6 Predictive Analytics Examples: How Companies Predict the Future

Predictive analytics is a form of data analytics that uses machine learning (ML) and other artificial intelligence (AI) methods to predict how humans and systems will behave in the future by examining the ways we have acted in the past. Data analytics helps you answer four main questions: what events happened, how events happened, what the next actions you should take are, and what events are likely to occur in the future. Predictive analytics focuses on answering the fourth question, giving us insight into the potential outcomes of our current actions. 

This article will explore examples of predictive analytics across various industries to demonstrate this technology's wide-ranging implications. 

What is predictive analytics?

This offshoot of data analytics uses various techniques to anticipate future consequences and effects. Predictive analytics uses data mining, data modeling, machine learning, and deep learning to create models that predict how likely specific outcomes will be in the future. This area of data analytics uses statistical models to make predictions that are free from human bias and based on historical data. 

Insight into future events gives companies a strategic advantage to react and adapt faster than the competition. With predictive analytics, companies can improve their data security, analyze insurance coverage, and improve decision-making for the company and the client. By using predictive analytics insights, companies can forecast seasonal sales, make smarter decisions about inventory and shipping logistics, set pricing strategies, and, in other ways, improve their bottom line.  

Predictive analytics models and techniques

You’ll use models specific to the problem you aim to solve. You can use various techniques to create predictive analytic models, including classification models, clustering models, time series models, and neural networks. These models do the actual work of understanding the data and providing insight into what the data could mean. 

• Classification models: A classification model separates data objects into subsegments, classifying each object by type. These models work best to answer yes or no questions about data and can be used to identify abnormal transactions or evaluate the creditworthiness of an individual or business. Common classification models include regression and decision trees.  

• Clustering models: Clustering models use unsupervised learning to make open-ended decisions, such as sorting customers into audience segments. The model identifies patterns in the data to return the most effective means of organizing the data. Examples of clustering models include k-means clustering, mean-shift clustering, hierarchical clustering, and the Gaussian clustering model. 

• Time series models: Time series models provide insight into when or how often events happen. For example, time series models could predict what time of the year will bring your business the highest volume of foot traffic. Two commonly used time series models are autoregressive and moving average models. 

• Neural networks: A neural network model simulates how human brains process information. This type of predictive analytics is best for complex situations where data will have a nonlinear relationship to one another. For example, neural networks power content moderation services that remove harmful online content by recognizing keywords and imagery. 

Read more: 6 Popular Data Analytics Certifications: Your Guide

6 predictive analytics examples

Predictive analytics can offer insight to companies across industries and even for public safety. For example, local weather forecasts run on predictive analytic technology. Let’s examine how big data and machine learning are changing the landscape of industries like the automotive industry, financial services, manufacturing, health care, marketing and retail, and the oil, gas, and utilities industries. 

1. Automotive industry

Predictive analytics and other forms of AI pave the way for self-driving vehicles by predicting what will happen in the immediate future while driving a car down the road. This process needs to happen continuously when a vehicle is in motion, drawing information from multiple sensors and making judgment calls about which potential actions would be a safety risk. 

Tesla uses predictive analytics in the form of neural network accelerators for their self-driving vehicles. A neural network model simulates how human brains use information to make decisions. 

Beyond autonomous vehicles, manufacturers and retailers can also use predictive analytics to their benefit. For example, predictive analytics helps factories create vehicles faster using fewer resources. Dealerships can use predictive analytics for retail and marketing operations, setting sales prices according to trending data. Regarding routine maintenance and repairs, predictive analytics can identify trends in vehicle maintenance, allowing dealerships to encourage customers to perform preventative maintenance. 

2. Financial services and risk reduction 

When you receive an alert to suspicious activity in your bank account, you can thank predictive analytics for determining that something doesn’t seem right based on deviations from your routine, such as a transaction in a different city. Financial institutions and other companies use predictive analytics to reduce credit risk, combat fraud, predict future cash flow, analyze insurance coverage, and look for new business opportunities. Companies use predictive analytics to determine how likely a person or company is to pay their debts or default on their obligations.  

