The importance of econometrics in the analytics toolkit is increasing every day. Econometric modeling helps uncover structural relationships in observational data. This paper highlights the many recent changes to the SAS/ETS^® portfolio that increase your power to explain the past and predict the future. Examples show how you can use Bayesian regression tools for price elasticity modeling, use state space models to gain insight from inconsistent time series, use panel data methods to help control for unobserved confounding effects, and much more.

Soon after the advent of the SAS^® hash object in SAS^® 9.0, its early adopters realized that the potential functionality of the new structure is much broader than basic 0(1)-time lookup and file matching. Specifically, they went on to invent methods of data aggregation based on the ability of the hash object to quickly store and update key summary information. They also demonstrated that the DATA step aggregation using the hash object offered significantly lower run time and memory utilization compared to the SUMMARY/MEANS or SQL procedures, coupled with the possibility of eliminating the need to write the aggregation results to interim data files and the programming flexibility that allowed them to combine sophisticated data manipulation and adjustments of the aggregates within a single step. Such developments within the SAS user community did not go unnoticed by SAS R&D, and for SAS^® 9.2 the hash object had been enriched with tag parameters and methods specifically designed to handle aggregation without the need to write the summarized data to the PDV host variable and update the hash table with new key summaries, thus further improving run-time performance. As more SAS programmers applied these methods in their real-world practice, they developed aggregation techniques fit to various programmatic scenarios and ideas for handling the hash object memory limitations in situations calling for truly enormous hash tables. This paper presents a review of the DATA step aggregation methods and techniques using the hash object. The presentation is intended for all situations in which the final SAS code is either a straight Base SAS DATA step or a DATA step generated by any other SAS product.

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

SAS^® Environment Manager helps SAS^® administrators and system administrators manage SAS resources and effectively monitor the environment. SAS Environment Manager provides administrators with a centralized location for accessing and monitoring the SAS^® Customer Intelligence environment. This enables administrators to identify problem areas and to maintain an in-depth understanding of the day-to-day activities on the system. It is also an excellent way to predict the usage and growth of the environment for scalability. With SAS Environment Manager, administrators can set up monitoring for CI logs (for example, SASCustIntelCore6.3.log, SASCustIntelStudio6.3.log) and other general logs from the SAS^® Intelligence Platform. This paper contains examples for administrators who support SAS Customer Intelligence to set up this type of monitoring. It provides recommendations for approaches and for how to interpret the results from SAS Environment Manager.

Predictive analytics has been widely studied in recent years, and it has been applied to solve a wide range of real-world problems. Nevertheless, current state-of-the-art predictive analytics models are not well aligned with managers' requirements in that the models fail to include the real financial costs and benefits during the training and evaluation phases. Churn predictive modeling is one of those examples in which evaluating a model based on a traditional measure such as accuracy or predictive power does not yield the best results when measured by investment per subscriber in a loyalty campaign and the financial impact of failing to detect a real churner versus wrongly predicting a non-churner as a churner. In this paper, we propose a new financially based measure for evaluating the effectiveness of a voluntary churn campaign, taking into account the available portfolio of offers, their individual financial cost, and the probability of acceptance depending on the customer profile. Then, using a real-world churn data set, we compared different cost-insensitive and cost-sensitive predictive analytics models and measured their effectiveness based on their predictive power and cost optimization. The results show that using a cost-sensitive approach yields to an increase in profitability of up to 32.5%.

Organisations find SAS^® upgrades and migration projects come with risk, costs, and challenges to solve. The benefits are enticing new software capabilities such as SAS^® Visual Analytics, which help maintain your competitive advantage. An interesting conundrum. This paper explores how to evaluate the benefits and plan the project, as well as how the cloud option impacts modernisation. The author presents with the experience of leading numerous migration and modernisation projects from the leading UK SAS Implementation Partner.

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.

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.

Companies are increasingly relying on analytics as the right solution to their problems. In order to use analytics and create value for the business, companies first need to store, transform, and structure the data to make it available and functional. This paper shows a successful business case where the extraction and transformation of the data combined with analytical solutions were developed to automate and optimize the management of the collections cycle for a TELCO company (DIRECTV Colombia). SAS^® Data Integration Studio is used to extract, process, and store information from a diverse set of sources. SAS Information Map is used to integrate and structure the created databases. SAS^® Enterprise Guide^® and SAS^® Enterprise Miner™ are used to analyze the data, find patterns, create profiles of clients, and develop churn predictive models. SAS^® Customer Intelligence Studio is the platform on which the collection campaigns are created, tested, and executed. SAS^® Web Report Studio is used to create a set of operational and management reports.

Managing and organizing external files and directories play an important part in our data analysis and business analytics work. A good file management system can streamline project management and file organizations and significantly improve work efficiency . Therefore, under many circumstances, it is necessary to automate and standardize the file management processes through SAS^® programming. Compared with managing SAS files via PROC DATASETS, managing external files is a much more challenging task, which requires advanced programming skills. This paper presents and discusses various methods and approaches to managing external files with SAS programming. The illustrated methods and skills can have important applications in a wide variety of analytic work fields.