This paper simply develops a new SAS^® macro, which allows you to scrap user textual reviews from Apple iTunes store for iPhone applications. It not only can help you understand your customers experiences and needs, but also can help you be aware of your competitors user experiences. The macro uses API in iTunes and PROC HTTP in SAS to extract and create data sets. This paper also shows how you can use the application ID and country code to extract user reviews.

The operational tempo of marketing in a digital world seems faster every day. New trends, issues, and ideas appear and spread like wildfire, demanding that sales and marketing adapt plans and priorities on-the-fly. The technology available to us can handle this, but traditional organizational processes often become the bottleneck. The solution is a new management approach called agile marketing. Drawing upon the success of agile software development and the lean start-up movement, agile marketing is a simple but powerful way to make marketing teams more nimble and marketing programs more responsive. You don't have to be small to be agile agile marketing has thrived at large enterprises such as Cisco and EMC. This session covers the basics of agile marketing what it is, why it works, and how to get started with agile marketing in your own team. In particular, we look at how agile marketing dovetails with the explosion of data-driven management in marketing by using the fast feedback from analytics to rapidly iterate and adapt new marketing programs in a rapid yet focused fashion.

The proliferation of textual data in business is overwhelming. Unstructured textual data is being constantly generated via call center logs, emails, documents on the web, blogs, tweets, customer comments, customer reviews, and so on. While the amount of textual data is increasing rapidly, businesses ability to summarize, understand, and make sense of such data for making better business decisions remain challenging. This presentation takes a quick look at how to organize and analyze textual data for extracting insightful customer intelligence from a large collection of documents and for using such information to improve business operations and performance. Multiple business applications of case studies using real data that demonstrate applications of text analytics and sentiment mining using SAS^® Text Miner and SAS^® Sentiment Analysis Studio are presented. While SAS^® products are used as tools for demonstration only, the topics and theories covered are generic (not tool specific).

Digital data has manifested into a classic BIG DATA challenge for marketers who want to push past the retroactive analysis limitations of traditional web analytics. The current groundswell of digital device adoption and variety of digital interactions grows larger year after year. The opportunity for 'digital intelligence' has arrived, as traditional web analytic techniques were not designed for the breadth of channels, devices, and pace that fuels consumer experiences. In parallel, today's landscape for data visualization, advanced analytics, and our ability to process very large amounts of multi-channel information is changing. The democratization of analytics for the masses is upon us, and marketers have the oppourtunity to take advantage of descriptive, predictive, and (most importantly) prescriptive data-driven insights. This presentation describes how organizations can use SAS^® products, specifically SAS^® Visual Analytics and SAS^® Adaptive Customer Experience, to overcome the limitations of web analytics, and support data-driven integrated marketing objectives.

In the credit card industry, there is a group of people who use credit cards as an interest-free loan by transferring their balances between cards during 0% balance transfer (BT) periods in order to avoid paying interest. These people are called gamers. Gamers generate losses for banks due to their behavior of paying no interest and having no purchases. It is hard to use traditional ways, such as risk scorecards, to identify them since gamers tend to have very good credit histories. This paper uses Naive Bayes classifier to classify gamers into three segments, according to the proportion of gamers. Using this model, the targeting policy and underwriting policy can be significantly improved and the function of tracking the proportion of gamers in population can be realized. This result has been accomplished by using logistic regression in SAS^® combined with a Microsoft Excel pivot table. The procedure is described in detail in this paper.

SAS^® Visual Analytics Designer enables you to create reports with different layouts. There are several basic graph objects that you can include in these reports. What if you wanted to create a report that wasn't possible with one of the out-of-the-box graph objects? No worries! The new SAS^® Visual Analytics Graph Builder available from the SAS^® Visual Analytics home page lets you create a custom graph object using built-in sample data. You can then include these graph objects in SAS Visual Analytics Designer and generate reports using them. Come see how you can create custom graph objects such as stock plots, butterfly charts, and more. These custom objects can be easily shared with others for use in SAS Visual Analytics Designer.

