MANAGING DIGITAL MEDIA SPEND ALLOCATION USING CALIBRATED USER-LEVEL RESPONSE DATA

摘要

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.

主权项

1. A computer implemented method comprising:
processing, in a computer, to determine a first set of channel spend allocation values for a plurality of media channels based on at least one channel response predictive model derived from one or more channel response measurements from the media channels, wherein the channel response predictive model accounts for one or more of cross-channel, seasonal, or external effects and the channel spend allocation values specify an allocation of spending of a budget across the channels for one or more media campaigns; training, using machine-learning techniques in a computer, a plurality of touchpoint encounters, that represent marketing messages exposed to a plurality of users, to generate a touchpoint response predictive model that determines a plurality of engagement stacks of touchpoint encounters that lead to a positive response to the marketing message and that determines a digital channel spend allocation for the budget, wherein the engagement stacks further specify an order of touchpoint encounters that range from weakest to strongest in eliciting a positive response to the marketing message; processing, in a computer, the touchpoint encounters to generate a plurality of calibrated touchpoint encounters by eliminating the weakest touchpoint encounters in the engagement stack for a channel until the channel spend allocation, output from channel response predictive model, falls within a specified amount of the digital channel spend allocation when applied to the touchpoint response predictive model; training, using machine-learning techniques in a computer, the calibrated touchpoint encounters to generate an updated touchpoint response predictive model; operating, on a computer, a media spend planning application accessible to one or more users, the media spend planning application receiving at least one budget for one or more media campaigns; and processing the budget in the media spend planning application by using the updated touchpoint response predictive model to generate a new channel spend allocation for the budget.