Personalized Recommendations for a Large Canadian Technology Company
Marketing AnalyticsBusiness Impacts
Hyper-personalized recommendations
Increase in revenue generated per user
Customer Key Facts
- Country : Toronto, Canada
- Industry : Technology | Marketing Analytics
- About : Canada-based technology company offering search and recommendation capabilities to their customers.
Problem Context
The current data stack was inhibiting the client from providing a personalized shopping experience to their customers. As the existing system failed to meet the business requirements, the client required a system that could provide a personalized shopping experience to each of its customers at a product line level.
Challenges
- Discrepancies in product catalog data
- User events not captured as per the recommended practices
- Lack of data transformation to maintain uniformity and enable seamless modeling
Technologies Used
Google Cloud Recommendation AI
Google Cloud Storage
Google Cloud Function
Google Cloud DataFlow
Google Cloud Pub/Sub
Providing personalized shopping experience to retail customers at a product line level
Solution
- Quantiphi helped the client in building a recommendation system for its customers using search and purchase data of users
- The ML model deployed were for three identified use cases - recommended for you, others you may like, and frequently bought together
Results
- Hyper-personalized recommendations
- Greater cross-selling and user engagements
- Improved CTR of provided recommendations
- Increased revenue generated per user
