A system and method for optimizing marketing campaigns is presented. Two marketing campaigns are received. Each is presented to a subset of users. The conversion rates of both marketing campaign are used to determine weighting of the two marketing campaigns. The weighting is determined using a range of conversion rates and maximizing the minimum expected value through the range of conversion rates. The process can be iteratively performed to converge upon an optimum weighting of the first and second conversion rates. More than two marketing campaigns can be used. The marketing campaign can be an email marketing campaign, a web marketing campaign, or an advertising keyword campaign. Other embodiments also are disclosed.

Methods and systems for displaying a summary of a performance of an advertising campaign are described. A system identifies advertising campaign goals of an advertising campaign. The system identifies, based on the identified goal, metrics that drive a performance of the campaign. The system identifies, for each metric, dimensions corresponding to the metric that influence a performance of the metric. The system determines, for a subset of the dimensions, that a predetermined number of criteria corresponding to the dimension contribute at least a predetermined threshold percentage of the metric. The system generates, for each metric, cards corresponding to the subset of the identified dimensions. Each card corresponds to a respective dimension and includes a graphic that identifies, for each of the predetermined number of criteria, a contribution provided by the criteria towards the performance of the metric. The system displays a subset of the cards in response to a request.

Information describing deliveries of content items and user actions associated with the content items is stored. Each delivery is performed by an online publisher to a user. A user action associated with a content item performed by a target user is detected. Information describing a set of online publishers that delivered the content item to the target user is retrieved. For each online publisher of the set, a likelihood that the user action would have occurred without the online publisher's delivery of the content item to the target user is determined. An estimated increase in the likelihood that the user action occurred due to the online publisher's delivery of the content item to the target user is determined. A performance metric is determined for the online publisher, wherein ratios of performance metrics for the set of online publishers are related based on corresponding ratios of the estimated increases in likelihoods.

Methods and apparatus to incorporate saturation effects into marketing mix models are disclosed. A disclosed method includes calculating adstocked gross rating points associated with an advertising campaign during segments of a period of time. The adstocked gross rating points are based on raw gross rating points corresponding to the advertising campaign. The raw gross rating points are delivered during the segments of the period of time. The example method further includes calculating an effective reach realized during the segments of the period of time for the advertising campaign. The effective reach realized is based on the adstocked gross rating points.

Advertisement quality measures (e.g., predicted click through rates) are modified in accordance with a user's preferences with respect to domains to which the advertisements relate.

The present invention relates to a system for outputting linked advertisement on basis of a smart phone which is capable of displaying an advertisement so that exposure frequency may be adjusted depending on an exposure index considering cost-per-click (CPC) price, possibility of selling products, and an amount of accumulated reward money, and displaying the advertisement information with a larger font size while more increasing possibility of access of users to advertisements having priority within a predetermined priority range.

The SYNTHETIC CONTROL GENERATION AND CAMPAIGN IMPACT ASSESSMENT APPARATUSES, METHODS AND SYSTEMS (“SCG”) provides a platform that, in various embodiments, is configurable to evaluate efficacy and/or return on investment of advertising and/or other media campaigns and/or to recommend actions for improvement thereof. In some implementations, multi-faceted campaigns of media and/or advertising behavior (e.g., including one or more of: internet advertising, television advertising, radio advertising, print advertising, social media publication, product placement, and/or the like) may be considered as a whole in relation to global metric behaviors and/or patterns in order to evaluate the efficacy and/or return on investment associated with the campaign as a whole.

A method for generating predictive insights for authoring messages is provided. The method includes receiving a message to be sent as an input. Key performance indicator (KPI) whose value is to be predicted for the message is identified from the input or marketing tool configuration. A plurality of feature vectors of the message are generated. KPI contributions for the plurality of feature vectors are determined using feature vectors of messages sent in past and tracked KPI values of the messages sent in past. The KPI contribution is a measure of contribution of feature vector to value of the KPI. Value of the KPI for the message is predicted by applying determined KPI contributions to the plurality of feature vectors. Apparatus for substantially performing the method as described herein is also provided.

Sequential hypothesis testing techniques are described, which involve testing sequences of increasingly larger number of samples until a winner is determined. In particular, sequential hypothesis testing techniques is based on whether a result of a statistic has reached statistical significance that defines a confidence level in the accuracy of the results. Sequential hypothesis testing also permits the user to “peek” into the test through use of a user interface (e.g., dashboard) to monitor the test in real time as it is being run. Real time output of this information in a user interface as a part of sequential hypothesis testing may be leveraged in a variety of ways. In a first example, a user may make changes as the test is run. In another example, flexible execution is also made possible in that the test may continue to run even if initial accuracy guarantees have been met.

A touchpoint exposure predictive model defines the relationship between a number of messages deployed in a message campaign and the response so as to model diminishing returns on the response due to the number of messages. A predicted message deployment—response curve is rendered on a display of a user computer depicts the effectiveness of the response to the messages. The user runs a simulation to increase the number of the messages in the campaign, and a modified message deployment—response curve for the messages, which incorporates diminishing returns, is rendered from the touchpoint exposure predictive model.