How is Predictive Analytics Being Utilized in Your Industry?
Predictive analytics 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. Today, advancements such as artificial intelligence (AI) elevate the usefulness of predictive analytics to unprecedented levels. In today’s data-driven world, predictive analytics helps organizations make smarter business decisions.
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. PredictiveAnalytics.com cites the LinkedIn 2017 U.S. Emerging Jobs Report, indicating that predictive analytics and related roles are in high demand in the workforce:
- The number of data analyst and data science roles has grown over 650 percent since 2012. Only about 35,000 people in the US have data science skills – while thousands of companies are hiring for those roles.
- Job growth in the next decade is expected to outstrip growth during the previous decade, creating 11.5 million data analysis jobs by 2026, according to the U.S. Bureau of Labor Statistics.
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.
Predictive analytics has applications in most industries. However, the industries most interested in predictive analytics today include finance, insurance, legal, healthcare, government, manufacturing, education, and marketing.
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.
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 early 2016, Willis Towers Watson reported that more than half of insurers surveyed (54%) used predictive models for underwriting and risk selection, but many additional uses are planned. It also reported that 28 percent of insurance companies were using predictive analytics in the area of insurance fraud. Over the next two years, that figure is expected to jump to 70 percent — meaning that insurance fraud detection will become the most popular application of predictive analytics within the insurance industry.
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, also known in the U.S. as voir dire, 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 the highly regulated healthcare industry, the opportunities to use predictive analytics are virtually limitless:
- The New York Times reported that research is being conducted to develop algorithms capable of identifying symptoms of lung cancer in CT scans.
- A machine-learning algorithm created at Carnegie Mellon University can predict heart attacks, according to The Economist. The algorithm has an 80 percent success rate — compared to a 30 percent success rate of manual systems — and made the prediction four hours in advance, which could allow time for intervention.
- Hospitals are already using predictive analytics to see which patients are likely to be admitted to the intensive care unit (ICU), who will be readmitted after discharge, or who will suffer from complications. This information is being used to determine the best course of treatment to prevent adverse outcomes.
- In a study published in the American Journal of Managed Care, researchers from the Mayo Clinic explain how predictive analytics can use co-morbidity and historical service utilization to identify patients at high risk of ending up in the emergency room or inpatient settings – and how this data can inform the decision-making for patient-centered medical homes and other coordinated care activities.
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 flier, or a TV ad.
Given worldwide efforts to stem acts of terrorism, The Department of Homeland Security is turning to data scientists to improve screening techniques at airports. The New York Times reported that the Department launched a contest to build computer algorithms that can identify concealed items in images captured by checkpoint body scanners.
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.
- Predictive data analytics is not only used at the individual car level, but aggregated data from multiple vehicles can point to a more global issue, such as a mechanical failure that might necessitate a recall.
Cyber security 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.
Education is another field that is increasingly reliant on predictive analytics. One analytical model, for instance, can determine with 75% accuracy how well students will perform in class. 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.
In general, predictive analytics can also drive overall marketing strategy. Insights attained 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.
In real estate, it’s all about location, location, location. In marketing, placement is important, but so are timing and messaging. Predictive analytics helps marketers target the right customers, via the right channels, at the right time, and with the right offer.
The 2017 Advanced and Predictive Analytics Market Study by Dresner Advisory Services underscores interest across the board in advanced and predictive analytics. Out of 30 rankings, advanced and predictive analytics came in seventh among technologies and initiatives considered strategic to business intelligence.
Regardless of whether you are starting or expanding your career, or what 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 advance your career and better utilize predictive analytics, 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, with marketable skills required by today’s top employers.