Client Overview

Our client is a leading e-commerce platform with a diverse range of products catering to a large and diverse customer base. They sought to enhance their customer experience by implementing a sophisticated recommender engine to personalise product recommendations.

Industry

Retail, eCommerce

Launch Timeline

3 Months

Services Provided :
Tech Stack Finalisation
Quality Assurance
Devops & Cloud Infra Support
Data Science & Artificial Intelligence

Key Challenges

Diverse Product Range: With thousands of products across various categories, it was challenging to provide relevant recommendations to each customer.

User Engagement: Increasing user engagement and driving repeat purchases required personalized recommendations that aligned with individual preferences.

Scalability: The solution needed to be scalable to handle the growing volume of products and users on the platform

Approach - Path to Solution

Data Collection and Processing: We collected and processed data on user interactions (e.g., clicks, purchases, ratings) and product attributes to build a comprehensive dataset.

Collaborative Filtering Algorithms: We implemented product-based collaborative filtering to recommend products similar to those a user has interacted with. We also used user-based collaborative filtering to recommend products based on the preferences of similar users.

Model Training and Validation: We trained and validated the collaborative filtering models using historical data to ensure accuracy and relevance of recommendations.

Platform Integration: We integrated the recommender engine with the client’s e-commerce platform, enabling real-time recommendations based on user interactions.

Testing and Optimization: We conducted extensive testing and optimization to fine-tune the recommender engine for optimal performance and relevance of recommendations

Results

Improved User Engagement

The personalized recommendations led to increased user engagement, with users more likely to discover and purchase relevant products

Higher Conversion Rates

The relevant product recommendations resulted in higher conversion rates and increased sales for the client

Enhanced Customer Satisfaction

Customers appreciated the personalised shopping experience, leading to higher satisfaction and loyalty

Conclusion

Dew Solutions’ collaboration with the e-commerce client demonstrates our ability to deliver innovative AI solutions that drive real-world impact. By leveraging collaborative filtering algorithms, we helped the client personalize product recommendations, leading to increased user engagement, higher conversion rates, and enhanced customer satisfaction

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