Rosetta Marketing Strategies Group

Rmsg LLC

Kurt E Holstein, Ii

Lawrence J Choi

Christopher B Kuenne

Jason R Whitney

George Tang

Chetna Bansal

Henry Andrew Cross

Joshua D Babbitt

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.

Method, system, and device for typing customers/prospects

A computer-assisted method for determining a plurality 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. The disclosed method also includes, assigning each of the plurality of observations to one of a plurality of clusters, and, for the plurality of observations and the plurality of variables, via cluster reassignment, maximizing a processor-determined fitness score representing a number of variables from the plurality of variables for which each observation's proxy value corresponds to a mode for that observation's assigned cluster.

Method and system for clustering optimization and applications

A computer-assisted method is disclosed for determining a potential effectiveness of a survey question. The disclosed method includes segregating a plurality of responses to a survey question into a plurality of categories, and determining a response distribution for each of the plurality of categories. The disclosed method also includes calculating a box score and a difference score for the survey question from the response distributions, and using the box score and the difference score to obtain an effectiveness score for the survey question.

Computer-assisted systems and methods for determining effectiveness of survey question

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 observation's 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 observation's 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.

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Princeton, NJ, United States of America

Kurt E Holstein, Ii