<img height="1" width="1" style="display:none" src="https://www.facebook.com/tr?id=1998336333988233&amp;ev=PageView&amp;noscript=1">

How a U.S. Furniture Brand Improved Price Accuracy by 43% and Protected Margin with Hypersonix AI

hero-image-HnW 1

THE RESULTS

Competitor AI Results43% improvement in price-match accuracy across style and material variants

Competitor AI Results18 hours/week saved in manual benchmarking and price reviews

Competitor AI Results2.7% average margin lift across top-selling living room and bedroom collections

Background

This U.S.-based mid-market furniture brand operates a multi-channel business: 200+ physical stores, an eCommerce site, and several retail partnerships.

Their catalog includes a mix of configurable products, sofas, beds, dining sets, with variation in fabric, finish, size, and material. Competitive pressure from DTC startups, online marketplaces, and value players led to frequent price shifts and comparison shopping.

The brand’s pricing and merchandising teams were flying blind, using incomplete or misaligned competitor data and static markup models. The result: over-discounting, internal pricing inconsistencies, and margin leakage.

The Challenge
  • Product matching issues, e.g., mistaking a faux-leather sofa for genuine leather, or a storage bed for a basic model
  • Manual price checks across multiple marketplaces and retailer sites
  • Price misalignment between online and store channels due to inconsistent update cadence
  • Lack of elasticity insights: leading to flat pricing even when demand patterns shifted
The Solution

The brand deployed Hypersonix Competitor AI and Pricing AI to build a cohesive, dynamic pricing strategy:

☑️ Competitor AI

  • AI-powered product matching across design, finish, material, and function
  • Daily pricing visibility across 10+ national competitors and marketplaces
  • Geofenced tracking of regional pricing variations

☑️ Pricing AI

  • Elasticity modeling for big-ticket and fast-moving SKUs
  • Predictive simulations to test margin and volume outcomes of pricing moves
  • Guardrails to prevent underpricing and ensure price integrity across channels

This eliminated guesswork and gave merchandising, pricing, and store ops teams a shared source of truth.

The Outcome
  • 43% fewer match errors leading to smarter, apples-to-apples competitive pricing
  • 2.7% average margin improvement on high-demand furniture sets
  • 18 hours/week saved in price validation and benchmarking
  • Better alignment between pricing, planning, and marketing teams

The Hypersonix Impact

  • CommerceLLM Matching: Correctly matched furniture by material, dimensions, design family
  • Daily Market Intelligence: No more lagging snapshots, fresh data enabled confident moves
  • Elasticity-Aware Pricing: Stopped flat pricing for high/low velocity items
  • Cross-Channel Sync: Unified pricing decisions across website, stores, and third-party retail partners

Still learning about ProfitGPT for Retailers and eCommerce? Get Profit Perspectives delivered to your inbox every week!