From the Hands of an Early Adopter's Avatar to Virtual Junkyards: Analysis of Virtual Goods' Lifetime Survival

One of the major questions in the study of economics, logistics, and business forecasting is the measurement and prediction of value creation, distribution, and lifetime in the form of goods. In "real" economies, a perfect model for the circulation of goods is impossible. However, virtual realities and economies pose a new frontier for the broad study of economics, since every good and transaction can be accurately tracked. Therefore, models that predict goods' circulation can be tested and confirmed before their introduction to "real life" and other scenarios. The present study is focused on the characteristics of early-stage adopters for virtual goods, and how they predict the lifespan of the goods. We employ machine learning and decision trees as the basis of our prediction models. Results provide evidence that the prediction of the lifespan of virtual objects is possible based just on data from early holders of those objects. Overall, communication and social activity are the main drivers for the effective propagation of virtual goods, and they are the most expected characteristics of early adopters.Comment: 28 page

Competences of IT Architects

The field of architecture in the digital world uses a plethora of terms to refer to different kinds of architects, and recognises a confusing variety of competences that these architects are required to have. Different service providers use different terms for similar architects and even if they use the same term, they may mean something different. This makes it hard for customers to know what competences an architect can be expected to have.\ud \ud This book combines competence profiles of the NGI Platform for IT Professionals, The Open Group Architecture Framework (TOGAF), as well as a number of Dutch IT service providers in a comprehensive framework. Using this framework, the book shows that notwithstanding a large variety in terminology, there is convergence towards a common set of competence profiles. In other words, when looking beyond terminological differences by using the framework, one sees that organizations recognize similar types of architects, and that similar architects in different organisations have similar competence profiles. The framework presented in this book thus provides an instrument to position architecture services as offered by IT service providers and as used by their customers.\ud \ud The framework and the competence profiles presented in this book are the main results of the special interest group “Professionalisation” of the Netherlands Architecture Forum for the Digital World (NAF). Members of this group, as well as students of the universities of Twente and Nijmegen have contributed to the research on which this book is based

Segmentation of life insurance customers based on their profile using fuzzy clustering

In the current competitive environment, companies will be able to adjust business strategies, they use market segmentation based on practical ways rather than using traditional approaches or incomplete and impractical mass marketing. In recent years, mining has gained attention and popularity in the business world. The goal of data mining projects is to convert the raw data into useful information. Clustering can also be used to explore differences in attitudes and intentions of the clients. In this study, we used fuzzy clustering on 1071 life insurance customers during March to October 2014. Results show that the optimal number of clusters was 2 which were named as "investment" and "life safety". Some suggestions are presented to improve the performance of the insurance company

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

Next best action – a data-driven marketing approach

Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsThe Next Best Action (NBA) is a framework that is built in order to assign to each client three (or more) actions that are considered to be the best actions to perform with the client. These actions can range from product offering to pro-active retention actions and upselling recommendations. It can be a useful tool to generate leads for ongoing campaigns but also an excellent tool for analysis and a driver for the creation of new campaigns, being a key element in Customer Relationship Management (CRM) as a Data-Driven Marketing approach. Initially planned as a joint collaboration between a Bank and an Insurance Company to improve the Bancassurance business model, three versions of the NBA were built with the first two being tested on a campaign setting showing promising results. The last version, NBA 3.0, later became a sole project of the Insurance Company due to GPDR compliance policies and due to time constraints could not be evaluated

Multiclass Classification of Motor Insurance Customers in Portugal

The insurance market is highly competitive. To stay in line with other companies in today's world, it is not enough for a company to have the best price. The most important move now is to make a personalized offer to each client. Insurance companies have an enormous amount of data that can be used to understand their customers better. What do they want? What offer would attract new clients, and what offer would keep existing customers from leaving? The project aims to classify customers’ profiles based on their individual preferences in motor insurance.Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytic

Predicting fraud behaviour in online betting

Project Work presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and ManagementFraud isn’t a new issue, there are discussions about this matter since the beginning of commerce. With the advance of the Internet this technique gained strain and became a billion-dollar business. There are many different types of online financial fraud: account takeover; identity theft; chargeback; credit card transactions; etc. Online betting is one of the markets where fraud is increasing every day. In Portugal, the regulation of gambling and online betting was approved in April 2015. In one hand, this legislation made possible the exploration of this business in a controlled and regulated environment, but on the other hand it encouraged customers to develop new ways of fraud. Traditional data analysis used to detect fraud involved different domains such as economics, finance and law. The complexity of these investigations soon became obsolete. Being fraud an adaptive crime, different areas such as Data Mining and Machine Learning were developed to identify and prevent fraud. The main goal of this Project is to develop a predicting model, using a data mining approach and machine learning methods, able to identify and prevent online financial fraud on the Portuguese Betting Market, a new but already strong business

Database Marketing In Travel And Tourism

An increasing number of organisations are developing customer databases in a bid to get closer to their customers and gain competitive advantage. This report investigates the practice of database marketing among different travel and tourism sectors, including airlines, hotels, museums and tour operators, and draws on UK and international examples. It compares direct marketing and database marketing and examines the different levels of sophistication at which database marketing can be practiced, the role of customer loyalty schemes, the ways in which a database can be segmented, the role of consumer data profiling companies and current developments in database marketing. The use of database marketing for customer retention and business acquisition is also investigated. In order to ensure true customer relationship building it is vital for the industry to leverage the information on their databases and provide customer recognition through the delivery of personalised service. Business acquisition through customer retention is likely to be a key strategy in future through the use of data-mining and cross-selling techniques. The report concludes that organisations must create a new marketing environment by moving away from transaction marketing towards the principles of customer relationship management