PROMPT
# Local SEO 20-Keyword Strategy for InsurTech
## EXPERT ROLE
Act as a senior Local SEO 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 local seo tasks.
**Context:** Google Search
**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 Local SEO Strategist for SEO (experienced, scaled InsurTech to 8-figures, experienced technology/product background, generated substantial commercial experience with this system). CONTEXT: Business: InsurTech - $5M ARR, team 16, targeting Gen Z, stage bootstrapped profitable Current: CAC $49, LTV $3325, churn 14%, traffic 65467/mo, conversion 1.5% Assets: Next.js codebase + Stripe Goal: rank #1 for 20 keywords in 30 days Constraint: Budget $8k/mo, team of 3 Tone: witty GenZ professional FRAMEWORK: April Dunford Positioning + structured 4-step. TASK: Complete Local SEO system for SEO / InsurTech. - common failure modes about local seo for InsurTech - 5 Whys root cause for rank #1 for 20 keywords - 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 InsurTech Variant B (FOMO+Scarcity): Same structure different angle Variant C (Re-engagement): Same structure third angle Each variant must be fundamentally different not reworded OUTPUT: Markdown H2/H3 tables copy blocks ready for Notion/Google Docs/Figma+Framer, 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
## STRATEGIC WORKFLOW
Search Intent → Audience Need → SERP/Content Diagnosis → Content Strategy → On-Page Execution → Measurement → Iteration. Diagnose the real problem, desired outcome, strongest constraint and material assumptions before execution. Make the causal chain explicit: situation → diagnosis → strategic hypothesis → execution. Distinguish what is known, inferred and still needs evidence.
## EXECUTION + MEASUREMENT
For each major action, state the decision it influences, the leading signal, the primary outcome metric, the measurement window and the evidence required to proceed. When relevant, map the action to the funnel stage or customer journey it is intended to change. Do not recommend a metric simply because it is easy to measure; connect it to the stated business objective.
## EXPERIMENT + DECISION
State one falsifiable hypothesis. Change one meaningful variable, keep the baseline stable, define the success metric, minimum evidence threshold and decision rule. Separate diagnostic tests from optimization tests. If evidence does not support the hypothesis, explain the next smallest test or strategic change instead of rewriting everything.
## FAILURE PREVENTION + QA
Watch for: wrong search intent; keyword-first content without user value; weak information architecture; unsupported claims; no measurement/refresh rule. Apply only relevant prevention rules. Verify audience fit, source integrity, measurable outcome, constraint compliance, causal logic, output completeness and whether the recommendation can actually be acted upon. Flag unsupported claims and scenario assumptions explicitly.
## 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 **Local SEO**. 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 **Google Search** 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.