Predictive analytics is an analytics process that uses statistics and modeling techniques to make informed decisions and predictions about future outcomes based on current and historical data. Businesses use these techniques, which include data mining, predictive modeling and machine learning, to assess risks and forecast business trends.

As a result, business leaders can use predictive analytics to increase the chances of success for many initiatives or to test a variety of scenarios quickly. This gives businesses across various industries a competitive edge and helps them make smarter, data-driven decisions.

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How does predictive analytics work?

Predictive analytics platforms look at historical data and try to spot patterns. The process relies on data such as customer purchases, weather information or banking habits, statistics such as regression analysis and assumptions that the future will follow trends from the past.

For example, predictive analytics has the potential to spot customers who are likely to cancel a service or not renew it, identify transactions that could be fraudulent or create a preventive maintenance schedule.

SEE: Learn more about predictive modeling.

Some types of predictive analytics software even use machine learning to revise algorithms based on learnings from the data collected over time, continuously improving prediction accuracy. These platforms use advanced algorithms to analyze large volumes of data and identify patterns, correlations and trends. They also utilize statistical models and techniques such as regression analysis, decision trees, neural networks and clustering to make predictions and provide insights based on historical trends.

What is the difference between predictive analytics and forecasting?

Predictive analytics involves using data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. As such, forecasting is a specific type of predictive analytics that focuses on predicting future values of a particular variable or set of variables based on past and present data.

Types of predictive analytical models

There are various types of predictive analytics models available. The best predictive analytics model is relative to the type and amount of data available, the nature of the problem a business wants to solve and the business’s desired outcome.

Clustering models

Clustering models are used to group similar observations based on their characteristics without predefined categories. There are various types of clustering models, including centroid-based clustering, density-based clustering, distribution-based clustering and hierarchical clustering.

Use cases: Customer segmentation, anomaly detection and market basket analysis.

Time series models

Time series models are used to predict future values of a variable based on a sequence of data points collected over time, patterns and trends observed in historical data.

Types of time series models include exponential smoothing, autoregressive integrated moving average models, autoregressive moving average, moving average and seasonal autoregressive integrated moving average.

Use cases: Forecasting stock prices, product demand, weather predictions and website traffic.

Classification models

A classification model is used to predict the correct label from the input values given for training. These models predict the category or class an observation belongs to based on its characteristics. Examples include logistic regression, decision trees, random forests and support vector machines.

Use cases: Fraud detection, customer segmentation, spam filtering, sentiment analysis and medical diagnosis.

Regression models

A regression model is a statistical model that attempts to predict the relationship between a dependent variable and one or more independent variables. Examples include linear regression, polynomial regression and multiple regression.

Use cases: Predict sales or financial data.

Ensemble models

​​Ensemble models combine predictions from multiple models to improve overall performance and robustness. Examples include random forests, gradient boosting and stacking models.

Use cases: Any prediction task where improved accuracy is desired.

Decision trees

Decision trees are a type of predictive model that uses a branching structure to map out a decision-making process. Each branch represents a different decision or outcome based on the input variables in the data.

Use cases: Classification problems where the goal is to assign each data instance to a predefined class or category.

Benefits of predictive analytics

  • Enhanced financial performance: Organizations can improve financial performance by minimizing risks and maximizing opportunities when they use predictive analytics to optimize financial processes, such as budgeting, forecasting and risk management.
  • Better customer targeting: Predictive analytics can segment customers based on their behavior, preferences and buying patterns. This enables businesses to tailor their marketing strategies and offers to specific customer segments.
  • Fraud detection: Financial services, insurance and e-commerce companies can use predictive analytics to prevent and minimize fraud, as it is effective in detecting unusual patterns or anomalies that may indicate fraudulent activities.
  • Improved decision-making: By leveraging historical data and advanced algorithms, predictive analytics can provide insights and predictions that help businesses make better decisions.

Predictive analytics use cases

Predictive analytics is becoming commonplace among various businesses, regardless of their industry. For example, it is good for financial forecasting, customer behavior analysis, risk management, fraud detection and operational optimization, making it useful in sectors that monitor and leverage these types of data.

Financial services

Financial institutions often use predictive analytics to identify patterns and anomalies in transaction data for fraud detection, assess the credit of borrowers to predict the likelihood of a customer defaulting on a loan and make data-driven investment decisions.


By analyzing factors such as transaction history, customer service interactions and demographic information, predictive models can flag customers with a higher likelihood of leaving and enable personalized retention efforts.

And by analyzing customer data and behavior, predictive models can identify customers who are more likely to purchase certain products or services, enhancing customer acquisition and retention.


Telcos can leverage predictive analytics capabilities to help them minimize downtime, improve service quality and enhance customer satisfaction. Using this technology to predict potential network issues based on past events and performance metrics allows for proactive maintenance and optimization.


Predictive analytics can be used in manufacturing to optimize equipment maintenance. By analyzing real-time sensor data and historical maintenance records, predictive models can identify patterns and predict when equipment is likely to fail. This allows manufacturers to schedule maintenance proactively, reducing downtime and improving overall operational efficiency.

Popular predictive analytics tools

Predictive analytics tools range from no-code tools to data lakes to machine learning algorithms. In addition, some platforms are complete workspaces, while others integrate with existing tools, and options for cloud and on-premises deployments will vary as well.

Businesses will need to pick a solution that fits the needs and expertise of each department. Some top predictive analytics tools include:

  • Alteryx allows users to create and deploy analytic models without the need for coding. It ranks best for data preparation, blending and analysis.
  • RapidMiner has 1,500+ native algorithms, data prep and data science functions and supports data integration from various sources. It offers visual workflows for creating and deploying predictive models.
  • Microsoft Azure Machine Learning is a cloud-based predictive analytics platform that offers a drag-and-drop interface and automated machine learning capabilities.

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