6 Predictive Analytics Examples: How Companies Predict the Future

Key takeaways

Predictive analytics is a form of data analytics that helps forecast the likelihood of future outcomes based on historical data.

• By using predictive analytics insights, companies can forecast seasonal sales, make smarter decisions about inventory, analyze insurance coverage, and more. 

• Financial institutions use predictive analytics to reduce credit risk and combat fraud.

• You can use various modeling techniques to support predictive analytics, including classification, clustering, time-series, and neural networks.

Learn how different industries apply predictive analytics and the factors that enable it. Afterward, if you’re ready to learn more about predictive analytics and pursue a career in data analytics, enroll in the University of Minnesota’s Analytics for Decision Making Specialization. Beginner-friendly, this program offers guidance on modeling and solving decision-making problems using predictive models, linear optimization, and simulation methods. 

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 (ML), 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.

What is an example of a predictive approach in business?

Companies might use a predictive approach when trying to manage supply chains. For example, investment in software that can predict when to order inventory helps avoid overstocking and provides a steady supply of products. 

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: Neural Network Examples, Applications, and Use Cases

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 analytics 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 artificial intelligence (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 its self-driving vehicles. A neural network model simulates how human brains use information to make decisions. 

Beyond autonomous vehicles, auto 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. 

Examples of companies that make predictive software for manufacturers include:

• Emerson: Focuses on field and rotational devices

• IBM: Features IBM Watson Studio

• LLlumin: Offers predictive maintenance across industries

• Siemens: Provides predictive maintenance and consulting

• Upkeep: Specializes in mobile devices

4. Health care industry

Predictive analytics can benefit the health care industry by predicting how chronic or dangerous conditions occur. Patients with asthma or chronic obstructive pulmonary disease (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. It built a data analytics system that could forecast its 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 better 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 quickly 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 its $5 footlong sandwich. Its 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. 

Keep track of trends in data analytics

Join Career Chat on LinkedIn to get weekly updates on popular skills, tools, and certifications in data analytics. Then, continue your learning journey with our free digital resources:

• Explore certifications: 7 data analytics certificates to know

• Watch on YouTube: AI Quick Tip for Supply Chain: Enhancing Efficiency with Predictive Analytics

• Hear from an expert: 7 Questions with a Data Analytics Professor

Accelerate your career growth with a Coursera Plus subscription. When you enroll in either the monthly or annual option, you’ll get access to over 10,000 courses.