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First Look – SAS Real Time Decision Manager


SAS Real-Time Decision Manager (RDM) is designed for inbound communications, complementing outbound communication solutions. It aims at real-time delivery of decisions and recommendations during a customer interaction to optimize that interaction to improve revenue, growth and retention. For example, in retail banking, a customer might come in with a new job with very different income and needs financial guidance – this must be real-time as the bank did not have this information earlier.

SAS Real-Time Decision Manager supports in person, call center, website, IVR, ATM/POS, mobile channels and is tightly integrated with SAS Marketing Automation – it shares a UI, data model, contact and response history etc. SAS Real-Time Decision Manager leverages data from customer profiles, historical data, real-time data being captured by the channel, other systems and SAS analytic models. Real-Time Decision Manager is designed to support business users, analytic users and technical administrators.

Real-Time Decision Manager is independent of the channels and plugs into the channel management systems to provide a decision. Real-Time Decision Manager gets web service requests from channel applications and responds by executing a decision flow and returning an answer – it acts, in other words, like a pure decision service. During the decision flow it can reach out to databases, other applications or external web services. It can execute analytic models and conditions and then returns the answer as a web service response.

The decision flow is specified by a business user and has a nice graphical interface common to many SAS applications. The flow can be defined using drag and drop from a palette of components including branches, filters etc. There is a task bar to manage the process and the diagrams can be annotated with notes and images. The flows start with a node for receiving request (the flow is exposed as a web service) and progress through a series of steps. These steps or activities can:

  • Use models (developed by SAS Enterprise Miner, for instance) to score the customer – the business user does not see the details of the model or need to drill into the details.
  • Execute a custom process (any web service call or SAS scripts) – provides a means to extend the capabilities of the solution with minimal development.  These could be rules-based servicesto handle things like eligibility
  • Access databases with SQL queries that are preconfigured so the user does not need to create queries themselves
  • Filter by applying conditions – essentially rules that are highly constrained in terms of action (only yes/no in terms of further evaluating the customer to determine a specific communication ) but allow conditions to be written against all the data being managed.
  • Branch based on data values
  • Assign transactions to a reporting cell for later analysis

The business user can bring up a test interface and specify input parameters to see what result you get. You can save and manage the tests you want to use and re-run them. The nodes executed and any errors are reported as part of this. Impact analysis can be set up using standard SAS BI/reporting capabilities to act on the response data stored by the system (though there is not an out of the box impact analysis report).

Analytic users can develop models using normal SAS tools and then register models with SAS Model Manager. This makes the model available to the business user working on decision processes. The model can be updated and versioned independently of the decision flow, allowing constant updating of the models for improved performance.

Most customers are focused, obviously, on marketing and many use it in conjunction with SAS’ outbound campaign management component, SAS Marketing Automation. Some however use it for customer service and other customer-related decisions. Competitively this goes up against Chordiant Decision Manager, Unica, Oracle RTD, Convergys etc. SAS sees their product as more visual, easier for a business user to use, and clearly it has much tighter integration with SAS’ modeling tools which is a big plus for companies with a large investment there. They also feel their inbound/outbound integration is stronger. SAS believes that models developed based on knowledge of the domain and the data are still more effective than adaptive analytic models, and this is another difference from their competitors many of whom are very focused on these kinds of analytics.  However SAS also knows that expert analysts can be a rare resource in many organizations, so it is developing a new solution called Rapid Predictive Modeling to enable non-modelers to develop expert models rapidly. I am getting a briefing on this soon and will blog about it when I do.


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