ShopEasy
ShopEasy Increased Checkout Conversion Rate by 28%
Company Overview
ShopEasy is a mid-sized e-commerce platform specializing in home goods and decor. Founded in 2018, they serve over 100,000 customers across North America with a catalog of more than 5,000 products from various suppliers and artisans.
The Challenge
ShopEasy was experiencing a high cart abandonment rate of 76%, well above the industry average. Their analytics showed that most abandonments occurred during the checkout process, but they weren't sure which specific aspects were causing friction. Traditional A/B testing would have taken months to test all the variables they wanted to explore, and they needed to fix the issue before the upcoming holiday shopping season.
The Solution
ShopEasy used Litmusly to rapidly test multiple checkout flow variations with AI personas that matched their customer demographics. This allowed them to identify the optimal design and user experience before implementing changes to their live site.
The Approach
Analyzing the Current Checkout
The team first used Litmusly to test their existing 5-page checkout process with 15 AI personas representing different customer segments. This revealed specific pain points including form complexity, shipping cost surprises, and account creation requirements.
Creating Design Variations
Based on the initial findings, the design team created four distinct checkout flow variations. These included a single-page checkout, a streamlined 3-step process, a guest checkout with optional account creation, and a version with upfront shipping calculation. Each variation was tested with the same set of AI personas.
Identifying Optimal Elements
Rather than selecting a single variation wholesale, the team identified the most successful elements from each design. The AI persona feedback showed that different aspects of each variation resonated with users, allowing the team to create a hybrid solution.
Final Design Validation
The team created a final checkout design that incorporated the best elements from each variation: guest checkout by default, upfront shipping costs, streamlined form fields, and a progress indicator. This final design was validated with an expanded set of 20 AI personas before implementation.
The Results
Increased Conversion
+28%
Checkout completion rate increased from 24% to 52% after implementing the new design.
Cart Abandonment
-32%
Overall cart abandonment rate decreased from 76% to 44%.
Revenue Impact
+$245,000
Estimated additional annual revenue based on improved conversion rates.
Testing Time
2 weeks vs. 3 months
Time to test all variations compared to traditional A/B testing approach.
Litmusly allowed us to test more checkout variations in two weeks than we could have tested in a quarter using traditional methods. The AI personas provided surprisingly nuanced feedback that helped us understand not just what was happening but why users were abandoning their carts. The impact on our bottom line has been substantial.
Elena Rodriguez
E-commerce Director, ShopEasy
Key Insights
Requiring account creation before checkout was the single biggest friction point; making it optional increased conversion by 15% alone.
Shipping cost surprises were a major abandonment trigger; showing estimated shipping costs earlier in the process improved trust.
Mobile users struggled with the multi-page approach more than desktop users; the single-page design performed better on mobile devices.
Form field optimization (reducing fields and using autofill) significantly reduced checkout time from 4.5 minutes to 2 minutes.
Different customer segments had different priorities—new customers valued simplicity, while returning customers wanted speed and saved information.
Conclusion
By using Litmusly to test multiple checkout variations, ShopEasy was able to identify and implement the optimal design in time for their holiday shopping season. The new checkout process not only improved conversion rates but also enhanced customer satisfaction and repeat purchase rates. The company has since adopted Litmusly as a core tool in their UX optimization process, using it to test new features and design changes before implementation.
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