We have done it so that you don’t have to – compiling a comprehensive list of predictive analytics examples across different sectors to assert how far artificial intelligence has evolved.
For years, companies have struggled to develop analytics capabilities to understand past performance and anticipate future trends and events, expediting their performance.
Today, companies are implementing predictive analytics to make services more efficient, develop better products, find potential threats accurately, optimize maintenance, and even save lives.
According to MARKETSANDMARKETS’ research:
The global market for predictive analytics is projected to grow from $ 10.5 billion in 2021 to $ 28.1 billion in 2026, with an overall annual growth rate (CAGR) of 21.7% over the forecast period.
Several factors, such as the increasing use of artificial intelligence and machine learning, and the acquisition and marketing of products in this industry, are expected further to guide the adoption of Predictive Analytics software and services.
What is Predictive Analytics, and How Does It Work?
Predictive analytics involves estimating future events based on historical data, to which various analytical, statistical, and machine learning techniques are applied.
Predictive analytics models are mathematical models that predict the behavior of a variable based on a set of other variables.
The more related the set of predictor variables is to the variable to be predicted (correlation), the more accurate the predictions will be. For example, the New York Police (NYPD) has a solution that, depending on the month, time of day, neighborhood, street, etc., can predict if a call to the emergency number has a high probability of response to an action constituting a crime.
Once the predictive algorithm has been developed, it is necessary to have a historical data set to construct these models. To do this, data is divided into two sets: one serves as the training data, and the other as test data. First, the model is fed with the training data to calibrate the prediction.
Then, it is provided with the test data. The forecast result is then compared to actual (historical) values to check its accuracy.
And here’s how predictive analytics works.
i) Data Collection
The development of a predictive analytics model begins with obtaining the data based on which the predictions are going to be made. The data may come from different sources, such as files, databases, sensors, etc. They are then explored to know their nature, structure, and quality.
ii) Sorting and Adaptation
The next step is to carry out the initial data processing to order them, transform them, and adapt them to the model’s needs. At this point, the extreme values that distort the model’s functioning are usually eliminated, and a single structure is created with the data processed from the different sources.
iii) Data Analysis
Once the data structure has been created, it is analyzed to identify its characteristics, detect patterns and trends in its values and obtain relevant information for developing the predictive algorithm, which is the basis of the model.
iv) Choosing the Mathematical Model
For developing the algorithm, you will have to use the information obtained from the data analysis. And based on it, the mathematical techniques allowing processing of the input data to the model and making the predictions (outputs) will be decided.
v) Parameter Optimization
Finally, the algorithm parameters will be optimized using a training data set (actual data of the input and output variables of the model). And its accuracy will be verified with a test data set (actual data of the input and output variables).
24 Examples of Predictive Analytics Across Different Industries
So, here’s the real deal – sharing all the predictive analytics examples you would ever need to conclude it’s helping make our businesses and lives significantly better!
1. Predictive Analytics in Finance
Every business needs regular financial records, and predictive analytics can play a significant role in predicting the future health of your organization. With historical data from previous financial statements and data from the overall industry, you can project sales, revenues, and expenses to take a picture of the future and make the best decisions.
Financial planning is one of the most critical elements for any business, regardless of industry. Many financial teams already use or plan to use predictive analytics to predict risks and returns, allocate resources efficiently, optimize operations to avoid additional costs, and more.
2. Predictive Analytics in Marketing
Marketers have long used data to understand and improve a campaign’s effectiveness. Over the years, these efforts have become more sophisticated.
Today’s customers can enjoy more options than ever before.
They are no longer limited to what’s available at their local store. As a result, competition between sellers, resellers, and service providers is fierce.
The only way to stay competitive is to be one step ahead of the trends and desires of consumers. Predictive analytics enables and helps resellers to understand consumer behavior and trends, anticipate future changes, and plan their campaigns accordingly.
Predictive analytics is a form of analysis performed with artificial intelligence and machine learning that combines the knowledge generated by different sets of data, algorithms, and models to predict future behavior.
Lessons learned from predictive analytics allow marketers to better identify what is likely to happen in the future and create effective marketing strategies.
