Network-oriented Customer Valuation and Social Engagement Analysis in Online Customer Networks

The ongoing development of the Internet in the last two decades has an increasing impact on society and business. The digital revolution changed the way how, at which frequency, and at which speed people are communicating and interacting with each other. In 2018, the number of internet users will reach the 4 billion mark, which is more than 50% of the global population. Among them, more than 3 billion people worldwide are already regarded as active social media users. The emergence of web 2.0 technologies had major consequences for the relationship between customers and companies. Web 2.0 has led to an increasing engagement of companies in online social networks as well as to the establishment of firm-sponsored online customer networks. The companies thereby aim at enhancing their knowledge of customers’ needs, preferences, and desires to increase customer-brand loyalty in the long term. An online customer network thus acts as a specialised online community for customers who want to share common social and commercial interests with other customers and interact with the sponsoring company. Many of the top 100 global companies host their own online customer network. Popular examples are the online customer networks of Oracle, SAP, or Lego, where millions of customers are connected to share experiences about products and services, ask and answer company-related questions, and help each other with specific issues related to the company and its products. Online customer networks display thereby the change of customers’ role from traditional passive consumers towards creators and publishers of information, opinions, and emotions. By using different forms of social engagement activities like the exchange of private messages, asking and answering product-related questions in public forums or rating products, customers can influence each other’s purchase decisions. Furthermore, from a sponsoring company’s perspective, customers’ social engagement activities in online customer networks allow enduring and emotional relationships not only between participating customers but also between customers and companies. Therefore, social engagement enables the establishment of a potential strategic competitive advantage in the form of increased brand awareness, established trust, and amplified customer loyalty. Sponsoring an online customer network, however, also poses a risk for companies as it requires a comparatively large initial investment for establishing the technical and organisational infrastructure. Companies also have to invest in marketing and public relations to increase customers’ awareness for the online customer network. Therefore, companies are interested in identifying, whether an online customer network and customers’ social engagement is beneficial for the company or not. Against this background, the dissertation focuses on the two complementary research topics “Social Engagement and Customer Profitability” (Topic 1) and “Network-Oriented Customer Valuation” (Topic 2). In Topic 1, the dissertation focuses on investigating the relationship between social engagement and profitability of customers participating in firm-sponsored online customer networks. Furthermore, the influence of different types of social engagement activities as well as the polarity of customers’ social engagement activities are the focus of this research topic. The findings aim at supporting researchers and practitioners alike to better identify and characterize potentially valuable customers within an online customer network. Furthermore, by investigating the varying influence of different types of social engagement activities, the identification of more beneficial social engagement activities is supported. With the help of text mining and sentiment analysis techniques, the content of customers’ social engagement activities is determined. Based on this research, the dissertation aims in the context of this research topic at broadening the understanding and knowledge of customers’ social engagement activities in firm-sponsored online customer networks. In Topic 2, the dissertation develops novel customer valuation approaches incorporating direct as well as indirect positive social influence exerted between customers participating in online customer networks. However, beside not only positive social influence but also negative social influence, for example in the form of negative WoM, has to be considered when calculating a network-oriented customer value. Therefore, this dissertation further develops an integrated approach in the context of this research topic to calculate a network-oriented customer value, including both positive and negative social influence exerted between customers participating in online customer networks. Negative social influence thereby can result in a lost value contribution, hence, a customer’s value contribution which is not realized due to the negative social influence of other customers on the purchase decision of a specific customer

Management of Customer Loyalty in the Context of Digitalization as a Means of Increasing Financial Stability of a Company

