Method for quantifying the propensity to respond to an advertisement

• US 20060235965A1
• Filed: 03/07/2006
• Published: 10/19/2006
• Est. Priority Date: 03/07/2005
• Status: Abandoned Application

• Current Assignee: Carhamm Limited LLC (Intellectual Ventures LLC)
• Original Assignee: Claria Corporation
• Inventors: Bennett, Dominic V., Paczkowski, Remigiusz K.
• Application Number:
  US11/369,334
• Publication Number:
  US 20060235965A1
• Time in Patent Office: Days
• US Class Current: 709/224
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

A method of quantifying the propensity of a consumer to respond positively to an advertisement. The process begins by producing a set of training factors from the entire set of user data available, one set of such factors being associated with each advertisement under study to indicate the probability of positive response to that advertisement. Once the training phase is complete, the application phase begins by receiving input data from a user in real time. The process continues by applying the training factors to the user data to identify the advertisement having the highest probability of positive response and then displaying the identified advertisement to the user.

Claims

1. A method of quantifying the propensity of a consumer to respond positively to an advertisement, comprising the steps of:

producing a set of training factors from the entire set of user data available, one set of such factors being associated with each advertisement under study to indicate the probability of positive response to that advertisement;

receiving input data from a user in real time;

applying the training factors to the user data to identify the advertisement having the highest probability of positive response;

displaying the identified advertisement to the user.

2. The method of claim 1, wherein producing the training factors includes the steps of:

gathering data from a large user population concerning user behavior while navigating the internet, including data concerning sites visited, links clicked and time spent per site;

selecting a subset of data for analysis, consisting of data related to a single banner advertisement;

removing outlier data from the dataset;

performing a primary components analysis to identify a set of eigenvectors and eigenvalues;

rotating the dataset axes by employing an orthogonal transformation matrix;

determining specific probabilities of action through a stepwise logistic regression.

3. The method of claim 2, wherein removing outlier data includes applying a k-means cluster algorithm to the dataset.