Jason E Whitney

US Patent:

6996560, Feb 7, 2006

Filed:

May 31, 2001

Appl. No.:

09/867582

Inventors:

Lawrence J. Choi - Lawrenceville NJ, US
Christopher B. Kuenne - Princeton NJ, US
Henry Andrew Cross - Moorestown NJ, US
Jason R. Whitney - New York NY, US
George Tang - New York NY, US
Chetna Bansal - New York NY, US
Joshua D. Babbitt - Jersey City NJ, US

Assignee:

RMSG LLC - Princeton NJ

International Classification:

G06F 17/30

US Classification:

707 6, 707 10, 707101, 7071041, 705 10, 705 14, 706 45, 706 48, 706 52, 434322, 434350

Abstract:

Exemplary embodiments provide a computer-assisted method for typing customers/prospects, including the activities of: refining a survey via bestfit clustering; refining survey results via composition analysis; and identifying clusters of customers/prospects from the survey results via champion/challenger cluster refinement and panel analysis.

US Patent:

6745184, Jun 1, 2004

Filed:

May 31, 2001

Appl. No.:

09/867802

Inventors:

Lawrence J. Choi - Lawrenceville NJ
Christopher B. Kuenne - Princeton NJ
Henry Andrew Cross - Moorestown NJ
George Tang - New York NY
Chetna Bansal - New York NY
Jason R. Whitney - New York NY
Joshua D. Babbitt - Jersey City NJ

Assignee:

Rosetta Marketing Strategies Group - Princeton NJ

International Classification:

G06F 1730

US Classification:

707 6, 705 10, 705 19, 706 45, 706 48, 706 52, 707101

Abstract:

A computer-assisted method for placing each of a plurality of observations into one of a predefined set of clusters is disclosed. The disclosed method includes, for each of a plurality of observations, obtaining a data set containing no more than one proxy value for each of a plurality of variables, each variable having a plurality of possible values, and obtaining a set of first dummy variables having a value indicative of an observations membership or non-membership (i. e. , membership status) in a particular cluster. The disclosed method also includes obtaining a set of second dummy variables each having a value indicative of an observations having a particular response or lack thereof for a given variable, and, for each observation, regressing the set of first dummy variables on the set of second dummy variables to obtain a linear approximation to a plurality of probabilities that an observation with particular provided values to the plurality of possible values belongs to each particular cluster from the predefined set of clusters.