PREDICTING CROSS-GAMING PROPENSITY USING E-CHAID ANALYSIS

Cross-selling different types of games could provide an opportunity for casino operators to generate additional time and money spent on gaming from existing patrons. One way to identify the patrons who are likely to cross-play is mining individual players’ gaming data using predictive analytics. Hence, this study aims to predict casino patrons’ propensity to play both slots and table games, also known as cross-gaming, by applying a data-mining algorithm to patrons’ gaming data. The Exhaustive Chi-squared Automatic Interaction Detector (E-CHAID) method was employed to predict cross-gaming propensity. The E-CHAID models based on the gaming-related behavioral data produced actionable model accuracy rates for classifying cross-gamers and non-cross gamers along with the cross-gaming propensity scores for each patron. Using these scores, casino managers can accurately identify likely cross-gamers and develop a more targeted approach to market to them. Furthermore, the results of this study would enable casino managers to estimate incremental gaming revenues through cross-gaming. This, in turn, will assist them in spending marketing dollars more efficiently while maximizing gaming revenues

Disposition of Federally Owned Surpluses

PDZ domains are scaffolding modules in protein-protein interactions that mediate numerous physiological functions by interacting canonically with the C-terminus or non-canonically with an internal motif of protein ligands. A conserved carboxylate-binding site in the PDZ domain facilitates binding via backbone hydrogen bonds; however, little is known about the role of these hydrogen bonds due to experimental challenges with backbone mutations. Here we address this interaction by generating semisynthetic PDZ domains containing backbone amide-to-ester mutations and evaluating the importance of individual hydrogen bonds for ligand binding. We observe substantial and differential effects upon amide-to-ester mutation in PDZ2 of postsynaptic density protein 95 and other PDZ domains, suggesting that hydrogen bonding at the carboxylate-binding site contributes to both affinity and selectivity. In particular, the hydrogen-bonding pattern is surprisingly different between the non-canonical and canonical interaction. Our data provide a detailed understanding of the role of hydrogen bonds in protein-protein interactions

Study of Relationship between Behavioral Characteristics and Demographic of Customers and Their Expected Advantages in Smart Cellphone Market

The aim of the present research is to study relationship between behavioral characteristics and demographic of customers and their expected advantages in smart cellphone market. The period of investigation is 2015 and its locations are cellphone stores and software and hardware repair shops in Bandar Abbas. So, 270 samples selected for analysis using questionnaire by two-phase sampling. Questionnaire contents validity studied, improved and finally confirmed by marketing and management professors in PHD degree, questionnaire reliability obtained using 0.879 Chronbach Alpha. One-way variance analysis test, Chi-squar independence Kruskal Wallis used to analyze behavioral and demographic characteristics. 15 factors selected as customer’s expected advantages include: phone vitals, longevity and durability of the phone, phone operation system, price, security, auxiliary facilities, quality, software feature, phone connectivity, memory, ease of use, the ability to capture and view photos and videos, after-sales service and processor 3 parts of market identified by cluster analysis implementation on extracted factors each part has its own characteristics. Clusters are different based on variables such as material status, employment status number and income of family members, education, loyalty and consumption; but there was not any difference between clusters in terms of sex and age. Keywords: customers’ expected advantages smart cellphones, behavioral characteristics, demographic characteristic

A participatory action research study of key account management changes

Pure Participatory Action Research projects in the IMP research tradition are rather rare. This paper describes both the process and the outcomes of such a project carried out for a major business to retail firm in the UK. The issue at hand was, and is, Key Account Management, defined in a very broad way. The process is one of changing the ways in which the actors in the firm at different levels work together to try to coordinate the long term strategy and short term operations in relation to powerful retail customers. The outcomes for the firm have, so far, been very positive. The outcomes for the researchers are too early to fully evaluate but look very promising

Transactional versus Relational Customer Orientation: Developing a Segmentation Tool in the French Banking Industry An exploratory study

The authors conduct an exploratory study in order to develop a measurement scale of customers' transactional/ relational orientation. The study is implemented in the context of French industry in both B.-to-C. and B.-to-B. environments. They show that transactional/ relational orientation can be measured following four dimensions: affective, technical, short-term and long-term dimensions. This scale is the first in this field and further research is necessary in order to improve its applications and functions. Moreover, this work remains limited in application to the French banking industry.Transactional orientation; Relational Orientation; Segmentation Tool; French Banking Industry

TAXONOMY DEVELOPMENT IN INFORMATION SYSTEMS: DEVELOPING A TAXONOMY OF MOBILE APPLICATIONS

The complexity of the information systems field often lends itself to classification schemes, or taxonomies, which provide ways to understand the similarities and differences among objects under study. Developing a taxonomy, however, is a complex process that is often done in an ad hoc way. This research-in-progress paper uses the design science paradigm to develop a systematic method for taxonomy development in information systems. The method we propose uses an indicator or operational level model that combines both empirical to deductive and deductive to empirical approaches. We evaluate this method by using it to develop a taxonomy of mobile applications, which we have chosen because of their ever-increasing number and variety. The resulting taxonomy contains seven dimensions with fifteen characteristics. We demonstrate the usefulness of this taxonomy by analyzing a range of current and proposed mobile applications. From the results of this analysis we identify combinations of characteristics where applications are missing and thus are candidates for new and potentially useful applications.taxonomy, design science, mobile application

Rude waiter but mouthwatering pastries! An exploratory study into Dutch aspect-based sentiment analysis

The fine-grained task of automatically detecting all sentiment expressions within a given document and the aspects to which they refer is known as aspect-based sentiment analysis. In this paper we present the first full aspect-based sentiment analysis pipeline for Dutch and apply it to customer reviews. To this purpose, we collected reviews from two different domains, i.e. restaurant and smartphone reviews. Both corpora have been manually annotated using newly developed guidelines that comply to standard practices in the field. For our experimental pipeline we perceive aspect-based sentiment analysis as a task consisting of three main subtasks which have to be tackled incrementally: aspect term extraction, aspect category classification and polarity classification. First experiments on our Dutch restaurant corpus reveal that this is indeed a feasible approach that yields promising results

Buyer Prediction Through Machine Learning

Targeted marketing has grown in popularity in recent years, as well as recognizing when a consumer will desire a commodity may be extremely important to a business. Predicting this demand, however, is a complex procedure. Businesses, promoters/marketers, and sellers are using machine learning approaches to execute buyer prediction. This study focuses on when a customer would buy fast-moving retail merchandise by evaluating a customer’s purchase history at partner vendors. The projections should be used to customize special discounts for customers who are about to make a purchase. In addition, buying behavior is a set of consumption habits that can be analyzed to help in predicting the needs of specific target audience. Knowing consumption habits, business is much more likely to formulate sales items tailored to the market. Thus, the chances of success and acceptance of products and services increase. Promotional offers can then be supplied to the most relevant clients (with alerts sent directly to buyers’ mobile devices) thus reducing the use of the traditional/general paper-based marketing. More specifically, I will create a machine learning model that predicts potential future buyers based on the supplied market dataset. I will use a data source that gathers clients’ consumer history to establish a solid basis for this approach. The study focuses on consumer groupings rather than individual purchasers to forecast purchasing. After analyzing which of these purchase behaviors fits the consumer\u27s decision-making of a product or service, it will be easy to establish appropriate/focused marketing and sales strategies