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 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.