AI-Powered Product Recommendations for E-Commerce
Our client, a healthcare innovator, provides full-spectrum diagnostic and preventive health solutions to corporate clients across India.
With a mission to drive proactive healthcare transformation, they aimed to leverage data for better monitoring, early intervention, and informed decision-making. Their goal was to build a scalable analytics foundation to visualize diagnostic trends and improve employee health outcomes.
View LiveE-Commerce | HVACR Industry
13 Weeks
- AI-Powered Recommendation Engine
- Machine Learning Model Development
- Product Recommendation Analytics
- Customer Behavior Analysis
- Data Collection & Exploration
- Data Cleaning & Structuring
- Real-Time E-Commerce Integration
- A/B Testing & Optimization
The Challenge
The client’s previous recommendation engine, powered by Proton.ai, failed to meet expectations. The system was not delivering relevant product suggestions, limiting personalization and reducing opportunities to increase revenue through cross-selling and upselling.
Inaccurate Recommendations
Suggestions were often irrelevant to user preferences, reducing engagement and weakening the online shopping experience.
Limited Personalization
The system lacked the ability to deliver tailored product recommendations based on user behavior, purchase patterns, and product relationships.
Missed Upselling Opportunities
The recommendation engine was unable to surface relevant complementary products, leading to lost revenue opportunities.
Our Solution
OnPoint Insights collaborated with the client to design and implement a highly personalized recommendation system using advanced AI and machine learning techniques.
The solution used behavioral, transactional, and product attribute data to deliver more accurate and dynamic product recommendations across the e-commerce journey.
Aggregated behavioral, transactional, and product attribute data to uncover meaningful patterns.
Handled missing values, cleaned outliers, and ensured consistency for high-quality model input.
Established a baseline using a simple popularity-based model to compare recommendation performance.
Conducted A/B testing to measure improvements in engagement, cart completion, and overall revenue impact.
Embedded the engine into the e-commerce platform to provide dynamic recommendations across product pages, search results, and checkout flows.
Implemented Matrix Factorization with Alternating Least Squares to surface relevant product recommendations based on latent user-item relationships.
Results & Impact
The AI-powered recommendation engine helped the client improve product discovery, personalize the shopping journey, and increase revenue opportunities across the platform.
Precision in Recommendations
Users received more relevant product suggestions, increasing engagement.
Personalized User Experience
The engine adapted to individual behavior, boosting conversion rates.
Revenue Growth
Improved cross-sell and upsell strategies led to significant revenue uplift.
Customer Satisfaction
A more intuitive and responsive shopping experience increased loyalty and repeat visits.
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