Abstract: Techniques are disclosed for automatically creating or updating predictive models, including digital marketing forecast models. A predictive model is updated in real-time or near real-time using a stochastic gradient descent optimization method based on one or more predictive values associated with an advertising impression that is won in an online advertising auction. Each predictive value, which is obtained from the predictive model, is encoded as an argument in a uniform resource locator (URL) corresponding to the ad impression being auctioned. If and when the ad impression is won, the predictive value(s) and other information can be tracked and immediately available for updating the model using information encoded in the URL.

Abstract: Techniques are disclosed for automatically creating or updating predictive models, including digital marketing forecast models. A predictive model is updated in real-time or near real-time using a stochastic gradient descent optimization method based on one or more predictive values associated with an advertising impression that is won in an online advertising auction. Each predictive value, which is obtained from the predictive model, is encoded as an argument in a uniform resource locator (URL) corresponding to the ad impression being auctioned. If and when the ad impression is won, the predictive value(s) and other information can be tracked and immediately available for updating the model using information encoded in the URL.

Abstract: Techniques for using interaction data of application users to target a social-networking advertisement are described herein, as well as other techniques. Interaction data of application users is received through applications associated with the social-networking platform. This interaction data can provide additional information about users to better enable marketers to target users of a social-networking platform. This improved targeting potentially saves advertising resources, increases effectiveness of advertisements, or improves users' impressions of a maker or brand associated with the advertisement.

Abstract: A censored observation for an online advertising campaign may be received for a first given time period. It may be determined that an amount spent on the online advertising campaign met a budget constraint such that the online advertising campaign was interrupted during the first given time period. Based on the received censored observation and one or more campaign parameters for the first given time period, a predictive model for predicting the result of a new online advertising campaign may be generated.