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Forecasting Granular Audience Size for Online Advertising
Orchestration of campaigns for online display advertising requires marketers to forecast audience size at the granularity of specific attributes of web traffic, characterized by the categorical nature of all attributes (e. g. {US, Chrome, Mobile}).
An RNN-Survival Model to Decide Email Send Times
We posit that emails are likely to be opened sooner when send times are convenient for recipients, while for other send times, emails can get ignored.
Surveys without Questions: A Reinforcement Learning Approach
To overcome these deficiencies we extract proxy ratings from clickstream data, typically collected for every customer's online interactions, by developing an approach based on Reinforcement Learning (RL).
B2B Advertising: Joint Dynamic Scoring of Account and Users
There is heterogeneity among individuals belonging to an account in seeking information and hence the seller needs to score the interest of each individual over a long horizon to decide which individuals must be reached and when.
Delivery Optimized Discovery in Behavioral User Segmentation under Budget Constraint
Following the discovery of segments, delivery of messages to users through preferred media channels like Facebook and Google can be challenging, as only a portion of users in a behavior segment find match in a medium, and only a fraction of those matched actually see the message (exposure).
