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FYN / CASE STUDY

Before/after PDP transformations.

Real examples of product detail pages restructured for AI selection.

◦ TYPE · CASE STUDYApril 2026◦ © FYNDABILITY

Real examples of how Fyndability transformed product detail pages, turning invisible products into top search results.

Case Study 1 — Food & Beverage Brand

Challenge: A mid-size CPG brand with 200+ SKUs was struggling with low search visibility across Walmart, Target, and Amazon. Product descriptions were generic, attributes were incomplete, and content was inconsistent across retailers.

◦ BEFORE

Title: “Premium Organic Snack”

Attributes: Missing dimensions, incomplete nutrition info

Description: “High quality organic snack perfect for your family”

Findability Score: 42%

◦ AFTER

Title: “Organic Gluten-Free Granola Bars, 12-Count, 1.2oz Each, Non-GMO, Kosher Certified”

Attributes: 1.2oz/bar, 12-count, gluten-free, non-GMO, kosher, organic certified

Findability Score: 78% (+36 points)

Results: Search visibility increased 28% within 3 weeks. Conversion rate improved 35%. The brand moved from page 3 to page 1 for key search terms.

Case Study 2 — Home & Garden Brand

Challenge: A home improvement brand with 150 SKUs was losing sales to competitors despite superior products. Their PDPs lacked retailer-specific optimization and semantic attributes.

◦ BEFORE

Title: “Garden Tool Set”

Attributes: Missing material, dimensions, weight

Findability Score: 38%

◦ AFTER

Title: “Stainless Steel 3-Piece Garden Tool Set with Ergonomic Handles, Rust-Resistant, 12" Trowel, 14" Transplanter, 16" Cultivator”

Attributes: Stainless steel, rust-resistant, ergonomic handles, 3-piece set, specific dimensions per tool

Findability Score: 82% (+44 points)

Results: Findability score increased 44 points. Sales increased 42% over 8 weeks. The brand now ranks #1 for “stainless steel garden tools” on major retailers.

Case Study 3 — Health & Personal Care

Challenge: A wellness brand with 80 SKUs had inconsistent content across retailers. Walmart had different attributes than Target, which had different copy than Amazon. Inconsistency hurt their overall findability.

Solution: Fyndability created retailer-specific content packages while maintaining semantic consistency.

Results:

  • Walmart findability: 45% → 76% (+31 points)
  • Target findability: 52% → 81% (+29 points)
  • Amazon findability: 58% → 84% (+26 points)
  • Overall brand findability: 51% → 80% (+29 points)

Common Patterns

  • Complete attribute coverage — every successful transformation included 100% attribute completion with semantic language.
  • Retailer-specific optimization — content tailored to each retailer’s search patterns.
  • Semantic language — copy that matches how customers actually search.
  • Consistent quality — same high-quality content across all retailers.
  • Ongoing monitoring — continuous tracking and correction of content drift.
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