Edward C. Malthouse

The Impact of New Media on Customer Relationships

Recent years have witnessed the rise of new media channels such as Facebook, YouTube, Google, and Twitter, which enable customers to take a more active role as market players and reach (and be reached by) almost everyone anywhere and anytime. These new media threaten long established business models and corporate strategies, but also provide ample opportunities for growth through new adaptive strategies. This paper introduces a new ‘‘pinball’’ framework of new media’s impact on relationships with customers and identifies key new media phenomena which companies should take into account when managing their relationships with customers in the new media universe. For each phenomenon, we identify challenges for researchers and managers which relate to (a) the understanding of consumer behavior, (b) the use of new media to successfully manage customer interactions, and (c) the effective measurement of customers’ activities and outcomes.

Nonlinear partial least squares

We propose a new nonparametric regression method for high-dimensional data, nonlinear partial least squares (NLPLS), which is motivated by projection-based regression methods, e.g. PLS, projection pursuit regression and feedforward neural networks. The model takes the form of a composition of two functions. The first function in the composition projects the predictor variables onto a lower-dimensional curve or surface yielding scores, and the second predicts the response variable from the scores. We implement NLPLS with feedforward neural networks. NLPLS often will produce a more parsimonious model (fewer score vectors) than projection-based methods. We extend the model to multiple response variables and discuss situations when multiple response variables should be modeled simultaneously and when they should be modeled with separate regressions. We provide empirical results that evaluate the performances of NLPLS, projection pursuit, and neural networks on response variable predictions and robustness to starting values.

Media consumption across platforms: Identifying user-defined repertoires:

New media have made available a wide range of platforms and content choices. However, audiences cope with abundant choices by using more narrowly defined repertoires. Unfortunately, we know little of how users create repertoires across media platforms. This study uses factor analysis to identify user-defined repertoires from data obtained by following 495 users throughout an entire day. Results indicate the presence of four repertoires that are powerfully tied to the rhythms of people’s daily lives. These were in turn explained by a combination of factors such as audience availability and individual demographics.

Value fusion: The blending of consumer and firm value in the distinct context of mobile technologies and social media

Purpose: In this article, we introduce the concept of Value Fusion to describe how value can emerge from the use of mobile, networked technology by consumers, firms, and entities such as non-consumers, a firm’s competitors, and others simultaneously.Design/methodology/approach: We discuss the combination of characteristics of mobile devices that enable Value Fusion. We discuss specific value and benefits to consumers and firms of being mobile and networked. We introduce and define Value Fusion and set it apart from related, other conceptualizations of value. We provide examples of Value Fusion and discuss the necessary conditions for Value Fusion to occur. We discuss under which conditions the use of mobile, networked technology by consumers and firms may lead to Value Confusion instead of Value Fusion. We end with several research questions, proposed to further enhance the understanding and management of Value Fusion.Findings: The combination of portable, personal, networked, textual/visual and converged characteristics of mobile devices enables firms and consumers to interact and communicate, produce and consume benefits, and create value in new ways that have not been captured by popular conceptualizations of value. These traditional conceptualizations include customer value, experiential value, customer lifetime value, and customer engagement value. Value Fusion is defined as value that can be achieved for the entire network of consumers and firms simultaneously, just by being on the mobile network. Value Fusion results from producers and consumers (i) individually or collectively, (ii) actively and passively, (iii) concurrently, (iv) interactively or in aggregation contributing to a mobile network (v) in real time and (vi) just-in-time.Implications: Mobile devices have revolutionized the way we live, and there is widespread expectation that they will have “game- changing” implications for marketing in the near future (MSI, 2012). Therefore, research is needed to help us understand how mobile technologies are likely to change conventional wisdom about how customers and firms connect, interact and do business, and finally culminate in mutual, synergetic value; a phenomenon which we label Value Fusion.Originality: This paper synthesizes insights from the extant value literature that by and large has focused on either the customer’s or the firm’s perspective, but rarely blended the two. The contribution of this paper is that the Value Fusion concept achieves such a blending in the distinct context of mobile technologies and social media. Value should be considered more holistically, and value to the consumer and firm should be jointly optimized (i.e., Value Fusion) rather than managed in isolation. In addition, both active and passive participation should be valued. This paper calls for a more holistic approach to conceptualize value and identifies unanswered questions about value in the distinct context of mobile technology and social media.