Particle swarm optimization is a heuristic global optimization method that was given by James Kennedy and Russell C. Eberhart in 1995. (James Kennedy and Russell C. Eberhart). The purpose of this paper develops a code for particle swarm optimization in SAS^® 9.2.

Evaluation of the efficacy of an intervention is often complicated because the intervention is not randomly assigned. Usually, interventions in marketing, such as coupons or retention campaigns, are directed at customers because their spending is below some threshold or because the customers themselves make a purchase decision. The presence of nonrandom assignment of the stimulus can lead to over- or underestimating the value of the intervention. This can cause future campaigns to be directed at the wrong customers or cause the impacts of these effects to be over- or understated. This paper gives a brief overview of selection bias, demonstrates how selection in the data can be modeled, and shows how to apply some of the important consistent methods of estimating selection models, including Heckman's two-step procedure, in an empirical example. Sample code is provided in an appendix.

New York City boasts a wide variety of cuisine owing to the rich tourism and the vibrant immigrant population. The quality of food and hygiene maintained at the restaurants serving different cuisines has a direct impact on the people dining in them. The objective of this paper is to build a model that predicts the grade of the restaurants in New York City. It also provides deeper statistical insights into the distribution of restaurants, cuisine categories, grades, criticality of violations, etc., and concludes with the sequence analysis performed on the complete set of violations recorded for the restaurants at different time periods over the years 2012 and 2013. The data for 2013 is used to test the model. The data set consists of 15 variables that capture to restaurant location-specific and violation details. The target is an ordinal variable with three levels, A, B, and C, in descending order of the quality representation. Various SAS^® Enterprise Miner^™ models, logistic regression, decision trees, neural networks, and ensemble models are built and compared using validation misclassification rate. The stepwise regression model appears to be the best model, with prediction accuracy of 75.33%. The regression model is trained at step 3. The number of critical violations at 8.5 gives the root node for the split of the target levels, and the rest of the tree splits are guided by the predictor variables such as number of critical and non-critical violations, number of critical violations for the year 2011, cuisine group, and the borough.

Response rates, churn models, customer lifetime value today's marketing departments are more analytically driven than ever. Marketers have had their heads down developing analytic capabilities for some time. The results have been game-changing. But it's time for marketers to look up and discover which analytic results from other departments can enhance the analytics of marketing. What if you knew the demand forecast for your products? What could you do? What if you understood the price sensitivity for your products? How would this impact the actions that your marketing team takes? Using the hospitality industry as an example, we explore how marketing teams can use the analytic outputs from other departments to get better results overall.

Ebony and Ivory was a number one song by Paul McCartney and Stevie Wonder about making music together, proper integration, unity, and harmony on a deeper level. With SAS^® Visual Analytics, current Enterprise Business Intelligence (BI) customers can rest assured that their years of existing BI work and content can coexist until they can fully transition over to SAS Visual Analytics. This presentation covers 10 inter-operability integration points between SAS^® BI and SAS Visual Analytics.

Given a time series data set, you can use automatic time series modeling software to select an appropriate time series model. You can use various statistics to judge how well each candidate model fits the data (in-sample). Likewise, you can use various statistics to select an appropriate model from a list of candidate models (in-sample or out-of-sample or both). Finally, you can use rolling simulations to evaluate ex-ante forecast performance over several forecast origins. This paper demonstrates how you can use SAS^® Forecast Server Procedures and SAS^® Forecast Studiosoftware to perform the statistical analyses that are related to rolling simulations.

In this interconnected world, it is becoming ever more important to understand not just details about your data, but also how different parts of your data are related to each other. From social networks to supply chains to text analytics, network analysis is becoming a critical requirement and network visualization is one of the best ways to understand the results. The new SAS^® Visual Analytics network visualization shows links between related nodes as well as additional attributes such as color, size, or labels. This paper explains the basic concepts of networks as well as provides detailed background information on how to use network visualizations within SAS Visual Analytics.

