Topics: Decision making, Business intelligence, Decision theory Pages: 8 (2192 words) Published: June 28, 2013

Predictive analytics
The rise and value of predictive analytics in enterprise decision making

“Give me a long enough lever and a place to stand, and I can move the Earth.” Archimedes, 250 B.C. In the past few years, predictive analytics has gone from an exotic technique practiced in just a few niches, to a competitive weapon with a rapidly expanding range of uses. The increasing adoption of predictive analytics is fueled by converging trends: the Big Data phenomenon, ever-improving tools for data analysis, and a steady stream of demonstrated successes in new applications. The modern analyst would say, “Give me enough data, and I can predict anything.” The way predictive models produce value is simple in concept; they make it possible to make more right decisions, more quickly, and with less expense. They can provide support for human decisions, making them more efficient and effective, or in some cases, they can be used to automate an entire decision-making process. A classic example of predictive analytics at work is credit scoring. Credit risk models, which use information from each loan application to predict the risk of taking a loss, have been built and refined over the years to the point where they now play indispensable roles in credit decisions. The consumer credit industry as we know it today could not operate without predictive credit risk models. Credit scoring is demonstrably better than unaided human judgment in both accuracy and efficiency when applied to high volume lending situations such as credit cards. So much so, that any company in the credit industry that does not use it is at a significant competitive disadvantage.


About this paper
Predictive analytics is on the rise as the number of successful applications continues to increase. Predictive models can be used to generate better decisions, greater consistency, and lower costs. Top areas in which predictive models are generating significant value for organizations include marketing, customer retention, pricing optimization and fraud prevention—and the list continues to grow. This paper discusses how predictive models are built, ideal situations for applying them, calculating their return on investment, key predictive modeling trends and more. With predictive analytics, organizations in both government and industry can get more value from their data, improve their decision making and gain a stronger competitive advantage.

Banks were early adopters, but now the range of applications and organizations using predictive analytics successfully have multiplied: Direct marketing and sales. Leads coming in from a company’s website can be scored to determine the probability of a sale and to set the proper follow-up priority. Campaigns can be targeted to the candidates most likely to respond. Customer relationships. Customer characteristics and behavior are strongly predictive of attrition (e.g., mobile phone contracts and credit cards). Attrition or “churn” models help companies set strategies to reduce churn rates via communications and special offers. Pricing optimization. With sufficient data, the relationship between demand and price can be modeled for any product and then used to determine the best pricing strategy. Analytical pricing and revenue management are used extensively in the air travel, hospitality, consumer packaged goods and retail banking sectors and are starting to enter new domains such as toll roads and retail e-commerce. Health outcomes. Models connecting symptoms and treatments to outcomes are seeing wider use by providers. For example, a model can predict the likelihood that a patient presenting a certain set of symptoms is actually suffering a heart attack, helping ER staff determine treatment and urgency. Insurance fraud. Many types of fraud have predictable patterns and can be identified using statistical models for the purpose of prevention or for after-the-fact...
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