10 Predictive Modeling Types (With Benefits and Uses)

Updated March 10, 2023

Organizations may use predictive modeling when they conduct predictive analysis, which often helps them make informed business decisions. These models help businesses learn more about their consumer bases, upcoming sales opportunities or account-related security alerts. If you're interested in learning how to ensure the success or improved functionality of your company, you may benefit from learning about these techniques. In this article, we define predictive modeling, describe the benefits of these techniques and list 10 predictive modeling types you can use for various business purposes.

Related: Everything You Need To Know About Predictive Analytics

What is predictive modeling?

Predictive modeling is a statistical technique in which an organization references known results and historical data to develop predictions for future events. Predictive models analyze patterns and observe trends within specific conditions to determine the most likely outcome. For example, financial organizations may use predictive modeling to gather a customer's credit history and other customer data. They might then use this information to determine an individual's credit score and the likelihood of them submitting credit payments on time. Another name for this technique is predictive analytics, which also includes machine learning and data mining to gather and assess data.

Various businesses and industries use predictive modeling and analytics to manage their services and customers effectively. The health care industry often uses predictive models to improve diagnostic practices and properly treat terminal or chronically ill patients, while banks may use these models to detect fraud. Human resources departments and companies may use them to hire employees.

Related: Data Analysis: Definition, Types and Benefits

10 predictive modeling types

There are two categories of predictive models: parametric and non-parametric. A model that uses a specific set of parameters, such as discrete numbers, is parametric. Non-parametric models consider data that doesn't come from a specific set of parameters or factors. Each type of model has a specific use and employs a particular type of data to achieve its purpose. They also employ different techniques, such as descriptive, diagnostic, predictive and prescriptive analytics. Here's a list of 10 common predictive modeling types and brief explanations for how and why organizations use them:

1. Classification model

This predictive modeling type is one of the most basic and commonly used models because it produces simple responses to questions that yield yes or no responses. A classification model uses historical data to produce a broad analysis of a query. Retail and finance businesses often use this because it quickly gathers and categorizes information to answer questions such as, "Is this applicant likely to default?" Other organizations also widely use this model because they can tailor it to include new or modified data when producing a response.

2. Forecast model

Forecast models are also one of the most common model types due to their versatility. These models produce numerical responses by analyzing historical data and estimating information based on that data. A business such as an online retailer may use forecast modeling to estimate how many orders they may receive over the next week. These models can also successfully manage multiple parameters simultaneously. For example, a restaurant estimating the amount of supplies to order may assign factors such as nearby events and upcoming holidays to this model.

Read more: The 4 Types of Forecasting Models With Examples

3. Clustering model

A clustering model separates data into different categories based on similar characteristics. It then uses the data from each group to determine large-scale outcomes for each cluster. This model works by using two types of clustering. Hard clustering categorizes data by determining whether each point completely belongs to a certain cluster. Soft clustering assigns a probability to each data point instead of separating them into distinct clusters. Businesses may use a clustering model to determine marketing strategies for certain groups of consumers.

Related: 10 Types of Marketing Models

4. Outliers model

An outliers model identifies unusual or outlying information within a dataset. It can analyze individual instances of unusual data or connections to other categories and numbers. Many financial companies use this model to detect fraud. For example, an outlier model may identify unusual transactions in a consumer's account, such as a large expense on jewelry in a city in which the consumer has never bought other items. The model can identify the dollar amount, location, time and type of purchase to determine whether a third party has breached a consumer's account.

5. Time series model

A time series model uses time sequences as input factors in a dataset. It considers past trends and data points from a specific time sequence to predict trends or occurrences throughout another specific time period. This model can forecast multiple trends and projects simultaneously or focus on a specific project. It can also analyze external factors that may affect future trends, such as seasons or periodical variations. For example, an electronic manufacturing company may use this model to analyze processing times over the last year. The model can then estimate the average processing speed for upcoming months.

6. Decision tree

A decision tree is an algorithm that graphs multiple sources of data into a tree-like structure to display the possible outcomes of different decisions. This model outlines different decisions into branches and then places possible outcomes underneath each decision. Organizations often use this to determine the most important variables in a certain dataset. They may also use them because the model can handle incomplete datasets to create possible outcomes. Decision trees are easily understandable, so many industries use them to ensure clarity among departments when presenting data.

7. Neural network

A neural network is a complex model that resembles the human brain. It involves many algorithms collaborating to identify patterns, cluster data and create categories for different datasets. Neural networks often include three layers. The input layer places data into the next layer, which is the hidden layer. The hidden layer includes functions that create predictors. The output layer collects the data from these predictors and produces the final, comprehensive outcome. Organizations may use these networks in conjunction with other predictive models, such as time series or clustering.

8. General linear model

A general linear model is a statistical tool that compares how certain variables affect continuous variables. This tool is often the foundation for other statistical tests, such as regression analysis. Companies employing predictive modeling often conduct regression analyses when creating and processing data to create a prediction. The general linear model determines whether the means of two dependent attributes in a prediction differ. A group of these models comprises a generalized linear model, in which an individual graphs multiple similar models.

Related: Calculating Linear vs. Logistic Regression: Definitions and Steps

9. Gradient boosted model

A gradient boosted model uses multiple related decision trees to generate rankings. It creates one tree at a time and corrects flaws from the first tree to create a second, improved tree. This process may include several trees, depending on the organization that creates it. Some organizations use these models to determine possible search engine outputs.

10. Prophet model

A Prophet model is an algorithm that an individual may use in conjunction with time series or forecast models to plan for a specific outcome. For example, a business might use a Prophet model to determine sales quotas or inventory requirements. This model, hosted by Facebook, is flexible and collaborates well with time series models that have multiple seasons or holidays included.

Please note that none of the companies or products mentioned in this article are affiliated with Indeed.

Benefits of predictive modeling

One main benefit of using predictive modeling is the potential to create improved marketing, sales and customer service strategies. Here's a list of additional benefits companies may experience while using predictive modeling:

  • Gaining a better understanding of competition

  • Employing strategies to gain a competitive advantage

  • Optimizing existing products or services

  • Understanding consumer needs

  • Understanding the general consumer base of an industry or company

  • Reducing time, effort and cost of estimating outcomes

  • Forecasting external factors that could affect productivity or workflow

  • Identifying financial risks

  • Forecasting inventory or resource management processes

  • Identifying future trends

  • Planning workforce or churn analyses


Explore more articles

  • How To Insert the Current Date in Word (With 4 Methods)
  • 5 Conflict Resolution Strategies: Steps, Benefits and Tips
  • How To Graph Functions in Excel (With 6 Steps and Tips)
  • 55 Inspirational Work-Life Balance Quotes for Motivation
  • 5 C’s of Marketing Analysis: Definition, Tips and Example
  • How To Add Individual Error Bars in Excel (Plus Benefits)
  • How To Inform Your Boss You're Running Late for Work
  • 7 Communication Failure Examples (With Definition)
  • 4 Modes of Persuasion and How To Use Them
  • Guide: Using the Question and Answer Format in Essays
  • How To Write a Nursing Progress Note
  • Valuing Employees: 15 Ways To Make Them Feel Valued