Location information plays a big role in business data. Everything that happens in a business happens somewhere, whether it s sales of products in different regions or crimes that happened in a city. Business analysts typically use the historic data that they have gathered for years for analysis. One of the most important pieces of data that can help answer more questions qualitatively, is the demographic data along with the business data. An analyst can match the sales or the crimes with the population metrics like gender, age groups, family income, race, and other pieces of information, which are part of the demographic data, for better insight. This paper demonstrates how a business analyst can bring the demographic and lifestyle data from Esri into SAS^® Visual Analytics and join the data with business data. The integration of SAS Visual Analytics with Esri allows this to happen. We demonstrate different methods of accessing Esri demographic data from SAS Visual Analytics. We also demonstrate how you can use custom shape files and integrate with Esri Portal for ArcGIS.

Data that are gathered in modern data collection processes are often large and contain geographic information that enables you to examine how spatial proximity affects the outcome of interest. For example, in real estate economics, the price of a housing unit is likely to depend on the prices of housing units in the same neighborhood or nearby neighborhoods, either because of their locations or because of some unobserved characteristics that these neighborhoods share. Understanding spatial relationships and being able to represent them in a compact form are vital to extracting value from big data. This paper describes how to glean analytical insights from big data and discover their big value by using spatial econometric methods in SAS/ETS^® software.

For all business analytics projects big or small, the results are used to support business or managerial decision-making processes, and many of them eventually lead to business actions. However, executives or decision makers are often confused and feel uninformed about contents when presented with complicated analytics steps, especially when multi-processes or environments are involved. After many years of research and experiment, a web reporting framework based on SAS^® Stored Processes was developed to smooth the communication between data analysts, researches, and business decision makers. This web reporting framework uses a storytelling style to present essential analytical steps to audiences, with dynamic HTML5 content and drill-down and drill-through functions in text, graph, table, and dashboard formats. No special skills other than SAS^® programming are needed for implementing a new report. The model-view-controller (MVC) structure in this framework significantly reduced the time needed for developing high-end web reports for audiences not familiar with SAS. Additionally, the report contents can be used to feed to tablet or smartphone users. A business analytical example is demonstrated during this session. By using this web reporting framework based on SAS Stored Processes, many existing SAS results can be delivered more effectively and persuasively on a SAS^® Enterprise BI platform.

Detection and adjustment of structural breaks are an important step in modeling time series and panel data. In some cases, such as studying the impact of a new policy or an advertising campaign, structural break analysis might even be the main goal of a data analysis project. In other cases, the adjustment of structural breaks is a necessary step to achieve other analysis objectives, such as obtaining accurate forecasts and effective seasonal adjustment. Structural breaks can occur in a variety of ways during the course of a time series. For example, a series can have an abrupt change in its trend, its seasonal pattern, or its response to a regressor. The SSM procedure in SAS/ETS^® software provides a comprehensive set of tools for modeling different types of sequential data, including univariate and multivariate time series data and panel data. These tools include options for easy detection and adjustment of a wide variety of structural breaks. This paper shows how you can use the SSM procedure to detect and adjust structural breaks in many different modeling scenarios. Several real-world data sets are used in the examples. The paper also includes a brief review of the structural break detection facilities of other SAS/ETS procedures, such as the ARIMA, AUTOREG, and UCM procedures.

Applying solutions for recommending products to final customers in e-commerce is already a known practice. Crossing consumer profile information with their behavior tends to generate results that are more than satisfactory for the business. Natura's challenge was to create the same type of solution for their sales representatives in the platform used for ordering. The sales representatives are not buying for their own consumption, but rather are ordering according to the demands of their customers. That is the difference, because in this case the analysts does not have information about the behavior or preferences of the final client. By creating a basket product concept for their sales representatives, Natura developed a new solution. Natura developed an algorithm using association analysis (Market Basket) and implemented this directly in the sales platform using SAS^® Real-Time Decision Manager. Measuring the results in indications conversion (products added in the requests), the amount brought in by the new solution was 53% higher than indications that used random suggestions, and 38% higher than those that used business rules.

