Managing a Profitable Interactive Email Marketing Program: Modeling and Analysis

Despite the popularity of mobile and social media, email continues to be the marketing tool that brings the highest ROI, according to the Direct Marketing Association’s “Power of Direct” (2011) study. An important reason for email marketing’s success is the application of an idea— “Permission Marketing,” which asks marketers to seek consent from customers before sending them messages. Permission-based email marketing seeks to build a two-way interactive communication channel through which customers can engage with firms by expressing their interests, responding to firms’ email messages and making purchases. This thesis consists of two essays that address several key questions that are related to the management of a profitable interactive permission-based email marketing program. Existing research has examined the drivers of customers’ opt-in and opt-out decisions, but it has investigated neither the timings of two decisions nor the influence of transactional activity on the length of time a customer stays with an email program. In the first essay, we adopt a multivariate copula model using a pair-copula construction method to jointly model opt-in time (from a customer’s first purchase to opt-in), opt-out time (from customer opt-in to opt-out) and average transaction amount. Through such multivariate dependences, this model significantly improves the predictive performance of the opt-out time in comparison with several benchmark models. The study offers several important findings (1) marketing intensity affects opt-in and opt-out times (2) customers with certain characteristics are more or less likely to opt-in or opt-out (3) firms can extend customer opt-out time and increase customer spending level by strategically allocating resources. Firms are using email marketing to engage with customers and encourage active transactional behavior. Extant research either focuses only on how customers respond to email messages or looks at the “average” effect of email on transactional behavior. In the second essay, we consider not only customers’ response to emails and their correlated transactional behavior, but also the dynamics that govern the evolving of the two types of customer relationship: email-response and purchase relationships. We model the email open count with a Binomial distribution and the purchase count with a zero-inflated negative binomial model. We capture the dependence between the two discrete distributions using a copula approach. In addition, we develop a hidden Markov model to model the effects of email contacts on purchase behavior. We also allow the relationship that represents customers’ responsiveness to email marketing to evolve flexibly along with the relationship of purchase. In the second essay, we apply the proposed model in a non-contractual context where a retailer operates a large-scale email marketing program. Through the empirical study, we capture a positive dependence between the opening of emails and purchase behavior. We identify three purchase-behavior states along with three email-response states. The empirical finding suggests that the customers who are in the medium relationship state have the highest intrinsic propensity to open an email, followed by the customers in the lowest and highest relationship state. Furthermore, we derive a dynamic email marketing resource allocation policy using the hidden Markov model, the purchase and email open model estimates. We demonstrate that a forward-looking agent could maximize the long-term profits of its existing email subscribers

Is cross-category brand loyalty determined by risk aversion?

The need to understand and leverage consumer-brand bonds has become critical in a marketplace characterized by increasing unpredictability, diminishing product differentiation, and heightened competitive pressure. This is especially true for fast moving consumer goods (FMCG) manufacturers and retailers. Knowing why a customer stays loyal to a brand in multiple product categories is necessary for deriving suitable marketing strategies in the context of a brand extension, yet research on the motives, characteristics, life styles and attitudes of cross-category brand loyal customers has been investigated only in a limited number of studies. We will fill a gap in the literature on cross-category brand choice behavior by analyzing revealed preference data with respect to brand loyalty in several categories in which a brand competes. Provided with purchase and corresponding survey data we investigate the product portfolio of a leading nonfood FMCG brand. We segment consumers on the basis of their revealed brand preferences and, focusing on consumers’ risk aversion, identify cross-category brand loyal customers’ personality traits as determinants of their brand loyal purchase behavior.cross-category brand loyalty, risk aversion, share of category requirements, customer segmentation

Applications of Business Analytics in Marketing: Joint Modeling of Correlated Multivariate Outcomes

