Predictive modelling refers to a collection of methods whereby specific data can be analyzed and interpreted to derive from it predictions for future events, trends or consumer behavior. These predictions are to be regarded as probabilities rather than actual statements about the future which will absolutely happen. The results of forecasting models are predictions about the respective subject area based on statistics, which depend in their probability on the size of the database. The more input data is available, the more accurate the output data will be. There is no guarantee for the actual occurrence of the prediction, however.
Predictive modelling is applied in a variety of areas and disciplines, including insurance and finance, telecommunications, science, e-commerce, customer relationship management, and business intelligence. The forecasts can be used in terms of economic applications as a basis for budget planning and the assessment of opportunities and risks. The best-known application of predictive modelling is the calculation of risks in life insurance. In a scientific context, it is about the confirmation or refutation of theories that describe the behavior of specific objects dependent on the topic. The demographic development of societies is an example of this.
Three phases can be distinguished in modeling. In order to increase the probabilities of certain statements, these phases are usually repeated several times and are therefore partly iterative.
In general, many different statistical models and multivariate regression analysis can be used to make predictions. In the context of applications in IT, it usually concerns data mining and machine learning. On the one hand relevant input data is found in large databases and on the other hand forecasting models should be self-learning, so that they automatically respond to new data.
Forecasts are always problematic in certain respects, since they are a form of inductive arguments. General application or statements will be concluded from empirical observations and individual cases. While it is reasonable to consider such conclusions to be true if the premise (the data) are true, it does not mean that the conclusions are logically stringent or can claim universality. Moreover, large amounts of data may enable more accurate predictions, however, more data does not always mean a better insight into the behavior of customers. Experts often recommend smart instead of big data in this context.
These limitations of predictive modelling become more apparent when it comes to human behavior. To predict the behavior of customers, the forecast model should know all the variables that affect this behavior. But people are influenced by hardly countable factors from their environment. Each model is therefore limited in certain respects. Nevertheless, some e-commerce platforms such as Netflix or Amazon managed to generate a high increase in sales with predictive modelling.
Using predictive modelling in the fields of web analytics, online marketing, search engine optimization, and social media, not only opportunities and risks can be assessed, but also expected sales can be calculated. Therefore, forecasts in these contexts can be used for planning and budgeting. Advertising companies can decide which marketing channels should be considered in view of their potential. This also applies to actions for online customer loyalty, customer relationship management, email and newsletter marketing, search engine advertising, and affiliate networks. The possible applications for Predictive Modelling are extremely diverse.