3. Predictive Analytics in Manufacturing
For the manufacturer, equipment failure can lead to business failure. Machine downtime can cost businesses millions in lost profits, repair costs, and lost production time.
By integrating predictive analytics into their applications, production managers can monitor equipment status and performance, predicting errors before they occur. They can plan and redistribute the load to other machines to reduce the impact on production.
Data may include maintained technology service protocols, particularly on older machines. Data from several machine sensors, including temperature, operating time, power level duration, and error messages, are handy for newer machines. The purpose of foreseeable maintenance is to inform the manufacturer of preventive activities related to industrial equipment. For example, if the conveyor belt in the distribution center breaks, it can stop production and cost the manufacturer.
Manufacturers can intervene before errors occur by processing large amounts of data, usually using the device’s built-in IoT sensors.
4. Predictive Analytics in Supply Chain Management
An under-optimized supply chain affects every area of your business. The following examples of predictive analytics show how your supply chain can benefit from this technique.
The data you collect is as large as possible so that the model can contain real-time data. It means that all your decisions are based on accurate and up-to-date information, not outdated reports.
You can be much more flexible in making decisions because the model predicts the impact of various variables on the efficiency of your supply chain. By identifying the least effective areas of your business and anticipating the impact of those inefficiencies, you can solve problems before they occur. It will lead to significant savings over time, as your supply chain’s efficiency will improve manifolds.
Predictive analytics can model different risk factors to see how they can influence your supply chain and integrate information from other locations or sources into a model to get the most accurate and relevant picture of your business.
5. Predictive Analytics in Retail
Probably the most extensive industry to use predictive analytics, retail is always looking to improve its sales position and build better customer relationships. One of the most ubiquitous examples is Amazon’s recommendations. When you purchase something, it displays a list of other similar items other shoppers have purchased.
Much of this is in the pre-sales area, with things like sales forecasting and market analysis, customer segmentation, business model reviews, IT alignment to business units, inventory management to accounting for seasonality, and finding the best retail locations. But it also acts after the sale, working to reduce returns, keep customers coming back, and extended warranty sales.
6. Predictive Analytics in HR
Human resources departments work with a large amount of workforce data, quite an ideal scenario to apply predictive analytics to their processes. The HR professionals can get forecasts of employee performance, employee turnover, the impact of different activities on employee distribution, and more using predictive analytics.
Aggregated and analyzed data can detect management pain and help managers create data-based labels for different functions. Analysis of workforce data can result in happier staff and higher productivity.
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.
7. Predictive Analytics in Insurance
Thanks to predictive analytics, despite some horrific disasters in 2017, insurance companies kept losses within risk tolerances. It helped them competitively in price underwriting, analyzing and estimating future losses, capturing fraudulent claims, planning marketing campaigns, and providing better risk selection insights.
8. Predictive Analytics in Healthcare
According to a recent International Data Corporation (IDC) report, data growth is affecting every industry today. As health data grows, the popularity of machine learning and predictive analytics grows.
There are three main areas where machine learning can help healthcare organizations: improve patient outcomes, improve healthcare, and detect fraud.
Predictive analysts predict that patients are at high risk, ensuring that patients in need of emergency care can get there faster. At the same time, parents can use their time and resources more effectively. Patients at higher risk are advised to come for an examination sooner than later.
Here it is essential to incorporate predictive analytics into your existing systems. Incorporate future insights and recommended actions into applications that your employees already use, allowing them to make informed decisions without having to jump into another system.
9. Predictive Analytics in Sports
The most famous example is Bing Predicts, Microsoft’s Bing search engine prediction system. It scored in the 80th percentile for singing contests like American Idol, the high 90th percentile in US House and Senate races, and 15 out of 15 in the 2014 World Cup. It uses statistics and sentiments in social networks to make its evaluations.
Another example is what is known as “Moneyball,” based on a book about how Oakland Athletics baseball teams used evidence-based data and analytics to build a competitive team.
10. Predictive Analytics in Social Media
Online social networks fundamentally change how information is produced, particularly concerning business. Tracking user feedback on social media allows companies to get immediate feedback and the opportunity to respond quickly.
