Ads & paid
AuraScore 81/100

Commercial Lending Paid Social Customer Acquisition Funnel Spec

Design a multi-stage paid social acquisition and lead qualification spec for business lending products.

Use this template to architect targeted LinkedIn and Meta paid social campaigns for commercial loans or business lines of credit. It defines audience gating, creative variations, lead form logic, and compliance controls under lending fairness regulations.

Template

Role: Senior Paid Social Growth Strategist specializing in commercial fintech and B2B lending.

Context

  • Lending provider: {{lender_brand_name}}
  • Financing product: {{loan_product_type}}
  • Target borrower revenue benchmark: {{target_business_revenue}}
  • Planned monthly social budget: {{monthly_paid_social_spend}}
  • Mandatory compliance rules: {{compliance_disclaimer_requirements}}
  • Primary ad platforms: {{primary_ad_platforms}}

Task

Produce an end-to-end paid social customer acquisition funnel specification for {{lender_brand_name}} that generates verified applications for {{loan_product_type}} from enterprises generating {{target_business_revenue}} while ensuring absolute adherence to {{compliance_disclaimer_requirements}}.

Method

  1. Map top-of-funnel through bottom-of-funnel customer stages tailored to decision-makers seeking {{loan_product_type}}.
  2. Define platform-specific targeting parameters across {{primary_ad_platforms}}, leveraging company size, seniority, and industry exclusions.
  3. Formulate creative testing matrices spanning static problem-solution cards, testimonial video treatments, and interactive rate calculators.
  4. Draft detailed native lead generation form architectures with custom progressive qualification questions that verify {{target_business_revenue}}.
  5. Establish automated webhook integration, CRM lead routing protocols, and speed-to-lead notification triggers.
  6. Integrate required statutory lending disclosures and APR transparency guidelines in adherence with {{compliance_disclaimer_requirements}}.
  7. Build a full-funnel retargeting cadence based on ad engagement depth, landing page drop-offs, and incomplete application states.

Constraints

  • MUST adhere to all fair lending and non-discrimination advertising policies under {{compliance_disclaimer_requirements}} without discriminatory targeting parameters.
  • MUST NOT promise guaranteed approval or conceal variable interest rate factors in any creative spec.
  • All lead form specifications must require business verification identifiers.
  • Creative specifications must explicitly provide exact pixel dimensions, character limits, and safe-zone layouts.

Output format

1. Funnel Architecture & Platform Budget Split

  • Stage-by-stage allocation of {{monthly_paid_social_spend}} across {{primary_ad_platforms}} with target CPL benchmarks.

2. Audience Segmentation & Exclusion Rules

  • Detailed targeting configurations, inclusion lists, and compliance exclusion boundaries.

3. Creative Asset Matrix & Copy Guidelines

  • 4 distinct creative concepts specifying format, headline, primary body copy, CTA button, and required footnotes.

4. Lead Form Logic & Data Pipeline Spec

  • Question sequencing, field validation rules, privacy policy links, and downstream webhook payloads.

Self-review

  • Confirm that no targeting parameter violates credit opportunity fairness standards.
  • Verify that the native form fields effectively qualify leads against {{target_business_revenue}}.
  • Ensure all disclaimers mandated by {{compliance_disclaimer_requirements}} are legibly positioned.
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
financial-services
paid-social
commercial-lending
fintech