This paper shares our experience integrating two leading data analytics and Geographic Information Systems (GIS) software products SAS^® and ArcGIS to provide integrated reporting capabilities. SAS is a powerful tool for data manipulation and statistical analysis. ArcGIS is a powerful tool for analyzing data spatially and presenting complex cartographic representations. Combining statistical data analytics and GIS provides increased insight into data and allows for new and creative ways of visualizing the results. Although products exist to facilitate the sharing of data between SAS and ArcGIS, there are no ready-made solutions for integrating the output of these two tools in a dynamic and automated way. Our approach leverages the individual strengths of SAS and ArcGIS, as well as the report delivery infrastructure of SAS^® Information Delivery Portal.

For decades, mixed models have been used by researchers to account for random sources of variation in regression-type models. Now, they are gaining favor in business statistics for giving better predictions for naturally occurring groups of data, such as sales reps, store locations, or regions. Learn about how predictions based on a mixed model differ from predictions in ordinary regression and see examples of mixed models with business data.

Have you ever seen SAS^® Visual Analytics reports that are somehow more elegant than a standard report? Which qualities make reports easier to navigate, more appealing to the eye, or reveal insights more quickly? These quick tips will reveal several SAS Visual Analytics report design characteristics to help make your reports stand out from the pack. We cover concepts like color palettes, content organization, interactions, labeling, and branding, just to name a few.

When first presented with SAS^® Enterprise Guide^®, many existing SAS^® programmers don't know where to begin. They want to understand, 'What's in it for me?' if they switch over. These longtime users of SAS are accustomed to typing all of their code into the Program Editor window and clicking Submit. This beginning tutorial introduces SAS Enterprise Guide 6.1 to old and new users of SAS who need to code. It points out advantages and tips that demonstrate why a user should be excited about the switch. This tutorial focuses on the key points of a session involving coding and introduces new features. It covers the top three items for a user to consider when switching over to a server-based environment. Attendees will return to the office with a new motivation and confidence to start coding with SAS Enterprise Guide.

Portfolio segmentation is key in all forecasting projects. Not all products are equally predictable. Nestl uses animal names for its segmentation, and the animal behavior translates well into how the planners should plan these products. Mad Bulls are those products that are tough to predict, if we don't know what is causing their unpredictability. The Horses are easier to deal with. Modern time series based statistical forecasting methods can tame Mad Bulls, as they allow to add explanatory variables into the models. Nestl now complements its Demand Planning solution based on SAP with predictive analytics technology provided by SAS^®, to overcome these issues in an industry that is highly promotion-driven. In this talk, we will provide an overview of the relationship Nestl is building with SAS, and provide concrete examples of how modern statistical forecasting methods available in SAS^® Demand-Driven Planning and Optimization help us to increase forecasting performance, and therefore to provide high service to our customers with optimized stock, the primary goal of Nestl 's supply chains.

Retail price setting is influenced by two distinct factors: the regular price and the promotion price. Together, these factors determine the list price for a specific item at a specific time. These data are often reported only as a singular list price. Separating this one price into two distinct prices is critical for accurate price elasticity modeling in retail. These elasticities are then used to make sales forecasts, manage inventory, and evaluate promotions. This paper describes a new time-series feature extraction utility within SAS^® Forecast Server that allows for automated separation of promotional and regular prices.

This presentation is for users who are familiar with SAS^® Enterprise Guide^® but might not be aware of the many useful new features added in versions 4.2 and beyond. For example, SAS Enterprise Guide allows you to: Format your SAS^® source code to make it easier to read. Easily schedule a project to run at a given time. Work with OLAP data in your enterprise. We will overview these and other features to help you become even more productive using this powerful application.

Power producers are looking for ways to not only improve efficiency of power plant assets but also to grow concerns about the environmental impacts of power generation without compromising their market competitiveness. To meet this challenge, this study demonstrates the application of data mining techniques for process optimization in a coal-fired power plant in Thailand with 97,920 data records. The main purpose is to determine which factors have a great impact on both (1) heat rate (kJ/kWh) of electrical energy output and (2) opacity of the flue gas exhaust emissions. As opposed to the traditional regression analysis currently employed at the plant and based on Microsoft Excel, more complex analytical models using SAS^® Enterprise Miner^™ help supporting managerial decision to improve the overall performance of the existing energy infrastructure while reducing emissions through a change in the energy supply structure.

