case study

Personalized Recommendations for a Large Canadian Technology Company

Marketing Analytics

Business 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 Recommendation AI

Google Cloud Storage

Google Cloud Storage

Google Cloud Function

Google Cloud Function

Google Cloud DataFlow

Google Cloud DataFlow

Google Cloud Pub/Sub

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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