What Is Predictive Analytics? Benefits, Examples, and More

Written by Coursera Staff • Updated on

Learn more about when—and why—businesses use predictive analytics and some of the benefits of working with this type of data analytics.

[Featured Image]: Data Analyst using predictive analytics tools.

Predictive analytics is one of the four key types of data analytics, and typically forecasts what will happen in the future, such as how sales will shift during different seasons or how consumers will respond to a change in price. Businesses often use predictive analytics to make data-driven decisions and optimize outcomes.

In this article, we'll go over more about predictive analytics, including how it's used, some common benefits, and what you can do to get started in it.

What is predictive analytics?

Businesses use data to understand what's happening—both now and in the future. Predictive analytics falls under the latter category. It uses 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.

Learn more: Data Science vs. Machine Learning: What's the Difference?

Benefits of predictive analytics

Predictive analytics can help businesses make stronger, more informed decisions. It can identify patterns and trends within data that enable different business functions to make a probabilistic determination about 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 even prioritize the risks that could be most detrimental.

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

  • Operational efficiency: By turning to historical data to understand resources and better manage them, predictive analytics can make companies operate more efficiently.

7 areas that use predictive analytics

Many industries use predictive analytics, including financial services, health care, retail, and manufacturing, and they each have different use cases. Let's review a few.

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 understand better 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 positive outlook in the US. Demand for data professionals is expected to grow by 36 percent—much faster than average—over the next decade, according to the US Bureau of Labor Statistics [1].

What's more, data science occupies the third spot on Glassdoor’s "50 Best Jobs in America for 2022" list [2]. Working in data science also tends to pay a higher-than-average salary. According to Glassdoor, the average annual salary for a predictive analyst is $83,948, once base pay and additional compensation are combined [3].

How to get started in predictive analytics 

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

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:

Explore predictive analytics further 

Learn more about predictive analytics or data analytics through Coursera. The University of Minnesota's Analytics for Decision Making Specialization emphasizes how to model and solve problems using predictive models, linear optimization, and simulation methods.

Or enroll in the Google Data Analytics Professional Certificate, which takes around six months to complete when you dedicate around 10 hours each week. You'll learn the fundamentals of data analytics, including data collection and data cleansing.

Article sources


US Bureau of Labor Statistics. "Data Scientists, https://www.bls.gov/ooh/math/data-scientists.htm." Accessed March 30, 2023.

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