Nothing makes a local business jump like a bad review on Yelp or makes a merchant respond like a bad review on Amazon. This makes it imperative to collect and classify vast amounts of social media data and create viable models to extract the valuable data.
11. Predictive Analytics in Cyber Security
More than 3 billion fraud reports were filed in 2018 with the FTC, resulting in $1.48 billion in total losses. This is up 38 percent in just one year.
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 recently.
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.
12. Predictive Analysis in IoT
IDC estimates that less than 1 percent of the data generated today is being analyzed and that the time will only increase as more IoT devices, such as smart cars and smart devices, become connected.
Predictive analytics are needed to help classify what’s coming in to weed out useless data and find what you need to take wise action. In one example, Cisco and Rockwell Automation helped a Japanese automation manufacturer reduce the downtime of its production robots to almost zero by applying predictive analytics to operational data.
13. Predictive Analytics in Software Testing
Predictive analytics improves performance throughout the lifecycle of your software test. Simplify analyzing a large amount of data generated in the software testing process by using this data to modify the results.
You can track your release schedule by following the terms and using predictable templates to determine how the delays will affect your project. By identifying these issues and their causes, you can make course adjustments in specific areas before a significant project is postponed.
14. Predictive Analytics in Weather Forecasting
Weather forecasting has improved significantly in recent times, thanks to predictive analytical models. Today’s 5-day forecast is as accurate as of the 80-day forecast. The 72-hour forecast for hurricane tracks is more accurate than a 24-hour forecast from 40 years ago.
15. Predictive Analytics in Computer Vision
Predictive analytics is used not only for language processing but also for image recognition. The software can identify people, places, animals, and melons (almost anything you want) based on videos, photos, and other images. Whether public or private, a large number of organizations increasingly use computer vision libraries such as OpenCV to accomplish this task.
By providing algorithms with a large number of marked visual inputs (e.g., images of cats marked in this way), the machine can learn to recognize and classify similar photos. It should not be a linear form of prediction, i.e., it does not predict the future directly, but it is always a form of predictive analysis. This is because it uses known data to classify unknown data using a technique known as guided learning.
Computer vision has many uses. By analyzing a considerable amount of video media, self-guided vehicles use image recognition to make independent decisions. An online stock photography database analyzes images to categorize, tag, and facilitate search. Also, have you ever noticed that your phone sometimes groups certain photos you take based on their shared features?
And, of course, face recognition – another subset of image recognition – is used to help find images, create deep fakes, tag people on social media images, and allow criminal agencies to identify criminals. The possibilities are endless, and who knows where they will take you??
16. Predictive Analytics in Fundraising
The predictive model allows you to plan a fundraising schedule strategically. It also helps ensure that the right message reaches the right people at the right time.
Dividing your donor base into actionable, logical segments, and addressing your reach instead of making general calls saves money on both sides of the effort and leads to higher conversion rates.
Fundraising data analysis also allows you to track your success with various fundraising efforts. It will enable you to see trends in the future to see if they continue or change.
17. Predictive Analytics in Higher Education
Some of the best examples of predictive analytics in higher education include enrollment management, fundraising, employment, and retention applications. Predictive analytics offers a remarkable advantage in providing intelligent insight that would otherwise be overlooked in each area.
Using data from a student’s high school year, the prediction model can assess each student and inform administrators how best to support them during their enrollment.
The templates can provide fundraisers with essential information on the best times and methods to contact potential and current donors.
The analysis allows recruiters to target their reach more accurately, leading to the greatest success at the lowest cost.
With the help of predictive analytics, educationists can also get insight into what factors make students stay in your school instead of moving to another school.
And you would be pretty wrong in confining AI’s contribution to education only to predictive analytics. There are some other interesting ways AI is helping improve education. Have a look at these fantastic benefits of augmented reality in the education sector.
18. Predictive Analytics in Real Estate
Property is a field in which there is a high demand for data and an area that benefits from predictive analytics tools. Thanks to predictive analytics, real estate agents can provide buyers with the expected value of a home. It’s also a great way to convince sellers that the price of their home is correct.
By applying predictive analytics to census data, real estate agents can identify homeowners who may be interested in early sales due to changes in living conditions. It means a more targeted and effective way to reach potential sellers.
