E-commerce Information Architecture Template

Building an intuitive e-commerce website starts with understanding how customers naturally organize and find products. This free template includes everything you need to run a card sorting study for your online store—from product categories to checkout flow.

Why E-commerce IA Matters

E-commerce conversion rates directly correlate with findability. Studies show:

37% of users abandon shopping if they can't find products quickly
Optimized navigation increases conversion rates by 15-25%
Clear categorization reduces support tickets by 30%

Card sorting reveals:

How customers naturally group products
What category names make sense to shoppers
Where features like "Wishlist" or "Compare" belong
Which filters and facets are most important

Template Overview

What's Included

Ready-to-use cards (40 items):

Product categories (Clothing, Electronics, Home & Garden, etc.)
Shopping features (Wishlist, Cart, Checkout, Compare)
Account features (Order History, Saved Addresses, Payment Methods)
Support & Info (Size Guide, Returns, Shipping Info, Live Chat)

Recommended study type: Open card sort → Closed card sort (two phases)

Suggested participants: 25-30 customers

Time to complete: 10-12 minutes per participant

Analysis time: 3-4 hours

The Template: E-commerce IA Card Sort

Phase 1: Open Card Sort (Discovery)

Cards for Product Categories (30 cards):

Men's Clothing
Women's Clothing
Kids' Clothing
Athletic Wear
Shoes & Sneakers
Accessories & Jewelry
Bags & Luggage
Watches
Sunglasses
Beauty & Skincare
Electronics
Smart Home Devices
Gaming & Consoles
Books & Media
Home Decor
Furniture
Kitchen & Dining
Bedding & Bath
Pet Supplies
Toys & Games
Outdoor & Sports
Fitness Equipment
Camping & Hiking
Art & Craft Supplies
Office Supplies
Tools & Hardware
Automotive
Health & Wellness
Seasonal & Holiday
Clearance & Sale Items

Cards for Features & Functions (15 cards):

Shopping Cart
Wishlist / Favorites
Product Comparison
Size Guide
Gift Registry
Order Tracking
Customer Reviews
Live Chat Support
Email Notifications
Saved Addresses
Payment Methods
Order History
Return Request
Shipping Calculator
Loyalty Program

Instructions for participants:

Welcome! We're redesigning our online store navigation.

Please group these products and features into categories that make sense to you.
Create category names that would help you find items quickly.

Think about how you naturally shop online. There are no right or wrong answers!

This will take about 12 minutes. Thank you!

What you'll learn:

Natural product groupings (by gender, category, or activity)
Whether customers think by "who" (men/women/kids) or "what" (clothing/shoes/accessories)
Where shopping features belong (top nav, account area, product pages)
Common category names customers use

Phase 2: Closed Card Sort (Validation)

After analyzing Phase 1 results, create a closed sort to validate your proposed structure.

Example proposed structure (from Phase 1 results):

Category 1: Shop by Category
Category 2: Shop by Who
Category 3: My Account
Category 4: Customer Service
Category 5: Special Offers

New cards to sort (25 different products): Use a fresh set of items to avoid bias. Ask participants to place each item into your proposed categories.

What you'll learn:

Agreement scores (do customers agree with your categories?)
Confusing items (which products don't fit clearly?)
Missing categories (do you need to add more?)
Category label clarity (do names make sense?)

Real-World Example: Fashion E-commerce

Before Card Sorting

Original navigation (designer's view):

├─ New Arrivals
├─ Collections
├─ Apparel
├─ Footwear
├─ Accessories
└─ Sale

Problem: 43% of users clicked "Collections" expecting all products, but it only showed curated sets.

Card Sort Results

25 customers sorted 40 fashion items. Key findings:

Strong groupings (75%+ agreement):

├─ "Women" / "Shop Women"
│  └─ Dresses, Tops, Pants, Shoes, Bags
├─ "Men" / "Shop Men"
│  └─ Shirts, Pants, Shoes, Accessories
├─ "Kids"
│  └─ Girls' Clothes, Boys' Clothes, Baby
├─ "My Stuff" / "Account"
│  └─ Orders, Wishlist, Profile, Addresses
└─ "Help" / "Support"
   └─ Size Guide, Returns, Contact, FAQ

Surprising insights:

68% grouped products by gender/age first, not by type
"Collections" meant "all products" to users, not "curated sets"
"Sale" items should appear in each gender category, not separately
Wishlist and Order History belong in same "My Account" area

Implemented Solution

New navigation (based on card sort):

├─ Women
├─ Men
├─ Kids
├─ Sale (with gender filters)
├─ New Arrivals
└─ [Account icon] My Account
    ├─ Orders
    ├─ Wishlist
    ├─ Profile
    └─ Addresses

Results after 3 months:

28% increase in products viewed per session
19% increase in add-to-cart rate
15% decrease in "Where is...?" support tickets
12% increase in overall conversion rate

Best Practices for E-commerce Card Sorting

1. Include Real Product Names

Don't use:

"Product A"
"Item 123"
"Category X"

Do use:

"Nike Air Max Sneakers"
"Organic Cotton T-Shirt"
"Stainless Steel Water Bottle"

Why: Real product names trigger actual shopping behavior and mental models.

2. Mix Product Types & Complexity

Include:

Clear-cut items (T-Shirt → obviously Clothing)
Ambiguous items (Yoga Mat → Fitness? Sports? Wellness?)
Cross-category items (Athletic Socks → Clothing? Sports? Footwear?)

Why: Edge cases reveal category boundaries and help you handle "doesn't quite fit" products.