In this dissertation I develop a general regression methodology for mixed multivariate outcomes. This methodology extends the generalized linear mixed model paradigm (glmm) to allow for correlated multivariate normal random effects across regression equations for differing outcomes. This methodology, referred to as joint modeling, is particularly useful in business and marketing applications where multiple outcomes of varying data type must be analyzed simultaneously with regression. I apply joint models to binary and continuous measures of customer loyalty in a large multinational survey of car owners. Survey respondents’ word-of-mouth and desire to switch brands were used as proxies for attitudinal loyalty and behavioral loyalty and were modeled as a function of product-related attributes, service-related attributes, marketing activities, and overall satisfaction of both their current car and alternatives together. My findings provide insights into customer loyalty in the context of both experience based loyalty and image based loyalty as well as cross-cultural consumer behavior and confirm the mediating role of satisfaction. Furthermore, I find that brand evaluation based on experience with the current brand, and alternative brand evaluations based on image both significantly affect customers’ overall satisfaction levels with varying degrees of impact. The study also identifies a significant moderating effect of culture between product-related attribute performance, service-related attributes performance, marketing activities, and satisfaction. The association between functional attribute performance and satisfaction is found to be stronger in collectivistic cultures than more individualistic cultures. A second study focuses on gaining a better understanding of the interplay between price promotion and consumption of both hedonic and utilitarian retail grocery items. A joint model relating three key outcomes, loyalty, cross-buy, and trip revenue was fit with price promotion, consumption type, and consumer demographic characteristics as explanatory variables. The findings indicate that in-store deal use is associated with significant store loyalty, variety-seeking behavior, and trip revenue for both hedonic and utilitarian goods. More interestingly, we find that coupon use for utilitarian goods is negatively associated with store-loyalty, cross-buy (variety- seeking), and trip revenue

Essays in Quantitative Risk Management for Financial Regulation of Operational Risk Models

An extensive amount of evolving guidance and rules are provided to banks by financial regulators. A particular set of instructions outline requirements to calculate and set aside loss-absorbing regulatory capital to ensure the solvency of a bank. Mathematical models are typically used by banks to quantify sufficient amounts of capital. In this thesis, we explore areas that advance our knowledge in regulatory risk management. In the first essay, we explore an aspect of operational risk loss modeling using scenario analysis. An actuarial modeling method is typically used to quantify a baseline capital value which is then layered with a judgemental component in order to account for and integrate what-if future potential losses into the model. We propose a method from digital signal processing using the convolution operator that views the problem of the blending of two signals. That is, a baseline loss distribution obtained from the modeling of frequency and severity of internal losses is combined with a probability distribution obtained from scenario responses to yield a final output that integrates both sets of information. In the second essay, we revisit scenario analysis and the potential impact of catastrophic events to that of the enterprise level of a bank. We generalize an algorithm to account for multiple level of intensities of events together with unique loss profiles depending on the business units effected. In the third essay, we investigate the problem of allocating aggregate capital across sub-portfolios in a fair manner when there are various forms of interdependencies. Relevant to areas of market, credit and operational risk, the multivariate shortfall allocation problem quantifies the optimal amount of capital needed to ensure that the expected loss under a convex loss penalty function remains bounded by a threshold. We first provide an application of the existing methodology to a subset of high frequency loss cells. Lastly, we provide an extension using copula models which allows for the modeling of joint fat-tailed events or asymmetries in the underlying process

Innovations in dependence modelling for financial applications

The contribution of this thesis is in developing and investigating novel dependence modelling techniques in financial applications. Furthermore, the aim is to understand the key factors driving the dynamic nature of such dependence. When modelling the multivariate distribution of the returns associated to a portfolio of financial assets one is faced with a multitude of considerations and potential choices. For example, in the currency studies undertaken in this thesis suitably heavy-tailed marginal time series models are developed for the returns of each currency exchange rate, and then the multivariate dependence structure of the returns of multiple-currency baskets at each time instant is considered. These dependence relationships can be studied via numerous concordance measures such as correlation, rank correlations and extremal dependences. Such studies can be undertaken in a static or dynamic setting and either parametrically or non-parametrically. Another important aspect of financial time series is the enormous amount of financial data available for statistical analysis and financial econometrics that can be used to better understand economic and financial theories. In this thesis, the focus is on the influence of dependence structures in complex financial data in two asset classes: currencies and commodities. These are challenging data structures as they contain temporal serial dependence, cross dependence and term-structural dependences. Each of these forms of dependence are studied in this thesis in both parametric and non-parametric settings

