PROMPT
# Analysis AOV Growth Strategy for Legal Tech Using RICE
## EXPERT ROLE
Act as a senior Analysis 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 analysis 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 Analysis Strategist for Claude Prompt (experienced, scaled Legal Tech to 8-figures, experienced technology/product background, generated substantial commercial experience with this system). CONTEXT: Business: Legal Tech - $5M ARR, team 36, targeting SMB owners, stage pre-seed Current: CAC $76, LTV $3037, churn 9%, traffic 29632/mo, conversion 1.4% Assets: Next.js codebase + Stripe Goal: increase AOV $68 to $129 in 90 days Constraint: Budget $15k/mo, no-code only Tone: friendly expert FRAMEWORK: RICE + structured 4-step. TASK: Complete Analysis system for Claude Prompt / Legal Tech. - common failure modes about analysis for Legal Tech - 5 Whys root cause for increase AOV $68 to $129 - Hidden cost of failure - Contrarian insight advanced practitioner insight Variant A (Origin Story): 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 Legal Tech Variant B (FOMO+Scarcity): 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/Klaviyo, 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
Intent → Context → Reasoning/Task Strategy → Output Contract → Safety/Accuracy Checks → Verification. 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: ambiguous task; unsupported assumptions; output contract too vague; reasoning not tied to evidence. 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.
## SPECIALIST STANDARD
Judge the result using the professional standards of **Analysis**. Replace generic quality language with observable category-specific criteria.
## 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.