This poster shows how to predict a past-due amount using traditional and machine learning techniques: logistic analysis, k-nearest neighbors, and random forest. The data set that was analyzed is about real-world commerce. It contains 305 categories of financial information from more than 11,787,287 unique businesses, from 2006 to 2014. The big challenge is how to handle the big and noisy real-world data sets. The first step of any model-building exercise is to define the outcome. A common prediction method in the financial services industry is to use binary outcomes, such as Good and Bad. For our research problem, we reduced past-due amounts into two cases, Good and Bad. Next, we built a two-stage model using the logistic regression method; that is, the first stage predicts the likelihood of a Bad outcome, and the second predicts a past-due amount, given a Bad outcome. Logistic analysis as a traditional statistical technique is commonly used for prediction and classification in the financial services industry. However, for analyzing big, noisy, or complex data sets, machine learning techniques are typically preferred to detect hard-to-discern patterns. To compare with both techniques, we use predictive accuracy, ROC index, sensitivity, and specificity as criteria.

Accessibility has become a hot topic on campus due to a flurry of recent investigations of discrimination against students with disabilities by the U.S. Department of Justice and the U.S. Department of Education. This paper provides an update on the latest improvements in SAS^® University Edition that are specifically targeted to enable students with disabilities to excel in the classroom and beyond. This paper covers the entire SAS University Edition user experience including installation, documentation, training, support, using SAS^® Studio, and the new accessibility features in the fourth maintenance release of SAS^® 9.4.

UNIX and Linux SAS^® administrators, have you ever been greeted by one of these statements as you walk into the office before you have gotten your first cup of coffee? Power outage! SAS servers are down. I cannot access my reports. Have you frantically tried to restart the SAS servers to avoid loss of productivity and missed one of the steps in the process, causing further delays while other work continues to pile up? If you have had this experience, you understand the benefit to be gained from a utility that automates the management of these multi-tiered deployments. Until recently, there was no method for automatically starting and stopping multi-tiered services in an orchestrated fashion. Instead, you had to use time-consuming manual procedures to manage SAS services. These procedures were also prone to human error, which could result in corrupted services and additional time lost, debugging and resolving issues injected by this process. To address this challenge, SAS Technical Support created the SAS Local Services Management (SAS_lsm) utility, which provides automated, orderly management of your SAS^® multi-tiered deployments. The intent of this paper is to demonstrate the deployment and usage of the SAS_lsm utility. Now, go grab a coffee, and let's see how SAS_lsm can make life less chaotic.

Let's walk through an example of communicating from the SAS^® client to SAS^® Viya . The demonstration focuses on how to use SAS^® language to establish a session, transport and persist data, and receive results. Learn how to establish communication with SAS Viya. Explore topics such as: What is a session? How do I make requests? What does my SAS log tell me? Get a deeper understanding of data location on the client and the server side. Learn about applying existing user formats, how to get listings or reports, and how to query sessions, data, and properties.

SAS^® is often deployed in a client/server architecture in which SAS^® Foundation is installed on a server and is accessed from each user's workstation. Many system administrators prefer that users not log on directly to the server to run SAS, nor do they want to set up a complex Citrix environment. SAS client applications are an attractive alternative for this type of architecture. But with the advent of multiple SAS^® Studio editions and ongoing enhancements to SAS^® Enterprise Guide^®, choosing the most suitable client application presents a challenge for many system administrators. To help guide you in this choice, this paper compares the administration of three SAS Foundation client applications that can be used in a client/server architecture: SAS Enterprise Guide, SAS^® Studio Basic, and SAS^® Studio Mid-Tier. The usage differences between SAS Studio and SAS Enterprise Guide have been addressed elsewhere. In this paper, we focus on differences that pertain specifically to system administration, including deployment, maintenance, and authentication. The information presented here will help system administrators determine which application best fits the needs of their users and their environment.

An interactive voice response (IVR) system is a powerful tool that automates routine inbound call tasks. Companies leverage this system and make substantial savings by cutting down call center costs while customers from do-it-yourselfers to non-tech-savvies take advantage of this technology rather than wait in line to speak to a Customer Care Representative (CSR). The flip side of the coin is that customers often see IVR as a barrier to overcome in order to talk to a real person. So it is important that IVR is managed in such a way that it is mutually beneficial for both a business and their customers. If managing IVR is critical, then measuring Customer Satisfaction (CSAT) scores is paramount as it helps in understanding customers better. The first section of this paper discusses analysis of different use cases on how CSAT scores correlate with customers' journeys inside IVR. West Corporation's leading financial services client offers a survey to their customers, and customers rate questions on a scale of 1 to 10 based on their IVR experience (10 being extremely satisfied). Analysis of survey ratings using SAS^® helped Operations understand challenges faced by customers traversing different sections of the IVR. The second section of the paper discusses how the research helped Operations to identify a population specification error that occurred while surveying customers. The error was rectified, and IVR CSAT scores improved by 3%.

