Self-service predictive analytics is here. Are you ready?


Self-reliant business users would like to use analytics more often to address changing market conditions and make quick decisions. Take, for example, a marketer or customer support specialist who might want to generate analytics and apply results to a variety of business issues, including:

  • Determine which inbound customer interactions are best candidates for an up-sell or cross-sell or a retention offer.
  • Recognize which customers are likely to respond and include them in the new campaign.
  • Measure the propensity for an active customer to churn.

Should that marketer have to rely on statisticians, modelers or data mining specialists to create predictive models every time? Are they frustrated because they have to wait for answers? Do they have to apply above issues to hundreds of product or service offerings? You bet! On the other hand, business analysts often do not understand all aspects of data mining and statistics. What the marketer in our example needs is a predictive modeling tool that is suitable for business analysts and subject matter experts. Today’s product launch at A2010 and immediate availability of SAS Rapid Predictive Modeler fulfills that need. SAS Rapid Predictive Modeler offers a guided, user-friendly interface for business analysts to conduct predictive analytics. Easy-to-interpret charts and reports are generated to help you quickly solve specific problems around customer intelligence, including customer segmentation, up-selling and cross-selling, campaign management, customer acquisition and churn. It eliminates complexities for business analysts and relies on behind-the-scenes automated data mining workflows, powered by SAS Enterprise Miner, to quickly produce the best possible model.

If we continue our example and take reducing customer churn as a specific scenario, our marketer has clear objectives in his or her mind to find out which customers have the highest propensity to churn and what are the key variables influencing churn. SAS Rapid Predictive Modeler helps them to become self-sufficient and responsive. It can generate a classification model to quickly and easily measure the propensity for an active customer to churn. Marketing or customer service managers can then take proactive steps to retain profitable customers before churn occurs or take corrective action against reasons influencing churn.

Does this mean that statisticians or modelers or data mining specialists are completely sidelined? Absolutely not! SAS Rapid Predictive Modeler is equally suitable for them. Analytic professionals can validate models generated by SAS Rapid Predictive Modeler. What if they want to further augment or customize the model generated using SAS Rapid Predictive Modeler? They can open up the model in SAS Enterprise Miner and do so offering a transparent, white box approach rather than a closed, black box approach to modeling.

Additionally analytic professionals with higher workloads can quickly generate baseline models using SAS Rapid Predictive Modeler. The time savings will allow modelers to focus on solving more complex issues like rate making in insurance, customer lifetime value, healthcare fraud detection, or market basket analysis using SAS Enterprise Miner.

To summarize, SAS Rapid Predictive Modeler helps to generate predictive models quickly and easily, and apply results to improve your decision making. It fosters collaboration between business analysts and analytic professionals and is a step in right direction to make analytics more pervasive. Want to learn more? Click here or check out a quick demo on YouTube.


About Author

Tapan Patel

Global Product Marketing Manager, SAS

Tapan Patel is Senior Manager (Product Marketing) at SAS. With 20 years of experience in the enterprise software market, Patel leads and manages product marketing efforts for Data Management, Artificial Intelligence, Decisioning and Cloud Providers.

Back to Top