What Is Predictive Analytics?

Predictive analytics is a type of data analysis that uses statistics, data science, machine learning and other techniques to predict what will happen in the future. Predictive analytics answers the question “What is most likely to happen in the future based on historical trends?”

Companies can use predictive analytics to identify possible risks and opportunities. Once established, predictive insights can then be utilized to prescribe the action a company should take.

Why Is Predictive Analytics Important?

Predictive analytics is important because it enables businesses to accurately estimate what is likely to happen next in a scenario. This enables organizations to detect and mitigate potential problems or outperform competition by quickly capitalizing on new opportunities.

Types of Predictive Modeling

Supervised learning and unsupervised learning are two different modeling approaches that can be used to build predictive models and solve specific problems.


Supervised learning involves teaching an algorithm to come to a specific conclusion based on historical data. For example, if the question is “Will this customer churn?” an analyst can look at historical data about who has churned in the past and train an algorithm to determine which customers are most likely to churn looks like based on that data. In a nutshell, an analyst creates a training data set with a known outcome (i.e. churn or not-churn) which the algorithm then uses to create a predictive model based on historical data.
Unsupervised learning involves teaching an algorithm to look for similarities or patterns in data and group things together based on that information without being taught what to look for. For example, a streaming media platform might use unsupervised learning to group users together based on similarities in viewing behavior. The streaming service may then use these clusters or segments to provide a recommendation on what to watch next.

Three types of algorithms used for predictive modeling are:


Classification: a supervised algorithm that predicts a category or “class label” based on historical data. Example: An email client labeling an email as “spam” based on a classification algorithm that considers past attributes of spam emails.
Regression: a supervised algorithm that predicts a value or number based on historical data. Example: Based on the location, size and other factors, a regression algorithm can predict the value of a house.
Clustering: an unsupervised algorithm that sorts data into groups based on similar patterns and characteristics. Example: An e-commerce website can use a clustering algorithm to sort customers based on browsing and purchasing history to help inform marketing strategy.

How Does Predictive Analytics Work?

Predictive analytics always starts with a business problem(customer churn and attrition, inefficient processes, etc.). Then, the predictive analytics process follows these steps:

Acquire the data required for decision: This might be behavioral data, equipment data, social data, or financial data —the historical data that will help predict future outcomes.

Integrate, blend, and cleanse training data: Make sure the data used to train the model is in the in the right shame and format for the analytic techniques to be used.

Build the predictive model: Select an algorithm and starting parameter values and begin the iterative process of comparing the model’s prediction with the correct output, repeatedly adjusting parameter values until the model is predicting accurately on the training data.

Validate predictive model: Show the model “unseen” historical data and compare its predictions to what actually happened to ensure the model is not overfitted to the training data.

Deploy predictive model: Host model to provide access to incoming data for scoring while monitoring model performance and retraining as needed.

Business system integration: Use the predictive score to take action (process improvement, predictive maintenance, equipment monitoring).

Predictive Analytics Use Cases

Predictive analytics can help different businesses and different departments meet important goals and solve problems.


Customer Success

  • Predict which customers are likely to churn within a given period so you can take action to prevent the loss of valuable customers
  • Categorize customers into predefined groups (aka segments) based on patterns to learn more about them


  • Predict which patients are likely to miss their appointments so you can improve clinicians’ productivity by ensuring minimal “downtime” due to no-shows
  • Predict which patients are likely to be unsatisfied and why; use that information to determine how to improve patient satisfaction


  • Predict which policyholders are likely to lapse and come up with a strategy to increase retention
  • Predict which claims are likely to be successfully subrogated so you can focus efforts on high-potential claims and optimize recovery of loss payments


  • Predict which survey recipients are likely to respond
  • Predict which customers are likely to respond to campaign messages and prioritize outreach to those customers


  • Predict which potential customers are likely to respond and prioritize contacting them
  • Predict which other products customers are likely to buy so you can focus cross-sell and upsell efforts

How to Get Started with Predictive Analytics

The Alteryx Analytics Automation Platform delivers predictive analytics within the complete analytics workflow. Data access, preparation, modeling, and sharing of analytic results all happens in the same place, in one easy-to-use platform.

You can also see how Alteryx makes predictive analytics more accessible and agile by downloading a free Predictive Analytics Starter Kit. The solution kit comes with analytics templates to help you learn how to use the low-code, no-code tools in Alteryx to predict customer spending, make time series forecasts, and optimize your pricing.

For more information on Alteryx predictive analytics solutions, contact us today.