Predictive analytics uses algorithms and machine learning techniques on historical structured and unstructured data to predict the likelihood of an event occurring in the future. This not only opens new possibilities for the business but also arms the management with data for effective decision making.
The most common kind of models are:
Predictive models: use known results to develop a new model which is very helpful to predict values for a different set of data.
Classification model: This model classifies the object as to whether “they will” or “they will not”.
Regression Models: to predict the revenue generation from a certain customer, product etc.
The predictive analytics have been around for some time now but the growing volumes of data, cheaper and faster computers, cheap storage facilities and above all fast and competitive environment has made organizations turn their attention to it now in a big way. The days of data representation through graphs and spreadsheets have run their course. Machine learning and AI are changing the way “BIG DATA” is now dealt with, with the single aim of providing a glimpse into the future.
The predictive analytics qualifies the leads and ascertain the effectiveness of the marketing campaigns. This would provide a clear picture of the resource allocation requirement based on the leads response. The resultant data can be a good data model for similar analysis in the future. Similarly, a propensity model that facilitate in predicting customer behavior
Predictive analytics can come in handy to improve operations and reducing risk. The predictive models are always beneficial to forecast inventory and future sales. Airlines and Hotel industry are one such business who leverage the capabilities of analytics to predict occupancy percentage and revenue generation. The insurance companies and banks use predictive analytics capabilities to find the creditworthiness of the patron.
Even the sports domain hasn’t been left behind when it comes to leveraging the capabilities of predictive analytics. A case in point is the Orlando Magic NBA basketball team. It uses the SAS predictive analytics to picking the best starting lineups.
Predictive analytics goes beyond the “present” and provides an insight into the future, based on factual data sets. It optimizes operations and increases value, positioning a company at a vantage point in a competitive landscape.