Pricing financial and insurance products in the multivariate setting

In finance and insurance there is often the need to construct multivariate distributions to take into account more than one source of risk, where such risks cannot be assumed to be independent. In the course of this thesis we are going to explore three models, namely the copula models, the trivariate reduction scheme and mixtures as candidate models for capturing the dependence between multiple sources of risk. This thesis contains results of three different projects. The first one is in financial mathematics, more precisely on the pricing of financial derivatives (multi-asset options) which depend on multiple underlying assets, where we construct the dependence between such assets using copula models and the trivariate reduction scheme. The second and the third projects are in actuarial mathematics, more specifically on the pricing of the premia that need to be paid by policyholders in the automobile insurance when more than one type of claim is considered. We do the pricing including all the information available about the characteristics of the policyholders and their cars (i.e. a priori ratemaking) and about the numbers of claims per type in which the policyholders have been involved (i.e. a posteriori ratemaking). In both projects we model the dependence between the multiple types of claims using mixture distributions/regression models: we consider the different types of claims to be modelled in terms of their own distribution/regression model but with a common heterogeneity factor which follows a mixing distribution/regression model that is responsible for the dependence between the multiple types of claims. In the second project we present a new model (i.e. the bivariate Negative Binomial-Inverse Gaussian regression model) and in the third one we present a new family of models (i.e. the bivariate mixed Poisson regression models with varying dispersion), both as suitable alternatives to the classically used bivariate mixed Poisson regression models

Comparison of Owned, Earned and Paid Website Visitors: a Case Study

This thesis explores website traffic and visitors by analysing website customer behaviour. The thesis expands the current research on web analytics to consider the rising categorization of media into owned, earned and paid media types. The research is first of its kind to further explore if there is significant difference between owned, earned and paid website visitors measured by web metrics. In addition to academic contributions, it is desired that the research helps marketers and publishers to invest their resources between generating each type of traffic in order to reach their individual goals and maximize the return-on-investment. In this paper, a framework for measuring owned, earned and paid website visitors is created. The research framework is tested in a case study where owned, earned and paid traffic is driven from Facebook to a fashion magazine’s online articles. Data on visitor-level website behavior of 2739 visitors is collected from the case website using Piwik analytics. The data was analyzed using two quantitative methods: chi-square test of homogeneity and one-way analysis of variance. These methods were used in order to determine whether statistically significant differences in website between owned, earned and paid visitor groups exists. Further, the case study demonstrates how to use the framework and appropriate techniques to effectively collect, extract, and analyze website visitor’s web behavior and the differences between owned, earned and paid website visitors. The empirical research reveals that significant differences between different types of website visitors exists. The chi-square test of homogeneity indicated a statistical significant difference of binomial proportions of ‘new / return user rate’, ‘bounce-rate’ and ‘mobile / desktop rate’ variables. One-way ANOVA indicated a statistical significant difference between the means of owned, earned and paid visitors of “visit count” and “actions”, but also a non-significant difference of “visit duration”. Thus also the usability of the research framework is confirmed. This thesis expands the research on clickstream data into social networking and earned media in media and journalism, and so contributes to the existing research on web analytics. This thesis also contributes to the existing literature on owned, earned and paid media and web analytics by adding owned and earned social media exposure to clickstream research and comparing them to paid social media exposure it in assessing user’s behavioral response in a cross-site context. Thus the thesis also combines social marketing with web analytics and expands the use ‘owned’, ‘paid’ and ‘earned’ jointly in a digital environment. This study is also first one to apply ‘heart rate monitoring’ measurement, redefined visit duration and bounce-rate metrics. The thesis provides useful technical and methodological information about website visitor tracking and web metrics for both academics and businesses seeking benefits from web analytics and online channels

Extreme Dependence in Asset Markets Around the Globe

The dependence between large stock returns is higher than the dependence between small to moderate stock returns. This is defined as extreme dependence, and it is particularly observed for large negative returns. Therefore, diversification gains calculated from the overall dependence will overestimate the true potential for diversification during turmoil periods. This thesis answers questions on how the dependence between large negative stock returns can appropriately be modelled. The main conclusions of this thesis read that extreme dependence is often present, can become rather strong, should not be ignored, and shows substantial time-variation. More specifically, extreme dependence shows up as contagion, with small local crashes evolving into more severe crashes. In addition, due to financial globalization, and emerging market liberalization in particular, extreme dependence between regional stock markets has substantially increased. Furthermore, extreme dependence can vary over time by becoming weaker or stronger, but it can also be subject to structural changes, such as a change from symmetric dependence to asymmetric dependence. Using return data at the highest possible level of detail, improves the accuracy of forecasting joint extreme negative returns. Finally, this thesis shows how different econometric techniques can be used for modelling extreme dependence. The use of copulas for financial data is relatively new, therefore a substantial part of this thesis is devoted to new copula models and applications. Other techniques used in this thesis are GARCH, regime-switching, and logit models

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.info:eu-repo/semantics/publishedVersio