This paper describes an effective real-time contextual marketing system based on a successful case implemented in a private communication company in Chile. Implementing real-time cases is becoming a major challenge due to stronger competition, which generates an increase of churn and higher operational costs, among other issues. All of these can have an enormous effect on revenue and profit. A set of predictive machine learning models can help to improve response rates of outbound campaigns, but it s not enough to be more proactive in this business. Our real-time system for contextual marketing uses the two SAS^® solutions: SAS^® Event Stream Processing and SAS^® Real-Time Decision Manager, which are connected in cascade. In this configuration, SAS Event Stream Processing can read massive amounts of data from call detail records (CDRs) and antennas, and SAS Real-Time Decision Manager receives the resulting golden events, which trigger the right responses. Time elapsed from the detection of a golden event until a response is processed is approximately 5 seconds. Since implementing seven use cases of this real-time system, the results show an average augmentation in revenue of two million dollars in a testing period of four months, thus returning the investment in a short-term period. The implementation of this system has changed the way Telef nica Chile generates value from big data. Moreover, an outstanding, long-term working relationship between Telef nica Chile and SAS has been started.

Many communication channels exist for customers to engage with businesses, yet an interactive voice response (IVR) system remains the most critical of them. The reason is is because IVR acts as the front end to consumer interaction and is the most effective method for customers to do business with companies in order to resolve their issues before talking to an agent. If the IVR interface is not designed properly, customers can be stuck in an endless loop of pressing buttons that can lead to consumer annoyance. The bottom line is: An IVR system should be set up to quickly resolve as many routine inbound inquires as possible and to allow customers to speak to an agent when necessary. In order to accomplish this, the IVR interface has to be optimized so that it is fully effective and provides a great customer experience. This paper demonstrates how SAS^® tools helped optimize the IVR system of a book publishing company. The data set used in this study was obtained from a telecom services company and contained IVR logs of more than 300,000 calls with 1.4 million observations. To gain insights into customer behaviors, path analysis was performed on this data using SAS^® Enterprise Miner and obstacles faced by customers were identified. This helped in determining underperforming prompts, and analysis using SAS procedures was conducted on such prompts. Prompts tuning was recommended and new self-service areas were identified that avoid transfers and can save clients thousands of dollars in investments in call centers.

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.

Airbnb is the world's largest home-sharing company and has over 800,000 listings in more than 34,000 cities and 190 countries. Therefore, the pricing of their property, done by the Airbnb hosts, is crucial to the business. Setting low prices during a high-demand period might hinder profits, while setting high prices during a low-demand period might result in no bookings at all. In this paper, we suggest a price recommendation methodology for Airbnb hosts that helps in overcoming the problems of overpricing and underpricing. Through this methodology, we try to identify key factors related to Airbnb pricing: factors influential in determining a price for a property; the relation between the price of a property and the frequency of its booking; and similarities among successful and profitable properties. The constraints outlined in the analysis were entered into SAS^® optimization procedures to achieve a best possible price. As a part of this methodology, we built a scraping tool to get details of New York City host user data along with their metrics. Using this data, we build a pricing model to predict the optimal price of an Airbnb home.

With the 2014 launch of SAS^® University Edition, the reach of SAS^® was greatly expanded to educators, students, researchers, non-profits, and the curious, who for the first time could use a full version of Base SAS^® software for free. Because SAS University Edition allows a maximum of two CPUs, however, performance is curtailed sharply from more substantial SAS environments that can benefit from parallel and distributed processing, such as environments that implement SAS^® Grid Manager, Teradata, or Hadoop solutions. Even when comparing performance of SAS University Edition against the most straightforward implementation of the SAS windowing environment, the SAS windowing environment demonstrates greater performance when run on the same computer. With parallel processing and distributed computing becoming the status quo in SAS production environments, SAS University Edition will unfortunately fall behind counterpart SAS solutions if it cannot harness parallel processing best practices and performance. To curb this disparity, this session introduces groundbreaking programmatic methods that enable commodity hardware to be networked so that multiple instances of SAS University Edition can communicate and work collectively to divide and conquer complex tasks. With parallel processing facilitated, a SAS practitioner can now harness an endless number of computers to produce blitzkrieg solutions with the SAS University Edition that rival the performance of more costly, complex infrastructure.

A Middle Eastern company is responsible for daily distribution of over 230 million liters of oil products. For this distribution network, a failure scenario is defined as occurring when oil transport is interrupted or slows down, and/or when product demands fluctuate outside the normal range. Under all failure scenarios, the company plans to provide additional transport capacity at minimum cost so as to meet all point-to-point product demands. Currently, the company uses a wait-and-see strategy, which carries a high operating cost and depends on the availability of third-party transportation. This paper describes the use of the OPTMODEL procedure to implement a mixed integer programming model to model and solve this problem. Experimental results are provided to demonstrate the utility of this approach. It was discovered that larger instances of the problem, with greater numbers of potential failure scenarios, can become computationally extensive. In order to efficiently handle such instances of the problem, we have also implemented a Benders decomposition algorithm in PROC OPTMODEL.

The challenge is to assign outbound calling agents in a telemarketing campaign to geographic districts. The districts have a variable number of leads, and each agent needs to be assigned entire districts with the total number of leads being as close as possible to a specified number for each of the agents (usually, but not always, an equal number). In addition, there are constraints concerning the distribution of assigned districts across time zones, in order to maximize productivity and availability. The SAS/OR^® CLP procedure solves the problem by formulating the challenge as a constraint satisfaction problem (CSP). Our use of PROC CLP places the actual leads within a specified percentage of the target number.