Valerie Lee.
Love, Bonito Β· 2019

Designing a mega
menu that scales with the brand.

Imagine walking into a supermarket where nothing is in the aisle you'd expect. That's what our website felt like. I reorganized the "store layout" online so it matched how customers naturally look for products. I tested it with real shoppers to make sure it worked, and after the redesign, people went from struggling to find products to finding them successfully most of the time, increasing the success rate from 12% to 80%.

Rethinking the Site Navigation of Love, Bonito

Read as

Love, Bonito is a vertically integrated, omni-channel fashion store across Asia. I joined when LB secured funding to invest in its technology stack, transforming the store from a marketing-led experience into a user-centred, omni-channel destination.

My role: I owned the whole web redesign but for the case study, I will focus on the navigation redesign, a critical e-commerce experience that helps users quickly find what they're looking for. As part of the research, I've conducted heuristic evaluation, competitive analysis, quantitative and qualitative research and prototyping for tree testing.

Business goals:

  • Mega menu: replace the dropdown menu with a structure that can actually hold the catalogue
  • Findability: let users browse by category and sub-category to find and purchase what they came for
  • Relevance: surface the right product types directly in the navigation
  • Scalability: a menu structure that accommodates future product additions without another rebuild
New mega menu navigation
Old dropdown navigation
Old New
Drag the handle to compare the old dropdown with the new mega menu
The problem

The catalogue kept growing. The dropdown didn't.

The gap. The old dropdown worked when the catalogue was small. At scale, it buried the products people actually came for: unclear groupings, hover menus that closed mid-click, and no room for the categories customers were searching.

The challenge. Replace the dropdown with a mega menu that lets customers browse by category and sub-category, surfaces the right product types directly in the navigation, and scales as the catalogue grows. The mega menu should also serve three very different shopping intents at once, deal-hunting, trend-following, and practical everyday buying, and stay consistent across desktop and mobile.

See heuristic review
Annotated heuristic review of the old navigation, desktop and mobile
Heuristic study

The dropdown failed accessibility, and that was the least of it.

The first step was identifying exactly where the existing navigation fell short, on both desktop and mobile, so every later decision could be traced back to a documented gap. Four surfaced.

Gap 1

Not accessibility compliant.

The site failed web content accessibility standards, a hard blocker for an enterprise-ready platform.

Gap 2

Unpredictable interactions.

Hover menus on desktop opened and closed unexpectedly, confusing users mid-navigation.

Gap 3

Unclear product groupings.

Styles, thematic items and non-product items were arranged in the dropdowns without a logical structure.

Gap 4

Wasted mobile space.

Unused white space throughout the mobile menu pushed content out of reach and slowed scanning.

Research

Competitors showed the pattern. Customers showed the intent. Analytics set the order.

With the gaps mapped, I triangulated three lenses, each feeding a different layer of the new hierarchy: competitors for the interaction pattern, customer research for the parent groups, and analytics for what goes where.

Lens 1 β†’ Competitive audit

I gathered real examples from e-commerce fashion sites to understand how other brands implement mega menus successfully.

What worked across competitors: organised image displays for thematic and non-product categories, and visual text-link images that were easy to click on desktop and tap on mobile.

What didn't: overly text-heavy layouts with too few images were difficult to scan. And even where top-level parent categories were well organised, sub-categories often required too much effort to navigate.

See competitive analysis
Competitive audit board of fashion e-commerce mega menus
Lens 2 β†’ Qualitative: shopping intent

A user-focused study helped us understand customer intent. After categorising shopping motivations, three primary user types emerged, each with their own preferred first click in the parent menu.

Value Researcher

Shops for the deal and the moment. Likely first clicks: New Arrivals, Sale, Gifting, Track Restocks.

Style Follower

Shops for what's current. Likely first clicks: New Designs, Latest Trends.

Practical Buyer

Shops for a purpose. Likely first clicks: Work Wear, Daily Wear.

See customer journey map
Customer journey mapping for a persona
Customer journey mapping for a persona
Lens 3 β†’ Quantitative: Google Analytics

To inform the navigation hierarchy, I analysed Sales Data, Page Value, Site Search terms and Product Quantity from Google Analytics.