Creating an environment that enables and empowers self-service and agile analytic capabilities requires a tremendous amount of working together and extensive agreements between IT and the business. Business and IT users are struggling to know what version of the data is valid, where they should get the data from, and how to combine and aggregate all the data sources to apply analytics and deliver results in a timely manner. All the while, IT is struggling to supply the business with more and more data that is becoming available through many different data sources such as the Internet, sensors, the Internet of Things, and others. In addition, once they start trying to join and aggregate all the different types of data, the manual coding can be very complicated and tedious, can demand extraneous resources and processing, and can negatively impact the overhead on the system. If IT enables agile analytics in a data lab, it can alleviate many of these issues, increase productivity, and deliver an effective self-service environment for all users. This self-service environment using SAS^® analytics in Teradata has decreased the time required to prepare the data and develop the statistical data model, and delivered faster results in minutes compared to days or even weeks. This session discusses how you can enable agile analytics in a data lab, leverage SAS analytics in Teradata to increase performance, and learn how hundreds of organizations have adopted this concept to deliver self-service capabilities in a streamlined process.

Tracking gains or losses from the purchase and sale of diverse equity holdings depends in part on whether stocks sold are assumed to be from the earliest lots acquired (a first-in, first-out queue, or FIFO queue) or the latest lots acquired (a last-in, first-out queue, or LIFO queue). Other inventory tracking applications have a similar need for application of either FIFO or LIFO rules. This presentation shows how a collection of simple ordered hash objects, in combination with a hash-of-hashes, is a made-to-order technique for easy data-step implementation of FIFO, LIFO, and other less likely rules (for example, HIFO [highest-in, first-out] and LOFO [lowest-in, first-out]).

Would you like to be more confident in producing graphs and figures? Do you understand the differences between the OVERLAY, GRIDDED, LATTICE, DATAPANEL, and DATALATTICE layouts? Finally, would you like to learn the fundamental Graph Template Language methods in a relaxed environment that fosters questions? Great this topic is for you! In this hands-on workshop, you are guided through the fundamental aspects of the GTL procedure, and you can try fun and challenging SAS^® graphics exercises to enable you to more easily retain what you have learned.

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Do you need to add annotations to your graphs? Do you need to specify your own colors on the graph? Would you like to add Unicode characters to your graph, or would you like to create templates that can also be used by non-programmers to produce the required figures? Great, then this topic is for you! In this hands-on workshop, you are guided through the more advanced features of the GTL procedure. There are also fun and challenging SAS^® graphics exercises to enable you to more easily retain what you have learned.

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When analyzing data with SAS^®, we often use the SAS DATA step and the SQL procedure to explore and manipulate data. Though they both are useful tools in SAS, many SAS users do not fully understand their differences, advantages, and disadvantages and thus have numerous unnecessary biased debates on them. Therefore, this paper illustrates and discusses these aspects with real work examples, which give SAS users deep insights into using them. Using the right tool for a given circumstance not only provides an easier and more convenient solution, it also saves time and work in programming, thus improving work efficiency. Furthermore, the illustrated methods and advanced programming skills can be used in a wide variety of data analysis and business analytics fields.

Linear regression, which is widely used, can be improved by the inclusion of the penalizing parameter. This helps reduce variance (at the cost of a slight increase in bias) and improves prediction accuracy and model interpretability. The regularization model is implemented on the sample data set, and recommendations for the practice are included.

Every organization, from the most mature to a day-one start-up, needs to grow organically. A deep understanding of internal customer and operational data is the single biggest catalyst to develop and sustain the data. Advanced analytics and big data directly feed into this, and there are best practices that any organization (across the entire growth curve) can adopt to drive success. Analytics teams can be drivers of growth. But to be truly effective, key best practices need to be implemented. These practices include in-the-weeds details, like the approach to data hygiene, as well as strategic practices, like team structure and model governance. When executed poorly, business leadership and the analytics team are unable to communicate with each other they talk past each other and do not work together toward a common goal. When executed well, the analytics team is part of the business solution, aligned with the needs of business decision-makers, and drives the organization forward. Through our engagements, we have discovered best practices in three key areas. All three are critical to analytics team effectiveness. 1) Data Hygiene 2) Complex Statistical Modeling 3) Team Collaboration

As a retailer, have you ever found yourself reviewing your last season's assortment and wondering, What should I have carried in my assortment ? You are constantly faced with the challenge of product selection, placement, and ensuring your assortment will drive profitable sales. With millions of consumers, thousands of products, and hundreds of locations, this question can often times be challenging and overwhelming. With the rise in omnichannel, traditional approaches just won't cut it to gain the insights needed to maximize and manage localized assortments as well as increase customer satisfaction. This presentation explores applications of analytics within marketing and merchandising to drive assortment curation as well as relevancy for customers. The use of analytics can not only increase efficiencies but can also give insights into what you should be buying, how best to create a profitable assortment, and how to engage with customers in-season to drive their path to purchase. Leveraging an analytical infrastructure to infuse analytics into the assortment management process can help retailers achieve customer-centric insights, in a way that is easy to understand, so that retailers can quickly take insights to actions and gain the competitive edge.