Standard SAS^® Studio tasks already include many advanced analytic procedures for data mining and other high-performance models, enabling point-and-click generation and execution of SAS^® code. However, you can extend the power of tasks by creating tasks of your own to enable point-and-click access to the latest SAS statistical procedures, to your own default model definitions, or to your previously developed SAS/STAT^® or SAS macro code. Best of all, these point-and-click tasks can be developed directly in SAS Studio without the need to compile binaries or build DLL files using third-party software. In this paper, we demonstrate three approaches to developing custom tasks. First, we build a custom task to provide point-and-click access to PROC IRT, including recently added functionality to PROC IRT used to analyze educational test and opinion survey data. Second, we build a custom task that calls a macro for previously developed SAS code, and we show how point-and-click options can be set up to allow users to guide the execution of complex macro code. Third, we demonstrate just enough of the underlying Apache Velocity Template Language code to enable developers to take advantage of the benefits of that language to support their SAS process. Finally, we show how these tasks can easily be shared with a user community, increasing the efficiency of analytic modeling across the organization.

In this course you will learn how to access and manage SAS and Excel data in SAS^® Viya .

This workshop provides hands-on experience with SAS^® Studio. Workshop participants will use SAS's new web-based interface to access data, write SAS programs, and generate SAS code through predefined tasks. This workshop is intended for SAS programmers from all experience levels.

This workshop provides hands-on experience performing statistical analysis with the Statistics tasks in SAS Studio. Workshop participants will learn to perform statistical analyses using tasks, evaluate which tasks are ideal for different kinds of analyses, edit the generated code, and customize a task.

In this technology-driven era, multi-channel communication has become a pivotal part of an effective customer care strategy for companies. Old ways of delivering customer service are no longer adequate. To survive a tough competitive market and retain current customer base, companies are spending heavily to serve customers in the manner in which they wish to be served. West Corporation helps their clients in designing a strategy that would provide their customers with a connected inbound and outbound communication experience. This paper illustrates how the Data Science team at West Corporation has measured the effect of outbound short message service (SMS) notification in reducing inbound interactive voice response (IVR) call volume and improving customer satisfaction for a leading telecom services company. As part of a seamless experience, customers have the option of receiving outbound SMS notifications at several stages while traversing inside IVR. Notifications can involve successful payment and appointment confirmations, outage updates in the area, and an option of receiving text with details to reset Wi-Fi password and activate new devices. This study was performed on two groups of customers one whose members opted to receive notifications and one whose members did not opt in. Also, analysis was performed using SAS^® to understand repeat caller behaviors within both groups. The group that opted to receive SMS notifications were less likely to call back than those who did not opt in.

As a SAS^® programmer, how often does it happen where you would like to submit some code but not wait around for it to finish? SAS^® Studio has a way to achieve this and much more! This paper covers how to submit and execute SAS code in the background using SAS Studio. Background submit in the SAS Studio interface allows you to submit code and continue with your work. You receive a notification when it is finished, or you can even disconnect from your browser session and check the status of the submitted code later. Or you can choose to use SAS Studio to submit your code without bringing up the SAS Studio interface at all. This paper also covers the ability to use a command-line executable program that uses SAS Studio to execute SAS code in the background and generate log and result files without having to create a new SAS Studio session. These techniques make it much easier to spin up long-running jobs, while still being able to get your other work done in the meantime.

SAS^® environments are evolving in multiple directions. Modern web interfaces such as SAS^® Studio are replacing the traditional SAS^® Display Manager system. At the same time, distributed analytic computing, centrally managed by SAS^® Grid Manager, is becoming the standard topology for many enterprises. SAS administrators are faced with the task of providing business users properly configured, tuned, and monitored applications. The tips included in this paper provide SAS administrators with best practices to centrally manage SAS Studio options and repositories, proper grid tuning, effective monitoring of user sessions, high-availability considerations and more.

SAS^® programming skills are much in-demand, and numerous free tools are available for students who want to develop those skills. This paper introduces students to SAS^® Studio and the Jupyter Notebook interface within SAS^® University Edition. To make this introduction more tangible, the paper uses a large data set of baseball statistics as an example. In particular, statistical analysis using SAS^® Studio examines the relationship between salary and performance for major leaguers. From importing text files to creating basic statistics to doing a more advanced analysis, this paper shows multiple ways to carry out tasks so that you can choose whichever method works best for you. Additional statistics that use t tests and linear regression are simple with SAS University Edition. For completeness, the paper shows the same code that is used in SAS Studio examples in the context of Jupyter Notebook in SAS University Edition. The paper also provides additional information about SAS e-learning and SAS Certification to show students how to be fully equipped in order to apply themselves to analytics and data exploration.

Time series analysis and forecasting have always been popular as businesses realize the power and impact they can have. Getting students to learn effective and correct ways to build their models is key to having successful analyses as more graduates move into the business world. Using SAS^® University Edition is a great way for students to learn analysis, and this talk focuses on the time series tasks. A brief introduction to time series is provided, as well as other important topics that are key to building strong models.

Students now have access to a SAS^® learning tool called SAS^® University Edition. This online tool is freely available to all, for non-commercial use. This means it is basically a free version of SAS that can be used to teach yourself or someone else how to use SAS. Since a large part of my body of writings has focused upon moving data between SAS and Microsoft Excel, I thought I would take some time to highlight the tasks that permit movement of data between SAS and Excel using SAS University Edition. This paper is directed toward sending graphs to Excel using the new ODS EXCEL destination.