A social network analysis of customer-level revenue distribution
- PDF / 201,630 Bytes
- 15 Pages / 439.37 x 666.142 pts Page_size
- 32 Downloads / 184 Views
A social network analysis of customer-level revenue distribution Michael Haenlein
Published online: 12 January 2010 # Springer Science+Business Media, LLC 2011
Abstract Social network analysis has been a topic of regular interest in the marketing discipline. Previous studies have largely focused on similarities in product/brand choice decisions within the same social network, often in the context of product innovation adoption. Not much is known, however, about the importance of social network effects once customers have been acquired. Using the customer base of a telecommunications company, our study analyzes network autocorrelation in the distribution of customerlevel revenue within a social network. Our results indicate a significant and substantial degree of positive network autocorrelation in customer-level revenue. High (low) revenue customers therefore tend to be primarily related to other high (low) revenue clients. Furthermore, we show that approximating communicative proximity by spatial proximity leads to a substantial underestimation of these effects. Keywords Social network analysis . Spatial statistics . Network autocorrelation . Moran’s I . Gary’s C
1 Introduction Interdependencies in consumer behavior among people who are connected to each other, or, more generally, the topic of social network analysis, have been of steady interest in marketing literature for over 25 years. Previous research has largely focused on brand choice decisions and brand congruence in interpersonal relations (Reingen et al. 1984) or, more generally, on product choice, specifically in the context of innovation diffusion (Goldenberg et al. 2009). These studies have provided consistent support for the fact that people who are related to each other by The author thanks Roger Bivand, Professor of Economics at the Norwegian School of Economics and Business Administration and developer of the spdep package, for his helpful comments during data analysis. M. Haenlein (*) ESCP Europe, 79 Avenue de la République, 75011 Paris, France e-mail: [email protected]
16
Mark Lett (2011) 22:15–29
strong tie relationships tend to show similarities in consumption behavior and brand preferences, caused by factors such as word-of-mouth communication (Goldenberg et al. 2001), social influences (i.e., compliance, identification, and internalization; see Kelman 1961), market embeddedness (i.e., utility derived from social capital next to basic product attributes; Frenzen and Davis 1990), or the use of similar information sources (Goldenberg et al. 2009). This finding has direct implications for the customer acquisition process and can, for example, be used in the context of network-based marketing campaigns (Hill et al. 2006). Customer acquisition or relationship initiation is, however, only one part of the firm’s CRM process, which also includes relationship maintenance and relationship termination (Reinartz et al. 2004). Nevertheless, not much is known about the impact of social networks on consumer behavior once customers have been acquir
Data Loading...
