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Countr Platform Enhancement: Advanced Recommendation Engine & User Behavior Analysis

netguru.com

eCommerce

Advertising & marketing

Retail

Challenge: Refining Product Discovery and Personalization

While Countr's initial version successfully launched a social shopping experience, the existing recommendation engine requires significant improvement to effectively personalize user feeds. Current limitations hinder optimal product discovery, leading to potential user dissatisfaction and reduced engagement. The need to leverage user behavior data to enhance recommendations is currently unmet.

About the Client

Countr is a social shopping app connecting users with trusted recommendations, style inspiration, and opportunities to earn through content creation. The platform facilitates shopping with friends and fosters a community around shared interests.

Objectives: Enhance User Engagement & Drive Sales

Improve the accuracy and relevance of product recommendations.
Increase user engagement with the platform through personalized content.
Enable learning from user actions (likes, dislikes, purchases) to further refine recommendations.
Enhance the overall user experience through a more intuitive and personalized shopping journey.
Maintain platform scalability to accommodate future growth.

Functional Requirements

Advanced Machine Learning Recommendation Algorithm: Utilizing collaborative filtering, content-based filtering, and potentially hybrid approaches.
User Behavior Tracking: Capturing and analyzing user interactions such as likes, dislikes, purchases, and browsing history.
Personalized Feed Customization: Dynamically adjusting product recommendations based on individual user preferences and behavior.
Integration with Retailer Data: Seamlessly incorporating product data and inventory information from over 160 retailers.
Reporting and Analytics Dashboard: Providing insights into recommendation engine performance and user behavior trends.

Preferred Technologies

Ruby on Rails (existing platform)

Elasticsearch (for enhanced search)

Searchkick (for improved search capabilities)

Google Vision API (for image recognition, potentially for product categorization)

Machine Learning Libraries (e.g., TensorFlow, PyTorch, scikit-learn)

Integrations Required

Retailer APIs (for product data and inventory)
Payment Gateway APIs (for seamless checkout)

Non-Functional Requirements

Scalability: The recommendation engine must be able to handle a growing user base and increasing data volume.
Performance: Recommendations should be generated quickly and efficiently to avoid impacting user experience.
Security: User data must be protected in accordance with privacy regulations.
Maintainability: The code should be well-documented and easy to maintain.

Expected Business Impact

This project is expected to significantly improve user engagement, increase conversion rates, and drive revenue growth for Countr. By providing more relevant and personalized product recommendations, the platform will foster a more satisfying shopping experience, encouraging repeat purchases and increased user loyalty. The improved recommendation engine will solidify Countr’s competitive advantage in the social shopping market, attracting new users and retaining existing ones. The ability to learn from user behavior will further enhance the platform's personalization capabilities over time.