Abstract: Embodiments of the present invention relate to allocating online advertising budget based on return on investment (ROI). Allocating an advertising budget based on ROI can facilitate advertisement bidding such that ROI can be optimized for the advertiser. In implementation, an online advertising budget is generally provided in connection with a budget time duration during which the online advertising budget is to be used. In determining a budget allocation in association with a particular feature, an indication of a ROI for each set of feature values is determined. The indication of the ROIs associated with each set of feature values can be used along with the advertising budget to identify an optimal allocation of the online advertising budget for the budget time duration. In some cases, constraints may also be applied to optimize budget allocation among the feature values.

Abstract: An ensemble model is described that is usable to predict revenue metrics for one or more keywords. The ensemble model may be formed using both a historical model and a user behavior model. In one or more implementations, weights are assigned to the historical model and/or the user behavior model based on one or more criteria. Various processing techniques of the ensemble model may utilize the historical model and the user behavior model to predict revenue metrics for one or more keywords.

Abstract: Techniques for predicting success rates for online search terms based on offline advertising are described herein. The techniques enable marketers to determine appropriate values for online search terms based on their predicted success rates. By so doing, marketers may decide whether to pay online search engines for preferences of their website in response to a search using these online search terms.

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: Performance data for online advertisement creatives may be received. A hierarchical model of the online advertisement creatives may be generated based on correlations among the online advertisement creatives. The hierarchical model may be used to estimate a respective performance value for each of at least some of the plurality of online advertisement creatives based on the received performance data. A creative quality score may be determined, for those online advertising creatives whose performance values were estimated, based on the estimated performance values.

Abstract: Methods and systems for testing, comparing, and optimizing creatives with multiple factors in digital advertising is presented. Experiments are designed for testing a plurality of factors combined to form a creative. Ad campaigns are launched or continue according to the design and the creatives' campaign performance data is collected. Statistical modeling and hypothesis testing are used to predict the performance of the creatives based on the performance data. The creatives are compared based on the predictions and either activated or deactivated based upon their relationship to statistical confidence levels. All the stages are executed automatically and iteratively.

Abstract: Disclosed are methods, apparatus, systems, and non-transitory, tangible computer-readable media associated with generating bids for multi-channel advertising environments, including in embodiments, generating a multi-channel advertising model. A multi-channel advertising model may be generated and used to estimate the effect of various advertisements and/or events that occur to an individual advertisee across various modeled advertising channels. An advertisee may be tracked across multiple channels, such as, for example, by using one or more cookies as the advertisee visits various web sites. Embodiments may calculate marginal contributions to a conversion event by various advertising events that have occurred along the sales funnel. Various revenue attributions may be generated as a function of a marginal contribution that an event had on the final conversion.