What Is Predictive Analytics? Definition, Examples, and More

Written by Coursera • Updated on

Use predictive analytics in business to make better decisions about the future. Discover what skills you'll need for a career in this subset of data science.

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

Predictive analytics uses data and advanced analytics to make predictions about the future, such as how sales will shift during different seasons or how consumers will respond to a change in price. Businesses use predictive analytics to make data-driven decisions and optimize outcomes. In this article, you'll learn more about predictive analytics, including how it's used, some common benefits, and what you can do to get started in the field. At the end, you'll also explore cost-effective, flexible courses that can help you gain job-relevant skills today.

Predictive analytics definition

Predictive analytics is a subfield of data analytics that deals with making predictions about future events or behaviors. Many diverse industries utilize predictive analytics, including financial services, health care, retail, and manufacturing.

Predictive analytics relies on numerous techniques from statistics, data analytics, artificial intelligence, and machine learning in order to forecast future events. Some common business applications of predictive analytics include detecting fraud, predicting customer behavior, and forecasting demand. In public health, predictive analytics is used to predict disease outbreaks and inform pandemic responses.

Predictive analytics examples

Predictive analytics is a powerful tool for improving decision-making in business, government, and other organizations. Here are some examples of how predictive analytics are used every day to improve business outcomes. 

Customer service

Predictive analytics is used in customer service to identify customers at risk of leaving or those likely to need more support. With a clearer understanding of the data, businesses can proactively prioritize and contact customers and offer them more personalized service.


One of the most common ways businesses use predictive analytics is to forecast sales and customer demand. These predictions then inform the decisions they make about inventory levels, production levels, and marketing campaigns.

Credit scoring

Predictive analytics is used in the credit scoring processes to identify customers who are likely to default on their loans or those likely to make late payments. This information helps financial institutions to set suitable credit limits and interest rates.


Predictive analytics is instrumental in marketing, which uses it to gain customer insights and direct marketing campaigns. By analyzing customer data, marketers can identify patterns and trends that indicate a need or desire for a particular product or service. By understanding the characteristics of potential buyers and how they’re likely to behave, businesses can better segment customers and create targeted marketing campaigns more likely to result in conversions.

Read more: Digital Marketing: What It Is and How to Get Started 


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 then can adjust premiums for individual policies or groups of policies or even deny coverage altogether.

Benefits of predictive analytics

Predictive analytics can help you to make better decisions. By analyzing past data, predictive analytics can identify patterns and trends that enable you to make a probabilistic determination about events in the future. 

You can then use this information to make decisions about:

• How to allocate resources 

• How to respond to potential risks

• How to take advantage of opportunities 

Predictive analytics can even help you to improve customer service by identifying potential issues before they occur and providing recommendations for how to resolve them.


Predictive analytics use cases across industries

You can find powerful predictive analytics use cases in diverse industries. Many sectors have come to rely on data science to remain competitive.

Retail: Predictive analytics is essential for retailers who want to understand customer behaviors and preferences better. Additional predictive power means you can make more informed decisions about product assortment, pricing, promotions, and other aspects of your business. 

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

Banking: Banks use predictive analytics to make more informed decisions about credit and investment products and even trade currency. As a banking predictive analytics specialist,  you might use large data sets to form patterns that identify customers at risk of defaulting on a loan. You might also use predictive analytics to identify customers likely to be interested in investing in a new financial product so that you can target them with impactful marketing campaigns.

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

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 you to adjust premiums and identify and target higher-risk customers with specific policies.

Social Media: Social media companies use predictive analytics to understand user behavior and trends. By analyzing the vast amount of data generated by users on social media platforms, you 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 you to target advertising more effectively.

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. You can use predictive analytics to create targeted prevention and treatment programs for those patients at the highest risk.

Working 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 you'll need to become a data professional specializing in predictive analytics:

Educational path

A data scientist typically has a strong background in mathematics and computer science, and many also have a master’s degree or PhD. In specific educational paths, you'll find a few different options that can lead you to a career in data science. 

One option is to study statistics, information technology, or another relevant field at the undergraduate or graduate level. Another option is to pursue a more general computer science degree and then specialize in data science through online courses, certifications, or by gaining professional experience. 

Some data scientists have taught themselves the necessary data science skills through online resources and personal projects. However, employers typically look for at least a bachelor's degree in a relevant subject.

Gaining 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. Many data scientists start their careers as data engineers or database administrators before transitioning into data science roles. 

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


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 boost your resume. 

Here are some more certifications that you might like to consider.

  • Microsoft Certified: Data Analyst Associate

  • Associate Certified Analytics Professional

  • IIBA Certification in Business Data Analytics (CBDA)

Job outlook and average salary

The job outlook for data scientists is extremely positive, with demand for data professionals expected to grow significantly in the coming years. Data science is number three in Glassdoor’s ‘50 Best Jobs in America for 2022’ [1]. Furthermore, according to Glassdoor, the average annual salary for a predictive analytics data scientist is $84,988, once base pay and additional compensation are combined [2].

Next steps 

Predictive analytics allows businesses to forecast future trends and customer behaviors, and make more informed business decisions. If you're interested in starting a career in data and predictive analytics, then you might consider taking a cost-effective, online specialization through Coursera.

In the University of Minnesota's Analytics for Decision Making Specialization, you'll learn how to model and solve decision-making problems using predictive models, linear optimization, and simulation methods. After completing the course, you'll receive a certificate that will demonstrate your accomplishment to potential future employers.



Introduction to Predictive Modeling

Welcome to Introduction to Predictive Modeling, the first course in the University of Minnesota’s Analytics for Decision Making specialization. This course ...


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Predictive Analytics, Data Preparation, Time Series Forecasting, Linear Regression

Article sources


Glassdoor. “50 Best Jobs in America for 2022, https://www.glassdoor.com/List/Best-Jobs-in-America-LST_KQ0,20.htm.” Accessed December 8, 2022.

Written by Coursera • Updated on

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