Predictive Analytics: Business Made Smarter

With business administration techniques reaching all time high’s there was an increasing demand for strategies that would make businesses more precise. With the advent of business intelligence into business, a new set of applications came into being. These are now considered to be the determining factor when it comes to the success of a business. Predictive analytics is one of these new age applications. The theory behind predictive analytics is quite simple. It involves myriad techniques that use past and current data to determine or predict future events. Rather than making predictions as pure statements, predictive analytical statements are expressed as values. It is the value in sync with the particular event that determines the chances of the trend occurring in the future.

When predictive analytics is used in business, it is often used to identify a potential opportunity or evaluate the risk with respect to a customer or a transaction. Many elements from the enormously huge database are considered before making these predictions. One of the key aspects that make predictive analytics so popular is that predictions made are mostly precise. Before completing or initiating a transaction with each customer, the predictive analytics model is used to determine the risk or the opportunity at hand. With this model in place, a business can easily isolate customers and classify them according to their salability. The further marketing plan or other plan of action can then be initiated with this data in hand. Hence you will find that customer decisions are mostly taken with predictive analytics. One of the most commonly used models of predictive analytics is credit scoring. This is used by financial organizations around the world to determine whether a customer is credit worthy or is a high risk proposition.

Ever since predictive analytics was first launched with respect to business, it has undergone several variations and the current models that are frequently used are predictive models, descriptive models and decision models. Predictive models focus mostly on analyzing past performances and data to predict to near perfection how a customer is most likely to behave in the future. Even the most minute data patterns are analyzed to utmost perfection and the result is prediction that guarantees results. Some models even perform complex calculations during live transactions. The other model called descriptive model is used to describe relationships that will allow customers to be classified into groups.

Although descriptive models allow the classification of customers, they do not rank them by the prospect of a future transaction by analyzing their past behavior. Last but not the least we have the decision models which are gaining a lot of popularity. These models are being used increasingly by businesses today to facilitate their complex decisions involving lots of details and numbers. These models are mostly used offline. Although the applications of predictive analytics are myriad, it is customer relationship management that has found most uses for it. It is also being used increasingly in marketing nowadays as marketing campaigns become more precise and sharp.