When large amounts of data are available, choosing the variables for inclusion in model building can be problematic. In this analysis, a subset of variables was required from a larger set. This subset was to be used in a later cluster analysis with the aim of extracting dimensions of human flourishing. A genetic algorithm (GA), written in SAS^®, was used to select the subset of variables from a larger set in terms of their association with the dependent variable life satisfaction. Life satisfaction was selected as a proxy for an as yet undefined quantity, human flourishing. The data were divided into subject areas (health, environment). The GA was applied separately to each subject area to ensure adequate representation from each in the future analysis when defining the human flourishing dimensions.

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With increasing regulatory emphasis on using more scientific statistical processes and procedures in the Bank Secrecy Act/Anti-Money Laundering (BSA/AML) compliance space, financial institutions are being pressured to replace their heuristic, rule-based customer risk rating models with well-established, academically supported, statistically based models. As part of their customer-enhanced due diligence, firms are expected to both rate and monitor every customer for the overall risk that the customer poses. Firms with ineffective customer risk rating models can face regulatory enforcement actions such as matters requiring attention (MRAs); the Office of the Comptroller of the Currency (OCC) can issue consent orders for federally chartered banks; and the Federal Deposit Insurance Corporation (FDIC) can take similar actions against state-chartered banks. Although there is a reasonable amount of information available that discusses the use of statistically based models and adherence to the OCC bulletin Supervisory Guidance on Model Risk Management (OCC 2011-12), there is only limited material about the specific statistical techniques that financial institutions can use to rate customer risk. This paper discusses some of these techniques; compares heuristic, rule-based models and statistically based models; and suggests ordinal logistic regression as an effective statistical modeling technique for assessing customer BSA/AML compliance risk. In discussing the ordinal logistic regression model, the paper addresses data quality and the selection of customer risk attributes, as well as the importance of following the OCC's key concepts for developing and managing an effective model risk management framework. Many statistical models can be used to assign customer risk, but logistic regression, and in this case ordinal logistic regression, is a fairly common and robust statistical method of assigning customers to ordered classifications (such as Low, Medium, High-Low, High-Medium, and High-High risk). Using ordinal logistic regression, a financial institution can create a customer risk rating model that is effective in assigning risk, justifiable to regulators, and relatively easy to update, validate, and maintain.

What do going to the South Pole at the beginning of the 20th century, winning the 1980 gold medal in Olympic hockey, and delivering a successful project have in common? The answer: Good teams succeed when groups of highly talented individuals often do not. So, what is your Everest and how do you gather the right group to successfully scale it? Success often hinges on not just building a team, but on assembling the right team. Join Scott Sanders, a business and IT veteran who has effectively built, managed, and been part of successful teams throughout his 27-year career. Hear some of his best practices for how to put together a good team and keep them focused, engaged, and motivated to deliver a project.

The impact of price on brand sales is not always linear or independent of other brand prices. We demonstrate, using sales information and SAS^® Enterprise Miner, how to uncover relative price bands where prices might be increased without losing market share or decreased slightly to gain share.

Learn how leading retailers are developing key findings in digital data to be leveraged across marketing, merchandising, and IT.

As many leadership experts suggest, growth happens only when it is intentional. Growth is vital to the immediate and long-term success of our employees as well as our employers. As SAS^® programming leaders, we have a responsibility to encourage individual growth and to provide the opportunity for it. With an increased workload yet fewer resources, initial and ongoing training seem to be deemphasized as we are pressured to meet project timelines. The current workforce continues to evolve with time and technology. More important than simply providing the opportunity for training, individual trainees need the motivation for any training program to be successful. Although many existing principles for growth remain true, how such principles are applied needs to evolve with the current generation of SAS programmers. The primary goal of this poster is to identify the critical components that we feel are necessary for the development of an effective training program, one that meets the individual needs of the current workforce. Rather than proposing a single 12-step program that works for everyone, we think that identifying key components for enhancing the existing training infrastructure is a step in the right direction.

