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Retail & E-commerce Intelligence

Smart Product Recommendation Engine Increases Conversion by 18%

Launched an AI-powered recommendation engine for an online fashion retail platform that personalized product suggestions based on user browsing behavior, purchase history, and clickstream data, improving engagement, conversion, and cross-sell effectiveness.

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18%
Increase in product page conversions
12%
Higher average order value due to personalized cross-sells
30k+
Active users served per day by the recommendation engine

Client Overview

Industry: Online Fashion Retail
Core business: E-commerce platform selling apparel and accessories to mid-market and enterprise customers
Employees: 120
Geography: US & EU

Smart Product Recommendation Engine Increases Conversion by 18% overview

Challenges

A fashion e-commerce platform aimed to improve engagement and sales performance but faced limitations in its recommendation capabilities. Despite having access to behavioral and transactional data, the system relied on static logic and lacked real-time adaptability.

Low engagement and conversion on product pages

Users were not interacting with existing recommendations, leading to missed sales opportunities and lower conversion rates.

Generic “bestseller” recommendations not tailored to users

The recommendation logic was static and did not account for individual user preferences, browsing behavior, or purchase history.

Difficulty predicting relevant cross-sell opportunities in real time

The platform lacked real-time analytics and adaptive models to dynamically suggest complementary products based on live user activity.

Solution

We designed and implemented a scalable AI-powered recommendation engine integrated with the client’s web platform to enable real-time personalization.

Personalized recommendation engine

Developed using collaborative filtering and content-based models to generate relevant product suggestions for each user.

Real-time user behavior tracking

Monitored clicks, purchases, and browsing history to continuously refine recommendations.

Dynamic “You Might Also Like” carousels

Displayed personalized product suggestions across product pages, homepage, and category pages.

Seamless web integration

Delivered recommendations through API endpoints to ensure smooth front-end rendering across desktop and mobile.

Business Impact

The AI-powered recommendation engine significantly improved engagement, conversion, and overall revenue performance.

Accelerated user engagement

Time on site and interaction with recommended products increased by 22%.

Improved conversion and cross-sell

Product page conversions increased by 18%, and average order value grew by 12% due to relevant cross-sell recommendations.

Scalable and reliable solution

The system served over 30,000 active users daily with real-time updates, maintaining stable performance without degradation.

Our Clients Say

“Overall, I was very satisfied with the work. Omnicode . The team managed the project well, delivered on time, and was AI completed a responsive to our needs. Their strong technical expertise and business understanding were key to the project's success.”
DENZEL HANKINSON, CEO
DHInfrastructure, USA
“Omnicode throughout the project, and collaborated closely with us. We have received a system that works flawlessly and is welldocumented. AI delivered a fully functional solution, communicated well Their deep knowledge of AI and machine learning really stood out.”
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Milk Interactive AG, Zurich, Switzerland
“Omnicode engagement. next-generation generative AI demonstrated excellent project management throughout the They delivered scalable, production-ready solutions using AI technologies. The team was highly responsive, adaptable to changing requirements, and maintained strong communication at all stages.”
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Strontium Corporation / COO, ECE Software, Canada
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GURGEN PETROSSIAN, Human Rights Consultant
Petrossian Consulting, Germany
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CEO, AI Social Media App Firm
Anonymous
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General Manager, ELAR FZ LLC
Anonymous
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Taron Simonyan, Founding Partner
ELL PARTNERSHIP

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