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Business Impact

  • 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
Challenges

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

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