Predictive analytics has grown in importance as a useful tool to help organizations make smarter business decisions.
What is predictive analytics? A good predictive analytics definition is the use of historical data, statistical algorithms, and machine learning techniques to estimate the likelihood of various future outcomes. It is a combination of data, technology, and processes. Advancements such as artificial intelligence (AI) have helped grow the reliability of predictive analytics and spread its use ever wider.
How companies use predictive analytics varies, but its use is growing. Find out more about predictive analytics below.
The Growth of Predictive Analytics Jobs
Systems and processes for collection of data may build the foundation, but the ability to learn from that data is a crucial skill in the marketplace. LinkedIn’s 2020 Emerging Jobs Report indicates that predictive analytics and related roles are in high demand in the workforce:
- Just over 33,000 people were employed in the data and mathematical science occupations by 2019, according to the U.S. Bureau of Labor Statistics.
- The BLS projects more than 10,000 new jobs will be added through the end of this decade, an increase of 31% in that period.
Women in particular are gaining ground in this field. With a nationwide focus on increasing the percentage of women in STEM (science, technology, engineering, and mathematics) professions, the outlook is bright in the coming years.
The Top Fields for Working in Predictive Analytics
Predictive analytics has applications in most industries. However, some of the industries most invested in predictive analytics today include education, finance, government, health care, insurance, legal, manufacturing, and marketing.
Here are a few examples of who uses predictive analytics and how predictive analytics can improve performance measurement.
Education is increasingly reliant on predictive analytics. Advocates of predictive analytics in higher education say the results can be used to optimize students' experiences on campus and in the classroom.
Predictive analytics is used to ensure academic integrity as well. Various platforms can detect plagiarism and can strengthen academic integrity, from undergrad studies to doctoral dissertations.
Institutions have long collected student data, but until relatively recently, such data was only used to look at prior achievements, not to look forward.
Banking and financial institutions seek innovative ways to prevent money laundering and insider trading and solve other problems unique to this highly regulated industry.
- Predictive analytics is frequently used to determine the likelihood of default on consumer loans. Logistic regression modeling and hierarchical Bayesian modeling, for example, are two modeling approaches used to score loan applicants.
- Banks also use predictive analytics to detect fraud. Incidences of fraud can damage a financial institution's reputation, causing customers and prospective customers to lose their trust in the institution—not to mention monetary losses.
- Investors use predictive analytics to anticipate stock market activity. Typical sources of data include financial reports, earnings, and share price activity. Other sources include credit card data, news articles, blog posts, and even satellite images.
It's no secret that political campaigns benefit from voter persuasion modeling. This type of model identifies which voters will be positively persuaded by various touch points, such as a phone call, neighborhood canvassing, a flyer, or a TV ad.
Authorities such as law enforcement agencies are making use of predictive analytics through AI for social network analysis of urban gangs, citywide alert systems, crime-spot prediction, and custody decision-making aids, according to a study by The Royal Institute of International Affairs. The study says refinements in these methods can be applicable to counterterrorism.
In the highly regulated health care industry, the opportunities to use predictive analytics are virtually limitless:
- Analytics is being used to deliver predictive care for at-risk patients in their home, according to Philips. It says these methods are also being used to identify equipment maintenance needs before they arise. Hospitals are using analytics to detect early signs of patient deterioration in the ICU and the general ward.
- An algorithm created at Carnegie Mellon University allows a computer to learn signals indicating that an injured patient’s cardio-respiratory system is deteriorating before the damage becomes irreversible.
The insurance industry uses predictive analytics to assess and control risk in underwriting, pricing/rating, claims, marketing, and other areas. As in other industries, insurance professionals rely on predictive analytics to maximize their return on investment (ROI), improve customer service, and work more efficiently.
In a study by Willis Towers Watson, more than two-thirds of insurers say predictive analytics helps reduce issues and underwriting expenses, and 60% say the resulting data has helped increase sales and profitability.
As predictive analytics grows, proper and casualty insurance companies expect to use it to help with, among other areas:
- Finding potential markets
- Focusing on customer loyalty
- Gaining a wider view of customers
- Identifying risk of fraud
- Managing data and modeling
- Noting customers at risk of cancellation
- Providing a personalized experience
- Tagging outlier claims
- Transforming the claims process
- Triaging claims
Predictive analytics can tell lawyers if a claim is likely to proceed to litigation. If the likelihood is high, steps can be taken early on to prevent it, or to settle.
Juror selection is another area where predictive analytics can help. The availability of precise algorithms can reveal attitudes, inclinations, and interests of potential jurors. Predictive analytics results in better jury selection and, ultimately, more favorable legal outcomes for firms using it. Information that once was only available from expensive jury consultants is now available to all parties, leveling the playing field.
In general, predictive analytics can also drive overall marketing strategy. Insights obtained can guide details such as marketing spend, marketing channel, and messaging. If content is king, Google Analytics could be considered the queen, offering rich data on which businesses base their online advertising spend.
Depending on the model used, marketers can glean information on demographics, product categories and preferences, brands, customer lifetime value, likelihood to engage and convert, and more. This information provides a competitive advantage, enabling professionals to obtain the best ROI from their marketing spend.
Product marketers rely on predictive analytics to decide what products or services to bring to market. Proper use of these insights can save a company from investing in a product that is unlikely to be successful in the marketplace. Predictive analytics is also extremely helpful in sizing up the current market and competition. Researching what works (and what doesn't) among competitors, and applying predictive models to that research, can prevent companies from making costly mistakes and steer companies down a more profitable path.
Research by Dresner Advisory Services says marketing, sales, and research and development functions are more likely to drive business intelligence initiatives, including predictive analytics, in technology organizations.
Predictive analytics is invaluable in supply chain management. The ROI of any given project can hinge on the gap between estimated and actual costs. Predictive analytics can help organizations more accurately project that data, enabling them to make more informed business decisions.
The automotive industry has jumped on the predictive analytics bandwagon:
- High-end cars, for example, use predictive collision avoidance systems.
- Predictive analytics for maintenance can give the car owner a warning before car repairs are necessary.
- Aggregated predictive data from multiple vehicles can point to a more global issue, such as a mechanical failure that might necessitate a recall.
Cybersecurity isn't just an issue for computers and mobile devices. Carmakers are using predictive analytics to thwart hacking, which could have fatal consequences if a hacker interferes with a car’s safety systems, for example.
Grow Your Career in Predictive Analytics
These predictive analytics examples show how the field is growing. Regardless of whether you are starting or expanding your career, or which field you are focusing on, predictive analytics is certain to play a role—and an increasingly important one at that.
If you are looking to better use predictive analytics and advance your career, consider the Wake Forest online Master of Science in Business Analytics (MSBA). The MSBA enables working professionals to develop deep, quantitative capabilities and technical expertise to create business and social value, while gaining marketable skills required by today's top employers.