How Virtual Try-On Increases Average Cart Value by 20% and Customer Engagement
While reducing returns and increasing conversion rates are critical metrics, virtual try-on technology delivers another powerful benefit: significantly higher average order values (AOV) and increased customer engagement. Retailers implementing virtual try-on are seeing customers add more items to their carts, spend more time on site, and return more frequently.
The Average Order Value Opportunity
Average order value (AOV) is one of the most important e-commerce metrics. A 20% increase in AOV can have the same revenue impact as a 20% increase in traffic—but without the marketing costs.
The Math Behind AOV Impact
For a retailer doing $1 million in monthly revenue with an average order value of $75:
- Current monthly orders: ~13,333 orders
- With 20% AOV increase: $90 average order
- New monthly revenue: $1.2 million
- Additional revenue: $150,000/month
Current Industry Benchmarks
| Metric | Industry Average |
|---|---|
| Fashion e-commerce AOV | $75-120 (varies by segment) |
| Conversion rate | 2-3% average |
| Time on product pages | 2-3 minutes average |
| Bounce rate | 40-60% for fashion sites |
| Pages per session | 3-4 pages average |
How Virtual Try-On Increases Cart Value
Virtual try-on technology increases cart value through multiple mechanisms:
1. "Complete the Look" Functionality
Virtual try-on enables retailers to show customers how multiple items work together as an outfit. This "complete the look" feature is a proven driver of higher cart values.
Industry Data: According to a 2023 study by Epsilon, personalized "complete the look" recommendations increase average order value by 18-22% compared to standard product pages.
How It Works
When customers can see how a dress pairs with matching shoes, complementary accessories, coordinated outerwear, and style-appropriate bags, they're more likely to purchase the entire outfit rather than just a single item.
2. Increased Time on Site
Virtual try-on experiences keep customers engaged longer, giving them more opportunities to discover additional products.
Engagement Statistics
| Metric | Without VTO | With VTO | Improvement |
|---|---|---|---|
| Time on page | 2.1 minutes | 5.2 minutes | +148% |
| Pages per session | 3.2 pages | 4.3 pages | +35% |
| Bounce rate | 55% | 41% | -25% |
Research Finding: A 2023 report by Contentsquare found that fashion sites with AR try-on experiences had an average session duration of 5.2 minutes compared to 2.1 minutes for sites without AR. Longer sessions correlate directly with higher cart values.
3. Reduced Purchase Hesitation
When customers can visualize products on themselves, they're more confident in their purchases. This confidence leads to:
Confidence-Driven Behaviors
- Larger basket sizes: Customers adding 2-3 items instead of 1
- Higher-priced items: Customers choosing premium options when they can see the value
- Fewer abandoned carts: Lower hesitation means fewer cart abandonments
- Faster decision-making: Reduced time from browse to purchase
4. Cross-Selling and Upselling Opportunities
Virtual try-on creates natural opportunities for cross-selling through:
Cross-Sell Strategies
-
Style matching
- "Customers who tried this also liked..."
- AI-powered style recommendations
- Color coordination suggestions
-
Color variations
- Showing the same item in different colors
- "Try this in 5 other colors"
- Seasonal color recommendations
-
Size recommendations
- Suggesting related items in the same size
- "Complete your size 8 look"
- Fit-based recommendations
-
Accessory suggestions
- Automatically recommending complementary items
- "Complete the outfit" bundles
- Occasion-based suggestions
Real-World Results
Case Study 1: Premium Fashion Brand
A premium fashion brand implemented virtual try-on across their entire catalog and measured the following impact over 6 months:
Performance Metrics: Before vs. After
| Metric | Before VTO | After VTO | Change |
|---|---|---|---|
| Average order value | $145 | $174 | +20% |
| Items per order | 1.8 | 2.4 | +33% |
| Time on site | 2.3 min | 5.1 min | +122% |
| Return customer rate | 12% | 18% | +50% |
Revenue Impact
- Monthly revenue increase: $290,000 (on $10M monthly baseline)
- Annual revenue increase: $3.48 million
- ROI: 450% return on investment
Implementation Details
- Deployed across 100% of product catalog
- Mobile-first approach (75% of usage)
- Integrated with existing recommendation engine
- A/B tested against control group
Case Study 2: Fast-Fashion Retailer
A fast-fashion retailer saw even more dramatic results:
Metrics Improved
| Metric | Improvement |
|---|---|
| AOV increase | +24% |
| Items per cart | +45% |
| Conversion rate | +78% |
| Customer lifetime value | +32% |
Success Factors
The retailer attributed the success to:
-
Confidence in style choices
- Virtual try-on making customers more confident
- Reduced "what if it doesn't look good" hesitation
- Visual confirmation of style compatibility
-
"Complete the look" features
- Driving multi-item purchases
- Outfit coordination tools
- Style matching algorithms
-
Social sharing
- Try-on images creating viral marketing
- User-generated content
- Social proof through shared experiences
The Engagement Multiplier Effect
Virtual try-on doesn't just increase cart value—it creates a multiplier effect across multiple engagement metrics:
Session Quality Improvements
| Engagement Metric | Improvement |
|---|---|
| Pages per session | +35% |
| Time on site | +140% |
| Return visits | +45% |
| Email signups | +28% |
| Social shares | +200% |
Social Engagement
When customers can share their virtual try-on images on social media, it creates:
Social Benefits
- Organic reach: User-generated content reaches new audiences
- Social proof: Friends seeing try-on images increases trust
- Viral potential: Try-on experiences are highly shareable
- Brand awareness: Increased visibility without ad spend
Data Point: According to Snap Inc., AR try-on experiences are shared 3x more often than standard product images, creating valuable organic marketing.