Predictive analytics can also connect relevant buyers with sellers who are not entirely willing to list their homes. A little extra motivation may convince the seller that it’s time to bring your home to market!!
19. Predictive Analytics in Entertainment
The entertainment industry, more specifically digital entertainment, benefits greatly from predictive analytics. Let’s look at how 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 of 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 include:
- The preferred genre of content
- Search keywords when looking for content
- 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 essential 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.
20. Predictive Analytics in Food Delivery
The food supply industry is a fast-growing industry ripe for data-driven development. Everything is measurable, from delivery times and zip codes to prices and customer satisfaction. These data points can be collected and processed to improve business and profitability while reducing losses.
GrubHub, one of the biggest names in the industry, uses demographics to make intelligent predictions about what a particular user might be interested in and then shows that the customer is advertising the product.
DoorDash monitors relationships between variables such as time of day, day of the week, and expected cooking time at specific restaurants (along with other factors such as major sporting events and weather) to predict climate.
21. Predictive Analytics in Energy
Power plant analysis can reduce unexpected equipment failures by predicting when a component may fail, reducing maintenance costs, and improving power availability.
The tool can also predict when customers have a high bill and send alerts to warn them that they will have a high bill that month. Smart meters allow the utility to alert users to peak loads at certain times of the day to know when to reduce power consumption.
22. Predictive Analytics in Law
A more surprising area in which predictive analytics is used is the natural language process (NLP). This linguistic subgroup of artificial intelligence involves discovering and understanding the nuances of human language. It has many (relatively speaking) everyday uses, such as spam detection or more humane machine interfaces. However, the notable application of this technology was in predicting the outcome of court proceedings.
Today, courts must publish their decisions in a publicly available database. It allows us to analyze legal text data on an unprecedented scale. Using NLP, a coalition of researchers from UCL and the University of Sheffield in the United Kingdom, and the University of Pennsylvania in the United States have predicted a decision by the European Court of Human Rights (ECtHR), boasting an unbelievable 79% accuracy.
Its algorithm used data on judgments from the ECtHR’s public database. It extracted the textual details available from the relevant parts of these judgments. Thanks to a combination of the linguistic characteristics of the subject (for example, what the law says) and indirect details, the algorithm was able to predict the outcome of each litigation with a high degree of accuracy.
23. Predictive Analytics in Life Sciences
Recent efforts in the field of big data analysis in the life sciences have focused primarily on applying advanced analytics to improve the effectiveness of its research and development. The industry believes that the cost of conducting clinical trials and launching a new drug, estimated at more than $ 1 billion, is clearly unbearable in the long run.
Using knowledge from large data sources, such as genetics and claim data, can reduce study costs by enrolling patients who are more likely to respond to treatment, improve study design, and shorten the duration of treatment.
One of the best-known examples of using big data analysis in genomics, is the study of human genes and their functions. Although the science of gene sequencing is not new, the ability to profile tens of thousands of genes in hundreds or thousands of patients has fundamentally changed the research of the science of life.
Researchers no longer have to focus on a few individuals or just a few genes. Now scientists can compare the behavior of thousands of individuals and look for similarities and patterns.
24. Predictive Analytics in Customer Service
Any consumer-oriented industry can benefit from the use of predictive analytics. Sellers and sales teams can understand when they target a particular ad or sales call to a customer based on their previous purchase history.
Using predictive analytics in customer service, brands get a clear idea of when it will be difficult and when it will be easy to employ more or fewer employees.
It can significantly improve customer service and product feedback processes by collecting data on customer preferences and anticipating trends that show which service techniques and engagement feedback methods lead to the best results.
These examples of predictive analytics show that their applications are broad and diverse. Whatever your industry, predictive analytics can help you solve problems you know you have and identify issues you don’t even know you have. The predictive model gives you a complete and accurate understanding of your organization and how it can be improved to lead to greater success in the future.
Regardless of your industry, predictive analytics can give you the insight you need for the next step. Whether you’re making financial decisions, formulating marketing strategies, changing the way you act, or trying to save lives, building a foundation in analytical skills can help.