With the latest release of SAS^® Business Rules Manager, decision-making using SAS^® Stored Processes is now easier with simplified deployment via a web service for integration with your applications and business processes. This paper shows you how a user can publish analytics and rules as SOAP-based web services, track its usage, and dynamically update these decisions using SAS Business Rules Manager. In addition, we demonstrate how to integrate with SAS^® Model Manager using SAS^® Workflow to demonstrate how your other SAS^® applications and solutions can also simplify real-time decision-making through business rules.

The telecommunications industry is the fastest changing business ecosystem in this century. Therefore, handset campaigning to increase loyalty is the top issue for telco companies. However, these handset campaigns have great fraud and payment risks if the companies do not have the ability to classify and assess customers properly according to their risk propensity. For many years, telco companies managed the risk with business rules such as customer tenure until the launch of analytics solutions into the market. But few business rules restrict telco companies in the sales of handsets to new customers. On the other hand, with increasing competition pressure in telco companies, it is necessary to use external credit data to sell handsets to new customers. Credit bureau data was a good opportunity to measure and understand the behaviors of the applicants. But using external data required system integration and real-time decision systems. For those reasons, we need a solution that enables us to predict risky customers and then integrate risk scores and all information into one real-time decision engine for optimized handset application vetting. After an assessment period, SAS^® Analytics platform and RTDM were chosen as the most suitable solution because they provide a flexible user friendly interface, high integration, and fast deployment capability. In this project, we build a process that includes three main stages to transform the data into knowledge. These stages are data collection, predictive modelling, and deployment and decision optimization. a) Data Collection: We designed a specific daily updated data mart that connects internal payment behavior, demographics, and customer experience data with external credit bureau data. In this way, we can turn data into meaningful knowledge for better understanding of customer behavior. b) Predictive Modelling: For using the company potential, it is critically important to use an analytics approach that is based on state-of-the-art tec hnologies. We built nine models to predict customer propensity to pay. As a result of better classification of customers, we obtain satisfied results in designing collection scenarios and decision model in handset application vetting. c) Deployment and Decision Optimization: Knowledge is not enough to reach success in business. It should be turned into optimized decision and deployed real time. For this reason, we have been using SAS^® Predictive Analytics Tools and SAS^® Real-Time Decision Manager to primarily turn data into knowledge and turn knowledge into strategy and execution. With this system, we are now able to assess customers properly and to sell handset even to our brand-new customers as part of the application vetting process. As a result of this, while we are decreasing nonpayment risk, we generated extra revenue that is coming from brand-new contracted customers. In three months, 13% of all handset sales was concluded via RTDM. Another benefit of the RTDM is a 30% cost saving in external data inquiries. Thanks to the RTDM, Avea has become the first telecom operator that uses bureau data in Turkish Telco industry.

In-database processing refers to the integration of advanced analytics into the data warehouse. With this capability, analytic processing is optimized to run where the data reside, in parallel, without having to copy or move the data for analysis. From a data governance perspective there are many good reasons to embrace in-database processing. Many analytical computing solutions and large databases use this technology because it provides significant performance improvements over more traditional methods. Come learn how Blue Cross Blue Shield of Tennessee (BCBST) uses in-database processing from SAS and Teradata.

Automated decision-making systems are now found everywhere, from your bank to your government to your home. For example, when you inquire for a loan through a website, a complex decision process likely runs combinations of statistical models and business rules to make sure you are offered a set of options for tantalizing terms and conditions. To make that happen, analysts diligently strive to encode their complex business logic into these systems. But how do you know if you are making the best possible decisions? How do you know if your decisions conform to your business constraints? For example, you might want to maximize the number of loans that you provide while balancing the risk among different customer categories. Welcome to the world of optimization. SAS^® Business Rules Manager and SAS/OR^® software can be used together to manage and optimize decisions. This presentation demonstrates how to build business rules and then optimize the rule parameters to maximize the effectiveness of those rules. The end result is more confidence that you are delivering an effective decision-making process.

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There are few business environments more dynamic than that of a casino. Serving a multitude of entertainment options to thousands of patrons every day results in a lot of customer interaction points. All of these interactions occur in a highly competitive environment where, if a patron doesn't feel that he is getting the recognition that he deserves, he can easily walk across the street to a competitor. Add to this the expected amount of reinvestment per patron in the forms of free meals and free play. Making high-quality real-time decisions during each customer interaction is critical to the success of a casino. Such decisions need to be relevant to customers' needs and values, reflect the strategy of the business, and help maximize the organization's profitability. Being able to make those decisions repeatedly is what separates highly successful businesses from those that flounder or fail. Casinos have a great deal of information about a patron's history, behaviors, and preferences. Being able to react in real time to newly gathered information captured in ongoing dialogues opens up new opportunities about what offers should be extended and how patrons are treated. In this session, we provide an overview of real-time decisioning and its capabilities, review the various opportunities for real-time interaction in a casino environment, and explain how to incorporate the outputs of analytics processes into a real-time decision engine.

This paper takes you through the steps for ways to modernize your analytical business processes using SAS^® Decision Manager, a centrally managed, easy-to-use interface designed for business users. See how you can manage your data, business rules, and models, and then combine those components to test and deploy as flexible decisions options within your business processes. Business rules, which usually exist today in SAS^® code, Java code, SQL scripts, or other types of scripts, can be managed as corporate assets separate from the business process. This will add flexibility and speed for making decisions as policies, customer base, market conditions, or other business requirements change. Your business can adapt quickly and still be compliant with regulatory requirements and support overall process governance and risk. This paper shows how to use SAS Decision Manager to build business rules using a variety of methods including analytical methods and straightforward explicit methods. In addition, we demonstrate how to manage or monitor your operational analytical models by using automation to refresh your models as data changes over time. Then we show how to combine your data, business rules, and analytical models together in a decision flow, test it, and learn how to deploy in batch or real time to embed decision results directly into your business applications or processes at the point of decision.

Streaming data is becoming more and more prevalent. Everything is generating data now--social media, machine sensors, the 'Internet of Things'. And you need to decide what to do with that data right now. And 'right now' could mean 10,000 times or more per second. SAS^® Event Stream Processing provides an infrastructure for capturing streaming data and processing it on the fly--including applying analytics and deciding what to do with that data. All in milliseconds. There are some basic tenets on how SAS^® provides this extremely high-throughput, low-latency technology to meet whatever streaming analytics your company might want to pursue.

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The era of mass marketing is over. Welcome to the new age of relevant marketing where whispering matters far more than shouting.' At ZapFi, using the combination of sponsored free Wi-Fi and real-time consumer analytics,' we help businesses to better understand who their customers are. This gives businesses the opportunity to send highly relevant marketing messages based on the profile and the location of the customer. It also leads to new ways to build deeper and more intimate, one-on-one relationships between the business and the customer. During this presentation, ZapFi will use a few real-world examples to demonstrate that the future of mobile marketing is much more about data and far less about advertising.

Managing and organizing external files and directories play an important part in our data analysis and business analytics work. A good file management system can streamline project management and file organizations and significantly improve work efficiency . Therefore, under many circumstances, it is necessary to automate and standardize the file management processes through SAS^® programming. Compared with managing SAS files via PROC DATASETS, managing external files is a much more challenging task, which requires advanced programming skills. This paper presents and discusses various methods and approaches to managing external files with SAS programming. The illustrated methods and skills can have important applications in a wide variety of analytic work fields.