Direct Marketing Performance Modeling Using Genetic Algorithms

Direct Marketing Performance Modeling Using Genetic Algorithms

Data analysts in direct marketing seek models to identify the most promising individuals to mail to and thus maximize returns from solicitations. A variety of criterion can be used to assess model performance, including response to or revenue generated from earlier solicitations. Given budgetary limitations, typically a fraction of the total customer database is selected for mailing. This depth...

Direct Marketing Response Models Using Genetic Algorithms

Direct marketing response models seek to identify individuals most likely to respond to marketing solicitations. Such models are commonly evaluated on classification accuracy and some measure of fit-to-data. Given large customer files and budgetary limitations, only a fraction of the total file is typically selected for mailing promotional material. This desired mailing-depth presents potential...

Uplift Modeling in Direct Marketing

Marketing campaigns directed to randomly selected customers often generate huge costs and a weak response. Moreover, such campaigns tend to unnecessarily annoy customers and make them less likely to answer to future communications. Precise targeting of marketing actions can potentially results in a greater return on investment. Usually, response models are used to select good targets. They aim ...

Using Genetic Algorithms to Breed Competitive Marketing Strategies

Genetic algorithms (GAs) have been used extensively in engineering and computer science to optimize specific functions, especially those which exhibit non-convexities and so are not amenable to calculus-based methods of optimization. A parallel use of GAs has been to solve algorithmic problems. A third domain in which GAs have been used is that of searching for mappings which optimize a repeate...

Direct-to-consumer marketing for genetic testing.