Abstract: A computer-implemented method, simulation and prediction system, and computer program product for advertising portfolio management are disclosed. An example method includes simulating, by executing a first simulated model utilizing a weight matrix, a first user provided scenario of an advertising campaign and collecting time series stimulus data and time series response data for marketing channels of the advertising campaign. The example method also includes reducing computational resource consumption associated with generating an updated version of the first simulated model by applying the time series stimulus data and the time series response data to the first simulated model to generate a second simulated model and simulating, by executing the second simulated model, a second user provided scenario of the advertising campaign to determine an effect of a change in a marketing media spend value of a first marketing channel on other ones of the marketing channels of the advertising campaign.

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: Fragmented user engagement stacks are generated from users that use multiple devices to view messages. The fragmented user engagement stacks include a universal unique identifier (UUID). A computer platform stores cross-device mapping information, derived from a shared characteristic between two or more devices, that associates the UUIDs of multiple devices to a single user. The computer platform processes the cross-device mapping data to identify the UUIDs from different devices associated with a single user and to join touchpoint encounters from the single user to generate at least one cross-device user engagement stack. The computer platform uses the cross-device user engagement stack and the response data to determine attribution as a measure of influence attributed to touchpoint encounters from a single user.

Abstract: An example apparatus includes a model generator to generate a learning model based on a correlation of stimulus data and response data, the learning model to predict user responses based on stimuli presented to the users in the channel or the sub-channel, the correlation indicative of stimuli contributing to user responses at a channel or a sub-channel level. The apparatus further includes an attribution engine to determine a media spend plan based on the learning model and a budget, the media spend plan including an allocation of the budget to stimuli corresponding to the channel or the sub-channel and a user interface to display the media spend plan to a user and update the media spend plan based on predictions of the learning model when the user adjusts the budget or allocations of the media spend plan in the user interface.

Abstract: A method, system, and computer program product identifies attribution of small signal stimulus in noisy response channels. Using machine-learning techniques in a computer, a small signal correlation engine correlates time series stimuli data vectors to time series response data vectors, and generates correlation coefficients that identify contributions of event notifications, including small signal attributes, to aggregated response data. Also using machine-learning techniques in a computer, a learning model simulates variations of stimuli data to predict user responses using the correlation coefficients, including computing a contribution of the small signal attributes of an event notification.

Abstract: A method, system, and computer program product identifies attribution of small signal stimulus in noisy response channels. Using machine-learning techniques in a computer, a small signal correlation engine correlates time series stimuli data vectors to time series response data vectors, and generates correlation coefficients that identify contributions of event notifications, including small signal attributes, to aggregated response data. Also using machine-learning techniques in a computer, a learning model simulates variations of stimuli data to predict user responses using the correlation coefficients, including computing a contribution of the small signal attributes of an event notification.

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: Fragmented user engagement stacks are generated from users that use multiple devices to view messages. The fragmented user engagement stacks include a universal unique identifier (UUID). A computer platform stores cross-device mapping information, derived from a shared characteristic between two or more devices, that associates the UUIDs of multiple devices to a single user. The computer platform processes the cross-device mapping data to identify the UUIDs from different devices associated with a single user and to join touchpoint encounters from the single user to generate at least one cross-device user engagement stack. The computer platform uses the cross-device user engagement stack and the response data to determine attribution as a measure of influence attributed to touchpoint encounters from a single user.

Abstract: A method, system, and computer program product for media spend management. An Internet media planning and purchasing application executes on a management interface device. Servers execute operations to predict various inventory and pricing effects that result from a particular Internet media planning and purchasing plan. Machine learning techniques are used to form a stimulus attribution predictive model based on stimulus data records and respective response data records received over a network path. Additional predictive models are formed, including (1) an ad inventory predictive model derived from ad inventory data records and (2) an ad pricing predictive model derived from ad pricing data records. A set of media spend allocation parameters are received from the management interface, and those parameters are used to produce predicted inventory changes that in turn affect parameters in the ad pricing predictive model.

Abstract: Touchpoint encounters, which represent exposure to messages transmitted through a network to users, include attributes that define universal unique identifiers (UUIDs) for user devices and at least one cross-device user engagement stack. The cross-device user engagement stack consolidates the touchpoint encounters from the user devices with different UUIDs but associated with a single user. A stimulus attribution predictive model outputs attribution parameters to estimate an effectiveness of the messages to elicit positive responses from the users. Media buy execution feed parameters are generated to quantify a set of spending amounts, based on the attribution parameters, so as to specify a cost-effective amount to deliver one of the messages to the users. The media buy execution feed parameters are delivered to programmatic media buying execution platforms that attempt to deliver the messages in accordance with the spending amounts.