For example, Orcolus is a program that businesses can use when determining someone’s credit eligibility. Orcolus uses AI and ML to offer a more stable solution for examining documents and avoiding fraud. 

3. Manufacturing and industrial automation

In a manufacturing setting, predictive analytics can anticipate significant equipment failures, which can be expensive and potentially dangerous to employees. By analyzing past equipment failures, this form of AI can determine imminent failure and notify an employee when conditions start to look dangerous. Similar predictive analytics methods can watch out for situations that pose a risk to employee health and safety, reducing workplace injury and potentially boosting employee morale as well. 

For example, in 2020, Ford Motor Company used predictive analytics to anticipate maintenance in its Valencia, Spain factory. By repairing equipment before it broke down and caused unplanned downtime, Ford saved more than $1 million in unplanned downtime [1]. 

4. Health care industry

Predictive analytics benefit the health care industry by predicting how chronic or dangerous conditions occur. Patients with asthma or COPD can use a wearable predictive analytics device to spot changes in their breathing patterns that could signal a problem. Similarly, a wearable device could detect allergic reactions as they occur and automatically give the patient epinephrine in response. 

Northern Light Health hospital system in Maine implemented predictive analytics in the face of the COVID-19 pandemic when it became more critical to anticipate future needs and maintain situational awareness. They built a data analytic system that could forecast their census, or patient population, in four-, eight-, and 12-hour blocks of time, in addition to a range of other functionalities. The result is that patients have been outcomes and receive care faster. 

Predictive analytics can predict the outcomes for infections like sepsis based on past patient health records to determine the best course of treatment. Health care professionals also use predictive analytics to gain insight into readmission rates, protect against ransomware and other cyberattacks, and process health insurance claims quicker with fewer errors. 

5. Marketing and retail

Marketing professionals use predictive analytics in many different ways: to tailor marketing to specific segments of their target audience, for seasonal sales forecasting, to improve customer relations, and to further engage customers. For example, a company might use predictive analytics to power a recommendation engine that suggests new products to customers based on products they’ve already viewed or purchased. Previous customer behavior can also help predict how customers progress through the sales funnel. This insight can help you place targeted touchpoints to engage proactively with customers. 

Subway used predictive analytics to decide whether to raise the price of their $5 Footlong sandwich. Their data showed that the low price point wasn’t causing them to sell enough sandwiches to make up for a bump in price. Using a predictive analytics program offered by Mastercard, Subway learned that customers purchasing Footlong sandwiches added additional items to their orders, such as a side of chips or a drink. With better information, Subway was able to make an informed decision about its price strategy. 

6. Oil, gas, and utilities

When it comes to oil, gas, and utilities, we can use predictive analytics to forecast demand for energy based on historical use and seasonal events like weather patterns. Likewise, utility companies can predict how prices will likely fluctuate over time. 

Similar to the manufacturing industry, utility companies can use predictive analytics to watch out for equipment failures and safety concerns. Due to the potentially catastrophic nature of equipment failure and malfunction in the utility industry, it’s vital for companies to invest in predictive analytics to keep things running as smoothly as possible. 

This technology can also create more reliable and safe conditions for workers in potentially dangerous energy production facilities. For example, ExxonMobil uses predictive analytics to power autonomous drilling stations in Guyana. Using AI and machine learning, Exxon predicts the ideal conditions for underwater drilling and enables a closed-loop automation system to minimize the need for personnel to intervene. 

Learn more with Coursera

If you’re ready to learn more about predictive analytics and pursue a career in data analytics, consider the Analytics for Decision Making Specialization from the University of Minnesota on Coursera. This beginner-friendly program can help you gain a foundation in business analytics, including predictive analytics.

If you have some experience already, consider the Advanced Data Analytics Professional Certificate offered by Google, also on Coursera. This seven-course program can help you qualify to become a data science analyst and sharpen your skills, including statistical analysis, data visualization, and communicating the insights you gain.