Improving the profitability of direct marketing : a quantile regression approach

Direct marketing is to target consumers who are most likely to respond. A number of target selection methods have been employed to select potential customers. These methods either only consider the customer response probability and ignore the profit issue or assume that the estimates of profit are homogenous across customers when considering the expected amount of profit. Furthermore, the traditional analytical techniques based on ordinary least squares (OLS) regression, which focus on the average customer, cannot examine the differences of various customer groups or account for customer heterogeneity in profitability estimates. Quantile regression, instead of the point estimate for the conditional mean, can be used to estimate the whole distribution, especially the upper tail which we are interested in. Quantile regression does not have strict model assumptions as OLS does and is not sensitive to outliers. To model consumer response profit in direct marketing, this thesis tested the endogeneity bias in the recency, frequency, monetary value (RFM) variables using the control function approach, made sample selection bias correction using Heckman’s procedure, and then adopted quantile regression to estimate customer profit and make forecast of the profit distribution of future values. Furthermore, we adopted the recentered influence function (RIF) regression methods proposed by Firpo et al. (2007) to perform unconditional quantile regression for customer profit estimation. The comparison of OLS, conditional and unconditional quantile regression shows that while OLS may induce possible misleading estimation results, conditional and unconditional quantile regression can provide more informative estimation results. The findings can help direct marketers augment the profitability of marketing campaigns and have meaningful implications for solving target marketing forecasting problems given the constraint of limited resources

Suffolk University Graduate Academic Catalog, College of Arts and Sciences and Sawyer Business School, 2013-2014

This catalog contains information for the graduate programs in the College of Arts and Sciences and the Sawyer Business School. The catalog is a pdf version of the Suffolk website, so many pages have repeated information and links in the document will not work. The catalog is keyword searchable by clicking ctrl+f. A-Z course descriptions are also included here as separate pdf files containing lists of CAS and SBS courses. Please contact the Archives if you need assistance navigating this catalog or finding information on degree requirements or course descriptions.https://dc.suffolk.edu/cassbs-catalogs/1167/thumbnail.jp

Suffolk University Graduate Academic Catalog, College of Arts and Sciences and Sawyer Business School, 2014-2015

This catalog contains information for the graduate programs in the College of Arts and Sciences and the Sawyer Business School. The catalog is a pdf version of the Suffolk website, so many pages have repeated information and links in the document will not work. The catalog is keyword searchable by clicking ctrl+f. A-Z course descriptions are also included here as separate pdf files with lists of CAS and SBS courses. Please contact the Archives if you need assistance navigating this catalog or finding information on degree requirements or course descriptions.https://dc.suffolk.edu/cassbs-catalogs/1169/thumbnail.jp

Customer metrics and their impact on financial performance

The need to understand the relationships among customer metrics and profitability has never been more critical. These relationships are pivotal to tracking and justifying firms’ marketing expenditures, which have come under increasing pressure. The objective of this paper is to integrate existing knowledge and research about the impact of customer metrics on firms’ financial performance. We investigate both unobservable or perceptual customer metrics (e.g., customer satisfaction) and observable or behavioral metrics (e.g., customer retention and lifetime value). We begin with an overview of unobservable and observable metrics, showing how they have been measured and modeled in research. We next offer nine empirical generalizations about the linkages between perceptual and behavioral metrics and their impact on financial performance. We conclude the paper with future research challenges

A conceptual framework for the direct marketing process using business intelligence

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Direct marketing is becoming a key strategy for organisations to develop and maintain strong customer relationships. This method targets specific customers with personalised advertising and promotional campaigns in order to help organisations increase campaign responses and to get a higher return on their investments. There are, however, many issues related to direct marketing, ranging from the highly technical to the more organisational and managerial aspects. This research focuses on the organisational and managerial issues of the direct marketing process and investigates the stages, activities and technologies required to effectively execute direct marketing. The direct marketing process integrates a complex collection of marketing concepts and business analytics principles, which form an entirely ‘self-contained’ choice for organisations. This makes direct marketing a significantly difficult process to perform. As a result, many scholars have attempted to tackle the complexity of executing the direct marketing process. However, most of their research efforts did not consider an integrated information system platform capable of effectively supporting the direct marketing process. This research attempts to address the above issues by developing a conceptual framework for the Direct Marketing Process with Business Intelligence (DMP-BI). The conceptual framework is developed using the identified marketing concepts and business analytics principles for the direct marketing process. It also proposes Business Intelligence (BI) as an integrated information system platform to effectively execute the direct marketing process. In order to evaluate and illustrate the practicality and impact of the DMP-BI framework, this thesis adopts a case study approach. Three case studies have been carried out in different industries including retailing, telecommunication and higher education. The aim of the case studies is also to demonstrate the usage of the DMP-BI framework within an organisational context. Based on the case studies’ findings, this thesis compares the DMP-BI framework with existing rival methodologies. The comparisons provide clear indications of the DMP-BI framework’s benefits over existing rival methodologies

