Pague Menos implements intelligent product recommendation for scaled conversionrates​​

01
The client​

Pague Menos is the second largest pharmacy chain in Brazil in number of stores, with over 1.6k units, and pioneered the country’s drugstore segment.​

With more than 70k SKUs and no product recommendation feature in its e-commerce, items on the navigation pages were exclusively promotional and not geared toward the customer's purchasing preference.​

Committed to always offering the best experience for its consumers, the pharmacy chain wanted to explore a more personalized recommendation system that targeted each shopper's individual preferences.​

02
The challenge​

Efficiency and timing are two of the most important features in retail. That’s why large companies are challenged to establish market share and engage consumers, improving customer experience and relationship while increasing consumption.​

Given this scenario, AI/R Compass UOL worked to deliver an intelligent recommendation system to introduce products for customers according to their preferences.​ 

03
The solution​

In less than 60 days, AI/R Compass UOL, in partnership with AWS, ran a product recommendation service for product ranking using Artificial Intelligence (AI) and Machine Learning (ML).​

Leveraging AI/R Compass UOL’s AI accelerators and Amazon Personalize for ML, Pague Menos can now offer the targeted recommendation of more than 70k SKUs directly in the shopping cart.​

 

With Amazon Personalize, the client accessed an ML service that allows the creation of personalized, scalable recommendation systems by using advanced ML algorithms that analyze patterns in historical data—items, customers, sales, and shopping baskets—to identify the most relevant items for each user.​

The system learns and is retrained with every new user interaction data, updating and refining its recommendations over time and allowing Pague Menos to offer highly personalized experiences with increased customer engagement, sales, and loyalty.​

04
Main results​

Improved accuracy of recommendations tailored to each customer profile​

Improved conversions and operational efficiency with up to 1% intended increase in monthly sales ​

Constant ML training after new user interactions​

Tailored delivery to each consumer with a login and interaction history in the e-commerce​

05
Our Impact

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