This paper establishes the conceptualization of the dimension of the shopping cart (or market basket) on apparel retail websites. It analyzes how the cart dimension (describing anonymous shoppers) and the customer dimension (describing non-anonymous shoppers) impact merchandise return behavior. Five data-mining techniques-namely logistic regression, decision tree, neural network, gradient boosting, and support vector machine-are used for predicting the likelihood of merchandise return. The target variable is a dichotomous response variable: return vs not return. The primary input variables are conceptualized as constituents of the cart dimension, derived from engineering merchandise-related variables such as item style, item size, and item color, as well as free-shipping-related thresholds. By further incorporating the constituents of the customer dimension such as tenure, loyalty membership, and purchase histories, the predictive accuracy of the model built using each of the five data-mining techniques was found to improve substantially. This research also highlights the relative importance of the constituents of the cart and customer dimensions governing the likelihood of merchandise return. Recommendations for possible applications and research areas are provided.

Optimizing delivery routes and efficiently using delivery drivers are examples of classic problems in Operations Research, such as the Traveling Salesman Problem. In this paper, Oberweis and Zencos collaborate to describe how to leverage SAS/OR^® procedures to solve these problems and optimize delivery routes for a retail delivery service. Oberweis Dairy specializes in home delivery service that delivers premium dairy products directly to customers homes. Because freshness is critical to delivering an excellent customer experience, Oberweis is especially motivated to optimize their delivery logistics. As Oberweis works to develop an expanding footprint and a growing business, Zencos is helping to ensure that delivery routes are optimized and delivery drivers are used efficiently.

Whether you are a current SAS^® Marketing Optimization user who wants to fine tune your scenarios, a SAS^® Marketing Automation user who wants to understand more about how SAS Marketing Optimization might improve your campaigns, or completely new to the world of marketing optimizations, this session covers ideas and insights for getting the highest strategic impact out of SAS Marketing Optimization. SAS Marketing Optimization is powerful analytical software, but like all software, what you get out is largely predicated by what you put in. Building scenarios is as much an art as it is a science, and how you build those scenarios directly impacts your results. What questions should you be asking to establish the best objectives? What suppressions should you consider? We develop and compare multiple what-if scenarios and discuss how to leverage SAS Marketing Optimization as a business decisioning tool in order to determine the best scenarios to deploy for your campaigns. We include examples from various industries including retail, financial services, telco, and utilities. The following topics are discussed in depth: establishing high-impact objectives, with an emphasis on setting objectives that impact organizational key performance indicators (KPIs); performing and interpreting sensitivity analysis; return on investment (ROI); evaluating opportunity costs; and comparing what-if scenarios.

Mediation analysis is a statistical technique for investigating the extent to which a mediating variable transmits the relation of an independent variable to a dependent variable. Because it is useful in many fields, there have been rapid developments in statistical mediation methods. The most cutting-edge statistical mediation analysis focuses on the causal interpretation of mediated effect estimates. Cause-and-effect inferences are particularly challenging in mediation analysis because of the difficulty of randomizing subjects to levels of the mediator (MacKinnon, 2008). The focus of this paper is how incorporating longitudinal measures of the mediating and outcome variables aides in the causal interpretation of mediated effects. This paper provides useful SAS^® tools for designing adequately powered studies to detect the mediated effect. Three SAS macros were developed using the powerful but easy-to-use REG, CALIS, and SURVEYSELECT procedures to do the following: (1) implement popular statistical models for estimating the mediated effect in the pretest-posttest control group design; (2) conduct a prospective power analysis for determining the required sample size for detecting the mediated effect; and (3) conduct a retrospective power analysis for studies that have already been conducted and a required sample to detect an observed effect is desired. We demonstrate the use of these three macros with an example.

