The customer engagement ecosystem

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

Understanding the effects of different review features on purchase probability

The role of electronic word-of-mouth (eWOM) has been recognized by marketers and academics, but little research has examined the impact of eWOM on purchase behavior. Building on dual-process models of persuasion, this study aims to disentangle the effect of different online review features (i.e. argument quality, review valence, review helpfulness, message sidedness, source credibility and reviewer recommendation). Using product reviews and purchase data from an online retailer website, we investigate the financial impact of online product reviews on purchase decisions. The results demonstrate the persuasive power of different review features that are derived from dual-process models of information processing. Managerial implications on how advertisers and companies should design and manage online product reviews are offered

“Understanding a fury in your words”: The effects of posting and viewing electronic negative word-of-mouth on purchase behaviors

Marketing scholars and practitioners have long recognized that the power of electronic negative word-of-mouth (e-NWOM) can influence brand revenues and firm performance, but most previous studies have only examined the effect of viewing. This study is one of the initial attempts to test the effects of e-NWOM on both posters and viewers. We also test the moderating effects of company usefulness and company apology in a separate study. Using an observational dataset that contains NWOM viewing and posting records and customers' purchase transactions from a real company, Study 1 finds that viewing e-NWOM has a negative effect on subsequent purchases, whereas posting e-NWOM has a positive interaction effect with company usefulness. Study 2 shows that a company's public apology has a positive effect on viewers, but not posters. We conclude with the theoretical, methodological, and managerial implications of e-NWOM and webcare research

CRM in data-rich multichannel retailing environments:A review and future research directions

Many retailers have collected large amounts of customer data using, for example, loyalty programs. We provide an overview of the extant literature on customer relationship management (CRM), with a specific focus on retailing. We discuss how retailers can gather customer data and how they can analyze these data to gain useful customer insights. We provide an overview of the methods predicting customer responses and behavior over time. We also discuss the existing knowledge on the application of marketing actions in a CRM context, while providing an in-depth discussion on CRM and firm value. We outline future research directions based on the literature review and retail practice insights. (C) 2009 Direct Marketing Educational Foundation, Inc. Published by Elsevier Inc. All rights reserved

Use of a high-value social audience index for target audience identification on Twitter

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Analysis of Surfaces Using Constrained Regression Models

We present a study of the relationship between the changes in the shape of the human ear due to jaw movement and acoustical feedback (AF) in hearing aids. In particular, we analyze the deformation field of the outer ear associated with the movement of the mandible (jaw bone) to understand its effect on AF and identify local regions that play a significant role. Our data contains ear impressions of 42 hearing aid users, in two different positions: open and closed mouth, and survey data including information about experienced discomfort due to AF. We use weighted support vector machines (WSVM) to investigate the separation between the presence and lack of AF and achieve classification accuracy of 80 % based on the deformation field. To robustly localize the regions of the deformation field that significantly contribute to AF we employ logistic regression penalized with elastic net (EN). By visualizing the selected variables on the mean surface, we provide clinical interpretations of the results

Analysis of Surfaces Using Constrained Regression Models

We present a study of the relationship between the changes in the shape of the human ear due to jaw movement and acoustical feedback (AF) in hearing aids. In particular, we analyze the deformation field of the outer ear associated with the movement of the mandible (jaw bone) to understand its effect on AF and identify local regions that play a significant role. Our data contains ear impressions of 42 hearing aid users, in two different positions: open and closed mouth, and survey data including information about experienced discomfort due to AF. We use weighted support vector machines (WSVM) to investigate the separation between the presence and lack of AF and achieve classification accuracy of 80 % based on the deformation field. To robustly localize the regions of the deformation field that significantly contribute to AF we employ logistic regression penalized with elastic net (EN). By visualizing the selected variables on the mean surface, we provide clinical interpretations of the results