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What is predictive modeling is a common question asked by people who work in business, information management, research, and related fields. Predictive modeling is the use of known results or collected data for analyses that allow a statistician, researcher, information specialist, or businessperson to validate a model that will be able to make a prediction about future results.

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What Predictive Modeling Means

Predictive modeling is a sort of data mining technique that is designed to answer the question, “What might occur in the future?” When a business takes a look at what has happened in the past, it can make a fairly accurate prediction of what might happen in the future, should a similar set of circumstances arise. The models use millions or even billions of points of data, which is too much for a human’s brain to calculate. Because of this, predictive modeling is conducted with the use of statistical analysis software packages. Some of the proprietary software products used for predictive modeling include SAS, STATA, and SPSS.

Where the Modeling Data Comes From

The rapid rise of customer service management systems, large databases, cloud computing and the collection of incredible amounts of data from every online activity, in-person transaction, and other activities means that businesses have a lot of data. They want to use that data in order to make predictions with as much accuracy and precision as possible. The data might be collected from a web browser when a person explores a website; from an eCommerce transaction, from opening emails and clicking on links; and from in-person visits to a business.

Types of Statistical Models Used in Predictions

Once a large amount of data is in a database, organized, and cleaned so that the formatting is in a way that a software package can make use of it, the researcher is able to choose a statistical technique for making the predictive model. Investopedia shares how there are two leading types of statistical techniques for predictive modeling. The first is regression, which uses data to determine how one variable behaves as other, independent variables change. Regression is a liner model. For example, a business might look at how customer satisfaction behaves as the price, availability, shipping speed, shipping cost, quality, size, and color of the product varies. The second type of predictive model is a neural network. A neural network uses interconnected hierarchical nodes in order to handle non-linear relationships between data points.

Considerations When Using Predictive Modeling

There are several considerations to keep in mind when using predictive modeling. One is that using the wrong type of model will yield useless results. Another consideration is that there may be variables that go unrecognized, so the model is missing a key piece that would allow it to make a better prediction. For example, the type of packaging on an item for sale might play a key role in customer satisfaction, but if it is left out of the model, the prediction will not be as accurate as it could have been.

The process of predictive modeling was developed by scientists studying the neural networks in the brain. The human brain uses past, known experiences in order to create expectations for the future, should similar conditions be met. Becoming familiar with what is predictive modeling could help a person in their career or gain a better understanding of the uses of data.


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