Retail pioneer in health and beauty products

Tasks

• Analyze customer behavior
• Identify upselling and cross-selling opportunities
• Leverage data analytics to boost revenues
• Increase the average basket size
• Understand key customer segments
• Retain customers in a competitive market

Challenges

Solutions

• Developed a new recommender engine to grow the average basket size
• Implemented a new churn retention strategy based on model’s predictions
• Leveraged data analytics to understand key customer segments and opportunities

Outcomes

• The new recommender engine allowed to grow the average basket size by 0.4 items
• The new churn retention strategy based on our model’s predictions allowed early detection of churning clients, which led to a decrease in churn from almost 20% to 15%

Technologies Used