Engagement with Online Media

Abstract Engagement has emerged as an important concept for newsorganizations. Yet lack of agreement within the industry and the academy on the definition of engagement has left news companies vulnerable to the definitions dictated by advertisers, who focus on brand placement not news content. This paper relies on quantitative and qualitative methodologies to define online engagement as a collection ofexperiences, illustrates that there are actually two important types of engagement (personal and social-interactive engagement) for media companies, and demonstrates their predictive validity by showing bothare associated with readership.

Managing Media and Advertising Change with Integrated Marketing

ABSTRACT This article defines the integrated marketing process and shows how it can be used to improve advertising. It discusses how integrated marketing thinks about brands, the consumer experience with products or services, and contact points. The role of media in delivering messages is reconsidered and ways of measuring the engagement with a medium are discussed. Integrated marketing also addresses the relationship between brands and customized contact points.

Ridge regression and direct marketing scoring models

Abstract The objective of a direct marketing scoring model is to pick a specified number of people to receive a particular offer so that the response to the mailing is maximized. This paper shows how ridge regression can be used to improve the performance of direct marketing scoring models. It reviews the key property of ridge regression—it can produce estimates of the slope coefficients having smaller mean squared error than ordinary least squares models. Next, it shows that ridge regression can be used to reduce the effective number of parameters in a regression model. Thus, ridge regression can be used as an alternative to variable subset selection methods such as stepwise regression to control the bias-variance tradeoff of the estimated values. This means that direct marketers can include more variables in a scoring model without danger of overfitting the data. Ridge regression estimates are compared with stepwise regression on direct marketing data. The empirical results suggest that ridge regression provides a more stable way of moderating the model degrees of freedom than dropping variables.

Assessing the performance of direct marketing scoring models

Direct marketers commonly assess their scoring models with a single-split, gains chart method: They split the available data into “training” and “test” sets, estimate their models on the training set, apply them to the test set, and generate gains charts. They use the results to compare models (which model should be used), assess overfitting, and estimate how well the mailing will do. It is well known that the results from this approach are highly dependent on the particular split of the data used, due to sampling variation across splits. This paper examines the single-split method. Does the sampling variation across splits affect ones ability to distinguish between superior and inferior models? How can one estimate the overall performance of a mailing accurately? I consider two ways of reducing the variation across splits: Winsorization and stratified sampling. The paper gives an empirical study of these questions and variance-reduction methods using the DMEF data sets.

Customer Satisfaction Across Organizational Units

This article examines customer satisfaction models for assessing the relationship of overall satisfaction with a product or service and satisfaction with specific aspects of the product or service for organizations having multiple units or subunits. These units could be stores, markets, dealers, divisions, and so on. The authors suggest a method for studying whether the drivers of overall satisfaction vary across such units. For cases where the drivers do vary across subunits, they show how additional variables can be included in a model to account for the variation. The authors illustrate this approach by studying customer satisfaction in the newspaper and health care industries. They use generalizability theory to evaluate the reliability of scales from multistage cluster sample designs. It is argued that the approach has important implications for both theory and practice.

The Customer Engagement Ecosystem

ABSTRACT Consumer engagement has been widely discussed in both the academic and practitioner literature, but there is no consensus about its meaning, what phenomena constitute engagement or what its antecedents and consequences are. Therefore, we propose that the term engagement should be eluded and that more specific terms should be used for the different phenomena. Building on the previous literature, we propose the customer engagement ecosystem, a conceptual model that encompasses brand actions, other actors, customer brand experience, shopping behaviours, brand consumption and brand-dialogue behaviours. The model posits that interactions between these elements are non-linear and reactive; meaning that each action causes a reaction of not only the intended recipient of the message, but the whole ecosystem. Hence, the model reflects the interconnected character of today’s marketing environment. It also recognises the growing importance of empowered consumers by distinguishing different forms of brand dialogue behaviours, which describe customers’ non-purchase focused behaviours.