In 2012, US Customs scanned nearly 4% and physically inspected less than 1% of the 11.5 million cargo containers that entered the United States. Laundering money through trade is one of the three primary methods used by criminals and terrorists. The other two methods used to launder money are using financial institutions and physically moving money via cash couriers. The Financial Action Task Force (FATF) roughly defines trade-based money laundering (TBML) as disguising proceeds from criminal activity by moving value through the use of trade transactions in an attempt to legitimize their illicit origins. As compared to other methods, this method of money laundering receives far less attention than those that use financial institutions and couriers. As countries have budget shortfalls and realize the potential loss of revenue through fraudulent trade, they are becoming more interested in TBML. Like many problems, applying detection methods against relevant data can result in meaningful insights, and can result in the ability to investigate and bring to justice those perpetuating fraud. In this paper, we apply TBML red flag indicators, as defined by John A. Cassara, against shipping and trade data to detect and explore potentially suspicious transactions. (John A. Cassara is an expert in anti-money laundering and counter-terrorism, and author of the book Trade-Based Money Laundering. ) We use the latest detection tool in SAS^® Viya , along with SAS^® Visual Investigator.

Ensemble models have become increasingly popular in boosting prediction accuracy over the last several years. Stacked ensemble techniques combine predictions from multiple machine learning algorithms and use these predictions as inputs to a second level-learning algorithm. This paper shows how you can generate a diverse set of models by various methods (such as neural networks, extreme gradient boosting, and matrix factorizations) and then combine them with popular stacking ensemble techniques, including hill-climbing, generalized linear models, gradient boosted decision trees, and neural nets, by using both the SAS^® 9.4 and SAS^® Visual Data Mining and Machine Learning environments. The paper analyzes the application of these techniques to real-life big data problems and demonstrates how using stacked ensembles produces greater prediction accuracy than individual models and na ve ensembling techniques. In addition to training a large number of models, model stacking requires the proper use of cross validation to avoid overfitting, which makes the process even more computationally expensive. The paper shows how to deal with the computational expense and efficiently manage an ensemble workflow by using parallel computation in a distributed framework.

Every sourcing and procurement department has limited resources to use for realizing productivity (cost savings). In practice, many organizations simply schedule yearly pricing negotiations with their main suppliers. They do not deviate from that approach unless there is a very large swing in the underlying commodity. Using cost data gleaned from previous quotes and SAS^® Enterprise Guide^®, we can put in place a program and methodology that move the practice from gut instinct to quantifiable and justifiable models that can easily be updated on a monthly basis. From these updated models, we can print a report of suppliers or categories that we should consider for cost downs, and suppliers or categories that we should work on to hold current pricing. By having all cost models, commodity data, and reporting functions within SAS Enterprise Guide, we are able to not only increase the precision and effectiveness of our negotiations, but also to vastly decrease the load of repetitive work that has been traditionally placed on supporting analysts. Now the analyst can execute the program, send the initial reports to the management team, and be leveraged for other projects and tasks. Moreover, the management team can have confidence in the analysis and the recommended plan of action.

Data is a valuable corporate asset that, when managed improperly, can detract from a company's ability to achieve strategic goals. At 1-800-Flowers.com, Inc. (18F), we have embarked on a journey toward data governance through embracing Master Data Management (MDM). Along the path, we've recognized that in order to protect and increase the value of our data, we must take data quality into consideration at all aspects of data movement in the organization. This presentation discusses the ways that SAS^® Data Management is being leveraged by the team at 18F to create and enhance our data quality strategy to ensure data quality for MDM.

Increasingly, customers are using social media and other Internet-based applications such as review sites and discussion boards to voice their opinions and express their sentiments about brands. Such spontaneous and unsolicited customer feedback can provide brand managers with valuable insights about competing brands. There is a general consensus that listening to and reacting to the voice of the customer is a vital component of brand management. However, the unstructured, qualitative, and textual nature of customer data that is obtained from customers poses significant challenges for data scientists and business analysts. In this paper, we propose a methodology that can help brand managers visualize the competitive structure of a market based on an analysis of customer perceptions and sentiments that are obtained from blogs, discussion boards, review sites, and other similar sources. The brand map is designed to graphically represent the association of product features with brands, thus helping brand managers assess a brand's true strengths and weaknesses based on the voice of customers. Our multi-stage methodology uses the principles of topic modeling and sentiment analysis in text mining. The results of text mining are analyzed using correspondence analysis to graphically represent the differentiating attributes of each brand. We empirically demonstrate the utility of our methodology by using data collected from Edmunds.com, a popular review site for car buyers.