What Is Predictive Analytics? Meaning, Examples, and More

Key takeaways

• Predictive analytics is a type of data analytics that uses historical data to forecast future outcomes, such as how sales will shift or how customers will respond to a price change.

• Common predictive analytics models include regression, decision trees, and neural networks.

• Predictive analytics in health care helps identify patients at risk of certain diseases by analyzing demographic data, health records, and genetic information.

Predictive analytics relies on statistics, AI, and machine learning to analyze historical data and forecast likely outcomes. Learn more about how predictive analytics works, common techniques, and real-world applications. If you're ready to expand your analytics skills, consider enrolling in the Google Advanced Data Analytics Professional Certificate. You'll have the opportunity to build regression and machine learning models to analyze data and communicate insights.

What is predictive analytics?

Predictive analysis uses historical data, or data gathered from past events, to make predictions about scenarios likely to occur in the future.

Businesses use data to understand what's happening, both now and in the future. Predictive analytics falls into the latter category. It uses data from the past (known as "historic" or "historical" data) to predict potential future events or behaviors so companies can better position themselves in the present.

In order to calculate the future, predictive analytics relies on a number of techniques from statistics, data analytics, artificial intelligence (AI), and machine learning. Some common business applications include detecting fraud, predicting customer behavior, and forecasting demand.

Benefits of predictive analytics

Predictive analytics can help businesses make stronger, more informed decisions. It can help you identify patterns and trends within data that enable different business functions to predict the likelihood of future events. Other benefits include:

• Decision making: Improve how a business function makes decisions by relying on data to determine potential outcomes

• Risk management: Develop risk management strategies for potential risks and prioritize the most detrimental risks

• Customer insights: Better understand potential customers and what they need so that you can develop more specific marketing campaigns to reach them

• Operational efficiency: Make companies operate more efficiently by turning to historical data to understand resources and better manage them

Predictive analytics models

There are numerous predictive analytics techniques out there, from the relatively simple to the strikingly complex. Here's a brief overview of three of the most popular: regression, decision trees, and neural networks, to give you a better idea of the techniques used for predictive analysis every day:

• Regression: Regression relies on statistics to forecast future outcomes based on historical data, essentially identifying patterns between variables within large data sets. For example, a regression model could predict how many customers will purchase an item at a given price point based on past consumer behavior.

• Decision trees: A decision tree represents decisions and their potential outcomes using a tree-like visual with branching subsets. In effect, decision trees reflect the natural decision-making process humans use every day by outlining a possible cause-and-effect sequence based on likely outcomes as described in a data set. Decision trees are popular because users can often easily understand the results, and they provide excellent visuals.

• Neural networks: Neural networks are a predictive analytics technique that uses AI to chart nonlinear data sets or data sets without defined mathematical relationships. They are a more sophisticated tool than simpler decision trees or regression methods, and those working in predictive analysis often use them to examine complex relationships.

7 predictive analytics examples

Many industries use predictive analytics, including financial services, health care, retail, and manufacturing. Each has different use cases as well.

1. Retail

Predictive analytics is essential for retailers who want to understand customer behaviors and preferences. With insights from data, you can make more informed decisions about product assortment, pricing, promotions, and other aspects.

For example, retailers might use predictive analytics to determine which products are most likely to be purchased together and then offer discounts on those items combined. They can also identify customers at risk of leaving for a competitor and take steps to keep them.

2. Banking

Banks use predictive analytics to make more informed decisions about credit and investment products and even trade currency. Banking-related data sets form patterns that identify customers at risk of defaulting on a loan.

Banks also use predictive analytics to identify customers likely to be interested in investing in a new financial product so that they can target them with impactful marketing messaging.

3. Sales

Sales teams use predictive analytics to better understand customers’ wants and needs. By analyzing past customer behaviors, they can more accurately predict which products or services a customer is likely to purchase. This allows sales teams to focus on selling the most appealing items to their prospects and ultimately increase their sales revenue.

