Case: Increasing Average Order Value through Cart Recommendation
Key Results:
+14% growth in Average Order Value (AOV)
+19% increase in Units Per Transaction (UPT)
+22% of users added at least one product from the “Frequently Bought Together” block
Recommendation block CTR reached 11.3%
Average time spent in the cart increased by 9 seconds
Cart abandonment rate remained stable
The new approach became the foundation for future on-site personalization
Client:
Industry: Online store for electrical equipment and related products
Target region: Ukraine
Services provided: UX/UI optimization, mathematical model development, recommendation system implementation
Company size: Large online store with thousands of SKUs
Goals and Objectives:
Increase average order value without discounts or promotions
Increase the number of items per order
Use existing sales data to generate personalized recommendations
Retain users at the cart stage
Workflow:
Problem:
The previous "Added to cart" logic provided no motivation to buy additional products. After clicking “Buy,” the user only saw a simple confirmation message without any product suggestions.
Approach:
A mathematical model was developed to generate recommendations based on historical data: “Users who bought this item also bought…”. This list appears in a pop-up cart after a product is added.
Challenges faced by Nikitishyn:
No ready-made module supported the custom recommendation logic
Maximum speed was crucial — recommendations had to load instantly
The pop-up window couldn’t be overloaded — only relevant products were allowed
Solution:
Developed a custom logic for product associations
Built a lightweight block displaying up to 4 frequently co-purchased products
Enabled quick-add functionality directly in the cart pop-up
All changes were tested via A/B testing before launch
Results:
In just the first few weeks:
– AOV and item count per order started growing
– Users actively interacted with the new block, adding suggested items
– Business achieved profit growth without discounts through effective upselling logic
#eCommerceAudit #UXUI #CartOptimization #AverageOrderValue
#RecommendationSystem #Personalization #SmartCart #AOVgrowth
#ConversionOptimization #UserBehavior #DigitalCommerce #ClientCase
#Nikitishyn #EcommerceGrowth #NoDiscountStrategy #UPT #AIRecommendations
#RetailTech #DataDrivenUX #CaseStudy
+14% growth in Average Order Value (AOV)
+19% increase in Units Per Transaction (UPT)
+22% of users added at least one product from the “Frequently Bought Together” block
Recommendation block CTR reached 11.3%
Average time spent in the cart increased by 9 seconds
Cart abandonment rate remained stable
The new approach became the foundation for future on-site personalization
Client:
Industry: Online store for electrical equipment and related products
Target region: Ukraine
Services provided: UX/UI optimization, mathematical model development, recommendation system implementation
Company size: Large online store with thousands of SKUs
Goals and Objectives:
Increase average order value without discounts or promotions
Increase the number of items per order
Use existing sales data to generate personalized recommendations
Retain users at the cart stage
Workflow:
Problem:
The previous "Added to cart" logic provided no motivation to buy additional products. After clicking “Buy,” the user only saw a simple confirmation message without any product suggestions.
Approach:
A mathematical model was developed to generate recommendations based on historical data: “Users who bought this item also bought…”. This list appears in a pop-up cart after a product is added.
Challenges faced by Nikitishyn:
No ready-made module supported the custom recommendation logic
Maximum speed was crucial — recommendations had to load instantly
The pop-up window couldn’t be overloaded — only relevant products were allowed
Solution:
Developed a custom logic for product associations
Built a lightweight block displaying up to 4 frequently co-purchased products
Enabled quick-add functionality directly in the cart pop-up
All changes were tested via A/B testing before launch
Results:
In just the first few weeks:
– AOV and item count per order started growing
– Users actively interacted with the new block, adding suggested items
– Business achieved profit growth without discounts through effective upselling logic
#eCommerceAudit #UXUI #CartOptimization #AverageOrderValue
#RecommendationSystem #Personalization #SmartCart #AOVgrowth
#ConversionOptimization #UserBehavior #DigitalCommerce #ClientCase
#Nikitishyn #EcommerceGrowth #NoDiscountStrategy #UPT #AIRecommendations
#RetailTech #DataDrivenUX #CaseStudy
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