The use of direct current distribution systems in delivering scalable charging infrastructure for battery electric vehicles

The use of low voltage direct current (LVDC) distribution is becoming recognised as a technology enabler that can be used to efficiently network native DC generators with DC loads, offer improved power sharing capabilities, reduce power system material resource requirements and enhance the performance of variable speed machinery. Practical deployment opportunities for LVDC range from small-scale microgrids in the context of energy for development to sophisticated, modern building-level power distribution systems for commercial office spaces, manufacturing applications and industrial processes. However, the incumbent AC distribution system benefits from existing technical product and safety standards, which makes the early adoption of LVDC systems challenging from a risk and cost perspective. Concurrently, the demand for native DC loads such as Battery Electric Transportation Systems is growing. This is especially significant in the area of private electric vehicles (EVs), taxis and buses, but the prospect of electric trucks, ferries and shortrange aircraft are also tangible opportunities. The success of this electric transport revolution depends on several factors, one of which is the availability of battery charging infrastructure that can cost effectively integrate with the existing electrical network, deliver adequate energy transfer rates and adapt to the rapid technical development of this industry. This thesis explores the application of two, novel LVDC distribution systems for the development of scalable EV charging networks; where charging infrastructure has the ability to scale with increasing EV adoption and has a lower risk of becoming a stranded asset in the future. The modelling is supported by real, rapid DC charger utilisation data from the national charging network in Scotland, comprising over 192 chargers and 400,000 charging events. During the work of this thesis, it was found that a combined heat and power (CHP) system can economically support short duration charging scenarios by providing additional power capacity in a congested electrical grid. In this case the highest system efficiency and Net Present Value (NPV) is achieved with a fuel cell directly connected to the DC charging network, compared to other gas reciprocating CHP options. Furthermore, the proposition of a reconfigurable LVDC charging network, interfaced to the public AC distribution network, reduces the capital outlay, offers a higher NPV and improved scalability compared to other charging solutions. For charging system designers and operators, it was found that rapid DC chargers can be classified by specific locations, each possessing a distinct Gaussian arrival pattern and Gamma distribution for charging energy delivered.The use of low voltage direct current (LVDC) distribution is becoming recognised as a technology enabler that can be used to efficiently network native DC generators with DC loads, offer improved power sharing capabilities, reduce power system material resource requirements and enhance the performance of variable speed machinery. Practical deployment opportunities for LVDC range from small-scale microgrids in the context of energy for development to sophisticated, modern building-level power distribution systems for commercial office spaces, manufacturing applications and industrial processes. However, the incumbent AC distribution system benefits from existing technical product and safety standards, which makes the early adoption of LVDC systems challenging from a risk and cost perspective. Concurrently, the demand for native DC loads such as Battery Electric Transportation Systems is growing. This is especially significant in the area of private electric vehicles (EVs), taxis and buses, but the prospect of electric trucks, ferries and shortrange aircraft are also tangible opportunities. The success of this electric transport revolution depends on several factors, one of which is the availability of battery charging infrastructure that can cost effectively integrate with the existing electrical network, deliver adequate energy transfer rates and adapt to the rapid technical development of this industry. This thesis explores the application of two, novel LVDC distribution systems for the development of scalable EV charging networks; where charging infrastructure has the ability to scale with increasing EV adoption and has a lower risk of becoming a stranded asset in the future. The modelling is supported by real, rapid DC charger utilisation data from the national charging network in Scotland, comprising over 192 chargers and 400,000 charging events. During the work of this thesis, it was found that a combined heat and power (CHP) system can economically support short duration charging scenarios by providing additional power capacity in a congested electrical grid. In this case the highest system efficiency and Net Present Value (NPV) is achieved with a fuel cell directly connected to the DC charging network, compared to other gas reciprocating CHP options. Furthermore, the proposition of a reconfigurable LVDC charging network, interfaced to the public AC distribution network, reduces the capital outlay, offers a higher NPV and improved scalability compared to other charging solutions. For charging system designers and operators, it was found that rapid DC chargers can be classified by specific locations, each possessing a distinct Gaussian arrival pattern and Gamma distribution for charging energy delivered

