Predictive Data Mining: Promising Future and Applications

Predictive analytics is the branch of data mining concerned with the prediction of future probabilities and trends. The central element of predictive analytics is the predictor, a variable that can be measured for an individual or other entity to predict future behavior. For example, an insurance company is likely to take into account potential driving safety predictors such as age, gender, and driving record when issuing car insurance policies. Multiple predictors are combined into a predictive model, which, when subjected to analysis, can be used to forecast future probabilities with an acceptable level of reliability. In predictive modeling, data is collected, a statistical model is formulated, predictions are made and the model is validated (or revised) as additional data becomes available. Predictive analytics are applied to many research areas, including meteorology, security, genetics, economics, and marketing. In this paper, we have done an extensive study on various predictive techniques with all its future directions and applications in various areas are being explaine

Intelligent data analysis approaches to churn as a business problem: a survey

Globalization processes and market deregulation policies are rapidly changing the competitive environments of many economic sectors. The appearance of new competitors and technologies leads to an increase in competition and, with it, a growing preoccupation among service-providing companies with creating stronger customer bonds. In this context, anticipating the customer’s intention to abandon the provider, a phenomenon known as churn, becomes a competitive advantage. Such anticipation can be the result of the correct application of information-based knowledge extraction in the form of business analytics. In particular, the use of intelligent data analysis, or data mining, for the analysis of market surveyed information can be of great assistance to churn management. In this paper, we provide a detailed survey of recent applications of business analytics to churn, with a focus on computational intelligence methods. This is preceded by an in-depth discussion of churn within the context of customer continuity management. The survey is structured according to the stages identified as basic for the building of the predictive models of churn, as well as according to the different types of predictive methods employed and the business areas of their application.Peer ReviewedPostprint (author's final draft

Implementing a bank sales analytics solution and a predictive model for the next best offer

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsIn the banking industry, the quantity of information that is processed is huge. Knowing also that clients are doted with changing needs every time, companies must adapt their approaches to attract clients with the best offers. That can be done by various machine learning and data mining techniques that enable them to understand better the clients. Also, internally, banks should be equipped with fast and efficient processes that enable them to take quickly the best decision. That is why real-time reporting tools should be implemented as an upper layer of the data sources. In this optic, this internship report is presenting 2 ambitious projects that aim to leverage Millennium BCP bank to a greater level in Analytics and Data Science. The first one is about building a Sales Analytics Solution to track weekly sales of retail products in the bank. The second one is about building a mechanism that will help reach to each client’s best adequate product to recommend

The impact of strategy on business analytics success

Business analytics systems are an important strategic investment for many organisations and can potentially contribute significantly to firm performance. In this paper we develop a theoretical model, based on the resource-based view, that explains how business analytics capabilities lead to benefits. We argue that the type of strategy, represented as enterprise architecture, moderates the benefits achieved. Two case studies are then presented, each with a different type of strategy, and we explain how and why benefits were achieved from business analytics systems in each. We then identify the similarities and differences between the two case studies and discuss these using five dimensions that emerge from the case studies: strategic alignment, governance, people, organizational culture and data and technology infrastructure.<br /

Clicking into Mortgage Arrears: A Study into Arrears Prediction with Clickstream Data

This research project investigates the predictive capability of clickstream data when used for the purpose of mortgage arrears prediction. With an ever growing number of people switching to digital channels to handle their daily banking requirements, there is a wealth of ever increasing online usage data, otherwise known as clickstream data. If leveraged correctly, this clickstream data can be a powerful data source for organisations as it provides detailed information about how their customers are interacting with their digital channels. Much of the current literature associated with clickstream data relates to organisations employing it within their customer relationship management mechanisms to build better relationships with their customers. There has been little investigation into the use of clickstream data in credit scoring or arrears prediction. Since the financial meltdown of 2008, financial institutions have being obliged to have mechanisms in place to deal with mortgage accounts which are in arrears or have a risk of entering arrears. A potentially crucial step in this process is the ability of an institution to accurately predict which of their mortgage accounts may enter arrears. In addition to traditional demographical and transactional data, this research determines the impact clickstream data can have on an arrears prediction model. A multitude of binary classifiers were reviewed in this arrears prediction problem. Of these classifiers, ensembles models proved to be the highest performing models achieving reasonably high recall accuracies without the inclusion of clickstream data. Once clickstream data was added to the models, it led to marginal increases in accuracy, which was a positive result

