Wine Recommendation Engine
Driving Competitive Advantage with Data-Driven Sales Strategy
From School Project to Real Product
This project began as a school assignment but evolved into a real-world solution that's being implemented by Grupo Pampa to transform their B2B wine sales approach.
Project Overview
Our team transformed Grupo Pampa's traditional B2B wine sales model into a scalable, data-driven recommendation engine. The goal was to reduce human bias in wine suggestions by leveraging historical sales data and machine learning.
By analyzing two years of sales and geolocation data, we were able to identify patterns in restaurant purchasing behavior and create a collaborative filtering model that suggests wines based on customer profiles and affordability.
The project culminated in a Power BI dashboard that visualizes restaurant clusters and provides real-time wine suggestions for salespeople and executives, significantly improving the efficiency and effectiveness of the sales process.