Asko Relas: Machine Learning for conversion optimization – How to be relevant at scale

This document discusses using machine learning techniques like multi-armed bandits and contextual bandits for automated conversion optimization across channels. It explains that A/B testing everything at scale is inefficient, while bandit algorithms allow earning while learning to continuously optimize conversions. The key aspects covered are: balancing exploration and exploitation when selecting the best performing variants, using user contextual data as input to a neural network to map users to optimal content, and combining bandit algorithms with product recommendation systems for personalized offers and promotions across channels.