes the result. When a model is specified, adapt only the relevant syntax or capability.
## DELIVERY FIT
Optimize the final artifact for **User-specified channel or workflow** only where platform behavior changes the format, attention pattern, constraints, or delivery.
## UNIQUENESS CHECK
Before finalizing, state why this prompt is materially different from nearby prompts: different intent, strategy, use case, audience, output, or decision—not merely different wording.