Three Revenue Prediction Models for United States Casinos Utilizing Competition and Site Attribute Variables PDF Download

Author: David Michael Pearlman
Publisher:
ISBN:
Category : Casinos
Languages : en
Pages : 458

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Author:
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 608

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Author: Omar Moufakkir
Publisher:
ISBN:
Category : Casinos
Languages : en
Pages : 594

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Author:
Publisher:
ISBN:
Category : Gambling
Languages : en
Pages : 212

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Author: Andrew Pearson
Publisher:
ISBN: 9781981764495
Category :
Languages : en
Pages : 444

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Book Description
The Predictive Casino is a casino that utilizes the latest technological developments to connect with its customers to deliver an exceptional personalized experience that will keep them coming back. Today, technology such as AI, Machine Learning, Augmented Reality, facial recognition, IoT, Real-time stream processing, social media, and wearables are altering the Customer Experience (CX) landscape and casino operators need to jump aboard this fast moving technology or run the risk of being left out in the cold. The Predictive Casino reveals how these and other technologies can help shape the customer journey as well as provide insights into every facet of the business. The book details how the five types of analytics-descriptive, diagnostic, predictive, prescriptive, and edge analytics-affect not only the customer journey, but also just about every department in a casino operation. Facial recognition technology can spot a customer stepping onto a casino bus at the Macau/China border and that can set off alerts which will notify he necessary departments, where that is a host on the floor, a restaurant manager, a dealer, or even the hotel's GM should the player be a high-end VIP. A whole other sequence of events can get triggered as the player's favorite table is prepared, his favorite meal is cooked, and his or her Theo gets added to a real-time table games revenue management model that takes into account his personal play. Labor needs are also considered and alerts can be sent to the required or unneeded staff. An IoT connected casino can make its operations smart. Connected devices can help with inventory optimization, supply chain management, labor management, and waste management. With machine learning algorithms, data centers can be kept green and their energy use smart. For a casino operator, social media is no longer a vanity platform, but rather a place to both connect with current customers as well as court new ones. It is also a powerful branding channel that can be utilized to both understand a casino's position in the market, as well as a place to benchmark its position against its competitors. Today, technology moves at break-neck speed and it can offer the power of deep customer understanding and insight, but it also comes with a confusing variety of technology and technological terms--Big Data, Cognitive Computing, CX, Data Lakes, Hadoop, Kafka, Personalization, Spark, etc., etc. The Predictive Casino will help make sense of it all, so that a casino executive can cut through the confusing clutters of technological jargons and understand why a Spark-based real-time stream processing data stream might be preferable over a TIBCO Streambase one, or an even IBM InfoSphere one. Or maybe not. This book will help casino executives break through the technological clutter so that they can deliver an unrivaled customer experience to each and every patron coming through their doors.

Author: Omer Artun
Publisher: John Wiley & Sons
ISBN: 1119037336
Category : Business & Economics
Languages : en
Pages : 217

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Make personalized marketing a reality with this practical guide to predictive analytics Predictive Marketing is a predictive analytics primer for organizations large and small, offering practical tips and actionable strategies for implementing more personalized marketing immediately. The marketing paradigm is changing, and this book provides a blueprint for navigating the transition from creative- to data-driven marketing, from one-size-fits-all to one-on-one, and from marketing campaigns to real-time customer experiences. You'll learn how to use machine-learning technologies to improve customer acquisition and customer growth, and how to identify and re-engage at-risk or lapsed customers by implementing an easy, automated approach to predictive analytics. Much more than just theory and testament to the power of personalized marketing, this book focuses on action, helping you understand and actually begin using this revolutionary approach to the customer experience. Predictive analytics can finally make personalized marketing a reality. For the first time, predictive marketing is accessible to all marketers, not just those at large corporations — in fact, many smaller organizations are leapfrogging their larger counterparts with innovative programs. This book shows you how to bring predictive analytics to your organization, with actionable guidance that get you started today. Implement predictive marketing at any size organization Deliver a more personalized marketing experience Automate predictive analytics with machine learning technology Base marketing decisions on concrete data rather than unproven ideas Marketers have long been talking about delivering personalized experiences across channels. All marketers want to deliver happiness, but most still employ a one-size-fits-all approach. Predictive Marketing provides the information and insight you need to lift your organization out of the campaign rut and into the rarefied atmosphere of a truly personalized customer experience.