The use, limits, and misuse of statistical models in different industries are propelling new techniques and best practices in forecasting. Until recently, many factors such as data collection and storage constraints, poor data synchronization capabilities, technology limitations, and limited internal analytical expertise have made it impossible to forecast intermittent demand. In addition, integrating consumer demand data (that is, point-of-sale [POS]/syndicated scanner data from ACNielsen/ Information Resources Inc. [IRI]/Intercontinental Marketing Services [IMS]) to shipment forecasts was a challenge. This presentation gives practical how-to advice on intermittent forecasting and outlines a framework, using multi-tiered causal analysis (MTCA), that links demand to supply. The framework uses a process of nesting causal models together by using data and analytics.

Ensemble models combine two or more models to enable a more robust prediction, classification, or variable selection. This paper describes three types of ensemble models: boosting, bagging, and model averaging. It discusses go-to methods, such as gradient boosting and random forest, and newer methods, such as rotational forest and fuzzy clustering. The examples section presents a quick setup that enables you to take fullest advantage of the ensemble capabilities of SAS^® Enterprise Miner^™ by using existing nodes, Start Groups and End Groups nodes, and custom coding.

Today's business needs require 24/7 access to your data in order to perform complex queries to your analytical store. In addition, you might need to periodically update your analytical store to append new data, delete old data, or modify some existing data. Historically, the size of your analytical tables or the window in which the table must be updated can cause unacceptable downtime for queries. This paper describes how you can use new SAS^® Scalable Performance Data Server 5.1 cluster table features to simulate transaction isolation in order to modify large sections of your cluster table. These features are optimized for extremely fast operation and can be done without affecting any on-going queries. This provides both the continuous query access and periodic update requirements for your analytical store that your business model requires.

More organizations are understanding the importance of geo-tagged data and the need for tools that can successfully combine location data with business metrics to provide intelligent outputs that are beyond a simple map. SAS^® Visual Analytics provides a robust and powerful platform for achieving location intelligence performed with a combination of SAS^® Analytics and GIS mapping technologies such as that offered by Esri. This paper describes the essentials for achieving location intelligence and demonstrates with industry examples how SAS Visual Analytics makes it possible.

When creating an OLAP cube, you have the option of specifying a drill-through table, also known as a Show Details table. This quick tip discusses the implications of using your detail table as your drill-through table and explores some viable alternatives.

Analyzing automobile policies and claims is an ongoing area of interest to the insurance industry. Although there have been many data mining projects in insurance sector over the past decade, the following questions How can insurance firms retain their best customers? Will this damaged car be covered and get claim payment? How much of loss of claims associated with this policy will be? do remain as common. This study applies data mining techniques using SAS^® Enterprise Miner^™ to enhance insurance policies and claims. The main focus is on assessing how corporate fleet customers policy characteristics and claim behavior are different from that of non-fleet customers. With more than 100,000 data records, implementing advanced analytics help create better planning for policy and claim management strategy.

Why would a SAS^® administrator need a dashboard? With the evolution of SAS^®9, the SAS administrator s role has dramatically changed. Creating a dashboard on a SAS environment gives the SAS administrator an overview on the environment health, ensures resources are used as predicted, and provides a way to explore. SAS^® Visual Analytics allows you to quickly explore, analyze, and visualize data. So, why not bring the two concepts together? In this session, you will learn tips for designing dashboards, loading what might seem like impossible data, and building visualizations that guide users toward the next level of analysis. Using the dashboard, SAS administrators will learn ways to determine the system health and how to take advantage of external tools, such as the Metacoda software, to find additional insights and explore problem areas.

