Personalisation Engine for a Leading Online Grocer

The online grocer delivers to residences and offices in the New York City metropolitan area. They custom-package groceries and meals for its customers and are popular for their distribution of organic food and locally grown items, as well as items that consumers see in supermarkets daily.

problem
Challenge
  • The current system relied on simple rules and algorithms to drive recommendations
  • Used only basic customer and product attributes and transaction history
  • Limited manual override capabilities
  • Limited business scenario support.
  • Limited real-time personalization capabilities
solution
Solution

Improving customer experience with hyper-personalization across all channels – StoreFront, Mobile, Customer Support with a recommendation engine for a leading online grocer

growth
Outcome
  • Advanced Machine Learning & Neural Networks to drive product ranking and personalization
  • Personalization cockpit to support manual inputs and overrides
  • Utilize much broader customer and product attribute sets like Geographic, Demographic, Life Events, Weather, Browsing history to drive recommendations
  • Support for real-time inferences and personalization across channels.