Post by simranratry20244 on Feb 12, 2024 1:31:47 GMT -5
Predictive analysis allows you to develop mathematical models to help better understand the variables that drive success. To do this, it is based on statistical formulas, game theory and data mining that allow historical and current events to be analyzed in order to make predictions about future events. In practice, this extracted knowledge is manifested in: Photo credits: istock Better understanding of customer behavior and motivations. More effective marketing methods. Greater capacity to attract and retain customers. Creation of more attractive and much more personalized offers, difficult to resist. Predicting consumer purchasing habits and insight into their preferences for different products requires an analytical framework capable of discovering significant patterns and relationships within the customer's own data, in order to achieve better targeting. First step towards achieving the goal of customer loyalty.
Judicious data mining Predictive models improve Colombia Telemarketing Data marketing effectiveness, but companies need to have ready customer databases (in quality conditions) to be able to apply data mining techniques or statistical formulas. Furthermore, they must know which of all the existing approaches is best for them to use. Among the most commonly used data mining techniques are decision trees, neural networks, genetic algorithms, regression models, choice models, induction rules and groupings. When choosing one you have to keep in mind that: The simplicity of the model facilitates its interpretation and the first contact with the data. However, an overly simplistic approach can make it difficult to discover non-linear or subtle dependencies. The choice of approach must be made with the aim of improving the predictive capacity, so it must encourage a correct interpretation of the data, carried out in sufficient depth.
Different criteria can also be applied for decision making, among which Wald's, Bayes', Laplace's, Savage's or Hurwicz's stand out. The selection of the technique and criterion must be made consciously for a correct application of statistics. In any case, the purely analytical perspective is not the key to success, at least not independently; but also the quality of the data must be taken as an imperative requirement for data mining actions to be effective. Therefore, when starting any data mining action, the following three premises must be taken into account: Data mining does not imply the instantaneous discovery of information, it requires a process. For effective data mining, it is necessary to know how to correctly formulate the problem to be solved in advance. Being clear about your objectives is as important as knowing how to "translate" them into analytical terms.