AI-Powered Smart Recommendation Solution
Retail & CPGBusiness Impacts
Improved Customer Experience
Diverse Recommendations
25% Take Rate
Increased Revenues
Customer Key Facts
- Location : Burbank, California
- Industry : Food & Beverage
Problem Context
The client, a leading US-based entertainent company, wanted a recommendation solution for restaurants in their theme parks that could recommend sides or beverages based on the cart items. The existing solution was incapable of providing the most relevant and accurate items to the user by filtering useful information from a huge pool of data. The client also requested to integrate a solution that could be further scaled to 100+ restaurants across their two major theme parks in the US.
Challenges
- Designing an intelligent recommendation system for restaurants
- Optimizing ML models to drive more revenue
- Scaling to divergent restaurants
Technologies
Python
XGBoost
AWS Sagemaker
AWS Lambda
AWS S3
AWS Cloudwatch
AWS Redshift
AWS Quicksight
Built a Bespoke Solution to Recommend Menu Items Using Intelligent ML Models
Solution
Quantiphi developed an AWS-based solution that would recommend menu items using advanced ML models and build an end-to-end solution that can be further scaled to 100+ restaurants across 2 major theme parks in the US.
Result
- Increased average order value
- Boost number of items per order
- Increased customer satisfaction
- Generate greater revenue