Medicaid programs are the second largest line item in each state's budget. In 2012, they contributed $421.2 billion, or 15 percent of total national healthcare expenditures. With US health care reform at full speed, state Medicaid programs must establish new initiatives that will reduce the cost of healthcare, while providing coordinated, quality care to the nation's most vulnerable populations. This paper discusses how states can implement innovative reform through the use of data analytics. It explains how to establish a statewide health analytics framework that can create novel analyses of health data and improve the health of communities. With solutions such as SAS^® Claims Analytics, SAS^® Episode Analytics, and SAS^® Fraud Framework, state Medicaid programs can transform the way they make business and clinical decisions. Moreover, new payment structures and delivery models can be successfully supported through the use of healthcare analytics. A statewide health analytics framework can support initiatives such as bundled and episodic payments, utilization studies, accountable care organizations, and all-payer claims databases. Furthermore, integrating health data into a single analytics framework can provide the flexibility to support a unique analysis that each state can customize with multiple solutions and multiple sources of data. Establishing a health analytics framework can significantly improve the efficiency and effectiveness of state health programs and bend the healthcare cost curve.

Your electricity usage patterns reveal a lot about your family and routines. Information collected from electrical smart meters can be mined to identify patterns of behavior that can in turn be used to help change customer behavior for the purpose of altering system load profiles. Demand Response (DR) programs represent an effective way to cope with rising energy needs and increasing electricity costs. The Federal Energy Regulatory Commission (FERC) defines demand response as changes in electric usage by end-use customers from their normal consumption patterns in response to changes in the price of electricity over time, or to incentive payments designed to lower electricity use at times of high wholesale market prices or when system reliability of jeopardized. In order to effectively motivate customers to voluntarily change their consumptions patterns, it is important to identify customers whose load profiles are similar so that targeted incentives can be directed toward these customers. Hence, it is critical to use tools that can accurately cluster similar time series patterns while providing a means to profile these clusters. In order to solve this problem, though, hardware and software that is capable of storing, extracting, transforming, loading and analyzing large amounts of data must first be in place. Utilities receive customer data from smart meters, which track and store customer energy usage. The data collected is sent to the energy companies every fifteen minutes or hourly. With millions of meters deployed, this quantity of information creates a data deluge for utilities, because each customer generates about three thousand data points monthly, and more than thirty-six billion reads are collected annually for a million customers. The data scientist is the hunter, and DR candidate patterns are the prey in this cat-and-mouse game of finding customers willing to curtail electrical usage for a program benefit. The data scientist must connect large siloed data sources, external data , and even unstructured data to detect common customer electrical usage patterns, build dependency models, and score them against their customer population. Taking advantage of Hadoop's ability to store and process data on commodity hardware with distributed parallel processing is a game changer. With Hadoop, no data set is too large, and SAS^® Visual Statistics leverages machine learning, artificial intelligence, and clustering techniques to build descriptive and predictive models. All data can be usable from disparate systems, including structured, unstructured, and log files. The data scientist can use Hadoop to ingest all available data at rest, and analyze customer usage patterns, system electrical flow data, and external data such as weather. This paper will use Cloudera Hadoop with Apache Hive queries for analysis on platforms such as SAS^® Visual Analytics and SAS Visual Statistics. The paper will showcase optionality within Hadoop for querying large data sets with open-source tools and importing these data into SAS^® for robust customer analytics, clustering customers by usage profiles, propensity to respond to a demand response event, and an electrical system analysis for Demand Response events.

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.

Although today's marketing teams enjoy large-scale campaign relationship management systems, many are still left with the task of bridging the well-known gap between campaigns and customer purchasing decisions. During this session, we discuss how Slalom Consulting and Celebrity Cruises decided to take a bold step and bridge that gap. We show how marketing efforts are distorted when a team considers only the last campaign sent to a customer that later booked a cruise. Then we lay out a custom-built SAS 9.3 solution that scales to process thousands of campaigns per month using a stochastic attribution technique. This approach considers all of the campaigns that touch the customer, assigning a single campaign or a set of campaigns that contributed to their decision.

