Potentially one of the most significant shifts in how a business will approach decision making – from hiring decisions to marketing tactics, is the use of predictive analytics and big data to justify choices that will achieve business objectives. We’re at the peak of a technology surge that has been, for the past ten years, changing the way people provide and use information. In 2018, it was estimated that the predictive analytics market was worth $6.2 billion, and by 2022, it will be worth $10.95 billion (Statistica).
What is predictive analysis, what are some of the industries at the forefront of this innovation, and what about Employee benefits and Employee Health?
What is Predictive Analysis?
Big Data is the term that refers to the mass conglomeration of consumer data combined from multiple sources, ultimately creating a consumer profile or profiles based on demographics and behavior. Big data is collected by your browsing history, the apps on your smartphone, your travel bookings, membership cards at retail outlets, responses to emails, or your credit card transactions (…just to name a few). Predictive Analytics takes this historical data, groups it with others with similar profiles or trends, and offers predictions for future behavior.
Big Data and Industry
Marketing
The industry currently the most advanced when it comes to big data utilization is sales and marketing. A great example of this is the movement away from adverting on traditional mass media channels (such as newspaper, television, or radio), and in favor of those that allow them to be more tailored to their specific, niche audience (such as Youtube, podcast marketing, and Google Adwords).
Today, when purchasing advertising space, you’re not only paying for exposure, you are also paying for access to a database and its segregation based on behaviors or expected behaviors.
Use of predictive analysis takes this to the next level by measuring how a user interacts with your communication. How many emails did they open, did they visit your website, what product were they interested in? Then, the sales funnel or marketing efforts are adjusted so that they are tailored to respond to the consumer behavior.
Hiring
Traditional hiring practices often still rely on (very) brief resume screenings and intuition-based interviews. Predictive hiring, however, relies on rich sets of data and smart algorithms to recommend best-fit candidates to recruiters and hiring managers.
Setting up data sources is the first step. Data sources could include structured interviews and scoring based on interview questions; personality tests; or pre-employee assessments. Using these data sources and then comparing them with employee performance and length of employment will allow you to draw parallels between hiring indicators, and quality of hire.
Another outcome of using data in hiring is in accessing the process itself. Time till hire, for instance, looks at the length of time it takes from the point you identify a hiring need, to when that person is hired. This enables you to eliminate various inefficiencies and hence hire faster and better. For one, because it automates part of the process, since all candidates go through the same pre-employment assessment.
Health Care
While it is currently in the preliminary stages of implementation, predictive analytics will soon revolutionize the health care system, assisting doctors in diagnosis, and reducing the need for multiple consultations, and therefore the overall length of time to diagnose and treat a patient.
Today, it is being integrated into some hospital servers to improve patient outcomes and reduce operating costs. An advanced system automates some of the guesswork from health experts, speeding up treatment without having multiple specialists see a patient. For example, a robust predictive analytics platform would examine a patient with a newly diagnosed condition and consider their age bracket, race, gender, allergies, medical history, and more. The software would then combine this data with the patient’s existing conditions and medications to find other patients with similar histories. Physicians would analyze this aggregation of data to discover the most fitting treatment option.
Employee Benefits Cost Management Solutions
In the benefits industry, predictive analysis involves collecting data on benefits usage, so that you can predict the needs of plan members, and implement better cost management. While still in early stages of development, it has the potential to become a powerful tool for plan sponsors because it can estimate the level of paramedical usage, or the incidence of certain diseases—such as diabetes—in workplace populations.
One reason for benefits lagging in adopting predictive analytics is that the way companies choose new benefits varies significantly from business to business. Data is often kept in disparate spreadsheets, and even if some HR departments conduct employee surveys or historical cost analyses, they often do not integrate the data about their workforce. Knowing how many employees are logging into a benefits platform is helpful; market standard benefit utilization reports provide this level of information, but they do not give insight into the underlying reason for an employee to utilize a benefit. More in-depth analytics is required to look deeper into employees’ behavior. Companies must understand not only their employees’ needs but also the underlying data related to these needs to provide valuable benefits offering. With this insight, they can make better choices and serve their workforce more effectively. At this year’s TELUS Executive Health Roundtable, participants noted that insurers are actively building the reporting to make this happen.
It can also provide a competitive advantage, noted Alan Kyte, director, pharmacy benefits at Manulife. “Employers often ask for benchmarks to know where they stand vis-à-vis somebody else,” he said, but benchmarking can be inaccurate. “Predictive analytics could help them to make better decisions to improve their plans.”
