System and method for direct mailing insurance solicitations utilizing hierarchical bayesian inference for prospect selection

A computer system for selecting prospects for insurance marketing activities, the computer system comprising:

• a processor;
• and
• a memory in communication with the processor and storing program instructions, the processor operative with the program instructions to:
• store at least one equation, the at least one equation defining a predictive model responsive to a plurality of independent variables and a plurality of parameters, and having an associated spatio-temporal error component, wherein the predictive model has a first dependent variable that represents a probability that a respective prospect will respond to a marketing offer, a second dependent variable that represents a probability that a respective prospect will be retained as a customer, and a third dependent variable that represents a predicted insurance premium amount that would be paid by a potential prospect, wherein the second discrete dependent variable has a value of noncontinuation for prospects expected to cancel a policy within a first time period, continuation for prospects expected to cancel a policy within a second time period that is longer than the first time period, and renewal for prospects expected to continue as policy-holders for a third time period that is longer than the second time period;
• evaluate the parameters to parameterize the predictive model in such a way as to reduce the error component;
• apply the parameterized predictive model to a data set, the data set representing a universe of potential prospects for insurance marketing activities and including, for each potential prospect, a spatial component represented as a distance matrix identifying a distance between each of the potential prospects;
• generate, based on results of applying the parameterized predictive model to the data set, a list of selected prospects, the list of selected prospects representing a subset of the universe of potential prospects, wherein the list is ranked according to a score based on (1) the first dependent variable that represents the probability that the respective selected prospect will respond to the marketing offer, (2) the second discrete dependent variable that represents the probability that the respective selected prospect will be retained as a customer, and (3) the third dependent variable that represents the predicted insurance premium that would be paid by the respective selected prospect, the predicted insurance premium determined based at least in part on the spatial component;
• and
• output the list of selected prospects;
• wherein the evaluation of the parameters includes iteratively applying Bayesian inference using a training sample data set.

A method for selecting prospects for insurance marketing activities, the method comprising:

• storing at least one equation in a computer, the at least one equation defining a predictive model responsive to a plurality of independent variables and a plurality of parameters, and having an associated spatio-temporal error component, wherein the predictive model has a first dependent variable that represents a probability that a respective prospect will respond to a marketing offer, a second dependent variable that represents a probability that a respective prospect will be retained as a customer, and a third dependent variable that represents a predicted insurance premium amount that would be paid by a potential prospect, wherein the second discrete dependent variable has a value of noncontinuation for prospects expected to cancel a policy within a first time period, continuation for prospects expected to cancel a policy within a second time period that is longer than the first time period, and renewal for prospects expected to continue as policy-holders for a third time period that is longer than the second time period;
• evaluating the parameters by the computer to parameterize the predictive model in such a way as to reduce the error component;
• applying the parameterized predictive model by the computer to a data set, the data set representing a universe of potential prospects for insurance marketing activities and including, for each potential prospect, a spatial component represented as a distance matrix identifying a distance between each of the potential prospects;
• generating by the computer, based on results of applying the parameterized predictive model to the data set, a list of selected prospects, the list of selected prospects representing a subset of the universe of potential prospects, wherein the list is ranked according to a score based on (1) the first dependent variable that represents the probability that the respective selected prospect will respond to the marketing offer, (2) the second discrete dependent variable that represents the probability that the respective selected prospect will be retained as a customer, and (3) the third dependent variable that represents the predicted insurance premium that would be paid by the respective selected prospect, the predicted insurance premium determined based at least in part on the spatial component;
• and
• outputting the list of selected prospects.