Abstract: The present disclosure provides a detailed description of techniques used in systems, methods, and computer program products for determining marketing touchpoint attributions in a segmented media campaign. Embodiments commence by forming a touchpoint attribution predictive model based on stimulus data records and Internet-collected touchpoint data records. A set of media campaign segments can be received or derived and then used for selecting corresponding segment touchpoint data records. Segmented touchpoint contribution values for the media campaign segments are generated by applying the segment touchpoint data records to the touchpoint attribution predictive model. The segmented touchpoint contribution values serve to relate a segment of users with varying engagement states experienced by that segment of users.

Abstract: A method, system, and computer program product for media spend management using real-time marketing campaign stimuli selection based on user response predictions. Embodiments commence upon identifying one or more users comprising an audience for one or more marketing campaigns. Observed touchpoint data records are collected based on audience responses to campaign stimuli. A collection of historical touchpoint data records are used to form a predictive model that captures relationships between the stimuli and the responses. At any moment in time, such as when a particular user is online, the predictive model is used to predict one or more next desired touchpoints based on a particular user's then-current online interactions. Marketing campaign stimuli that has a known historical effectiveness with respect to the desired touchpoints is reported. A marketing manager can increase the prevalence of such effective stimuli so as to increase the likelihood of desired responses by the particular user.

Abstract: A method, system, and computer program product for forming and validating a predictive model. The predictive model is based on empirically-determined data taken from one or more user encounters. A first portion of a set of user data corresponds to a first set of respective users that have performed at least some first conversion activity after experiencing a touchpoint encounter. A second set of user data corresponds to second respective users that have experienced at least one of the touchpoint encounters. The user data from the first portion are parsed to identify characteristics that are used for calculating propensity to convert scores. The cookies and scores are used to generate a predictive model that forms a prediction for a given user to convert. The predictive model is validated by comparing the predictions to empirically-determined conversion data (e.g., taken from the second set of user data).

Abstract: A method, system, and computer program product for managing Internet advertising campaigns. Embodiments commence upon receiving (e.g., over a network) advertisement touchpoint data pertaining to a plurality of touchpoints. The advertisement touchpoint data comprises measured stimulation data (e.g., impressions) and measured response data (e.g., conversions). The stimulation data and response data is formatted into an initial succession of candidate touchpoint contribution values where each of the individual touchpoints contributes its respective portion of the total contribution from the total set of measured responses. A non-linear model is applied over the succession of candidate touchpoint contribution values to form a non-linear succession of candidate touchpoint contributions. Individual touchpoints receive an apportionment based on the non-linear succession of candidate touchpoint contributions.

Abstract: A system and method for allocating credit for an advertising conversion among various advertising touchpoints encounter by the consumer is provided. The system and method comprise receiving data pertaining to touchpoints and conversions of an advertising campaign across multiple channels. Users are correlated across the channels and the various conversions, touchpoints, and touchpoint attributes are identified. Each touchpoint attribute and touchpoint attribute value is assigned a weight. An attribution algorithm is selected, and coefficients are calculated using the assigned weights. The algorithm is executed and true scores corresponding to the touchpoints encountered by each converting user are computed.

Abstract: The present disclosure provides a detailed description of techniques used in systems, methods, and in computer program products for real-time marketing portfolio optimization and reapportioning. The claimed embodiments enable calculating and saving a maximum efficiency response curve of a media portfolio, the maximum efficiency response curve comprising a range of response values resulting from a set of media portfolio input characteristics. A user interacts with an interactive graphical user interface which accepts an input characteristic change to alter a spending apportionment in the media portfolio, relative to the calculated and saved a maximum efficiency response curve. Using the stored maximum efficiency response curve as a baseline, results of the reapportionment(s) are generated and displayed.

Abstract: A series of techniques, methods, systems, and computer program products for advertising portfolio management is disclosed herein. More specifically, the herein disclosed techniques enable receiving data comprising a plurality of marketing stimulations, and receiving data comprising a plurality of engagement metrics. The received data is analyzed to determine a set of engagement weights associated with the engagement metrics. The determined engagement weights are in turn used to calculate the effectiveness of particular marketing stimulations through a set of marketing channels. Additional data in the form of measured responses (e.g., sales figures, survey results, etc.) are used to form a learning model wherein the learning model comprises one or more of, a stimulus-response predictor, a stimulus-engagement predictor, and an engagement-response predictor.

Abstract: A method, system, and computer program product for computer-aided management of marketing and advertising campaigns. Operations commence upon displaying a maximum efficiency response curve of a media portfolio, where the maximum efficiency response curve comprises a range of response values resulting from a given set of media portfolio input characteristics. A maximum efficiency ROI curve of the media portfolio is displayed, where the maximum efficiency ROI curve comprises a range of ROI values resulting from the set of media portfolio input characteristics. A user provides a prospective quantitative change to alter the media portfolio input characteristics in the media portfolio. The output response of the media portfolio to the prospective change is modeled, and suggested media plan reallocation values are displayed. An ROI value resulting from the prospective set of media portfolio input characteristics is displayed in juxtaposition to the maximum efficiency ROI curve.

Abstract: A computer-implemented method, simulation and prediction system, and computer program product for advertising portfolio management. Embodiments commence upon receiving data comprising a plurality of marketing stimulations and respective measured responses, both pertaining to a first time period. A computer is used to form a multi-channel simulation model, where the simulation model accepts the marketing stimulations then outputs simulated responses. The simulation model is used for determining cross-channel weights to apply to the respective measured responses pertaining to the first time period. The simulation model is updated to reflect updated marketing stimulations pertaining to a second time period. The updated marketing stimulations overwrite some of the plurality of marketing stimulations captured in the first time period.

Abstract: 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.

Abstract: A system and method for allocating credit for an advertising conversion among various advertising touchpoints encounter by the consumer is provided. The system and method comprise receiving data pertaining to touchpoints and conversions of an advertising campaign across multiple channels. Users are correlated across the channels and the various conversions, touchpoints, and touchpoint attributes are identified. Each touchpoint attribute and touchpoint attribute value is assigned a weight. An attribution algorithm is selected, and coefficients are calculated using the assigned weights. The algorithm is executed and true scores corresponding to the touchpoints encountered by each converting user are computed.

Abstract: A system and method for allocating credit for an advertising conversion among various advertising touchpoints encounter by the consumer is provided. The system and method comprise receiving data pertaining to touchpoints and conversions of an advertising campaign across multiple channels. Users are correlated across the channels and the various conversions, touchpoints, and touchpoint attributes are identified. Each touchpoint attribute and touchpoint attribute value is assigned a weight. An attribution algorithm is selected, and coefficients are calculated using the assigned weights. The algorithm is executed and true scores corresponding to the touchpoints encountered by each converting user are computed.

Abstract: A system and method for allocating credit for an advertising conversion among various advertising touchpoints encounter by the consumer is provided. The system and method comprise receiving data pertaining to touchpoints and conversions of an advertising campaign across multiple channels. Users are correlated across the channels and the various conversions, touchpoints, and touchpoint attributes are identified. Each touchpoint attribute and touchpoint attribute value is assigned a weight. An attribution algorithm is selected, and coefficients are calculated using the assigned weights. The algorithm is executed and true scores corresponding to the touchpoints encountered by each converting user are computed.