Last updated: December 26, 2025
Data Quality & Dispute Policy
Accurate price data is the foundation of PriceMapper. Learn how we ensure data quality and how you can report or dispute incorrect information.
1. How We Ensure Data Quality
1.1 Automated Validation
Every price observation goes through automated checks:
- Range Validation: Prices outside expected ranges are flagged
- Duplicate Detection: Identical submissions from same user are deduplicated
- Anomaly Detection: Statistical outliers are reviewed before inclusion
- Freshness Scoring: Older data is weighted less in calculations
1.2 Community Consensus
We use consensus-based validation:
- Prices confirmed by multiple users get higher confidence
- Single-source prices are marked as "Low Confidence"
- Contradictory reports trigger manual review
1.3 Manual Review
Our moderation team reviews:
- Flagged anomalies and outliers
- User-reported disputes
- High-variance products
- Suspicious submission patterns
2. Data Confidence Indicators
Every price shown includes a confidence indicator based on:
| Factor | Weight |
|---|---|
| Number of observations | 40% |
| Data freshness (days since last observation) | 30% |
| Source diversity (unique contributors) | 20% |
| Price consistency (low variance) | 10% |
3. Reporting Wrong Prices
In-App Reporting
- Tap on the product with incorrect price
- Select "Report Issue" from the detail view
- Choose the issue type (price wrong, store wrong, product wrong)
- Optionally upload a receipt showing the correct price
- Submit the report
Email Reporting
Include: product name, store, date, incorrect price shown, actual price, evidence if available.
4. Dispute Resolution Process
- 1Report Received
Your report is logged and queued for review
- 2Evidence Gathering
We collect all observations for the disputed price
- 3Resolution Method
We apply the appropriate resolution:
- •Majority Consensus: Most reported price wins
- •Newest Receipt: Most recent dated receipt wins
- •External Verification: Store flyer or website check
- 4Correction Applied
Data is updated and suspicious observations flagged
- 5Reporter Notified
You receive notification of the resolution
5. Handling Suspicious Data
5.1 Flagging
- Observation is marked as "Under Review"
- Excluded from public aggregations temporarily
- User notified if their submission is flagged
5.2 Review
- Pattern analysis across user's submissions
- Comparison with other sources
- Assessment of user's contribution history
5.3 Outcomes
- Cleared: Data restored to normal status
- Corrected: Data adjusted based on evidence
- Removed: Data permanently deleted
- Down-weighted: Data included but with reduced influence
6. Data Freshness Policy
Price data relevance decays over time:
| Age | Weight in Calculations |
|---|---|
| 0-7 days | 100% |
| 8-14 days | 80% |
| 15-30 days | 50% |
| 31-60 days | 25% |
| 60+ days | Historical only (not in current price) |
7. Transparency Commitment
Our Commitment
- Confidence indicators are always visible
- Data source (your data vs community) is clearly marked
- Observation counts are shown for each product
- Last updated timestamps are displayed
8. Contact
For data quality concerns:
support@pricemapper.ca