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.

Risk managers and traders know that some knowledge loses its value quickly. Unfortunately, due to the computationally intensive nature of risk, most risk managers use stale data. Knowing your positions and risk intraday can provide immense value. Imagine knowing the portfolio risk impact of a trade before you execute. This paper shows you a path to doing real-time risk analysis leveraging capabilities from SAS^® Event Stream Processing Engine and SAS^® High-Performance Risk. Event stream processing (ESP) offers the ability to process large amounts of data with high throughput and low latency, including streaming real-time trade data from front-office systems into a centralized risk engine. SAS High-Performance Risk enables robust, complex portfolio valuations and risk calculations quickly and accurately. In this paper, we present techniques and demonstrate concepts that enable you to more efficiently use these capabilities together. We also show techniques for analyzing SAS High-Performance data with SAS^® Visual Analytics.

As a consequence of the financial crisis, banks are required to stress test their balance sheet and earnings based on prescribed macroeconomic scenarios. In the US, this exercise is known as the Comprehensive Capital Analysis and Review (CCAR) or Dodd-Frank Act Stress Testing (DFAST). In order to assess capital adequacy under these stress scenarios, banks need a unified view of their projected balance sheet, incomes, and losses. In addition, the bar for these regulatory stress tests is very high regarding governance and overall infrastructure. Regulators and auditors want to ensure that the granularity and quality of data, model methodology, and assumptions reflect the complexity of the banks. This calls for close internal collaboration and information sharing across business lines, risk management, and finance. Currently, this process is managed in an ad hoc, manual fashion. Results are aggregated from various lines of business using spreadsheets and Microsoft SharePoint. Although the spreadsheet option provides flexibility, it brings ambiguity into the process and makes the process error prone and inefficient. This paper introduces a new SAS^® stress testing solution that can help banks define, orchestrate and streamline the stress-testing process for easier traceability, auditability, and reproducibility. The integrated platform provides greater control, efficiency, and transparency to the CCAR process. This will enable banks to focus on more value-added analysis such as scenario exploration, sensitivity analysis, capital planning and management, and model dependencies. Lastly, the solution was designed to leverage existing in-house platforms that banks might already have in place.

The goal of this presentation is to provide user group an update on retail solution releases in past one year and the roadmap moving forward.

The two primary objectives of multi-tiered causal analysis (MTCA) are to support and evaluate business strategies based on the effectiveness of marketing actions in both a competitive and holistic environment. By tying the performance of a brand, product, or SKU at retail to internal replenishment shipments at a point in time, the outcome of making a change to the marketing mix (demand) can be simulated and evaluated to determine the full impact on supply (shipments). The key benefit of MTCA is that it captures the entire supply chain by focusing on marketing strategies to shape future demand and to link them, using a holistic framework, to shipments (supply). These relationships are what truly define the marketplace and all marketing elements within the supply chain.

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.

In 2014, for the first time, mid-market banks (consisting of banks and bank holding companies with $10-$50 billion in consolidated assets) were required to submit Capital Stress Tests to the federal regulators under the Dodd-Frank Act Stress Testing (DFAST). This is a process large banks have been going through since 2011. However, mid-market banks are not positioned to commit as many resources to their annual stress tests as their largest peers. Limited human and technical resources, incomplete or non-existent detailed historical data, lack of enterprise-wide cross-functional analytics teams, and limited exposure to rigorous model validations are all challenges mid-market banks face. While there are fewer deliverables required from the DFAST banks, the scrutiny the regulators are placing on the analytical modes is just as high as their expectations for Comprehensive Capital Analysis and Review (CCAR) banks. This session discusses the differences in how DFAST and CCAR banks execute their stress tests, the challenges facing DFAST banks, and potential ways DFAST banks can leverage the analytics behind this exercise.

