PROMPT
# LinkedIn CAC Reduction Strategy for Legal Tech Using ACC
## EXPERT ROLE
Act as a senior LinkedIn 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 linkedin tasks.
**Context:** LinkedIn
**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 LinkedIn Strategist for Social Media (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 8, targeting B2B founders, stage bootstrapped profitable Current: CAC $82, LTV $1458, churn 12%, traffic 8659/mo, conversion 3.2% Assets: Notion docs + SOPs Goal: reduce CAC 40% in 60 days Constraint: Budget $3k/mo, team of 3 Tone: witty GenZ professional FRAMEWORK: ACC + structured 4-step. TASK: Complete LinkedIn system for Social Media / Legal Tech. - common failure modes about linkedin for Legal Tech - 5 Whys root cause for reduce CAC 40% - 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 (Objection Crusher): 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
## CROSS-CLUSTER STRATEGIC DIFFERENTIATION
**Why this version exists:** This prompt belongs to a semantic overlap cluster. It must solve a materially different strategic problem from its sibling records.
**Primary strategic lens:** Acquisition / Reach
**Decision priority:** Prioritize discovering and attracting the highest-fit audience. Optimize the first step of the journey and measure qualified reach, response or acquisition efficiency.
**What must change:** Make the diagnosis, recommended action, evidence/metric and output reflect this lens.
**What must remain stable:** Preserve the supplied facts, audience context, constraints and core task unless they directly conflict with this strategic lens.
**Do not differentiate by wording alone:** A different framework name, adjective or formatting style is insufficient. The resulting recommendation/output must lead to a different professional decision or use-case outcome.
## STRATEGIC WORKFLOW
Audience Situation → Content Objective → Hook/Angle → Platform Execution → Measurement → Experiment → Decision. 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: weak first-glance hook; platform-inappropriate format; generic audience framing; engagement without business/content objective; no measurable test. 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.