Author: Olivia Parr-Rud
Publisher: SAS Institute
ISBN: 1629593273
Category : Business & Economics
Languages : en
Pages : 182

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Book Description
This tutorial for data analysts new to SAS Enterprise Guide and SAS Enterprise Miner provides valuable experience using powerful statistical software to complete the kinds of business analytics common to most industries. This beginnner's guide with clear, illustrated, step-by-step instructions will lead you through examples based on business case studies. You will formulate the business objective, manage the data, and perform analyses that you can use to optimize marketing, risk, and customer relationship management, as well as business processes and human resources. Topics include descriptive analysis, predictive modeling and analytics, customer segmentation, market analysis, share-of-wallet analysis, penetration analysis, and business intelligence. --

Author: William M. Bolstad
Publisher: John Wiley & Sons
ISBN: 1118593227
Category : Mathematics
Languages : en
Pages : 608

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"...this edition is useful and effective in teaching Bayesian inference at both elementary and intermediate levels. It is a well-written book on elementary Bayesian inference, and the material is easily accessible. It is both concise and timely, and provides a good collection of overviews and reviews of important tools used in Bayesian statistical methods." There is a strong upsurge in the use of Bayesian methods in applied statistical analysis, yet most introductory statistics texts only present frequentist methods. Bayesian statistics has many important advantages that students should learn about if they are going into fields where statistics will be used. In this third Edition, four newly-added chapters address topics that reflect the rapid advances in the field of Bayesian statistics. The authors continue to provide a Bayesian treatment of introductory statistical topics, such as scientific data gathering, discrete random variables, robust Bayesian methods, and Bayesian approaches to inference for discrete random variables, binomial proportions, Poisson, and normal means, and simple linear regression. In addition, more advanced topics in the field are presented in four new chapters: Bayesian inference for a normal with unknown mean and variance; Bayesian inference for a Multivariate Normal mean vector; Bayesian inference for the Multiple Linear Regression Model; and Computational Bayesian Statistics including Markov Chain Monte Carlo. The inclusion of these topics will facilitate readers' ability to advance from a minimal understanding of Statistics to the ability to tackle topics in more applied, advanced level books. Minitab macros and R functions are available on the book's related website to assist with chapter exercises. Introduction to Bayesian Statistics, Third Edition also features: Topics including the Joint Likelihood function and inference using independent Jeffreys priors and join conjugate prior The cutting-edge topic of computational Bayesian Statistics in a new chapter, with a unique focus on Markov Chain Monte Carlo methods Exercises throughout the book that have been updated to reflect new applications and the latest software applications Detailed appendices that guide readers through the use of R and Minitab software for Bayesian analysis and Monte Carlo simulations, with all related macros available on the book's website Introduction to Bayesian Statistics, Third Edition is a textbook for upper-undergraduate or first-year graduate level courses on introductory statistics course with a Bayesian emphasis. It can also be used as a reference work for statisticians who require a working knowledge of Bayesian statistics.

Author: Carlos Andre Reis Pinheiro
Publisher: John Wiley & Sons
ISBN: 1118347609
Category : Business & Economics
Languages : en
Pages : 256

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Employ heuristic adjustments for truly accurate analysis Heuristics in Analytics presents an approach to analysis that accounts for the randomness of business and the competitive marketplace, creating a model that more accurately reflects the scenario at hand. With an emphasis on the importance of proper analytical tools, the book describes the analytical process from exploratory analysis through model developments, to deployments and possible outcomes. Beginning with an introduction to heuristic concepts, readers will find heuristics applied to statistics and probability, mathematics, stochastic, and artificial intelligence models, ending with the knowledge applications that solve business problems. Case studies illustrate the everyday application and implication of the techniques presented, while the heuristic approach is integrated into analytical modeling, graph analysis, text analytics, and more. Robust analytics has become crucial in the corporate environment, and randomness plays an enormous role in business and the competitive marketplace. Failing to account for randomness can steer a model in an entirely wrong direction, negatively affecting the final outcome and potentially devastating the bottom line. Heuristics in Analytics describes how the heuristic characteristics of analysis can be overcome with problem design, math and statistics, helping readers to: Realize just how random the world is, and how unplanned events can affect analysis Integrate heuristic and analytical approaches to modeling and problem solving Discover how graph analysis is applied in real-world scenarios around the globe Apply analytical knowledge to customer behavior, insolvency prevention, fraud detection, and more Understand how text analytics can be applied to increase the business knowledge Every single factor, no matter how large or how small, must be taken into account when modeling a scenario or event—even the unknowns. The presence or absence of even a single detail can dramatically alter eventual outcomes. From raw data to final report, Heuristics in Analytics contains the information analysts need to improve accuracy, and ultimately, predictive, and descriptive power.

Author: Paul C. Cozby
Publisher: WCB/McGraw-Hill
ISBN:
Category : Psychology
Languages : en
Pages : 300

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Book Description
For undergradute social science majors. A textbook on the interpretation and use of research. Annotation copyright Book News, Inc. Portland, Or.