SAS^® Enterprise Guide^® has become the place through which many SAS^® users access the power of SAS. Some like it, some loathe it, some have never known anything else. In my experience, the following attitudes prevail regarding the product: 1) I don't know what SAS is, but I can use a mouse and I know what my business needs are. 2) I've used SAS before, but now my company has moved to SAS Enterprise Guide and I love it! 3) I've used SAS before, but now my company has done something really stupid. SAS Enterprise Guide offers a place to learn as well as work. The product offers environments for point-and-click for those who want that, and a type-your-code-with-semi-colons environment for those who want that. Even better, a user can mix and match, using the best of both worlds. I show that SAS Enterprise Guide is a great place for building up business solutions using a step-by-step method, how we can make the best of both environments, and how we can dip our toes into parts of SAS that might have frustrated us in the past and made us run away and cry I ll do it in Excel! I demonstrate that there are some very nice aspects to SAS Enterprise Guide, out of the box, that are often ignored but that can improve the overall SAS experience. We look at my personal nemeses, SAS/GRAPH^® and PROC TABULATE, with a side-trip to the mysterious world that is ODS, or the Output Delivery System.

Universities in the UK are now subject to League Table reporting by a range of providers. The criteria used by each League Table differ. Universities, their faculties, and individual subject areas want to understand how the different tables are constructed and calculated, and what is required in order to maximize their position in each league table in order to attract the best students to their institution, thereby maximizing recruitment and student-related income streams. The School of Computing and Maths at the University of Derby is developing the use SAS^® Visual Analytics to analyse each league table to provide actionable insights as to actions that can be taken to improve their relative standing in the league tables and also to gain insights into feasible levels of targets relative to the peer groups of institutions. This paper outlines the approaches taken and some of the critical insights developed that will be of value to other higher education institutions in the UK, and suggests useful approaches that might be valuable in other countries.

This workshop provides hands-on experience using SAS^® Enterprise Miner^™. Workshop participants will do the following: open a project create and explore a data source build and compare models produce and examine score code that can be used for deployment

This workshop provides hands-on experience using SAS^® Forecast Server. Workshop participants will do the following: create a project with a hierarchy generate multiple forecasts automatically evaluate the accuracy of the forecasts build a custom model

This workshop provides hands-on experience using SAS^® Office Analytics. Workshop participants will complete the following tasks: use SAS^® Enterprise Guide^® to access and analyze data create a stored process that can be shared across an organization access and analyze data sources and stored processes using the SAS^® Add-In for Microsoft Office

This workshop provides hands-on experience with SAS^® Visual Analytics. Workshop participants will do the following: explore data with SAS^® Visual Analytics Explorer design reports with SAS^® Visual Analytics Designer

Business analysts commonly use Microsoft Excel with the SAS^® System to answer difficult business questions. While you can use these applications independently of each other to obtain the information you need, you can also combine the power of those applications, using the SAS Output Delivery System (ODS) tagsets, to completely automate the process. This combination delivers a more efficient process that enables you to create fully functional and highly customized Excel worksheets within SAS. This paper starts by discussing common questions and problems that SAS Technical Support receives from users when they try to generate Excel worksheets. The discussion continues with methods for automating Excel worksheets using ODS tagsets and customizing your worksheets using the CSS style engine and extended tagsets. In addition, the paper discusses tips and techniques for moving from the current MSOffice2K and ExcelXP tagsets to the new Excel destination, which generates output in the native Excel 2010 format.

SAS^® Visual Analytics enables you to conduct ad hoc data analysis, visually explore data, develop reports, and then share insights through the web and mobile tablet apps. You can now also share your insights with colleagues using the SAS^® Office Analytics integration with Microsoft Excel, Microsoft Word, Microsoft PowerPoint, Microsoft Outlook, and Microsoft SharePoint. In addition to opening and refreshing reports created using SAS Visual Analytics, a new SAS^® Central view enables you to manage and comment on your favorite and recent reports from your Microsoft Office applications. You can also view your SAS Visual Analytics results in SAS^® Enterprise Guide^®. Learn more about this integration and what's coming in the future in this breakout session.

