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Revolutionizing Offline Retail Pricing & Promotions with ML — Daniel Guhl

Everybody uses price promotions in retail. However, individual pricing is seldom used, particularly in offline retail. Marketing literature has been advocating the use of individual price discrimination for decades. Furthermore, product recommendations, ever-present in e-commerce, are also not often found in offline retail. We show the machine learning driven system behind a new promotion channel that enables retailers and manufacturers alike to target individual customers in offline retail. Lessons learned, technologies used, and machine learning approaches driving our system will be shown.


Daniel Guhl has a background in economics & marketing, and got interested in data modeling during his Ph.D.. Currently, he is working as a data scientist at a Berlin based Start-up and is pursuing a postdoc at Humboldt University. He enjoys learning everyday and focuses on solving real world problems.

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Jacek Dabrowski has a university background in mathematics, computer science and psychology. He is also a startup veteran with experience in financial technology and online advertising. His current focus is on building distributed real-time systems, big data pipelines and machine learning engines. He is also passionate about deep learning applications.

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Later Event: March 15
Coffee break