Purchase-Driven Taxonomy Restructuring
Redesigned information architecture based on purchase intent vs. browse behavior. Improved finding rates and aligned category structure with SEO demand.
The Problem
What I found in week 1: The navigation was too simple. Only top-level categories visible (e.g., 'Shop', 'Inspiration'). No way to navigate directly to 'Portraits' or 'Abstract paintings' from the homepage.
The bigger problem: Visits ≠ Purchases. I pulled Google Analytics data and found a critical insight: People browsed 'Nudes' out of curiosity but rarely purchased. Meanwhile, 'People and portraits' ranked lower in visits but higher in purchases.
The strategic question: Should navigation prioritize browse behavior (what people click) or purchase behavior (what people buy)?
My decision: Prioritize purchase behavior. Navigation should surface what converts, not just what attracts clicks.
Research & Discovery
1. Google Analytics audit: Analyzed category page traffic, bounce rates, and conversion paths. Identified which subjects/styles drove purchases vs. curiosity clicks.
2. Competitor analysis (Saatchi Art, Rise Art, Singulart): all competitors exposed 2-3 levels of navigation hierarchy.
Key insight: Every major art marketplace made subcategories visible in navigation. Artfinder was the outlier.
3. SEO agency collaboration: Worked with an external SEO agency who provided keyword research and category naming recommendations. Identified high-volume search terms that should be reflected in navigation.
4. Taxonomy audit: Found art terminology was inconsistent or incorrect (e.g., 'Impressionistic' → 'Impressionism'). Corrected terminology to match proper art world conventions.
5. Stakeholder workshops: Aligned on business priorities: increase discoverability, improve SEO, maintain artist relationships.
Process: Purchase-Driven IA
Design principle: 'Prioritize what people buy over what people browse.' High-purchase subjects become top-level navigation items.
The new structure: 2-level hierarchy. Top Level (Medium/Category) suffix Second Level (Subject, Style, Medium filters).
Example: PAINTINGS ├─ By Subject: Portraits, Landscapes... ├─ By Style: Impressionism, Surrealism... └─ By Medium: Oil, Acrylic...
The 5 iterations: I created 5 IA versions, iterating based on SEO recommendations, purchase data prioritization, and stakeholder feedback.
Version 5 was approved because it balanced SEO-friendly naming, purchase-driven hierarchy, and artist terminology accuracy.
Key Design Decisions
1. Purchase rank determines navigation order: Positioned 'People and portraits' higher because it drives more revenue. Buyers should see high-converting categories first.
2. Expose subcategories in navigation: Shows all subjects/styles/mediums upfront to reduce cognitive load, and improves SEO.
3. Correct art world terminology: Changed 'Impressionistic' → 'Impressionism', etc. Credibility matters—incorrect labels signal amateur curation.
4. SEO-aligned category names: Used keyword data to match category names to actual search queries (e.g., 'Modern art' vs 'Contemporary art').
The Process
Google Analytics audit (visits vs. purchases)
Competitor benchmarking
SEO agency kickoff
Taxonomy audit
Created 5 versions of information architecture
Tested different hierarchy models (subject-first vs. medium-first)
Stakeholder reviews and refinement
Designed navigation components in Figma
Created desktop mega menu and mobile drawer variations
Documented responsive behavior
Created taxonomy guide for content team
Documented category naming conventions
Handed off to engineering for implementation
Conclusion
What shipped: 2-level navigation hierarchy, Purchase-driven category prioritization, SEO-optimized category names, Corrected art terminology.
What happened after I left: The navigation structure launched and remains in use today. It solved the discoverability problem, improved SEO, and aligned with business goals.
What I'd Do Differently
1. Push for a phased launch with measurement. I'd stay through launch + 2 weeks to validate the purchase-driven prioritization with real data.
2. Test category names with users, not just SEO. I assumed correct terminology = better UX, but didn't test if users understood 'Impressionism' vs 'Impressionistic'.
3. Document the 'visits vs. purchases' insight more thoroughly. Stronger documentation = stronger case for future navigation decisions.
Key Learnings
Browse behavior ≠ purchase behavior. Navigation should prioritize what converts.
Shallow navigation hides inventory. Exposing subcategories = perceived abundance.
Art world terminology matters. Incorrect labels signal amateur curation.
Iteration is the process. 5 versions were necessary to reach the right solution.