Method and apparatus for data-driven multi-touch attribution determination in multichannel advertising campaigns
AssigneeTurn Inc.
InventorXuhui SHAO
Life of this patent
Estimating the term…
Abstract
A bivariate metric is disclosed, in which one variable measures the variability of the estimate, and the other measures the accuracy of classifying the positive and negative users. A bagged logistic regression model is used, which achieves a comparable classification accuracy as a usual logistic regression, but a much more stable estimate of individual advertising channel contributions. Embodiments of the invention also provide an intuitive and simple probabilistic model to quantify the attribution of different advertising channels directly. Both the bagged logistic model and the probabilistic model are then applied to a real world data set from a multichannel advertising campaign.
Claims14
A computer implemented method for multi-touch attribution determination in a multichannel advertising campaign, comprising:
- with a processor, executing program instructions that implement a bivariate metric in which a first variable measures variability of an estimate of an individual's advertising channel contributions to a conversion event, and a second variable measures accuracy of classifying positive and negative users;
- and
- with said processor, effecting multi-touch attribution for said conversion event with said bivariate metric.
An apparatus for multi-touch attribution determination in a multichannel advertising campaign, comprising:
- a processor executing program instructions that implement a bivariate metric in which a first variable measures variability of an estimate of an individual's advertising channel contributions to a conversion event, and a second variable measures accuracy of classifying positive and negative users, said processor effecting multi-touch attribution for said conversion event with said bivariate metric.
Dependencies are inferred from the claim text.