Abstract: Machine learning-based techniques are described that enable modifying an event timing schedule in a multi-tenant computing environment. The multi-tenant computing environment stores tenant data of multiple tenants. Each tenant of the multiple tenants offers subscription services to subscribers. Multiple events involving multiple subscribers of a particular tenant are attempted. The multiple events include a first subset of successfully executed events and a second subset of unsuccessfully executed events. One or more training datasets are generated based on the first subset and the second subset. The one or more training datasets include contextual information corresponding to each of the multiple events. The contextual information includes multilevel data. A machine learning model is trained to output a timing schedule for retrying a particular unsuccessfully executed event of a particular subscriber.

Abstract: The present invention provides techniques for use in providing advertisers and other entities with information relating to target audiences. Techniques are provided in which, in reply to an advertiser query, the advertiser is provided with, in connection with a specified target audience, topics of interest, levels of interest per topic, and a level or levels of engagement with the advertiser. Other information may also be provided, including topic of interest trending information, as well as topics of interest that best differentiate between the target audience and a specified comparison audience.