Millions of users browse Walmart.com each day with varying levels of intent. Many of them end up making a purchase in the same session and most, well, do not. Display retargeting channels, with ads over open web and your favourite social media sites, are then used to reach out to the potential customers with relevant content. The ad serving comes at a cost and optimizing these costs becomes especially important given the huge scale. Predicting a user’s purchase (or click) propensity and bidding appropriately is crucial for reaching out to the right user with the right content and at the right time. We discuss how we, at WalmartLabs, build the user propensity prediction models to efficiently bid for ad impressions. We start from ground zero - understanding data nuances and formulating the problem. We delve into the finer aspects of offline data crunching and building models and pipelines on top of petabytes of user data. We further elaborate the critical stage of deploying models into the real world, where the model scores are just not enough! We also discuss the effect of multiple user touchpoints on these models and how ‘robust’ algorithms come to the rescue. View the slides from this presentation here: http://tinyurl.com/wsdm19-priyanka-bhatt https://www.youtube.com/watch?v=RrAc3dc0dro&list=PLn0nrSd4xjjaQQvmtd3zCca0u625MgEg0&index=7&t=25s