These days especially businesses cannot afford to make the wrong moves, especially when it comes to predicting what their most valued assets: What their customers do next. Instead they need to accurately and quickly analyze and model customer behaviors i.e. predictive analytics, to better understand their buyers so that they can meet their needs with precision.

SAS, which makes business analytics software and services, has responded to its customers’ and the marketplace needs with the SAS (News - Alert) Rapid Predictive Modeler. It provides a quick, easy and effective way to develop predictive models that address a broad range of business scenarios, including customer segmentation, up-sell, cross-sell, campaign management, customer acquisition and customer churn across many industries.

“SAS Rapid Predictive Modeler brings predictive analytics functionality to business users looking to improve their operational decision making,” says Dan Vesset, program vice president of IDC's (News - Alert) business analytics research. “This shift to self-service analytics by business users frees advanced analytics experts to focus on more complex analyses.”

When nonstatisticians can generate models in a few simple steps, more people can contribute to solving problems and seizing opportunities, explains SAS. Business users just select data and choose inputs for the outcome they desire. The software automatically processes the input variables and selects the best predictive model. Easy-to-interpret charts deliver dynamic results, helping business analysts determine which predictions will be most valuable.  

Here are the SAS Rapid Predictive Modeler highlights:

*          Enables business analysts and subject-matter experts to explore and analyze their data using the familiar, visual interfaces of either SAS Enterprise Guide or Microsoft (News - Alert) Excel surfaced via the SAS Add-In for Microsoft Office

*          Offers a choice of basic, intermediate and advanced prebuilt models that use a broad range of classical and modern data modeling techniques

*          Automatically treats the data to handle outliers, missing values, rare target events, skewed data, correlated variables, variable selection and model selection

*          Lets users develop modeling and scoring tables using the data preparation tasks to stack, transpose, join, filter and sample data

*          Presents results in a visual interface to help users understand model performance, including scorecards, lift charts and ranking of key variables: and in business terms that are easy to understand, including scorecards, lift charts and rankings of key variables

*          Generates descriptive reports and graphs using tasks such as summary statistics, distributional analysis and one-way frequencies

*          Lets users interact with and explore data using bar charts, scatter plots, box plots, histograms and other graphic tasks

SAS Rapid Predictive Modeler is a component of SAS Enterprise Miner, which analytic professionals can use to further refine and customize the models generated by business users. It integrates with SAS Model Manager for central management, promotion and performance monitoring and with SAS Scoring Accelerator, allowing the models to be deployed and then executed directly within the database environment to further increase speed.

“Organizations are constantly looking for ways to speed up both the development and deployment of analytic applications; SAS Rapid Predictive Modeler provides key functionality in both areas,” said Mike Rote, Teradata (News - Alert) director of the SAS and Teradata Center of Excellence. “The models generated by SAS Rapid Predictive Modeler can be deployed in-database for quicker utilization of analytic results and faster decisions. This new offering will be well received by our mutual customers."

“SAS Rapid Predictive Modeler ushers our ‘non’ data miners into the power of predictive analytics,” added Tim Rey, manager of the Advanced Analytics group for The Dow Chemical Company. “Early in the process, users get to see if their target outcomes are likely to be explained by the input variables they chose - saving time by providing a quick reality check. SAS Rapid Predictive Modeler also ensures best practices are used for both the data mining methodology and processes so we can maximize both accuracy and ROI.”