Pode ajudar as empresas a identificar e responder rapidamente a novas oportunidades (artigo em inglês).
Enterprises can gain significant long-term benefits by applying predictive analytics to their operational and historical data, analysts and IT managers said at Computerworld’s BI & Analytics Perspectives conference being held here this week.
Unlike traditional business intelligence practices, which are more backward looking in nature, predictive analytic approaches are focused on helping companies glean actionable intelligence based on historical data.
If applied correctly, predictive analytics can enable companies to identify and respond to new opportunities more quickly, they said.
In a keynote address, James Taylor, CEO of Decision Management Solutions, said predictive analytics are especially useful in situations where companies need to make quick decisions with large volumes of data.
Predictive analytics practices can help companies in three key areas: minimizing risk, identifying fraud and pursuing new revenue opportunities, Taylor said.
For instance, predictive analytics can help companies fine tune their ability to identify risk in areas such as loan and credit originations, or fraud in areas such as insurance claims, he said.
Importantly, by embedding predictive analytics into operational data, companies can put themselves in a better position to identify new revenue opportunities, Taylor said. For example, by looking at a customer’s historical purchase patterns, companies can make reasonable predictions on the kind of promotional offers and coupons that are likely to resonate with that customer.
Blue Cross and Blue Shield System (BCBS) is one organization that is already deriving considerable benefits from predictive analytics. As an organization that provides healthcare insurance to nearly one in three Americans, Blue Cross Blue Shield System has amassed a huge amount of claims-related data over the years.
A few years ago, the BCBS Association, the entity that holds the Blue brands, created a single database called Blue Health Intelligence (BHI) to consolidate all the claims information maintained by each of the 39 companies that are part of BCBS. The database is one of the largest repositories of de-identified healthcare data anywhere and contains claims-related information on more than 100 million people.
BHI operates as an independent unit and provides a range of business intelligence services that is enabling better health care services for members while also transforming the manner in which BCBS manages it costs.
The impetus for the effort came from the need for BCBS, like other health insurers, to control spiraling costs, said Swati Abbot, president and CEO of BHI, during a presentation.
A disproportionate share of healthcare costs goes toward the care of people with chronic illnesses, Abbot said. In fact, the top 5% of healthcare users account for more than 55% of healthcare costs, she said.
By applying predictive analytic technologies to its vast trove of claims data, BCBS has been getting better at not only identifying the risk factors that lead to several chronic diseases but also in identifying individuals who are at heightened risk of getting such diseases, she said.
“For every member enrolled in a health plan we have a health score” that represents the likelihood of that individual needing lifelong treatment for a chronic illness, Abbot said. BHI has even developed disease specific modules, such as one for diabetes, that predict an individual’s risk of getting diabetes based on previous data, she said.
The goal is to be able to use the data to get doctors to provide better, more targeted care for high-risk patients so as to reduce their need for expensive, long-term treatment, she said. The predictive modeling is enabling BCBS to move toward a more incentive-based healthcare model in which doctors get incented for performance, Abbot said.
Online dating site Match.com is another company that relies heavily on predictive analytics to run its service. The company collects and maintains a lot of information, some collected from subscribers and some collected from monitoring their interactions on Match.com.
The company’s challenge is to find a way to improve revenue per subscriber by delivering the best matches possible based on each subscriber’s preferences, said Jim Talbott, director of consumer insights at Match.com
It is a task that is complicated by the fact that subscribers might indicate a specific set of requirements for a potential partner but then interact with people who fall outside their own specified range of preferences, he said.
To meet the challenge, Match.com has developed a predictive model that matches people based not just on their stated preferences but also on their site behavior and their interactions with other people.
Companies interested in predictive modeling need to have a clear idea of their objectives before they start, Taylor said. They need to know what sort of decisions will be powered by their predictive analytics and work backwards from there, he said.
To develop a good predictive model, enterprises need to focus on defining a clear set of business rules for each decision and then focus their analytics on driving the best decisions, he said.
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