Mobica's internationalisation project : analysis and recommendations for the South American market

The purpose of this company-based project is to provide recommendations on which South American low-cost location should Mobica, an UK-based software engineering and integration services firm, establish its new R&D centre. This will be achieved through developing an international market selection framework which the company can use in future internationalisation studies in order to support the expansion of its global low-cost strategy. The methodology adopted consists of a systematic contractible ranking approach which results in a formal sequential decision-making structure composed of three stages of analysis according to a general consensus in International Market Selection literature: a screening stage, where the initial set of South American countries is reduced to a short-list of countries according to macro-economic criteria; an identification stage, where a specific country is selected from the previous short-list according to micro-economic criteria; and a final selection stage, where the analysis indicates three best-possible city options for location according to the firm’s strategic goals and resource availability. Recommendations are then provided regarding one specific city. This will be achieved through the implementation of an Analytic Hierarchy Process method which allows prioritising each indicator through pairwise comparisons surveys. Data collection methods are mainly focused on collecting secondary data through documentation and evidence from reputable sources such as databases, study reports as well as company insight. Hence, one of this study’s limitations is related to the availability of reliable data for each stage of analysis, particularly the city-level stage. A further study limitation regards the interdependence of the various analysis criteria, which leads to problems in terms of priority interpretation. For future projects, it is advisable that the firm opts for a survey group answer, in order to allow for the combination of different professionals’ opinions but as well as for analysis objectivity. Findings indicate Chile as the best possible location, given the country’s general good performance in every indicator, and the project’s final recommendations address the three cities of Temuco, Santiago and Viña del Mar

The Use of Online Panel Data in Management Research: A Review and Recommendations

Management scholars have long depended on convenience samples to conduct research involving human participants. However, the past decade has seen an emergence of a new convenience sample: online panels and online panel participants. The data these participants provide—online panel data (OPD)—has been embraced by many management scholars owing to the numerous benefits it provides over “traditional” convenience samples. Despite those advantages, OPD has not been warmly received by all. Currently, there is a divide in the field over the appropriateness of OPD in management scholarship. Our review takes aim at the divide with the goal of providing a common understanding of OPD and its utility and providing recommendations regarding when and how to use OPD and how and where to publish it. To accomplish these goals, we inventoried and reviewed OPD use across 13 management journals spanning 2006 to 2017. Our search resulted in 804 OPD-based studies across 439 articles. Notably, our search also identified 26 online panel platforms (“brokers”) used to connect researchers with online panel participants. Importantly, we offer specific guidance to authors, reviewers, and editors, having implications for both micro and macro management scholars

Portfolio Construction: The Efficient Diversification of Marketing Investments

Efforts in the marketing sciences can be distinguished between the analysis of individual customers and the examination of portfolios of customers, giving scarce theoretical guidance concerning the strategic allocation of promotional investments. Yet, strategic asset allocation is considered in financial economics theory to be the most important set of investment decisions. The problem addressed in this study was the application of strategic asset allocation theory from financial economics to marketing science with the aim of improving the financial results of investment in direct marketing promotions. This research investigated the components of efficient marketing portfolio construction which include multiattribute numerical optimization, stochastic Brownian motion, peer index tracking schemes, and data mining methods to formulate unique investable asset classes. Three outcomes resulted from this study on optimal diversification: (a) reduced saturative promotional activities balancing inefficient advertising cost and enterprise revenue objectives to achieve an investment equilibrium state; (b) the use of utility theory to assist in the lexicographic ordering of goal priorities; and (c) the solution approach to a multiperiod linear goal program with stochastic extensions. A performance test using a large archival set of customer data illustrated the benefits of efficient portfolio construction. The test asset allocation resulted in significantly more reward than that of the benchmark case. The results of this grounded theory study may be of interest to marketing researchers, operations research practitioners, and functional marketing executives. The social change implication is increased efficiency in allocation of large advertising budgets resulting in improved corporate performance