4. Insurance

Insurance companies use predictive analytics to determine the likelihood that a particular customer will make a policy claim. By analyzing claims history, demographics, and lifestyle choices, insurers can develop models that help them predict which customers are most likely to file a claim. This information allows them to adjust premiums and identify and target higher-risk customers with specific policies.

5. Social media

Social media teams use predictive analytics to understand user behavior and trends. By analyzing the vast amount of data generated by users on social media platforms, they can gain insights into the things that people care about, what they are talking about, and how they interact with each other. This information improves the user experience on social media platforms and enables them to target advertising more effectively.

6. Underwriting

The process of underwriting insurance policies routinely uses predictive analysis. By analyzing data on past claims, insurers identify patterns that may indicate a higher risk of future claims. Armed with probabilities and predictions, they can then adjust premiums for individual policies or groups of policies or even deny coverage altogether.

7. Health

Predictive analytics in health care can identify patients at risk of developing certain diseases or conditions. By analyzing demographic data, health records, and genetic information, doctors and researchers can develop models that help them create health policies and interventions. They can then use predictive analytics to create targeted prevention and treatment programs for those patients at the highest risk.

Predictive analytics: Job outlook and salary

Predictive analytics falls within the larger umbrella of data science, which has a very positive outlook in the US. Demand for data professionals is expected to grow by 34 percent between 2024 and 2034, a rate that is much faster than the average of 3 percent for all US occupations, according to the US Bureau of Labor Statistics [1].

What's more, data science occupies the eighth spot on US News and World Report's "Best Jobs in America in 2026" list [2]. Working in data science also tends to pay a higher-than-average salary. According to Glassdoor, the median total pay for a predictive analyst is $115,000 [3]. This figure includes base salary and additional pay, which may represent profit-sharing, commissions, bonuses, or other compensation.

What jobs use predictive analytics?

The field of predictive analytics offers a range of exciting career opportunities. Some of the most common jobs include:

• Data scientist

• Data analyst

• Business analyst

• Marketing analyst

• Risk analyst

• Quantitative analyst

• Machine learning engineer

• Fraud analyst

• Pricing analyst

• Customer success manager

How to get started in predictive analytics

To work in predictive analytics, you’ll need to be comfortable working with large data sets, have a strong grasp of data analytics and statistics, and be able to communicate your findings clearly to non-technical audiences. Ways you can gain the skills needed to become a data professional specializing in predictive analytics include the following:

1. Education

A data scientist typically has a strong background in mathematics and computer science and holds at least a bachelor's degree with a major in data science or a related subject, like IT, statistics, or business. That being said, many data scientists have taught themselves the necessary skills through online resources and personal projects.

Learn more: Data Science Jobs Guide: Resources for a Career in Tech

2. Professional experience

In addition to formal education, gaining professional experience is essential for becoming a data scientist. You can gain experience in predictive analytics through internships, working with datasets in freelancing projects, and working in junior or entry-level roles.

Many employers place great value on relevant work experience, so previous experience working with data and analytics tools can be helpful. You'll want to build your skill set and experience to work in predictive analytics. Your resume may look more robust if you have demonstrable experience in the following:

• Predictive modeling

• Regression analysis

• Classification algorithms

• Decision trees

• Neural networks

• Support vector machines

3. Certifications

When you're pivoting into data analytics, earning a Professional Certificate or certification can be a great way to learn about the subject and gain the skills you need to do the work.

Several certifications are available for predictive analytics professionals, such as the Certified Analytics Professional (CAP) certification offered by INFORMS. Certificates are not always required for employment, but they can strengthen your resume.

Common certifications and certificates include:

• Google Data Analytics Professional Certificate

• Microsoft Certified: Power BI Data Analyst Associate

• INFORMS Associate Certified Analytics Professional (aCAP)

• IIBA Certification in Business Data Analytics (CBDA)

No-fluff help when you're stuck

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