The article highlights the issue of forming and managing customer loyalty in the service sector by using digital tools. The importance of customer loyalty as one of the most powerful components of an enterprise’s intellectual capital is emphasized. The types of loyalty are characterized according to the criteria of attitude to the brand (positive/negative) and the frequency of repeated transactions (absolute, hidden, false, zero). Loyalty indicators and how to calculate them are covered: customer retention rate, customer long-term value, Net Promoter Score, customer satisfaction surveys, customer effort evaluation, cause and effect questions, repeated purchases and referrals, engagement. It was noted that among the various types of loyalty (bonus program, fixed discount, temporary discount, multi-level discount program, product as a gift, partner loyalty program, paid loyalty program) the non-commercial program deserves the most attention, because in the long term it forms and testifies to absolute customer loyalty. The main types of loyalty management in the context of digitalization include transactional, social-network, related to the influence on engagement, emotions, consumer behavior, and intentions to recommend a service. It is emphasized that automated systems, such as software for managing business processes and online services for booking procedures, are becoming an integral part of modern beauty salons and offer a wide range of possibilities: online booking for services through a website or mobile application, reminders about visits by messages or e-mail; a calendar of appointments for services with the possibility of synchronization with personal calendars of beauty masters; automatic control of consumables balances, formation of supply orders, cash book management, income and expense accounting, profitability analysis; newsletters about promotions and special offers, maintenance of pages in social networks; storage of information about customers, their preferences, history of visits through the CRM system. The positive consequences of using automation in beauty salons include increasing efficiency and productivity, improving customer service, increasing profitability, expanding marketing and advertising opportunities, improving ratings and reviews, but among the negative ones, we single out dependence on technology, loss of personal contact, and threats to data privacy. A developed set of advertising events with seasonal specialization using online tools (conducting master classes and virtual consultations, seasonal loyalty programs, charity initiatives, promotions for special dates) is presented

The role of social effects and perceived risk in driving profitable online customer interactions

The emergence of online channels has been of special relevance, as it has promoted a more active participation of consumers in the value creation process. In this study, we draw from the Stimulus-Organism-Response model to provide a theoretical understanding of the role played by two critical factors that drive online customer initiated interactions (OnCICs): social effects and perceived risk. In addition, we also investigate their consequences by establishing a direct link between these interactions and customer profitability. Merging longitudinal objective data with subjective data for a sample of 1,990 customers in the financial services and applying Partial Least Squares (PLS), the results reveal that social effects influence perceived risk. Perceived risk consequently promotes the development of OnCICs, while social effects reduce the need for such interactions. In addition, OnCICs help promote high-quality relationships and leads to higher performance

Four papers on the effect of non-economic customer club structures on club performance

University of Technology, Sydney. School of Marketing.Organizations are increasingly developing customer clubs as part of their marketing strategy. Customer clubs such as airline frequent flyer programs, credit card loyalty programs, retail frequent purchase programs, and business-to-business channel programs are created to drive customer demand, shorten purchase cycles, achieve a higher share of wallet, introduce switching costs, and create sustainable customer loyalty. Despite the purposeful design of these customer clubs, success is not guaranteed. Indeed, the literature is replete with examples of both customer club failures and customer club successes. The question arises as to what makes a successful customer club, where success is defined as meeting the objectives of the firm sponsoring the customer club. The proposition presented in this paper is that the structure of the customer club has a significant bearing upon its ability to meet the objectives of the sponsoring organization. In other words, there is a relationship between club structure and club performance. The literature pertaining to customer club design suggests that customer clubs incorporate both 'hard' economic structures (that lead to economic benefits for club members) and 'soft' non-economic structures (leading to both non-economic benefits and indirectly to economic benefits) for club members. Economic structures, such as the accumulation of points or credits and discounts, can be easily matched by competitors and may result in discount wars yielding parity, eroding margins, and lowering profitability all around. Non-economic structures, such as member identification with the club, trust in the club, and the development of social networks within the club, may prove to be more difficult for competitors to replicate. The literature examining customer club performance has tended to concentrate on the impact of economic structures and how they drive club performance rather than examining the impact of non-economic structures. The nature of non-economic club structures, I argue, relates to a social interaction, which is captured by concepts such as social capital, and social networks. Social capital, predicts that through levels of engagement within a network of relationships, individuals can gain access to resources otherwise unavailable. Social network theory focuses on the interdependence of actors in a network and how their positions in these networks influence their opportunities and constraints. Both theories are grounded in social interactions and thus provide a suitable context to identify possible non-economic club structures and their influence on customer club performance. Customer clubs are, however, constrained by their capabilities and by available resources. Thus, my conceptual model of club structure is couched within a dynamic capabilities framework, allowing the organization to identify and apply its competences and capabilities to create non-economic structures efficiently and effectively and respond to changing market circumstances. In two separate studies involving customer club managers and customer club members, drawn from diverse geographical regions and industries, I test the conceptual model outlined above. The empirical results from both studies support the propositions that noneconomic structures, drawn from concepts embedded in social capital and network theories, drive the consumption of a club's goods and services by club members, which in tum has a positive impact on customer club performance. Feedback from customer club managers during the qualitative interviews indicated that a number of additional factors might have an impact on club performance. Specifically, a customer club's market orientation, a customer club's innovativeness in regards to its administration and to its products and services, product performance, and club member purchase involvement. I account for these factors by including them as control variables, and also for possible sources of systematic bias that could affect customer club performance. My results suggest that firms operating customer clubs should allocate resources to building structural club capital in the form of networks among club members to create strong social bonds. Clubs should also encourage information sharing, which may create additional business and personal opportunities for club members, thus increasing the overall value proposition of the customer club. Building structural club capital may be operationalized in the form of club member networking functions and an online communication infrastructure that allows members to interact and share information. Further, a club should invest in building cognitive club capital by creating a sense of shared identity, creating a common language for the club that can be used frequently by the members and allocate resources that help build trust between the customer club and its members. The findings and recommendations contained in this thesis provide a preliminary guide for customer club managers as to the types of non-economic structures that can be deployed in a customer club, that is not dependent upon a single market, industry type or customer segment. Perhaps the careful consideration of the guidelines presented will assist in a customer club achieving its stated performance objectives

