Abstract: Techniques for determining a prioritized listing of offers for use to contact potential customers are described. The technique includes generating an ordered listing of offers from a set of offers, by which to contact a potential customer from a group of potential customers by considering the potential customer independently from others of the potential customers in the group, during generating of the ordered listing of offers for the potential customer.

Abstract: A computer-implemented method is described for determining a response attribution value that represents a credit assignment to a communication, for an associated response received from a recipient during a marketing campaign.

Abstract: A computer-implemented method is described for determining a response attribution value that represents a credit assignment to a communication, for an associated response received from a recipient during a marketing campaign.

Abstract: A computer-implemented method is described for determining a response attribution value that represents a credit assignment to a communication, for an associated response received from a recipient during a marketing campaign.

Abstract: A computer-implemented method is described for determining a response attribution value that represents a credit assignment to a communication, for an associated response received from a recipient during a marketing campaign.

Abstract: A computer-implemented method is described for determining a response attribution value that represents a credit assignment to a communication, for an associated response received from a recipient during a marketing campaign.

Abstract: A computer-implemented method is described for determining a response attribution value that represents a credit assignment to a communication, for an associated response received from a recipient during a marketing campaign.

Abstract: Methods, systems, and software for conducting a marketing campaign are described. One or more scores associated with sending offers to proposed contacts are received; and an overall campaign score corresponding to a sum of the scores is optimized to generate optimal assignments of offers to customers. The optimal assignments are included in the marketing campaign.

Abstract: Methods, systems, and software for conducting a marketing campaign are described. One or more scores associated with sending offers to proposed contacts are received; and an overall campaign score corresponding to a sum of the scores is optimized to generate optimal assignments of offers to customers. The optimal assignments are included in the marketing campaign.

Abstract: A computer-implemented method is described for determining a response attribution value that represents a credit assignment to a communication, for an associated response received from a recipient during a marketing campaign.

Abstract: Methods, systems, and software for conducting a marketing campaign are described. One or more scores associated with sending offers to proposed contacts are received; and an overall campaign score corresponding to a sum of the scores is optimized to generate optimal assignments of offers to customers. The optimal assignments are included in the marketing campaign.

Abstract: A computer-implemented method is described for determining a response attribution value that represents a credit assignment to a communication, for an associated response received from a recipient during a marketing campaign.

Abstract: Methods, systems, and software for conducting a marketing campaign are described. One or more scores associated with sending offers to proposed contacts are received; and an overall campaign score corresponding to a sum of the scores is optimized to generate optimal assignments of offers to customers. The optimal assignments are included in the marketing campaign.

Abstract: Methods, systems, and software for combinatorial optimization are described. A first combination of offers are selected from a list of combinations of offers that pass a first set of rules belonging to a first category of rules, and it is determined whether the first combination of offers passes a second set of rules belonging to a second, different category of rules. If the first combination passes the second set of rules, the first combination is returned as a solution.

Abstract: A method for optimizing transactions to customers from a list of transactions is described. The method includes generating a frequency distribution of scores for each offer based on at least one constraint, determining a score threshold based on the frequency distribution of scores, and adding or removing transactions from the list of transactions based on the score threshold.

Abstract: A method executed on a computer for modeling expected behavior is described. The method includes scoring records of a dataset that is segmented into a plurality of data segments using a plurality of models and converting scores of the records into probability estimates. Two of the techniques described for converting scores into probability estimates are a technique that transforms scores into the probabilities estimates based on an equation and a binning technique that establishes a plurality of bins and maps records based on a score for the record to one of the plurality of bins.

Abstract: Marketing strategy optimization includes organizing a marketing strategy by plans and programs with each of plans and programs having input metrics having a causal relationship to output measurements that describe the outcome of the strategy. Optimization includes determining input measurements that optimize a given output for the strategy. Also described are graphical user interfaces for the optimization view that depicts a hierarchical organization of plans and programs associated with the market strategy, a region having target roll up values that are calculated by a marketing strategy optimization and a region having edit window to enter values for metrics associated with the plans and programs.

Abstract: Techniques for optimizing budget allocations among a plurality of marketing campaigns are described. The techniques include providing an expected return on investment (ROI) for potential contacts in target populations for each of a plurality of marketing campaigns and ordering the potential contacts by ROI for the marketing campaigns. The techniques also include determining a threshold ROI satisfying an overall budget, where potential contacts with ROI greater than the threshold level are recommended for actual contact.

Abstract: Techniques for determining a prioritized listing of offers for use to contact potential customers are described. The technique includes generating an ordered listing of offers from a set of offers, by which to contact a potential customer from a group of potential customers by considering the potential customer independently from others of the potential customers in the group, during generating of the ordered listing of offers for the potential customer.

Abstract: A method executed on a computer for modeling expected behavior is described. The method includes scoring records of a dataset that is segmented into a plurality of data segments using a plurality of models and converting scores of the records into probability estimates. Two of the techniques described for converting scores into probability estimates are a technique that transforms scores into the probabilities estimates based on an equation and a binning technique that establishes a plurality of bins and maps records based on a score for the record to one of the plurality of bins.