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UXJayley

Information Architecture Restructure

Making e-commerce navigation work with real user behaviour.

IAAnalyticsSystem design
Information Architecture Restructure cover image

My Role

UX/UI Designer (Sole Designer)

I was the only UX/UI designer working within a cross-functional team including marketing, graphic design, social media, IT, and an external development agency.

Challenge

The core challenge was not traffic, but conversion and retention.

Specifically:

- Users struggled to find products efficiently

- Filters and navigation didn’t support exploration

Goal

My goal was to:

- Improve findability and decision-making across the purchase journey

- Deliver measurable business impact quickly

Overview

Jayley is a fast-growing fashion brand offering luxury-inspired products at accessible price points. While the brand was performing strongly in physical retail, its B2C e-commerce platform was underperforming, showing low engagement, high abandonment, and weak customer retention.

The website was functional but fragmented: inconsistent navigation, ineffective filters

The Challenge

The original mobile menu: opening Womenswear reveals a long list of clothing types with overlapping items such as ponchos, wraps, capes and kimonos, followed by long colour and material lists.

One of the main issues was the confusion around categories:

Categories were:

  • Repeated with overlapping meanings
  • Organised internally rather than from a user perspective
  • Difficult to navigate on mobile
  • Poorly indexed by search and filters

This lack of information architecture had a cascading effect:

  • High drop-off from the home page
  • Broken filtering and unreliable search results
  • Delivery mistakes caused by misclassified products
  • Customer support overloaded with avoidable requests
Product cards with near-identical generic names: two 'Devore Tasseled Kimono Jacket' items at £89 and £94, a 'Luxury Wool Blended Ponchos' and a 'Luxury Wool Blended Cape'.

Strategy: Fix the System, Not the Labels

Given the constraints (no access to users, no A/B testing), I adopted a data-informed, iterative IA strategy. My approach focused on:

  • Reducing cognitive load on mobile
  • Aligning category logic with user expectations
  • Improving findability without increasing complexity

I prioritised the main navigation, as analytics showed the highest abandonment occurred immediately after landing.

How it was Tree diagram of the original information architecture, showing overlapping and poorly organised categories Proposal Tree diagram of the proposed information architecture, showing the simplified and reorganised category structure

Core Actions on the Main Menu

I worked on five interconnected levers:

  1. Renaming ambiguous categories
  2. Reducing the number of subcategories
  3. Aggregating categories based on behavioural data
  4. Eliminating repetitions
  5. Reordering categories based on real usage (not conventions)

Because we couldn’t run card sorting or A/B tests, I proposed monthly incremental changes, measuring impact through analytics. This controlled rollout allowed us to validate decisions while limiting business risk. The process ran over ~12 months.

1. Renaming, Aggregating and Reordering High-Impact Categories

The menu before and after: Online Exclusive, Weddings and Inspiration replaced by Sale and Trending.

Two categories required immediate attention:

“Online Exclusive” → Sales

“Online Exclusive” was effectively a hidden sale category, yet analytics showed it had one of the highest conversion rates — strongly aligned with one of our personas (“discount chaser”).

The label masked its value and reduced discoverability. I proposed making it explicit and enabling price-led browsing, increasing clarity without cannibalising other channels.

“Weddings” → Trending

The “Weddings” category mirrored B2B wholesale logic and performed well only during short seasonal windows. For a B2C e-commerce, this created dead weight in the main navigation. We deprioritised it in favour of trend-led discovery, targeting the 24–35 audience interested in inspiration rather than occasion-based shopping.

To guide users through familiar patterns, I:

  • Grouped capes and wraps under Womenswear, near Jackets and Coats
  • Aggregated low-range categories (e.g. Jackets + Coats)
  • Reordered categories based on analytics rather than alphabetical logic

We consciously reduced overall “findability” in favour of:

  • Faster recognition
  • Better experience for returning users
  • This trade-off was mitigated by simplifying the navigation structure.

For fast-growing categories, I deliberately expanded subcategories to preserve consistency and support scalability.

2 & 5. Reducing categories, Clarifying Hybrids.

Product names before and after: 'Luxury Wool Blended Cape' becomes 'Emma, Faux Suede Cape with Fox Fur Collar', and the poncho becomes 'Olivia, Cashmere Blend Coney Fur Poncho'.

A major IA challenge was hybrid products (ponchos, wraps, capes), which didn’t match customer mental models.

Internally, products were categorised by technical definitions and uploaded by IT, resulting in:

  • Near-identical names
  • Confusing categorisation
  • High customer support volume

I intervened upstream in the system:

  • Took ownership of product naming
  • Introduced distinctive, human-readable product names
  • Aligned naming, category placement, and descriptions

This improved:

  • Search performance
  • Filter accuracy
  • Support team efficiency

Outcomes

Through a customer-centred redesign of UX, IA, and UI, the e-commerce platform achieved:

  • +300% engagement
  • +17% returning customers
  • +35% conversion rate

Improvements were driven primarily by:

  • A restructured information architecture
  • A redesigned search and filtering experience
  • Better SEO performance due to cleaner structure and content hierarchy
  • A clearer, more confident visual and interaction language
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