Abstract: 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.

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 method, system, and computer program product for promotional offer spend management. A computer implementation commences upon performing calculations to predict future market response of presenting a particular offer to an audience. The prediction calculations include machine-learning processing of historical offer specification parameters that at least partially characterize the offer. The offer specification comprises a unique combination of parameters and respective media channels, which combination was not measured as pertains to that particular specific unique combination. A predicted market response to the unique offer delivered being over selected media channels is forecasted by using a predictive model.

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 method, system, and computer program product for classifying, weighting, and quantifying audience responses to stimulation. A method commences by forming a predictive model comprising parameters derived from response data records taken from the Internet and stimulus data records takers item the performance or execution of a media plan. A database of user configurations is consulted to access brand engagement event weighting parameters. The brand engagement event weighting parameters are combined with simulation data to generate weighted touchpoint contribution values that can in turn be used to predict future responses front the audience or a similar future audience. The weighted touchpoint contribution values are used to calculate audience engagement scores. A selected set of audience engagement scores are used to determine spending in a media plan.

Abstract: A method, system, and computer program product for forming correlations and measurements between online data items and offline data items. An online and offline touchpoint attribution model is constructed by collating user records that correspond to an audience of online users taken from an audience of users that have interacted with both online and offline touchpoints. Individual user interactions with particular touchpoints are codified as touchpoint records. Online user interactions are captured from online observations taken at the time of the interaction. Offline user interactions are collected by an agent and are imported into the attribution model. A set of transitions through both online and offline touchpoints can be aggregated to form commonly-traversed progression paths through touchpoints that reach a conversion event. A contribution value that quantifies influence attributable to each of the respective ones of the touchpoints is calculated and used to manage makeup and spending in media plans.

Abstract: Methods for digital media campaign management. Embodiments determine a set of channel spend allocation values for a plurality of media channels based on a predictive model derived from observed channel response measurements. A stream of one or more touchpoint attribute records that characterize user responses to the media channels are captured and used to calibrate further incoming touchpoint attribute records. The calibrated incoming touchpoint attribute records are used to generate a calibrated to touchpoint response predictive model. Outputs of the calibrated touchpoint response predictive model are used to adjust spending in digital media campaigns so as to increase effectiveness. Some embodiments perform calibration by analyzing a series of observed touchpoint events and then reducing the credit applied to the touchpoint events that are farthest from respective conversion events so as to reconcile the touchpoint observations with observed spending in media campaign.

Abstract: Methods for digital media campaign management. Embodiments determine a set of channel spend allocation values for a plurality of media channels based on a predictive model derived from observed channel response measurements. A stream of one or more touchpoint attribute records that characterize user responses to the media channels are captured and used to calibrate further incoming touchpoint attribute records. The calibrated incoming touchpoint attribute records are used to generate a calibrated touchpoint response predictive model. Outputs of the calibrated touchpoint response predictive model are used to adjust spending in digital media campaigns so as to increase effectiveness. Some embodiments perform calibration by analyzing a series of observed touchpoint events and then reducing the credit applied to the touchpoint events that are farthest from respective conversion events so as to reconcile the touchpoint observations with observed spending in media campaign.

Abstract: A system, method, and computer program product for determining media spend apportionment performance. A set of historical stimulus and response data is used to form a stimulus response predictive model for generating correlations and for generating historical performance results. The historical stimulus and historical performance results are used to determine a set of recommended stimuli that are applied to the stimulus response predictive model to simulate or predict responses that in turn are used to further predict the performance of sets of recommended stimuli. New spending on the recommended stimuli produces new responses. The new responses to a set of newly-deployed stimuli (such as changed spending in accordance with the recommended stimuli) can be measured so as to generate performance results pertaining to the newly-deployed stimuli. Individual stimuli and/or combinations of historical stimuli, recommended stimuli, and/or the deployed stimuli are analyzed against media spend apportionment plans.

Abstract: The present disclosure provides a detailed description of techniques used in systems, methods, and computer program products for marketing touchpoint attribution bias correction. More specifically, the herein disclosed techniques enable identifying a collection of marketing touchpoints (e.g., associated with a desired conversion) and receiving touchpoint data and/or conversion data associated with those touchpoints. The received data is used to determine contribution values for each of the respective touchpoints that indicate the probability of conversion generated by the respective touchpoints. Some contribution values can have attribution biases. Such biases are addressed by identifying a low contribution value associated with the collection of touchpoints and reducing or eliminating the low contribution value from the remaining contribution values to generate corrected contribution values for the remaining touchpoints.