When 33% of Black Friday deals turn out to be fake—priced higher than their lowest observed prices earlier in the season—consumers need better tools. A 2025 Visualping study tracking 1,659 products revealed that fake deals averaged 37% higher than products’ true lowest prices. This is where price comparison platforms like AVLUZ become essential shopping infrastructure.
The $350 Billion Market Opportunity
The global price comparison market reached $25.6 billion in 2024 and is projected to surge to $350.6 billion by 2032—a robust 12.8% annual growth rate. In the United States alone, the market stood at $13.35 billion in 2024 with projections of $21.88 billion by 2032.
These aren’t just impressive figures—they represent a fundamental transformation in how Americans shop. Recent research reveals that 61% of shoppers now use AI-powered tools for price comparison, while 73% have moved more shopping online in 2024. Perhaps most tellingly, 37% of US consumers say price comparison is essential—not optional, but necessary.
How AVLUZ Exposes Fake Deals
AVLUZ’s 90-day historical price charts instantly expose deceptive pricing tactics. Where retailers advertise “Save $200!” banners, AVLUZ’s trend line shows whether that discount is measured against a real historical price or an artificial reference point.
The platform tracked multiple Black Friday 2025 examples: a MacBook Air advertised at “$260 off” was actually $102 cheaper in early November. A Dyson vacuum’s “Cyber Monday Exclusive” price of $999 was $50 more expensive than its October 17 price of $949. These manipulation patterns—inflate prices weeks before sales, then “discount” back to baseline—become instantly visible through historical data.
Multi-Retailer Advantage Over Competitors
Unlike single-retailer tools like CamelCamelCamel (Amazon-only) or corporate-owned platforms like Honey (PayPal acquisition raising conflict-of-interest concerns), AVLUZ aggregates pricing from 50+ major retailers including Amazon, Walmart, Best Buy, Target, and Costco.
This breadth matters. A consumer shopping for a KitchenAid Stand Mixer might find it listed on seven different sites. Single-retailer tools show only one price history. AVLUZ shows all seven simultaneously, with trend data for each, enabling true comparison of which merchant consistently offers better value.
Performance-Based Business Model
AVLUZ operates on transparent performance-based affiliate commissions, earning 1-8% when users complete purchases through retailer links. Critically, the platform displays all prices without preferential placement based on commission rates—a transparent approach contrasting with platforms accused of steering users toward higher-paying partners.
For retail partners, AVLUZ traffic represents high-intent shoppers with conversion rates 2-3x higher than general web traffic. Users arrive ready to purchase, having already researched products and decided to buy—they’re simply choosing where to complete the transaction.
Technology Roadmap: AI Price Predictions
AVLUZ is developing AI-powered features that analyze historical patterns to forecast future price movements. Machine learning models will provide predictions like “Based on historical patterns, this television typically drops 18% in mid-January (82% confidence)” or “Price has reached seasonal low; predicted to increase in 2 weeks.”
The roadmap includes browser extensions for seamless integration, digital wallet connectivity for one-click checkout, and community-verified deal sharing with reputation systems. The vision transforms AVLUZ from a comparison tool into a shopping intelligence platform answering not just “what’s the price?” but “should I buy now or wait?”.
In a market where one-third of advertised deals are provably fake, see how AVLUZ’s price intelligence protects your wallet. By combining multi-retailer breadth, historical price depth, and transparent business practices, AVLUZ positions itself as the consumer advocate in a $350 billion market defined by opacity.
Never overpay again. Join thousands of smart shoppers using AVLUZ to make data-driven shopping decisions.