This study explores the role of customers' social network in their defection from a service provider. The authors use data on communication among one million customers of a cellular company to create ...a large-scale social system composed of customers' individual social networks. The study's results indicate that exposure to a defecting neighbor is associated with an increase of 80% in the defection hazard, after controlling for a host of social, personal, and purchase-related variables. This effect is comparable in both magnitude and nature to social effects observed in the highly researched case of product adoption: The extent of social influence on retention decays exponentially over time, and the likelihood of defection is affected by tie strength and homophily with defecting neighbors and by these neighbors' average number of connections. Highly connected customers are more affected, and loyal customers are less affected by defections that occur in their social networks. These results carry important implications for the theoretical understanding of the drivers of customer retention and should be considered by firms that aim to predict and affect customer retention.
In word-of-mouth seeding programs, customer word of mouth can generate value through market expansion; in other words, it can gain customers who would not otherwise have bought the product. ...Alternatively, word of mouth can generate value by accelerating the purchases of customers who would have purchased anyway. This article presents the first investigation exploring how acceleration and expansion combine to generate value in a word-of-mouth seeding program for a new product. The authors define a program's "social value" as the global change, over the entire social system, in customer equity that can be attributed to the word-of-mouth program participants. They compute programs' social value in various scenarios using an agent-based simulation model and empirical connectivity data on 12 social networks in various markets as input to the simulation. The authors show how expansion and acceleration integrate to create programs' social value and illustrate how the role of each is affected by factors such as competition, program targeting, profit decline, and retention. These results have substantial implications for the design and evaluation of word-of-mouth marketing programs and of the profit impact of word of mouth in general.
Historically, when targeting potential adopters of a new product, firms have tended to focus first on people with disproportional effect on others, often labeled "opinion leaders." The authors ...highlight the benefit of targeting customers with high lifetime value (CLV), or "revenue leaders." The authors argue that targeting revenue leaders can create high value both by accelerating adoption among these customers and because of the higher-than-average value that revenue leaders generate by affecting other customers with similarly high CLV. The latter phenomenon is driven by network assortativity, whereby people's social networks tend to be composed of others who are similar to themselves. Analyzing an agent-based model of a seeding program for a new product, the authors contrast revenue leader seeding with opinion leader seeding and compare the factors that influence the effectiveness of each. They show that the distribution of CLV in the population and the seed size play a major role in determining which seeding approach is preferable, and they discuss the managerial implications of these findings.
In light of the emerging discourse on AI systems' effect on society, whose perception swings widely between utopian and dystopian, we conduct herein a critical analysis of how artificial intelligence ...(AI) affects the essential nature of customer relationship management (CRM). To do so, we survey the AI capabilities that will transform CRM into AI-CRM and examine how the transformation will influence customer acquisition, development, and retention. We highlight in particular how AI-CRM's improving ability to predict customer lifetime value will generate an inexorable rise in implementing adapted treatment of customers, leading to greater customer prioritization and service discrimination in markets. We further consider the consequences for firms and the challenges to regulators.
•Artificial Intelligence will have noticeable impact on how firms manage customer relationships.•We present a critical view of expected implications of AI-CRM emergence.•AI-CRM much improve the ability to predict customer lifetime value and to adapt treatment of customers.•This will lead to greater customer prioritization and increase in service discrimination.
The increasing emphasis on understanding the antecedents and consequences of customer-to-customer (C2C) interactions is one of the essential developments of customer management in recent years. This ...interest is driven much by new online environments that enable customers to be connected in numerous new ways and also supply researchers’ access to rich C2C data. These developments present an opportunity and a challenge for firms and researchers who need to identify the aspects of C2C research on which to focus, as well as develop research methods that take advantage of these new data. The aim here is to take a broad view of C2C interactions and their effects and to highlight areas of significant research interest in this domain. The authors look at four main areas: the different dimensions of C2C interactions; social system issues related to individuals and to online communities; C2C context issues including product, channel, relational and market characteristics; and the identification, modeling, and assessment of business outcomes of C2C interactions.
The Diffusion of Services Libai, Barak; Muller, Eitan; Peres, Renana
Journal of marketing research,
04/2009, Letnik:
46, Številka:
2
Journal Article
Recenzirano
Many of the products introduced during the past two decades have been services rather than goods. An important influence on the growth and long-term profits of these services is customer attrition, ...which can occur at the category level (disadoption) or between firms (churn). However, the literature has rarely modeled how services penetrate a market and has not evaluated the effect of attrition on growth. The authors combine diffusion modeling with a customer relationship approach to investigate the influence of attrition on growth in service markets. In particular, the authors model the effects of disadoption and churn on evolution of a category and on growth of individual firms in a competitive environment. The authors show how neglecting disadoption can bias parameter estimation and, especially, market potential. They also derive an expression for the customer equity of a growing service firm and apply it to valuation of firms operating in competitive industries. The results for six of seven firms in four service categories are remarkably close to stock market valuations, an indicator for the role of customer equity in valuations of growing service firms.
Consumer choice is influenced in a direct and meaningful way by the actions taken by others. These "actions" range from face-to-face recommendations from a friend to the passive observation of what a ...stranger is wearing. We refer to the set of such contexts as "social interactions" (SI). We believe that at least some of the SI effects are partially within the firm's control and that this represents an exciting research opportunity. We present an agenda that identifies a list of unanswered questions of potential interest to both researchers and managers. In order to appreciate the firm's choices with respect to its management of SI, it is important to first evaluate where we are in terms of understanding the phenomena themselves. We highlight five questions in this regard: (1) What are the antecedents of word of mouth (WOM)? (2) How does the transmission of positive WOM differ from that of negative WOM? (3) How does online WOM differ from offline WOM? (4) What is the impact of WOM? (5) How can we measure WOM? Finally, we identify and discuss four principal, non-mutually exclusive, roles that the firm might play: (1) observer, (2) moderator, (3) mediator, and (4) participant.
Seeding, Referral, and Recommendation Haenlein, Michael; Libai, Barak
California management review,
02/2017, Letnik:
59, Številka:
2
Journal Article
Recenzirano
In recent years, word-of-mouth (WOM) marketing has been the subject of considerable interest among managers and academics alike. However, there is very little common knowledge on what drives the ...value of WOM programs and how they should be designed to optimize value. Firms therefore frequently rely on relatively simple metrics to measure the success of their WOM marketing efforts and mainly use rules of thumb when making crucial program design decisions. This article proposes a new method to measure WOM program value that is based on the impact of WOM on the firm’s customer equity. It then provides recommendations for the five main questions managers face when planning a WOM program: Who to target? When to launch the program? Where to launch it? Which incentives to offer? and How many participants to include?
Using data on a large number of innovative products in the consumer electronics industry, the authors find that between one-third and one-half of the sales cases involved the following pattern: an ...initial peak, then a trough of sufficient depth and duration to exclude random fluctuations, and eventually sales levels that exceeded the initial peak. This newly identified pattern, which the authors call a "saddle," is explained by the dual-market phenomenon that differentiates between early market adopters and main market adopters as two separate markets. If these two segments-the early market and the main market-adopt at different rates, and if this difference is pronounced, then the overall sales to the two markets will exhibit a temporary decline at the intermediate stage. The authors employ both empirical analysis and cellular automata, an individual-level, complex system modeling technique for generating and analyzing data, to investigate the conditions under which a saddle occurs. The model highlights the importance of cross-market communication in determining the existence of a saddle. At low levels of this parameter, more than 50% of the cases of new product growth involved a saddle. This percentage gradually decreased as the parameter increased, and at values close to the within-market parameters, the proportion of saddle occurrences dropped below 5%.
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