Fact-checking
AuraScore 81/100

Omnichannel Retail Price and Comparative Promotion Fact-Checking Checklist

Validate promotional price baselines, discount math, and competitor comparison claims against pricing datasets.

Execute this template prior to launching major retail sales campaigns or price-matching promotions across digital and physical stores. It produces a verification checklist auditing reference prices, strikethrough baselines, and comparative claims to prevent deceptive pricing exposure.

Template

Role: Director of Retail Trade Compliance and Pricing Integrity

Context

  • Retail organisation: {{retail_chain}}
  • Promotional campaign title: {{promotional_campaign_name}}
  • Proposed promotional discounts: {{advertised_discounts}}
  • Historical pricing logs (90 days): {{historical_pricing_dataset}}
  • Competitor pricing benchmarks: {{competitor_benchmark_data}}
  • Target sales channels: {{distribution_channels}}

Task

Deliver an advanced pricing and promotion fact-checking checklist that verifies the mathematical validity and legal defensibility of all reference pricing, discounts, and comparative market claims in {{promotional_campaign_name}} for {{retail_chain}} across {{distribution_channels}}.

Method

  1. Analyze all advertised savings claims (e.g., 'Was $X / Now $Y', 'Save up to 50%', 'Lowest Price Anywhere') in {{advertised_discounts}}.
  2. Cross-verify reference original prices against bona fide historical selling prices in {{historical_pricing_dataset}} over the statutory lookback period.
  3. Validate comparative benchmark claims against verified competitor matching data in {{competitor_benchmark_data}}.
  4. Audit fine print, expiration triggers, and limitation clauses across all declared {{distribution_channels}}.
  5. Test multi-buy, bundled discount, and coupon stacking logic to verify mathematical accuracy.
  6. Verify inventory depth rules to ensure promotional stock levels prevent deceptive bait-and-switch exposure.
  7. Check compliance with Federal Trade Commission Guides Against Deceptive Pricing and local consumer protection rules.
  8. Produce a modular verification checklist delineating compliant promotions, pricing inaccuracies, and mandatory operational corrections.

Constraints

  • Every discount statement MUST be backed by a verified 30-to-90-day regular selling price history.
  • You MUST NOT approve 'Lowest Price' claims unless competitor data confirms absolute market leadership across identical SKUs.
  • Strikethrough pricing without demonstrable regular sales volume must be flagged as non-compliant.
  • Clear corrective actions must accompany every mathematically erroneous or legally deceptive promotion.

Output format

  • Campaign Pricing Integrity Assessment (100-140 words)
  • Section 1: Reference Pricing & Strikethrough Verification Checklist (8-12 checkpoints detailing Reference Price, Actual 90-Day Baseline, Status, and Legal Risk)
  • Section 2: Comparative & Competitor Claims Audit (SKU, Competitor Price, Advertised Margin, Verification Outcome)
  • Section 3: Channel Execution & Fine-Print Compliance Checklist (5-8 operational checkpoints covering exclusions, inventory thresholds, and disclaimers)

Self-review

  • Did I rigorously check original reference prices against the historical sales log rather than manufacturer suggested prices?
  • Are all omnichannel variations (in-store vs. online) accounted for in the channel verification section?
  • Is every comparative price claim verified against identical SKU specifications?
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.

research-analysis
research-fact-checking
retail-consumer-goods
fact-checking
pricing-compliance
retail