Data revolution in business is making it move from mere reporting to predicting crucial outcomes. By the very definition, Predictive Analytics is the practice of culling out insights from existing operational and historical data sets in order to find patterns and predict future outcomes and trends. Predictive analytics does not tell you with certainty what will happen in the future. It prognosticates what might happen in the future with a reasonable level of reliability, and includes what-if scenarios and risk assessment. Increased revenue, improved customer satisfaction and increased operational efficiency are few among the many benefits of Predictive Analytics.

B2B businesses interested in predictive modeling need to have a clear idea of their objectives before they start. To develop a good predictive model, businesses need to focus on defining a clear set of business rules for each decision and then focus their analytics on driving the best decisions. The potential of business outcomes of a predictive modeling is immense. It can help companies in three key areas: minimizing risk, identifying fraud and pursuing new revenue opportunities. According to Radius, the leading business-to-business predictive marketing platform, B2B companies which applied predictive analytics to demand generation met objectives 55% of the time compared to 30% for B2B marketers not using analytics.

Marketers who focus on data accuracy and use predictive marketing typically have a more effective demand generation process.