A conceptual framework for the direct marketing process using business intelligence

Direct marketing is becoming a key strategy for organisations to develop and maintain strong customer relationships. This method targets specific customers with personalised advertising and promotional campaigns in order to help organisations increase campaign responses and to get a higher return on their investments. There are, however, many issues related to direct marketing, ranging from the highly technical to the more organisational and managerial aspects. This research focuses on the organisational and managerial issues of the direct marketing process and investigates the stages, activities and technologies required to effectively execute direct marketing. The direct marketing process integrates a complex collection of marketing concepts and business analytics principles, which form an entirely ‘self-contained’ choice for organisations. This makes direct marketing a significantly difficult process to perform. As a result, many scholars have attempted to tackle the complexity of executing the direct marketing process. However, most of their research efforts did not consider an integrated information system platform capable of effectively supporting the direct marketing process. This research attempts to address the above issues by developing a conceptual framework for the Direct Marketing Process with Business Intelligence (DMP-BI). The conceptual framework is developed using the identified marketing concepts and business analytics principles for the direct marketing process. It also proposes Business Intelligence (BI) as an integrated information system platform to effectively execute the direct marketing process. In order to evaluate and illustrate the practicality and impact of the DMP-BI framework, this thesis adopts a case study approach. Three case studies have been carried out in different industries including retailing, telecommunication and higher education. The aim of the case studies is also to demonstrate the usage of the DMP-BI framework within an organisational context. Based on the case studies’ findings, this thesis compares the DMP-BI framework with existing rival methodologies. The comparisons provide clear indications of the DMP-BI framework’s benefits over existing rival methodologies.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

SIS 2017. Statistics and Data Science: new challenges, new generations

The 2017 SIS Conference aims to highlight the crucial role of the Statistics in Data Science. In this new domain of ‘meaning’ extracted from the data, the increasing amount of produced and available data in databases, nowadays, has brought new challenges. That involves different fields of statistics, machine learning, information and computer science, optimization, pattern recognition. These afford together a considerable contribute in the analysis of ‘Big data’, open data, relational and complex data, structured and no-structured. The interest is to collect the contributes which provide from the different domains of Statistics, in the high dimensional data quality validation, sampling extraction, dimensional reduction, pattern selection, data modelling, testing hypotheses and confirming conclusions drawn from the data

Factors Influencing Customer Satisfaction towards E-shopping in Malaysia

Online shopping or e-shopping has changed the world of business and quite a few people have decided to work with these features. What their primary concerns precisely and the responses from the globalisation are the competency of incorporation while doing their businesses. E-shopping has also increased substantially in Malaysia in recent years. The rapid increase in the e-commerce industry in Malaysia has created the demand to emphasize on how to increase customer satisfaction while operating in the e-retailing environment. It is very important that customers are satisfied with the website, or else, they would not return. Therefore, a crucial fact to look into is that companies must ensure that their customers are satisfied with their purchases that are really essential from the ecommerce’s point of view. With is in mind, this study aimed at investigating customer satisfaction towards e-shopping in Malaysia. A total of 400 questionnaires were distributed among students randomly selected from various public and private universities located within Klang valley area. Total 369 questionnaires were returned, out of which 341 questionnaires were found usable for further analysis. Finally, SEM was employed to test the hypotheses. This study found that customer satisfaction towards e-shopping in Malaysia is to a great extent influenced by ease of use, trust, design of the website, online security and e-service quality. Finally, recommendations and future study direction is provided. Keywords: E-shopping, Customer satisfaction, Trust, Online security, E-service quality, Malaysia