8 Powerful Predictive Analytics Examples Across Industries

“Only one in four jokes ever work, and I still can't predict what people will laugh at,” said long-time American comedian Steven Wright.

While prediction may not work in an industry like comedy, there are plenty of other industries where being able to predict outcomes and next steps are essential to short and long-term successes.

Of course, these predictions aren’t off-the-cuff. Instead, they’re backed by data, translated into models, and interpreted by skilled professionals using predictive analytics software.

In this roundup article, we’ll explore some predictive analytics examples and look into how it’s used across 8 prominent industries today.

A few years ago, predictive analytics may have been looked at as niche and accessible for a select few, but now, more and more companies are using it in their day-to-day.

To put its influence in perspective, we’re going to start off our roundup with perhaps the most significant user of predictive analytics today – the retail industry.

1. Predicting buying behavior in retail

With the retail industry seeing nearly $7.24 trillion in sales annually, it’s no wonder why enterprises like Amazon and Walmart regularly use predictive analytics to learn all they can about their customers.

For example, in 2004, Walmart mined transaction data in its stores to understand buying habits at certain points in time. They found that right before hurricanes hit, strawberry Pop-Tart sales rose by seven times, along with beer. Of course, Walmart used this as an opportunity to stock its shelves. We discuss the technique they used in our intro guide on data mining.

Amazon has already used predictive analytics in the past to create personalized product recommendations based on buying patterns.

Most recently, Amazon is looking to use predictive analytics for anticipatory shipping. In other words, shipping products to customers before they even buy them based on their behavior on Amazon’s platform. This could lead to creepily-fast shipping times.

What if I’m not an enterprise?

Predictive analytics isn’t reserved for the big players. Many of today’s retail POS software is great at gathering customer data and integrating with other systems like CRM, supply chain, and inventory management to be used for predictive analysis.

Successful retailers are able to collect and combine data from all touchpoints, like e-commerce sites, mobile apps, store locations, social media platforms, and more. Analyzing this data will help you understand your customers on a deeper level and predict their behaviors in a more personalized way.

2. Detecting sickness in healthcare

There are more than 30 million patients in U.S. hospitals alone, which can only imagine how much health data this amounts to. However, the healthcare industry isn’t so much focused on the consumer journey as it is on analyzing data to improve diagnoses and predict outcomes based on specific health factors. 

“We worked with Element AI to produce an algorithm that successfully predicted negative health events in seniors (in their homes). Seniors would take a series of vitals every day  and the algorithm digested the vitals and combined that with the clients’ ICD-10 diagnosis, age, and gender. We successfully reduced hospitalizations and ER visits by 73% and 64% amongst a chronically ill patient set.”

Jeff Howell
Director of Growth at AlayaCare

This two-year study is just one of many ways predictive analytics and AI is used in healthcare for more personalized, proactive patient care.

3. Curating content in entertainment

The entertainment industry, more specifically digital entertainment, benefits greatly from the use of predictive analytics. Let’s look at some of the ways today’s digital media and entertainment giants harness big data to shape viewer experiences.

We know there are more than 100 million active Netflix accounts today, amounting to billions of hours streaming digital content. All of this data helps Netflix build predictive models for keeping their consumers satisfied and exposing them to relevant shows.

So, what are some types of data Netflix uses for its models and algorithms? Some of the user data includes:

• The preferred genre of content.
• Search keywords when looking for content.
• Ratings.
• The preferred device to watch content.
• Dates watched, and in some cases, re-watched.
• Time spent watching content previews.
• When content is paused, and at what point.

These metrics, and many more, are important to the success of entertainment streaming services. As a matter of fact, Netflix used this data to craft its show House of Cards, claiming they already knew it would be a success based on the results of predictive data analysis.

4. Predicting maintenance in manufacturing

This example is uniquely tied in with the Internet of Things (IoT) since the manufacturing industry is moving in a more automated direction. Perhaps the most prominent example of predictive analytics used in manufacturing is predictive maintenance.

What is predictive maintenance?

The purpose of predictive maintenance is to notify manufacturers of cautious activity regarding industrial equipment. For example, if a conveyor belt in a distribution center breaks down or experiences a malfunction, this could paralyze production and cost the manufacturer money.

By taking it large amounts of data, typically through the use of IoT-embedded sensors on the equipment, manufacturers are able to intervene before a breakdown occurs.

5. Detecting fraud in cybersecurity

In 2024, the average cost of a data breach reached a record high of $4.88 million.

What’s one way to tackle the billions of dollars lost to fraud every year? Well, the use of predictive analytics has become a more prominent solution in the cybersecurity industry.

This is done by analyzing typical fraudulent activity, training predictive models to recognize patterns in this behavior, and finding anomalies. Better monitoring of suspicious financial activity should lead to earlier detection of fraud.

6. Predicting employee growth in HR

Is it really possible to predict employee success through the use of analytics? The short answer is yes, although HR is still a relatively new industry tapping the benefits of predictive analytics.

There are a few ways this can be done. One way is through aggregating data to manage workflows and boost productivity. Employee data can show pain points and productivity spikes in their day-to-day, and this data only gets better with time.

Using a performance management system to collect this data can help businesses predict future employee performance. More data can be used to build baselines of where employees should be at which stages in their careers.

Predictive analytics can also help during the hiring process. Gathering data on everything from company review sites and social media to job growth rates and evolving skill sets, predictive analytics can help recruiters find the right matches for their job postings faster and more efficiently. This can also reduce turnover rates in the long run.

As a matter of fact, applicant tracking software like Greenhouse is one of the few solutions today that utilize predictive analytics and machine learning for this very purpose.

7. Predicting performance in sports

Professional sports may be fun to watch, but at the end of the day, it’s still an industry where franchises are always looking for ways to gain a competitive edge. The trendiest way to do so now is through predictive analytics.

Baseball has pioneered the use of predictive analytics when it comes to professional sports. It’s most widely used today for predicting the future value of a player, along with his regression, based on a complex series of metrics. This helps teams when it comes time to structure expensive contracts.

It’s no wonder why professional sports teams everywhere are on the prowl for data analysts and scientists with sports acumen.

Read Wharton’s blog to learn more about how small-market baseball teams have been able to maximize their budgets using predictive analytics.

8. Forecasting patterns in weather

Today’s weather forecasts are wildly more accurate than they were 40 years ago. You can thank predictive analytics for this.

By analyzing weather patterns using satellite imagery and historical data, we can see accurate estimates of weather forecasts up to 30 days in advance.

More importantly, this information can also be used to help us understand the impacts of global warming. For example, predictive models paired with data visualization can show us rising sea and carbon dioxide levels – and where these levels may be headed. After the results are interpreted, action can be taken to mitigate adverse effects.

What can we learn from these examples?

Of all these examples, there’s one common theme you may have noticed – the sheer volume of data required to derive value from predictive analytics.

Aside from volume, this data also needs to be relevant to the purpose of the model. But collecting and cleaning through this data is much easier said than done, and it’s why the roles of data analysts and scientists are in heavy demand.

Explore the comprehensive guide on the data analysis process to make data-driven and informed business decisions.

This article was originally published in 2023. It has been updated with new information.