The vast and increasing demands of fraud detection and description have promoted the broad application of statistics and machine learning in fields as diverse as banking, credit card application and usage, insurance claims, trader surveillance, health care claims, and government funding and allowance management. SAS^® Visual Scenario Designer enables you to derive interactive business rules, along with descriptive and predictive models, to detect and describe fraud. This paper focuses on building interactive decision trees to classify fraud. Attention to optimizing the feature space (candidate predictors) prior to modeling is also covered. Because big data plays an increasingly vital role in fraud detection and description, SAS Visual Scenario Designer leverages the in-memory, parallel, and distributed computing abilities of SAS^® LASR™ Analytic Server as a back end to support real-time performance on massive amounts of data.

Tracking responses is one of the most important aspects of the campaign life cycle for a marketing analyst; yet this is often a daunting task. This paper provides guidance for how to determine what is a response, how it is defined for your business, and how you collect data to support it. It provides guidance in the context of SAS^® Marketing Automation and beyond.

Building a holistic view of the customer is becoming the norm across industries. The financial services industry and retail firms have been at the forefront of striving for this goal. Firm ABC is a large insurance firm based in the United States. It uses multiple campaign management platforms across different lines of business. Marketing campaigns are deployed in isolation. Similarly, responses are tracked and attributed in silos. This prevents the firm from obtaining a holistic view of its customers across products and lines of business and leads to gaps and inefficiencies in data management, campaign management, reporting, and analytics. Firm ABC needed an enterprise-level solution that addressed how to integrate with different types of data sources (both external and internal) and grow as a scalable and agile marketing and analytics organization; how to deploy campaign and contact management using a centralized platform to reduce overlap and redundancies and deliver a more coordinated marketing messaging to customers; how to perform more accurate attribution that, in turn, drives marketing measurement and planning; how to implement more sophisticated and visual self-service reporting that enables business users to make marketing decisions; and how to build advanced analytics expertise in-house. The solution needed to support predictive modeling, segmentation, and targeting. Based on these challenges and requirements, the firm conducted an extensive RFP process and reviewed various vendors in the enterprise marketing and business intelligence space. Ultimately, SAS^® Customer Intelligence and SAS^® Enterprise BI were selected to help the firm achieve its goals and transition to a customer-centric organization. The ability for SAS^® to deliver a custom-hosted solution was one of the key drivers for this decision, along with its experience in the financial services and insurance industries. Moreover, SAS can provide the much-needed flexibility and scala bility, whether it is around integrating external vendors, credit data, and mail-shop processing, or managing sensitive customer information. This presentation provides detailed insight on the various modules being implemented by the firm, how they will be leveraged to address the goals, and what their roles are in the future architecture. The presentation includes detailed project implementation and provides insights, best practices, and challenges faced during the project planning, solution design, governance, and development and production phases. The project team included marketers, campaign managers, data analysts, business analysts, and developers with sponsorship and participation from the C suite. The SAS^® Transformation Project provides insights and best practices that prove useful for business users, IT teams, and senior management. The scale, timing, and complexity of the solution deployment make it an interesting and relevant case study, not only for financial clients, but also for any large firm that has been tasked with understanding its customers and building a holistic customer profile.

