Marketplace ops
AuraScore 81/100

Flash Campaign Merchant Allocation and Co-Op Merchandising Protocol

Build an algorithmic qualification and slotting framework for marketplace promotional campaigns and co-op ad matching.

Use this operational protocol to evaluate, score, and allocate high-visibility promotional placements among competing marketplace sellers. It guarantees fair promotional governance while maximizing seasonal GMV and fulfillment compliance.

Template

Role: Head of Marketplace Merchandising and Campaign Operations

Context

  • Platform Vertical: {{platform_vertical}}
  • Supported Fulfillment Models: {{seller_fulfillment_models}}
  • Campaign Cadence: {{promotional_calendar_type}}
  • Gross Margin Floor: {{margin_thresholds}}
  • Baseline SLA Compliance: {{sla_compliance_rates}}
  • Merchant Co-Op Ad Tiers: {{ad_spend_tiers}}

Task

Construct an operational framework and scoring rubric that governs merchant campaign eligibility, slot allocation, and promotional co-op investment for {{promotional_calendar_type}} events within {{platform_vertical}}.

Method

  1. Define mandatory baseline qualification gates assessing historical order defect rates, inventory depth, and compliance with {{sla_compliance_rates}}.
  2. Create a multi-factor placement scoring algorithm balancing discount depth, historical conversion rate, fulfillment model reliability from {{seller_fulfillment_models}}, and co-op ad commitment across {{ad_spend_tiers}}.
  3. Establish deal slotting tiers (e.g., Hero Spotlight, Category Deal, Grid Inclusion) with differentiated traffic guarantees and inventory commitment requirements.
  4. Design automated inventory holding safeguards and real-time out-of-stock de-listing triggers to protect customer experience.
  5. Model promotional subsidy sharing mechanisms ensuring net revenue maintains platform limits defined in {{margin_thresholds}}.
  6. Formulate post-campaign audit criteria to measure sell-through efficiency, merchant ROI, and compliance breach penalties.
  7. Detail an exception management workflow for key strategic merchant accounts without compromising overall platform fairness.

Constraints

  • Deal slotting allocations MUST require full fulfillment validation across {{seller_fulfillment_models}}.
  • The scoring rubric MUST prioritize customer delivery reliability over raw {{ad_spend_tiers}} spend.
  • MUST NOT award premium promotional placement to any SKU failing the threshold established in {{margin_thresholds}}.
  • Every campaign tier MUST include explicit minimum discount percentages and inventory reserves.

Output format

Deliver the operational framework across four structured modules:

  1. Campaign Eligibility and Gating Matrix (Mandatory baseline criteria, disqualifiers, and exception rules)
  2. Weighted Placement Scoring Engine (Scoring formula, weight distributions, and variable definitions)
  3. Slotting Tier Architecture (Tier designations, required inventory units, co-op commitments, and traffic assets)
  4. Execution and Compliance Runbook (Pre-campaign lock, real-time de-listing triggers, and post-mortem review steps)

Self-review

  • Confirm that scoring weights balance ad revenue from {{ad_spend_tiers}} with operational metrics in {{sla_compliance_rates}}.
  • Verify that each fulfillment model within {{seller_fulfillment_models}} has explicit SLA lead-time validations.
  • Ensure margin safeguards prevent loss-making promotion approvals.
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.

ecommerce-retail
ecom-operations
business-strategy-marketing-sales
merchandising
promotions
marketplace-ops