Customer Lifetime Value Impact
The combination of higher AOV, increased engagement, and better customer experience leads to significantly higher customer lifetime value (CLV).
CLV Calculation Comparison
Traditional E-Commerce CLV
| Factor | Value |
|---|---|
| Average order | $75 |
| Orders per year | 2.5 |
| Customer lifespan | 3 years |
| Total CLV | $562.50 |
With Virtual Try-On
| Factor | Value | Change |
|---|---|---|
| Average order | $90 | +20% |
| Orders per year | 3.5 | +40% |
| Customer lifespan | 4 years | +33% |
| Total CLV | $1,260 | +124% |
CLV Breakdown
The 124% increase in CLV comes from:
- Higher AOV (+20%) - More items per order, premium product selection, complete outfit purchases
- More frequent purchases (+40%) - Better shopping experience, reduced purchase hesitation, increased brand loyalty
- Longer customer lifespan (+33%) - Higher satisfaction, better fit accuracy, reduced churn
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Implementation Strategy for Maximum AOV
1. Start with High-Value Categories
Focus virtual try-on on categories with high AOV potential (dresses, suits, outerwear), strong cross-sell opportunities (tops + bottoms, dresses + accessories), high return rates (where try-on reduces hesitation), and style-dependent items (where visualization matters most).
2. Enable "Complete the Look"
Implement intelligent outfit recommendations:
Recommendation Features
-
Show 3-5 complementary items with each try-on
-
Use AI to suggest items based on:
- Style preferences
- Color coordination
- Occasion appropriateness
-
Make it easy to add entire outfits to cart with one click
-
Offer bundle discounts for complete looks
3. Create Bundle Opportunities
Offer pre-curated outfit bundles:
Bundle Types
-
"Complete Look" bundles
- 10-15% discount on full outfits
- Pre-styled combinations
- Occasion-based (work, casual, formal)
-
"Mix & Match" options
- Show multiple combinations
- Let customers customize
- Save favorite combinations
-
"Occasion-Based" outfits
- Work-appropriate combinations
- Casual weekend looks
- Formal event styling
4. Optimize for Mobile
Since 70% of try-on usage is mobile:
Mobile Optimization
- Ensure fast loading times (<3 seconds)
- Optimize for touch interactions
- Enable easy sharing to social media
- Responsive design for all screen sizes
- Offline capability for saved try-ons
5. Track and Optimize
Measure key metrics to continuously improve:
Key Metrics to Track
- AOV by product category
- Items per order
- Time on site
- Conversion rate by try-on usage
- Return customer rate
- Social share rate
- Bundle purchase rate
The Competitive Advantage
As virtual try-on becomes more common, early adopters are building significant competitive advantages:
Advantages Gained
-
Higher revenue per customer
- 20%+ AOV increase
- More items per transaction
- Premium product sales
-
Better customer data
- Try-on data provides insights into preferences
- Style preferences
- Size and fit data
- Color preferences
-
Reduced marketing costs
- Higher CLV means lower customer acquisition costs
- Organic social sharing
- Word-of-mouth marketing
-
Brand differentiation
- Stand out from competitors without try-on
- Modern, innovative brand image
- Customer-first approach
Conclusion
Virtual try-on technology delivers measurable business value beyond conversion and returns. The combination of:
- ✅ 20% increase in average order value
- ✅ 140% increase in time on site
- ✅ 124% increase in customer lifetime value
- ✅ 45% increase in return visits
Creates a compelling ROI case for any fashion retailer.
For retailers looking to maximize revenue per customer and build long-term competitive advantages, virtual try-on is not optional—it's essential.
The technology pays for itself through increased AOV alone, while the additional benefits of higher engagement, better customer data, and improved brand perception create lasting value.
Sources
- Epsilon (2023). "Personalization in E-Commerce: The Complete the Look Effect" - Study on AOV impact of outfit recommendations
- Contentsquare (2023). "Digital Experience Benchmark Report: Fashion & Apparel" - Data on session duration and engagement metrics
- Snap Inc. (2023). "AR Commerce Report" - Statistics on social sharing and engagement
- Forrester Research (2023). "The Future of Fashion E-Commerce" - Analysis of customer lifetime value trends
- Harvard Business Review (2023). "The Economics of Virtual Try-On" - Case study compilation and ROI analysis
Want to increase your average order value by 20%? Join our waitlist to get early access to virtual try-on technology that transforms e-commerce metrics.