10 Types of Predictive Modeling: Benefits and Practical Applications

Modeling techniques in predictive analytics are statistical methods that rely on established data to forecast future outcomes. Businesses use predictive analytics to identify patterns that allow them to optimize operations, make informed decisions, reduce risks and improve customer experiences.¹

Using historical data as a foundation, predictive modeling incorporates statistical modeling, data mining techniques and machine learning to increase profitability and gain a competitive advantage. This article will explore 10 types of predictive analytics modeling techniques and how they’re used to drive business strategy.¹

You can use multiple statistical methods for predictive analytics. The right choice will depend on what you’re trying to forecast and what data you’re using.

Linear regression helps you make predictions by finding relationships between two variables. This is a simple predictive analytical model that’s not computationally complex, so it’s easy to scale. Linear regression is frequently used in areas such as sales forecasting, sports analysis and predicting investment performance.²

For example, businesses can use linear regression to predict how much inventory they need to keep on hand to meet demand during the holiday season. With an accurate sales forecast, a company can optimize its inventory levels—avoiding too much or too little inventory.²

Another statistical analysis method used in predictive modeling is logistic regression. It estimates the probability of an event occurring. Businesses can use logistic regression to predict how many customers are likely to leave, or “churn.” Because it’s based on a binary classification model, logistic regression separates events into two distinct categories. You can use logistic regression to evaluate the results of a customer experience improvement initiative by comparing your predicted churn rate to your actual churn rate.³

With a decision tree, you can map out the potential outcomes, including costs and consequences, of decisions. It’s a predictive method flowchart that lists the different courses of action you may take, along with the probability of each outcome. Decision trees provide clarity in complex situations, driving effective decision-making. Leaders can use decision trees for optimizing operations, budget planning and project management.⁴

Multiple decision trees can give you a more accurate predictive model. Random forest is a machine learning algorithm that uses multiple decision trees to predict the best outcome. It’s capable of handling various types of data and can be used in real-time, making it a good choice for multiple use cases. Financial institutions use random forest algorithms to identify fraudulent transactions by identifying transactions made from suspicious locations or for unusually large amounts.⁵

When you need to understand how data has changed over time, time series analysis is a useful tool. A time series analysis records and analyzes data points at regular time intervals, from seconds to decades or longer. Business leaders can use time series analysis to measure financial performance, such as stock prices; to reduce operational costs; for price forecasting; or to uncover hidden sales opportunities.⁶

Grouping data points based on similarities can help you uncover unexpected trends that can lead to strategic business opportunities. Clustering models are used in machine learning algorithms, pattern recognition and information retrieval models to better understand large datasets. Companies often use clustering models for customer segmentation to create targeted and effective marketing strategies.⁷

As the name implies, neural networks are roughly designed to mimic human brain activity. These deep learning algorithms are a fundamental artificial intelligence (AI) technology widely used in generative AI applications such as chatbots and image generation software. Neural networks can identify patterns and complex relationships in large datasets. They can take what they’ve learned from datasets and use it to generate content from existing data.⁸

A support vector machine is another type of classification model frequently used in machine learning. It has a high degree of accuracy, although it’s more computationally complex than other types of logistic regression models. Businesses can use it for customer segmentation, sentiment analysis, financial market prediction, medical diagnoses and speech recognition.⁹

The Naïve Bayes Classifier is a simple but effective machine learning algorithm that predicts probabilities. It assumes that each data point is independent of all the others. Although this is rarely true in the real world, it works well for probability predictions in the model. Common business use cases for the Naïve Bayes Classifier include content recommendation engines, fraud detection, customer sentiment analysis and credit risk assessment.¹⁰

No one prediction model is perfect, so ensemble models combine multiple methods to take advantage of the strengths of many different ones. It uses methods such as stacking models and training them in sequence to improve the accuracy and stability of the final prediction. This allows the model to handle complex predictive tasks that would otherwise be too difficult for a single model.¹¹

Predictive analysis with statistical modeling is as close as you can come to seeing into the future. Predictive models allow you to make better decisions and formulate effective strategies based on historical data. Business leaders can use these models to improve efficiency, reduce costs, create more effective advertising campaigns and mitigate risks. In today’s data-driven economy, predictive analytics can power business success and dramatically increase profitability.¹² 

If you’re looking to power up your career with data science and analytics, an Online Master of Science in Business Analytics from William & Mary can equip you with the knowledge and skills you need. Our well-known, highly-ranked program will help you stand out in a competitive market and prepare for a rewarding career that’s in high demand in as few as 15 months. Contact one of our admissions outreach advisors today to apply.