How does historical production data relate a story about subsurface oil and gas reservoirs? Business and domain experts must perform accurate analysis of reservoir behavior using only rate and pressure data as a function of time. This paper introduces innovative data-driven methodologies to forecast oil and gas production in unconventional reservoirs that, owing to the nature of the tightness of the rocks, render the empirical functions less effective and accurate. You learn how implementations of the SAS^® MODEL procedure provide functional algorithms that generate data-driven type curves on historical production data. Reservoir engineers can now gain more insight to the future performance of the wells across their assets. SAS enables a more robust forecast of the hydrocarbons in both an ad hoc individual well interaction and in an automated batch mode across the entire portfolio of wells. Examples of the MODEL procedure arising in subsurface production data analysis are discussed, including the Duong data model and the stretched exponential data model. In addressing these examples, techniques for pattern recognition and for implementing TREE, CLUSTER, and DISTANCE procedures in SAS/STAT^® are highlighted to explicate the importance of oil and gas well profiling to characterize the reservoir. The MODEL procedure analyzes models in which the relationships among the variables comprise a system of one or more nonlinear equations. Primary uses of the MODEL procedure are estimation, simulation, and forecasting of nonlinear simultaneous equation models, and generating type curves that fit the historical rate production data. You will walk through several advanced analytical methodologies that implement the SEMMA process to enable hypotheses testing as well as directed and undirected data mining techniques. SAS^® Visual Analytics Explorer drives the exploratory data analysis to surface trends and relationships, and the data QC workflows ensure a robust input space for the performance forecasting methodologies that are visualized in a web-based thin client for interactive interpretation by reservoir engineers.

Real-time web content personalization has come into its teen years, but recently a spate of marketing solutions have enabled marketers to finely personalize web content for visitors based on browsing behavior, geo-location, preferences, and so on. In an age where the attention span of a web visitor is measured in seconds, marketers hope that tailoring the digital experience will pique each visitor's interest just long enough to increase corporate sales. The range of solutions spans the entire spectrum of completely cloud-based installations to completely on-premises installations. Marketers struggle to find the most optimal solution that would meet their corporation's marketing objectives, provide them the highest agility and time-to-market, and still keep a low marketing budget. In the last decade or so, marketing strategies that involved personalizing using purely on-premises customer data quickly got replaced by ones that involved personalizing using only web-browsing behavior (a.k.a, clickstream data). This was possible because of a spate of cloud-based solutions that enabled marketers to de-couple themselves from the underlying IT infrastructure and the storage issues of capturing large volumes of data. However, this new trend meant that corporations weren't using much of their treasure trove of on-premises customer data. Of late, however, enterprises have been trying hard to find solutions that give them the best of both--the ease of gathering clickstream data using cloud-based applications and on-premises customer data--to perform analytics that lead to better web content personalization for a visitor. This paper explains a process that attempts to address this rapidly evolving need. The paper assumes that the enterprise already has tools for capturing clickstream data, developing analytical models, and for presenting the content. It provides a roadmap to implementing a phased approach where enterprises continue to capture clickstream data, but they bring that data in-house to be merg ed with customer data to enable their analytics team to build sophisticated predictive models that can be deployed into the real-time web-personalization application. The final phase requires enterprises to continuously improve their predictive models on a periodic basis.

Data visualization is synonymous with big data, for which billions of records and millions of variables are analyzed simultaneously. But that does not mean data scientists analyzing clinical trial data that include only thousands of records and hundreds of variables cannot take advantage of data visualization methodologies. This paper presents a comprehensive process for loading standard clinical trial data into SAS^® Visual Analytics, an interactive analytic solution. The process implements template reporting for a wide variety of point-and-click visualizations. Data operations required to support this reporting are explained and examples of the actual visualizations are presented so that users can implement this reporting using their own data.

The experiences of the programmer role in a large SAS^® shop are shared. Shortages in SAS programming talent tend to result in one SAS programmer doing all of the production programming within a unit in a shop. In a real-world example, management realized the problem and brought in new programmers to help do the work. The new programmers actually improved the existing programmers' programs. It became easier for the experienced programmers to complete other programming assignments within the unit. And, the different programs in the shop had a standard structure. As a result, all of the programmers had a clearer picture of the work involved and knowledge hoarding was eliminated. Experienced programmers were now available when great SAS code needed to be written. Yet, they were not the only programmers who could do the work! With multiple programmers able to do the same tasks, vacations were possible and didn't threaten deadlines. It was even possible for these programmers to be assigned other tasks outside of the unit and broaden their own skills in statistical production work.