Digital Marketing for Sustainable Growth: Business Models and Online Campaigns Using Sustainable Strategies

t: In recent years, digital marketing has transformed the way in which companies communicate with their customers around the world. The increase in the use of social networks and how users communicate with companies on the Internet has given rise to new business models based on the bidirectionality of communication between companies and Internet users. Digital marketing, new business models, online advertising campaigns, and other digital strategies have gathered user opinions and comments through this new online channel. In this way, companies have started to see the digital ecosystem as not only their present, but also as their future. From this long-term perspective, companies are concerned about sustainability and the growth of their business models. There are new business models on the Internet that support social causes, new platforms aimed at supporting social and sustainable projects, and digital advertising campaigns promoting sustainability. The overarching aim of this Special Issue was to analyze the development of these new strategies as well as their influence on the sustainability of digital marketing strategies. Therefore, we aimed to analyze how companies adopt these new technologies in a digital environment that is increasingly concerned with the sustainability of business models and actions on the Internet

Using big data for customer centric marketing

This chapter deliberates on “big data” and provides a short overview of business intelligence and emerging analytics. It underlines the importance of data for customer-centricity in marketing. This contribution contends that businesses ought to engage in marketing automation tools and apply them to create relevant, targeted customer experiences. Today’s business increasingly rely on digital media and mobile technologies as on-demand, real-time marketing has become more personalised than ever. Therefore, companies and brands are striving to nurture fruitful and long lasting relationships with customers. In a nutshell, this chapter explains why companies should recognise the value of data analysis and mobile applications as tools that drive consumer insights and engagement. It suggests that a strategic approach to big data could drive consumer preferences and may also help to improve the organisational performance.peer-reviewe

Beyond Personalization: Research Directions in Multistakeholder Recommendation

Recommender systems are personalized information access applications; they are ubiquitous in today's online environment, and effective at finding items that meet user needs and tastes. As the reach of recommender systems has extended, it has become apparent that the single-minded focus on the user common to academic research has obscured other important aspects of recommendation outcomes. Properties such as fairness, balance, profitability, and reciprocity are not captured by typical metrics for recommender system evaluation. The concept of multistakeholder recommendation has emerged as a unifying framework for describing and understanding recommendation settings where the end user is not the sole focus. This article describes the origins of multistakeholder recommendation, and the landscape of system designs. It provides illustrative examples of current research, as well as outlining open questions and research directions for the field.Comment: 64 page

Are black friday deals worth it? Mining twitter users' sentiment and behavior response

The Black Friday event has become a global opportunity for marketing and companies’ strategies aimed at increasing sales. The present study aims to understand consumer behavior through the analysis of user-generated content (UGC) on social media with respect to the Black Friday 2018 offers published by the 23 largest technology companies in Spain. To this end, we analyzed Twitter-based UGC about companies’ offers using a three-step data text mining process. First, a Latent Dirichlet Allocation Model (LDA) was used to divide the sample into topics related to Black Friday. In the next step, sentiment analysis (SA) using Python was carried out to determine the feelings towards the identified topics and offers published by the companies on Twitter. Thirdly and finally, a data-text mining process called textual analysis (TA) was performed to identify insights that could help companies to improve their promotion and marketing strategies as well as to better understand the customer behavior on social media. The results show that consumers had positive perceptions of such topics as exclusive promotions (EP) and smartphones (SM); by contrast, topics such as fraud (FA), insults and noise (IN), and customer support (CS) were negatively perceived by customers. Based on these results, we offer guidelines to practitioners to improve their social media communication. Our results also have theoretical implications that can promote further research in this area