Various examples are directed to systems and methods for adaptively generating leads. A marketing system may determine that a first lead score for a first lead is greater than a first lead score threshold and determine that a second lead score for a second lead is less than the first lead score threshold. The marketing system may generate a set of filtered leads including the first lead information from the first lead. The marketing system may determine a scrub rate that describes a portion of first execution cycle data having lead scores greater than the first lead score threshold and determine that the scrub rate is greater than an analysis window scrub rate by more than a scrub rate threshold. The marketing system may select a second lead score threshold that is lower than the first lead score threshold.

Systems, methods, and computer-readable storage media that may be used to generate competitor-specific bidding recommendations are provided. One method includes identifying, at a computerized analysis system, at least one competitor of a content provider within a plurality of content auctions for displaying content items. The method further includes calculating, at the analysis system, at least one bidding action estimated to improve performance of the content provider in future content auctions with respect to the at least one competitor from a current level to a goal level of the performance metric. The method further includes providing a recommendation to the content provider to implement the at least one bidding action.

The disclosed embodiments provide a system for evaluating a performance of a mobile application. During operation, the system obtains, for a statistical model used in a quasi-experimental design, a first predicted outcome produced from a first set of data that is collected from two substantially identical versions of a mobile application. Next, the system uses the first predicted outcome to assess a bias of the statistical model. The system then improves an accuracy of the statistical model by using the assessed bias to normalize a second predicted outcome of the statistical model.

Embodiments of the present invention provide systems, methods, and computer storage media directed at controlling a campaign. In embodiments, a method includes receiving event values respectively associated with corresponding events. The method can then utilize these event values in calculating an estimated impression value for a present logical interval of the campaign. The method can further include generating a price control signal based on a desired return on investment (ROI) associated with the campaign and an observed ROI of a previous logical interval of the campaign. Based on the estimated impression value and the price control signal, a bid price can be computed for the current logical interval of the campaign. This bid price can then be transmitted to a market associated with the campaign. Other embodiments may be described and/or claimed herein.

A system and method for managing advertisements in a syndicated feed is described. One embodiment includes a method for expiring an advertisement space in a feed. This method includes the following actions: receiving a data item associated with a feed from a publisher; determining whether an advertisement space should be associated with the data item; inserting a markup of the advertisement space into the data item; providing the data item for a user; receiving a request from the user to view the advertisement corresponding to the advertisement space and the data item; determining whether the data item is older than a threshold age; providing an advertisement to the user if the data item is not older than a threshold age, wherein the advertisement is viewable in the advertisement space; and providing a blank advertisement to the user if the data item is older than a threshold age.

Embodiments of the invention provide a technical solution by generating a value index score based on aggregation of a value from a combination of features as a unit. In one embodiment, instead of generating a value index score based on a collection of features with each feature being a discrete parameter, aspects of the invention generate the value index score while accounting for weights of a combination of features as a unit. Furthermore, embodiments of the invention generate a weight value for each feature and that the weight, not only will it be a factor in the calculation, but also be modifiable in response to other factors of the features.

A system and method for providing options for scheduling ads is provided. In example embodiments, a list of one or more ads is presented to an operator. A selection of an ad to be placed into a schedule is received. Placement analysis is performed, using a hardware processor, to identify a plurality of placement options based on placing the ad into the schedule. The placement analysis includes determining a net difference metric value for each placement option. The net difference metric value is determined based on displacement of at least one previously scheduled ad in each placement option. The plurality of placement options are sorted according to a score derived from at least one value metric to create a result. The result is presented to the operator, whereby the result includes the score derived from the at least one value metric for each placement option.

A method, system, and computer program product for advertising portfolio management. The method form processes steps for determining effectiveness of marketing stimulations in a plurality of marketing channels included in a marketing campaign. The method commences upon receiving data comprising a plurality of marketing stimulations and respective measured responses, then determining from the marketing stimulations and the respective measured responses, a set of cross-channel weights to apply to the respective measured responses, where the cross-channel weights are indicative of the influence that a particular stimulation applied to a first channel has on the measure responses of other channels. The cross-channel weights are used in calculating the effectiveness of a particular marketing stimulation over an entire marketing campaign. The marketing campaign can comprise stimulations quantified as a number of direct mail pieces, a number or frequency of TV spots, a number of web impressions, a number of coupons printed, etc.

A system and method of predicting a value of a key performance indicator (KPI) of a target advertisement campaign may include receiving a plurality of campaign data elements, such as campaign types, campaign geographies, campaign dates, and historic KPI values corresponding to a respective plurality of campaigns; processing the plurality of campaign data elements to produce one or more training batches; training a machine-learning (ML) model to predict a value of a campaign KPI, based on the one or more training batches. In a subsequent inference stage, embodiments may receive at least one new campaign data element, corresponding to a target campaign; and applying the trained ML model on the at least one new campaign data element to predict a value of a target KPI of the target campaign.

In accordance with one or more aspects of selecting between client-side and server-side market detection, a determination is made at a device as to which of a client-side detected market and a server-side detected market is to have priority for a service. An application of the device is configured in accordance with a client-side market configuration setting if the client-side detected market has priority, and is configured in accordance with a server-side market configuration setting if the server-side detected market has priority.