Predictive Analytics

Banking & Financial Services

The financial industry, with huge amounts of data and money at stake, has long embraced predictive analytics to detect and reduce fraud, measure credit risk, maximize cross-sell/up-sell opportunities and retain valuable customers. Commonwealth Bank uses analytics to predict the likelihood of fraud activity for any given transaction before it is authorized – within 40 milliseconds of the transaction initiation.

Retail

Since the now infamous study that showed men who buy diapers often buy beer at the same time, retailers everywhere are using predictive analytics for merchandise planning and price optimization, to analyze the effectiveness of promotional events and to determine which offers are most appropriate for consumers. Staples gained customer insight by analyzing behavior, providing a complete picture of their customers, and realizing a 137% ROI.

Oil, Gas & Utilities

Whether it is predicting equipment failures and future resource needs, mitigating safety and reliability risks, or improving overall performance, the energy industry has embraced predictive analytics with vigor. Salt River Project is the second-largest public power utility in the US and one of Arizona's largest water suppliers. Analyses of machine sensor data predicts when power-generating turbines need maintenance.

Governments & the Public Sector

Governments have been key players in the advancement of computer technologies. The US Census Bureau has been analyzing data to understand population trends for decades. Governments now use predictive analytics like many other industries – to improve service and performance; detect and prevent fraud; and better understand consumer behavior. They also use predictive analytics to enhance cybersecurity.

Health Care

In addition to detecting claims fraud, the health care industry is taking steps to identify patients most at risk of chronic disease and find what interventions are best. Express Scripts, a large pharmacy benefits company, uses analytics to identify those not adhering to prescribed treatments, resulting in a savings of $1,500 to $9,000 per patient.

Manufacturing

For manufacturers it's very important to identify factors leading to reduced quality and production failures, as well as to optimize parts, service resources and distribution. Lenovo is just one manufacturer that has used predictive analytics to better understand warranty claims – an initiative that led to a 10 to 15 percent reduction in warranty costs.

Regression (linear and logistic) is one of the most popular method in statistics. Regression analysis estimates relationships among variables. Intended for continuous data that can be assumed to follow a normal distribution, it finds key patterns in large data sets and is often used to determine how much specific factors, such as the price, influence the movement of an asset. With regression analysis, we want to predict a number, called the response or Y variable. With linear regression, one independent variable is used to explain and/or predict the outcome of Y. Multiple regression uses two or more independent variables to predict the outcome. With logistic regression, unknown variables of a discrete variable are predicted based on known value of other variables. The response variable is categorical, meaning it can assume only a limited number of values. With binary logistic regression, a response variable has only two values such as 0 or 1. In multiple logistic regression, a response variable can have several levels, such as low, medium and high, or 1, 2 and 3.

Decision trees are classification models that partition data into subsets based on categories of input variables. This helps you understand someone's path of decisions. A decision tree looks like a tree with each branch representing a choice between a number of alternatives, and each leaf representing a classification or decision. This model looks at the data and tries to find the one variable that splits the data into logical groups that are the most different. Decision trees are popular because they are easy to understand and interpret. They also handle missing values well and are useful for preliminary variable selection. So, if you have a lot of missing values or want a quick and easily interpretable answer, you can start with a tree.

Neural networks are sophisticated techniques capable of modeling extremely complex relationships. They’re popular because they’re powerful and flexible. The power comes in their ability to handle nonlinear relationships in data, which is increasingly common as we collect more data. They are often used to confirm findings from simple techniques like regression and decision trees. Neural networks are based on pattern recognition and some AI processes that graphically “model” parameters. They work well when no mathematical formula is known that relates inputs to outputs, prediction is more important than explanation or there is a lot of training data. Artificial neural networks were originally developed by researchers who were trying to mimic the neurophysiology of the human brain.