3. Test with Your Actual Customers

Target participants:

60% existing customers (know your store)
30% prospects (fresh perspective)
10% frequent shoppers (power users)

Why: Different customer segments may shop differently (browse vs. search, impulse vs. research).

4. Include Non-Product Cards

Don't just test products. Include:

Shopping features (Wishlist, Compare)
Service elements (Live Chat, Size Guide)
Account features (Order History, Saved Payments)

Why: Navigation isn't just about products. Users need to access features and services too.

5. Follow Up with Tree Testing

After card sorting: Validate with tree testing

Give users tasks: "You want to find women's running shoes. Where would you go?"
Test if they can actually find items in your new structure
Measures findability, not just groupings

Common E-commerce IA Patterns

Pattern 1: Shop by Category (Product-first)

├─ Clothing
├─ Shoes
├─ Accessories
├─ Electronics
└─ Home & Garden

Best for: General merchandise stores with diverse products

Pros: Intuitive for browsers, easy to understand Cons: Can become overwhelming with many categories

Pattern 2: Shop by Audience (People-first)

├─ Women
├─ Men
├─ Kids
├─ Home
└─ Pets

Best for: Fashion, apparel, lifestyle brands

Pros: Reduces cognitive load, targets specific shoppers Cons: Some products fit multiple audiences (gifts, unisex items)

Pattern 3: Shop by Activity (Use case-first)

├─ Running
├─ Yoga
├─ Hiking
├─ Gym & Training
└─ Recovery

Best for: Specialty retailers (sports, hobbies, activities)

Pros: Matches shopping intent, easy upselling Cons: Requires customers to know their activity/need

Pattern 4: Hybrid (Multi-axis navigation)

Top nav: Women | Men | Kids | Sale
Secondary nav (within Women):
  ├─ Clothing
  ├─ Shoes
  ├─ Accessories
  ├─ By Activity (Running, Yoga, Casual)
  └─ By Occasion (Work, Weekend, Athletic)

Best for: Large catalogs, complex product lines

Pros: Multiple paths to same product, serves different shopping styles Cons: More complex to build and maintain

Using This Template

Step 1: Customize the Cards (15 minutes)

Replace the generic products with your actual product names:

Generic: "Men's Shirt"
Your store: "Organic Cotton Henley - Men's"

Generic: "Electronics"
Your store: Specific products like "Bluetooth Speaker" or "Wireless Earbuds"

Include:

Your top 20-30 best-selling products
5-10 new products you're adding
5-10 products customers struggle to find (from support tickets)
Key features and services

Step 2: Set Up Study (5 minutes)

Create study with this template →

Settings:

Type: Open card sort
Number of cards: 35-45 (don't exceed 50)
Estimated time: 10-12 minutes
Collect: Name, Email (for follow-up)

Step 3: Recruit Participants (1-2 days)

Email template:

Subject: Help us improve our online store (10 min, $10 gift card)

Hi [Name],

We're redesigning our website to make shopping easier. Would you help by organizing products into categories that make sense to you?

It takes 10-12 minutes and you'll receive a $10 store credit.

[Link to study]

Thanks!

Target: 25-30 participants

Step 4: Analyze Results (3-4 hours)

Look for:

Product pairs grouped together 70%+ of the time (strong relationships)
Most common category names (top 5-7)
Products that don't fit anywhere (rethink inclusion or create new category)
Differences by customer segment (new vs. returning, age, gender)

Tools: Free Card Sort provides similarity matrix, dendrograms, and category analysis

Step 5: Design Navigation (1 day)

Based on results:

Create 5-7 main categories (top-level navigation)
Add 3-5 subcategories under each
Place features and services in appropriate sections
Create logical paths for edge cases

Step 6: Validate with Closed Sort or Tree Test (3 days)

Before building, validate your proposed structure with a second study.

Metrics to Track Post-Launch

Navigation metrics:

% of sessions using top nav vs. search
Clicks to product detail page
Bounce rate on category pages
Use of filters and facets

Business metrics:

Conversion rate (overall and by category)
Products viewed per session
Add-to-cart rate
Cart abandonment rate

Support metrics:

"Where is...?" support tickets
Navigation-related feedback
User testing task completion

Target improvements:

15-20% increase in conversion rate
25-30% reduction in navigation support tickets
30-40% improvement in findability (tree testing scores)

Related Templates

Frequently Asked Questions

Q: Should I test with mobile users separately? A: Not necessarily. Card sorting reveals mental models, which are consistent across devices. However, mobile users may prioritize certain features differently (e.g., Quick View, One-Click Checkout). Consider including mobile-specific features in your card set.

Q: How often should I re-run this study? A: Every 12-18 months, or when:

Adding new product categories
Expanding to new audiences
Support tickets indicate navigation confusion
Conversion rates decline

Q: Can I test multiple languages? A: Yes! Run separate studies per language/region. Mental models and category names often differ by culture.

Q: What if results don't match our current structure? A: That's the point! Card sorting shows user mental models, not your internal org chart. If results diverge significantly, user research is telling you to adapt.

Ready to Optimize Your E-commerce IA?

Use this template now (free) →

What you'll get:

Pre-configured card sorting study
Automatic analysis & visualizations
Similarity matrix & dendrograms
Export results to CSV/Excel

No credit card required. 3 free studies.

Next Steps

Create free account (2 minutes)
Load e-commerce template (1 click)
Customize products (15 minutes)
Send to 25 customers (1 day)
Analyze results (3 hours)
Redesign navigation (1 week)
Increase conversion rates (ongoing)

Start optimizing your e-commerce IA today.

E-commerce Information Architecture Template | Free Card Sort