How we define accurate
Match accuracy: 98%+ across monitored domains
Data completeness: 92%+ catalog coverage rate
Products monitored: 10B+ refreshed daily
Wiser measures accuracy across two distinct dimensions:
Match accuracy: of all the products we match to your catalog, how many are correct matches? This is calculated as correct matches ÷ total matches validated.
Data completeness: of all the products in your catalog that are carried by a given retailer, how many did we successfully find and match? This is calculated as matches found ÷ potential matches.
Both matter. A tool with high match accuracy but low completeness leaves gaps in your view. High completeness with poor match accuracy fills your dashboard with noise. Wiser optimizes for both simultaneously.
How we maintain accuracy at scale
Domain-specific crawling: Rather than performing generic internet crawls, Wiser builds dedicated extraction rules for each retailer domain. When a site updates its layout, our rules are updated accordingly, minimizing disruption to your data feed.
AI-assisted matching: Our machine learning models identify product relationships (including bundles, multipacks, and private label equivalents) that rule-based systems miss. This is especially valuable in complex categories where retailers use inconsistent naming conventions.
Automated anomaly detection: Wiser deploys automated alerts for statistical outliers in price, assortment, and availability. When data deviates from expected patterns, it's flagged for immediate review before it reaches your dashboard.
Human QA layer: Data analysts continuously review match rates and refine extraction logic. This ongoing process compounds over time and allows our systems to learn from corrections.
Continuous UAT testing: Data undergoes daily accuracy checks, weekly sampling, and ongoing user acceptance testing to ensure long-term stability across every domain we monitor.
What this means in practice
Data accuracy has a direct impact on the quality of decisions you can make:
Competitive price comparisons reflect real market positioning, not extraction artifacts.
You spend less time auditing data and more time acting on it.
Mispriced products and false violations are less likely to appear in MAP enforcement alerts.
Accurate data ultimately gives you confidence behind every decision.
Watch the videos below to find out more: