Companies that offer subscription-based services (such as telecom and electric utilities) must evaluate the tradeoff between month-to-month (MTM) customers, who yield a high margin at the expense of lower lifetime, and customers who commit to a longer-term contract in return for a lower price. The objective, of course, is to maximize the Customer Lifetime Value (CLV). This tradeoff must be evaluated not only at the time of customer acquisition, but throughout the customer's tenure, particularly for fixed-term contract customers whose contract is due for renewal. In this paper, we present a mathematical model that optimizes the CLV against this tradeoff between margin and lifetime. The model is presented in the context of a cohort of existing customers, some of whom are MTM customers and others who are approaching contract expiration. The model optimizes the number of MTM customers to be swapped to fixed-term contracts, as well as the number of contract renewals that should be pursued, at various term lengths and price points, over a period of time. We estimate customer life using discrete-time survival models with time varying covariates related to contract expiration and product changes. Thereafter, an optimization model is used to find the optimal trade-off between margin and customer lifetime. Although we specifically present the contract expiration case, this model can easily be adapted for customer acquisition scenarios as well.

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In our management and collection area, there was no methodology that provided the optimal number of collection calls to get the customer to make the minimum payment of his or her financial obligation. We wanted to determine the optimal number of calls using the data envelopment analysis (DEA) optimization methodology. Using this methodology, we obtained results that positively impacted the way our customers were contacted. We can maintain a healthy bank and customer relationship, keep management and collection at an operational level, and obtain a more effective and efficient portfolio recovery. The DEA optimization methodology has been successfully used in various fields of manufacturing production. It has solved multi-criteria optimization problems, but it has not been commonly used in the financial sector, especially in the collection area. This methodology requires specialized software, such as SAS^® Enterprise Guide^® and its robust optimization. In this presentation, we present the PROC OPTMODEL and show how to formulate the optimization problem, create the programming, and process the data available.

As organizations strive to do more with fewer resources, many modernize their disparate PC operations to centralized server deployments. Administrators and users share many concerns about using SAS^® on a Microsoft Windows server. This paper outlines key guidelines, plus architecture and performance considerations, that are essential to making a successful transition from PC to server. This paper outlines the five key considerations for SAS customers who will change their configuration from PC-based SAS to using SAS on a Windows server: 1) Data and directory references; 2) Interactive and surrounding applications; 3) Usability; 4) Performance; 5) SAS Metadata Server.

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

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Walt Disney World Resort is home to four theme parks, two water parks, five golf courses, 26 owned-and-operated resorts, and hundreds of merchandise and dining experiences. Every year millions of guests stay at Disney resorts to enjoy the Disney Experience. Assigning physical rooms to resort and hotel reservations is a key component to maximizing operational efficiency and guest satisfaction. Solutions can range from automation to optimization programs. The volume of reservations and the variety and uniqueness of guest preferences across the Walt Disney World Resort campus pose an opportunity to solve a number of reasonably difficult room assignment problems by leveraging operations research techniques. For example, a guest might prefer a room with specific bedding and adjacent to certain facilities or amenities. When large groups, families, and friends travel together, they often want to stay near each other using specific room configurations. Rooms might be assigned to reservations in advance and upon request at check-in. Using mathematical programming techniques, the Disney Decision Science team has partnered with the SAS^® Advanced Analytics R&D team to create a room assignment optimization model prototype and implement it in SAS/OR^®. We describe how this collaborative effort has progressed over the course of several months, discuss some of the approaches that have proven to be productive for modeling and solving this problem, and review selected results.

Investment portfolios and investable indexes determine their holdings according to stated mandate and methodology. Part of that process involves compliance with certain allocation constraints. These constraints are developed internally by portfolio managers and index providers, imposed externally by regulations, or both. An example of the latter is the U.S. Internal Revenue Code (25/50) concentration constraint, which relates to a regulated investment company (RIC). These codes state that at the end of each quarter of a RIC's tax year, the following constraints should be met: 1) No more than 25 percent of the value of the RIC's assets might be invested in a single issuer. 2) The sum of the weights of all issuers representing more than 5 percent of the total assets should not exceed 50 percent of the fund's total assets. While these constraints result in a non-continuous model, compliance with concentration constraints can be formalized by reformulating the model as a series of continuous non-linear optimization problems solved using PROC OPTMODEL. The model and solution are presented in this paper. The approach discussed has been used in constructing investable equity indexes.

Dynamic pricing is a real-time strategy where corporations attempt to alter prices based on varying market demand. The hospitality industry has been doing this for quite a while, altering prices significantly during the summer months or weekends when demand for rooms is at a premium. In recent years, the sports industry has started to catch on to this trend, especially within Major League Baseball (MLB). The purpose of this paper is to explore the methodology of applying this type of pricing to the hockey ticketing arena.

A leading killer in the United States is smoking. Moreover, over 8.6 million Americans live with a serious illness caused by smoking or second-hand smoking. Despite this, over 46.6 million U.S. adults smoke tobacco, cigars, and pipes. The key analytic question in this paper is, How would e-cigarettes affect this public health situation? Can monitoring public opinions of e-cigarettes using SAS^® Text Analytics and SAS^® Visual Analytics help provide insight into the potential dangers of these new products? Are e-cigarettes an example of Big Tobacco up to its old tricks or, in fact, a cessation product? The research in this paper was conducted on thousands of tweets from April to August 2014. It includes API sources beyond Twitter--for example, indicators from the Health Indicators Warehouse (HIW) of the Centers for Disease Control and Prevention (CDC)--that were used to enrich Twitter data in order to implement a surveillance system developed by SAS^® for the CDC. The analysis is especially important to The Office of Smoking and Health (OSH) at the CDC, which is responsible for tobacco control initiatives that help states to promote cessation and prevent initiation in young people. To help the CDC succeed with these initiatives, the surveillance system also: 1) automates the acquisition of data, especially tweets; and 2) applies text analytics to categorize these tweets using a taxonomy that provides the CDC with insights into a variety of relevant subjects. Twitter text data can help the CDC look at the public response to the use of e-cigarettes, and examine general discussions regarding smoking and public health, and potential controversies (involving tobacco exposure to children, increasing government regulations, and so on). SAS^® Content Categorization helps health care analysts review large volumes of unstructured data by categorizing tweets in order to monitor and follow what people are saying and why they are saying it. Ultimatel y, it is a solution intended to help the CDC monitor the public's perception of the dangers of smoking and e-cigarettes, in addition, it can identify areas where OSH can focus its attention in order to fulfill its mission and track the success of CDC health initiatives.

A Chinese wind energy company designs several hundred wind farms each year. An important step in its design process is micrositing, in which it creates a layout of turbines for a wind farm. The amount of energy that a wind farm generates is affected by geographical factors (such as elevation of the farm), wind speed, and wind direction. The types of turbines and their positions relative to each other also play a critical role in energy production. Currently the company is using an open-source software package to help with its micrositing. As the size of wind farms increases and the pace of their construction speeds up, the open-source software is no longer able to support the design requirements. The company wants to work with a commercial software vendor that can help resolve scalability and performance issues. This paper describes the use of the OPTMODEL and OPTLSO procedures on the SAS^® High-Performance Analytics infrastructure together with the FCMP procedure to model and solve this highly nonlinear optimization problem. Experimental results show that the proposed solution can meet the company's requirements for scalability and performance. A Chinese wind energy company designs several hundred wind farms each year. An important step of their design process is micro-siting, which creates a layout of turbines for a wind farm. The amount of energy generated from a wind farm is affected by geographical factors (such as elevation of the farm), wind speed, and wind direction. The types of turbines and their positions relative to each other also play critical roles in the energy production. Currently the company is using an open-source software package to help them with their micro-siting. As the size of wind farms increases and the pace of their construction speeds up, the open-source software is no longer able to support their design requirements. The company wants to work with a commercial software vendor that can help them resolve scalability and performance issues. This pap er describes the use of the FCMP, OPTMODEL, and OPTLSO procedures on the SAS^® High-Performance Analytics infrastructure to model and solve this highly nonlinear optimization problem. Experimental results show that the proposed solution can meet the company's requirements for scalability and performance.

Mathematical optimization is a powerful paradigm for modeling and solving business problems that involve interrelated decisions about resource allocation, pricing, routing, scheduling, and similar issues. The OPTMODEL procedure in SAS/OR^® software provides unified access to a wide range of optimization solvers and supports both standard and customized optimization algorithms. This paper illustrates PROC OPTMODEL's power and versatility in building and solving optimization models and describes the significant improvements that result from PROC OPTMODEL's many new features. Highlights include the recently added support for the network solver, the constraint programming solver, and the COFOR statement, which allows parallel execution of independent solver calls. Best practices for solving complex problems that require access to more than one solver are also demonstrated.

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