What is predictive modeling?

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In predictive modeling, future events are predicted based on statistical analysis. Read this guide to understand how predictive modeling works and how it can benefit your business.

The rapid adoption of digital products and services has created vaster volumes of data than we’ve ever seen before. As a result, an increasing number of organizations are using big data analytics and strategies to derive value from available data.

This data is often too complex and extensive for humans to analyze manually, especially for organizations that want to derive future insights from existing data sets. Organizations are instead relying on predictive modeling tools to connect data points and identify patterns in data. With the right predictive modeling tools and strategies, companies are able to make predictions about future events, customer behaviors and market trends.

Predictive modeling, a component of predictive analysis, is a statistical process used to predict future outcomes or events using historical or real-time data. Businesses often use predictive modeling to forecast sales, understand customer behavior and mitigate market risks. It is also used to determine what historical events are likely to occur again in the future.

Predictive modeling solutions frequently use data mining technologies to analyze large sets of data. Common steps in the predictive modeling process include gathering data, performing statistical analysis, making predictions, and validating or revising the model. These processes are repeated if additional input data becomes available.

Organizations use predictive modeling to reduce the time, effort and resources that are needed to forecast business outcomes. Here are the top benefits of using predictive modeling:

One of the challenges or limitations of predictive modeling is that the results are only as good as the data used to construct the model. To ensure predictive modeling is as effective as it can be, organizations should implement data quality tools to keep data accurate, safe and reliable. They should also prepare the data for business use by cleansing and formatting it for predictive modeling needs.

There are various predictive modeling techniques. The two most prevalent techniques involve using neural networks and regression, respectively. In statistics, regression refers to establishing a relationship between input and output variables. The predictive model could be linear or nonlinear, depending on the variables.

In neural networks, predictive modeling tools use interconnected nodes in hierarchical levels, a model inspired by the human brain. These nodes create patterns and relationships between variables to establish future trends. Beyond these two most popular predictive modeling techniques, businesses also use clustering, outliers and classification models.

Traditionally, predictive modeling was handled manually by a data analytics team. But as the process has become more complex and data quality efforts have increased exponentially, using computer software for predictive modeling has become increasingly popular. As a result, most organizations use predictive modeling tools such as Oracle Crystal Ball, RapidMiner Studio and SAP Predictive Analytics.

Many industries rely on predictive modeling to help with key business decisions. These are some common use cases for predictive modeling:

Predictive modeling is used in banking to identify fraud and illegal activities. For example, the amount and frequency of transactions are analyzed to recognize patterns or trends in money laundering.

In supply chain management, predictive modeling is used to forecast the impact of multiple variables on the inventory. Different risk factors can be plugged into the calculations to check their effect on the efficiency and reliability of the supply chain.

In digital marketing, predictive modeling can help market research analysts improve their understanding of customer behavior, which can reduce customer acquisition costs and improve sales conversion rates. This is done by modeling customer buying trends and online engagement based on historical data.