Ads & paid
AuraScore 81/100

Graduate Program Paid Acquisition Audit and Unit Economics Analysis

Audit higher education paid media efficiency, attribution decay, and student acquisition unit economics across channels.

Use this template when evaluating multi-channel paid ad campaigns for higher education or professional certificate programs. It assesses conversion leaks, cost-per-enrolled-student dynamics, and creative fatigue.

Template

Role: Principal Paid Acquisition Specialist with 12+ years optimizing high-ticket higher education and professional certification recruitment funnels.

Context

  • Target Institution: {{institution_name}}
  • Program Portfolio: {{program_portfolio}}
  • Active Paid Channels: {{ad_channels_active}}
  • Target Student Profile: {{target_student_persona}}
  • Unit Economics Baseline: {{current_cac_and_lcv}}
  • Pipeline Conversion Baseline: {{lead_to_enrollment_rate}}

Task

Deliver an advanced diagnostic audit analyzing paid campaign efficiency across {{institution_name}}'s media mix. The analysis must identify budget waste, evaluate CAC-to-LTV sustainability for {{program_portfolio}}, and isolate conversion drop-offs between inquiry generation and matriculation.

Method

  1. Evaluate blended and channel-specific acquisition costs against {{current_cac_and_lcv}} benchmarks.
  2. Dissect channel attribution models across {{ad_channels_active}} to detect top-of-funnel credit inflation versus bottom-of-funnel capture.
  3. Map inquiry-to-enrollment friction points against {{lead_to_enrollment_rate}} across high-intent search and social placements.
  4. Analyze audience segment exhaustion and creative decay rates relative to {{target_student_persona}}.
  5. Audit landing page conversion pathways for degree requirements, financial aid disclosures, and mobile responsiveness.
  6. Model marginal return on ad spend (mROAS) curves to pinpoint over-invested and under-scaled ad sets.
  7. Formulate a budget reallocation matrix with scenario modeling for 15% CAC reduction.

Constraints

  • MUST calculate implied payback periods assuming a 4-year tuition cycle or upfront certificate fee.
  • MUST NOT recommend top-of-funnel brand awareness expansion without proving assisted conversion value.
  • Data interpretations MUST prioritize cost-per-enrolled-student (CPES) over raw cost-per-lead (CPL).
  • Analysis must strictly preserve academic brand integrity guidelines.

Output format

  • Section 1: Executive KPI Scorecard (4 core metrics with current vs benchmark variance).
  • Section 2: Channel Efficiency and Attribution Breakdown (Structured markdown table).
  • Section 3: Funnel Leakage & Audience Decay Diagnostic (3-4 bulleted root causes).
  • Section 4: Budget Reallocation and mROAS Optimization Matrix (Scenario table: Conservative, Target, Aggressive).
  • Total length: 700-1100 words.

Self-review

  • Did I tie all recommendations back to {{program_portfolio}} and {{target_student_persona}}?
  • Are CPES calculations mathematically aligned with {{lead_to_enrollment_rate}}?
  • Have I explicitly flagged any channel with negative marginal return?
AuraScore breakdown
81/100Provisional
Instruction clarity15/15 · Strong

Explicit role, a named task, and discrete steps the model can follow.

Context architecture12/12 · Strong

Background, inputs and variables the model needs before it starts.

Constraint engineering12/12 · Strong

Hard boundaries — what the model must and must not do.

Output specification6/14 · Thin

A named, field-level shape for the response.

Reasoning structure10/10 · Strong

Ordered work items that force analysis before an answer.

Model compatibility10/10 · Strong

Length and structure that travel across frontier models.

Token efficiency5/10 · Thin

Signal density — instruction weight without padding.

Reusability7/7 · Strong

Documented variables so the scaffold adapts to new inputs.

Robustness3/5 · Adequate

Quality bar, assumptions and behaviour when inputs are thin.

Observed performance1/5 · Thin

How much real usage the template has behind it.

marketing
marketing-ads
research-productivity-operations
edtech
paid-acquisition
higher-education