Global businesses must react to daily changes in market conditions over multiple geographies and industries. Consuming reputable daily economic reports assists in understanding these changing conditions, but requires both a significant human time commitment and a subjective assessment of each topic area of interest. To combat these constraints, Dow's Advanced Analytics team has constructed a process to calculate sentence-level topic frequency and sentiment scoring from unstructured economic reports. Daily topic sentiment scores are aggregated to weekly and monthly intervals and used as exogenous variables to model external economic time series data. These models serve to both validate the relationship between our sentiment scoring process and also as near-term forecasts where daily or weekly variables are unavailable. This paper will first describe our process of using SAS^® Text Miner to import and discover economic topics and sentiment from unstructured economic reports. The next section describes sentiment variable selection techniques that use SAS/STAT^®, SAS/ETS^®, and SAS^® Enterprise Miner^™ to generate similarity measures to economic indices. Our process then uses ARIMAX modeling in SAS^® Forecast Studio to create economic index forecasts with topic sentiments. Finally, we show how the sentiment model components are used as a matrix of economic key performance indicators by topic and geography.

Once upon a time, a writer compared a desert to a labyrinth. A desert has no walls or stairways, but you can still find yourself utterly lost in it. And oftentimes, when you think you found that oasis you were looking for, what you are really seeing is an illusion, a mirage. Similarly, logical fallacies and misleading data patterns can easily deceive the unaware data explorer. In this paper, we discuss how they can be recognized and neutralized with the power of the SAS^® Visual Analytics Explorer. Armed with this knowledge, you will be able to safely navigate the dunes to find true insights and avoid false conclusions.

SAS^® Add-In for Microsoft Office remains a popular tool for people who are not SAS^® programmers due to its easy interface with the SAS servers. In this session, you'll learn some of the many tricks that other organizations use for getting more value out of the tool.

SAS^® Visual Analytics is a unique tool that provides both exploratory and predictive data analysis capabilities. As the visual part of the name suggests, the rendering of this analysis in the form of visuals (crosstabs, line charts, histograms, scatter plots, geo maps, treemaps, and so on) make this a very useful tool. Join me as I walk you down the path of exploring the capabilities of SAS Visual Analytics 6.3, starting with data stored in a desktop application as multiple Microsoft Excel files. Together, we import the data into SAS Visual Analytics, prepare the data using the data builder, load the data into SAS^® LASR™ Analytic Server, explore data, and create reports.

This presentation will teach the audience how to use SAS^® ODS Graphics. Now part of Base SAS^®, ODS Graphics is a great way to easily create clear graphics that enable any user to tell their story well. SGPLOT and SGPANEL are two of the procedures that can be used to produce powerful graphics that used to require a lot of work. The core of the procedures are explained, as well as the options available. Furthermore, we explore the ways to combine the individual statements to make more complex graphics that tell the story better. Any user of Base SAS on any platform will find great value from the SAS ODS Graphics procedures.

Especially in this current financial climate, many of us are being asked to do more with less. For several years, the Office of Institutional Research and Testing at Baylor University has been using SAS^® software to increase the efficiency of the office and of the University as a whole. Reports that were once prepared manually have been automated. Data quality processes have been implemented in order to reduce the number of duplicate mailings. Predictive modeling is used to focus recruiting efforts on those prospective students most likely to respond. A web-based portal has been created to provide self-service report generation for many administrators across campus. Along with this, a number of data processing functions have been centralized, eliminating the need for additional programming skills and software support. This presentation discusses these improvements in more detail and provides examples of the end results.

A European utility company has several thousand service engineers who provide its customers with services that range from performing routine maintenance to handling emergency breakdowns. Each service engineer is assigned to a work area that consists of a set of postal sectors. The company wants to understand how it should configure its work areas to improve customer satisfaction, minimize travel time for its full-time service engineers, and minimize the costs of overtime and subcontractor hours. This paper describes the use of SAS/OR^® optimization procedures to model this problem and configure optimal work areas, and the use of SAS^® Simulation Studio to simulate how the optimal configurations might satisfy the customer service requirements. The experimental results show that the proposed solution can satisfy customer demand within the desired service-time window, with significantly less travel time for the engineers, and with lower overtime and subcontractor costs.