Data Science Applications in Retail Pricing and Promotions
Many retailers have been slow in incorporating analytics as well as data science tools when making decisions on optimal prices and promotions for their products and instead tend to rely on traditionally used methods. In this presentation I will first share my experiences from working on a newly formed Pricing Data Science team at Staples over the last two years, the challenges faced and lessons learned. Then, I will talk about various applications in Pricing that can benefit from the use of machine learning techniques and deep dive into one of those – predicting the effectiveness of retail promotions, particularly price drops.
Key Takeaways: • Which applications related to retail pricing and promotions can benefit from the use of analytics and machine learning tools? • How to organize a data science team dedicated to pricing? What are the skills required? • Project deep dive: predicting the effectiveness of retail promotions (product price drops)
Ioanna is a Data Scientist based in Boston, Massachusetts. Over the last two years she worked as a Senior Pricing Statistician at Staples where she focused on optimizing pricing strategies and promotions for retail. The retail sector is undergoing a big transformation and Ioanna is passionate about applying data science to help overcome the challenges that this sector is facing as well as automating processes and creating tools that help others generate insights from data. She is also passionate about constantly learning new skills and sharing her knowledge. Before joining Staples Ioanna worked as an economic consultant at The Brattle Group and also as a Research Assistant at MIT where she did energy and climate modeling. She holds a Master in Technology and Policy from MIT and a Bachelor in Electrical and Computer Engineering from the National Technical University of Athens, Greece.