  • Sales data set the secondary category hierarchyβ†’ Dresses 39% Β· Tops 32.3% Β· Bottoms 16% Β· One-Piece 7.9%
  • Page value revealed high-interaction parent categoriesβ†’ Singapore: Tops, Bottoms, Clothing Β· Malaysia: Bottoms, Tops, New In
  • Top search terms flagged what to surface directly in the menuβ†’ Knit (material) Β· Shorts (product) Β· Denim (material) Β· Culottes (style)
Sales data, page value, user flow and top search term analysis
What the data shaped in the menu
  • Three shopping intents emergedβ†’ three parent groups: product-specific (clothing, shoes), non-product (living, gifts), thematic (sale, bestsellers)
  • Dresses drove 39% of salesβ†’ first position in the category hierarchy
  • Knit, Shorts, Denim and Culottes topped site searchβ†’ surfaced directly in the menu instead of buried in filters
  • Strong social following, campaign-aware shoppersβ†’ a featured marketing section inside the mega menu
Improvement ideas

Restructuring the tree, category by category.

With the hierarchy set by the research, I rebuilt the full navigation tree, weighing each existing category against the data before deciding whether to keep, rename, or restructure it.

Key observations by category:

  • Collections were rarely searched and the copy wasn't resonating, customers needed a clearer route to occasion and thematic products
  • New In and Clothing had high conversion rates and needed to stay prominent
  • Lifestyle and Gifts needed copy improvements
Old navigation tree next to the proposed new treeClick to view the full tree
Fig. 1: The old navigation tree beside the restructured tree
Wireframing

From a flat dropdown to a full mega menu

Wireframe proposal. Content was divided into two areas: the main navigation and a utility bar. All utility bar items became direct links, freeing the main navigation to expand into a full mega menu.

Mega menu concept. Most users begin their journey browsing by product style, occasion, latest trends, or sale items. Given Love, Bonito's strong social media following and regularly updated feed, I also designed a featured marketing section for users who are already familiar with, and loyal to, the brand.

Workshop and stakeholder review. After designing the wireframes, I ran an internal workshop with key stakeholders including the Founder, COO, Customer Care, Merchandising, Marketing, and Product teams. Adjustments were made based on their input before moving into tree testing.

Animated wireframes of the main navigation and utility bar
On-site tree testing

Ten shoppers, six tasks, tested on the sidewalk outside the store.

Rather than recruiting a panel, I took the test to where the customers already were: outside the Love, Bonito store at 313@Somerset, with walk-in and window shoppers.

10 participants 30 min per session 1 moderator Β· 1 notetaker

Scenario tasks. A mind map helped identify the most critical access points. Six scenarios were selected based on search history and upper-management priorities; all correct answers were locatable within the header and sub-header navigation.

Objectives: compare performance between the old and new site navigation, and identify the pathways users take to find a specific product.

Metrics captured:

  • Success rate: percentage of users who found the correct category for each task
  • Task performance: time taken on old versus new navigation
  • Directness: users who found the right category immediately, without backtracking
  • Pathways: observed first clicks and nominated answers using a think-aloud approach
On-site tree testing with walk-in shoppers outside the Love, Bonito store at 313@Somerset
Testing where the customers already were, outside Love, Bonito at 313@Somerset
The new navigation

The mega menu, shipped across desktop and mobile.

The redesign shipped as part of Love, Bonito's re-platformed storefront: a full mega menu on desktop, a restructured menu on mobile, a utility bar of direct links, and a featured marketing section for the brand's loyal social audience.

Desktop mega menu
Desktop
The mega menu, expanded.
Mobile navigation
Mobile
The restructured mobile menu.
Fig. 2: The shipped navigation across desktop and mobile
Findings

From 12% to 80% completion in new mega menu

Turning data into action. The first place people clicked told us a lot. When first clicks for the same task landed all over the menu, it meant shoppers carried different mental maps of where a product lives. So instead of forcing one correct path, those items were listed in every category people expected to find them. One surprise came after launch: far more people started using the search bar. Rather than guessing why, it became the first question for the next round of testing.

12%task completion, old nav

All six tasks showed performance issues, and even the successes ran with high completion times.

80%task completion, new nav

Most tasks performed well; the small gap traced to one fixable usability issue during testing. Users were also able to complete the task quickly.

↑search-bar usage

A significant rise since launch, logged as a new assumption for the next round of testing.

What I learned

What was hard, and what I'd do differently.

What was hard. Budget constraints meant the test ran on manual setup and manual data cleaning, which took far longer than planned. And because key stakeholders weren't in the room during testing, the results had to be re-proven afterwards, with video recordings and written reports, before the final design was approved.

What I'd do next. Bring stakeholders into the sessions from the start, so the evidence lands live instead of second-hand, and budget for a dedicated testing tool up front.

Ask me about Valerie's work