1. A selection method, comprising: providing a database system comprising a computer processor, a database manager software application, a first database structure storing a first list identifying marketing events, a second database structure storing a second list of candidates, and a third database structure storing a data model consisting of credit card balances, purchasing history, returned-items history, past marketing events that have been accepted by said candidates, and past spending habits associated with said second list of candidates, wherein said third database structure further comprises a predetermined total budget for said candidates regarding an amount of money that an entity offering said marketing events is willing to spend to offer said marketing events to the said candidates, wherein said database manager software application is stored on a computer readable medium, wherein said database manager software application comprises an optimization tool and a computing tool, and wherein each marketing event from said first list comprises a marketing offer associated with a credit based offer and an identified channel means for communicating said marketing offer; calculating, by said computing tool, value scores associated with said marketing events from said first list, wherein each value score of said value scores is associated with a marketing event from said first list, wherein said value scores are associated with an expected profit gain associated with each said marketing offer, and wherein each said value score is calculated based on said data model, said predetermined total budget for said candidates, and an annual return of credit repayments vs infrastructure costs balanced against a risk of a candidate defaulting on said credit repayments; computing by said computing tool, response probability scores for said marketing events from said first list for a first candidate from said second list, wherein each of said response probability scores are computed using said data model and said predetermined total budget, and wherein each of said response probability scores are computed simultaneously in parallel; computing by said computing tool, ranking scores for said marketing events from said first list and said first candidate from said second list, wherein each ranking score for each of said marketing events from said first list is associated with said first candidate from said second list, and wherein each of said ranking scores are computed by multiplying each said response probability score with an associated value score of said third list; sorting by said optimization tool for said first candidate, each said ranking score for each of said marketing events; generating , by said computer processor in response to said sorting, a ranking list comprising a ranked version of said marketing events and said ranking scores; determining, by said optimization tool, a date for offering each marketing event of said marketing events on said ranking list; and storing, by said database system, said ranking list; applying, by said optimization tool, timing constraint data to each ranking score of said ranking scores, wherein said timing constraint data comprises timing related data associated with a maximum number of offerings for each said marketing event and timing between each said marketing offer of each said marketing event; determining, by said optimization tool, if any of said marketing events comprises a same type of marketing offer; removing, by said optimization tool in response to results of said applying said timing constraint data and results of said determining if any of said marketing events comprises a same type of marketing offer, a group of marketing events and associated ranking scores from said ranking list resulting in a second ranking list comprising first remaining marketing events and first remaining ranking scores, wherein said group of marketing events comprise marketing events comprising timing constraints specified by said timing constraint data; applying, by said optimization tool, monetary constraint data to said first remaining value scores, wherein said monetary constraint data consists of an amount of money regarding execution costs associated with channels used for offering said remaining marketing events to said first candidate, wherein said execution costs consist of costs for promotional materials, mailing costs, telemarketing costs, and infrastructure costs, and wherein said applying said monetary constraint data comprises subtracting said monetary constraint data from said predetermined total budget; removing, by said optimization tool in response to results of said applying said monetary constraint data, a second group of marketing events and associated ranking scores from said second ranking list resulting in a third ranking list comprising second remaining marketing events and second remaining ranking scores; ranking in simultaneously in parallel, by said optimization tool, said second remaining ranking scores; generating , by said computer processor in response to said ranking said second remaining ranking scores, a final ranking list comprising said second remaining marketing events of said marketing events and said second remaining ranking scores in a specified order resulting from said ranking.