The Concept of Predictive Modelling – CIOReview

The use of computer software for predictive modeling has become increasingly popular as the process has become more complex and data quality efforts have increased exponentially.

In recent years, digital products and services have created more data than we've ever seen before. Consequently, more organizations are using big data analytics to derive value from their data.

What is predictive modeling?

Predictive analysis uses historical or real-time data to predict future events or outcomes. Forecasting sales, understanding customer behavior, and reducing market risks are some of the reasons businesses use predictive modeling. In addition to predicting future events, it determines what historical events are likely to occur again.

Data mining technologies are frequently used in predictive modeling solutions to analyze large data sets. As part of predictive modeling, data is collected, statistical analysis is performed, predictions are made, and the model is validated or revised. In the case of additional input data, these processes are repeated.

Benefits of predictive modelling

Predictive modeling reduces the time, effort, and resources needed to forecast business outcomes. Predictive modeling has the following top benefits:

An organization can use a predictive model to predict cyberattacks, fraudulent transactions, and other types of risks.

Marketing campaigns can be optimized by using predictive modeling to uncover customer insights.

Predictive modeling can be used to forecast inventory, create pricing strategies, and predict sales revenue.

Predictive modeling can help an organization prioritize resources in several ways. Getting lists of expected leads to convert allows sales teams to devote more time and effort to these high-priority leads.

How to build a predictive model

A variety of predictive modeling techniques are available. Neural networks and regression are two of the most prevalent techniques. As a statistical concept, regression describes the relationship between inputs and outputs. Depending on the variables, the predictive model could be linear or nonlinear.

An interconnected network of nodes in a hierarchical structure is the basis of neural networks, a predictive modeling tool based on the human brain. By establishing patterns and relationships between variables, future trends can be predicted. Aside from these two most popular predictive modeling techniques, businesses also use clustering, outliers, and classification models.

Data analytics teams traditionally handled predictive modeling manually. The use of computer software for predictive modeling has become increasingly popular as the process has become more complex and data quality efforts have increased exponentially. Because of this, most organizations use predictive modeling tools such as Oracle Crystal Ball, RapidMiner Studio, and SAP Predictive Analytics.

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The Concept of Predictive Modelling - CIOReview

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